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Use of Mobile Messaging System for Self-Management of Chemotherapy Symptoms in Patients with Advanced Cancer (FULL)
Cancer and cancer-related treatment can cause a myriad of adverse effects.1,2 Early identification and management of these symptoms is paramount to the success of cancer treatment completion; however, clinic and telephonic strategies for addressing symptoms often result in delays in care.1 New strategies for patient engagement in the management of cancer and treatment-related symptoms are needed.
The use of online self-management tools can result in improvement in symptoms, reduce cancer symptom distress, improve quality-of-life, and improve medication adherence.3-9 A meta-analysis concluded that online interventions showed promise, but optimizing interventions would require additional research.10 Another meta-analysis found that online self-management was effective in managing several symptoms.11 An e-health method of collecting patient self-reported symptoms has been found to be acceptable to patients and feasible for use.12-14 We postulated that a mobile text messaging strategy may be an effective modality for augmenting symptom management for cancer patients in real time.
In the US Departmant of Veterans Affairs (VA), “Annie,” a self-care tool utilizing a text-messaging system has been implemented. Annie was developed modeling “Flo,” a messaging system in the United Kingdom that has been used for case management of chronic obstructive pulmonary disease, heart failure, stress incontinence, asthma, as a medication reminder tool, and to provide support for weight loss or post-operatively.15-17 Using Annie in the US, veterans have the ability to receive and track health information. Use of the Annie program has demonstrated improved continuous positive airway pressure monitor utilization in veterans with traumatic brain injury.18 Other uses within the Veterans Health Administration (VHA) include assisting patients with anger management, liver disease, anxiety, asthma, diabetes, HIV, hypertension, weight loss, and smoking cessation.
Methods
The Hematology/Oncology division of the Minneapolis VA Healthcare System (MVAHCS) is a tertiary care facility that administers about 260 new chemotherapy regimens annually. The MVAHCS interdisciplinary hematology/oncology group initiated a quality improvement project to determine the feasibility, acceptability, and experience of tailoring the Annie tool for self-management of cancer symptoms. The group consisted of 2 physicians, 3 advanced practice registered nurses, 1 physician assistant, 2 registered nurses, and 2 Annie program team members.
We first created a symptom management pilot protocol as a result of multidisciplinary team discussions. Examples of discussion points for consideration included, but were not limited to, timing of texts, amount of information to ask for and provide, what potential symptoms to consider, and which patient population to pilot first.
The initial protocol was agreed upon and is as follows: Patients were sent text messages twice daily Monday through Friday, and asked to rate 2 symptoms per day, using a severity scale of 0 to 4 (absent, mild, moderate, severe, or disabling): nausea/vomiting, mouth sores, fatigue (Figure 1), trouble breathing, appetite, constipation, diarrhea (Figure 2), numbness/tingling, pain. In addition, patients were asked whether they had had a fever or not. Based on their response to the symptom inquiries, the patient received an automated text response. The text may have provided positive affirmation that they were doing well, given them advice for home management, referred them to an educational hyperlink, asked them to call a direct number to the clinic, or instructed them to report directly to the emergency department (ED). Patients could input a particular symptom on any day, even if they were not specifically asked about that symptom on that day. Patients also were instructed to text, only if it was not an inconvenience to them, as we wanted the intervention to be helpful and not a burden.
Results
Through screening new patient consults or those referred for chemotherapy education, 15 male veterans enrolled in the symptom monitoring program over an 8 month period. There were additional patients who were not offered the program or chose not to participate; often due to not having texting capabilities on their phone or not liking the texting feature. The majority of those who participated in the program (n = 14) were enrolled at the start of Cycle 1; the other patient was enrolled at the start of Cycle 2. Patients were enrolled an average of 89 days (range 8-204). Average response rate was 84.2% (range 30-100%).
Although symptoms were not reviewed in real time, we reviewed responses to determine the utilization of the instructions given for the program. No veteran had 0 symptoms reported. There were numerous occurrences of a score of 1 or 2. Many of these patients had baseline symptoms due to their underlying cancer. A score of 3 or 4 on the system prompted the patient to call the clinic or go to the ED. Seven patients (some with multiple occurrences) were prompted to call; only 4 of these made the follow-up call to the clinic. All were offered a same day visit, but each declined. Only 1 patient reported a symptom on a day not prompted for that symptom. Symptoms that were reported are listed in order of frequency: fatigue, appetite loss, numbness, pain, mouth sore, and breathing difficulty. There were no visits to the ED.
Program Evaluation
An evaluation was conducted 30 to 60 days after program enrollment. We elicited feedback to determine who was reading and responding to the text message: the patient, a family member, or a caregiver; whether they found the prompts helpful and took action; how they felt about the number of texts; if they felt the program was helpful; and any other feedback that would improve the program. In general, the patients (8) answered the texts independently. In 4 cases, the spouse answered the texts, and 3 patients answered the texts together with their spouses. Most patients (11) found the amount of texting to be “just right.” However, 3 found it to be too many texts and 1 didn’t find the amount of texting to be enough.
Three veterans did not have enough symptoms to feel the program was of benefit to them, but they did feel it would have been helpful if they had been more symptomatic. One veteran recalled taking loperamide as needed, as a result of prompting. No veterans felt as though the texting feature was difficult to use; and overall, were very positive about the program. Several appreciated receiving messages that validated when they were doing well, and they felt empowered by self-management. One of the spouses was a registered nurse and found the information too basic to be of use.
Discussion
Initial evaluation of the program via survey found no technology challenges. Patients have been very positive about the program including ease of use, appreciation of messages that validated when they were doing well, empowerment of self-management, and some utilization of the texting advice for symptom management. Educational hyperlinks for constipation, fatigue, diarrhea, and nausea/vomiting were added after this evaluation, and patients felt that these additions provided a higher level of education.
Staff time for this intervention was minimal. A nurse navigator offered the texting program to the patient during chemotherapy education, along with some instructions, which generally took about 5 minutes. One of the Annie program staff enrolled the patient. From that point forward, this was a self-management tool, beyond checking to ensure that the patient was successful in starting the program and evaluating use for the purposes of this quality improvement project. This self-management tool did not replace any other mechanism that a patient would normally have in our department for seeking help for symptoms. The MVAHSC typical process for symptom management is to have patients call a 24/7 nurse line. If the triage nurse feels the symptoms are related to the patient’s cancer or cancer treatment, they are referred to the physician assistant who is assigned to take those calls and has the option to see the patient the same day. Patients could continue to call the nurse line or speak with providers at the next appointment at their discretion.
Conclusion
Although Annie has the option of using either text messaging or a mobile application, this project only utilized text messaging. The study by Basch and colleagues was the closest randomized trial we could identify to compare to our quality improvement intervention.5 The 2 main, distinct differences were that Basch and colleagues utilized online monitoring; and nurses were utilized to screen and intervene on responses, as appropriate.
The ability of our program to text patients without the use of an application or tablet, may enable more patients to participate due to ease of use. There would be no increased in expected workload for clinical staff, and may lead to decreased call burden. Since our program is automated, while still providing patients with the option to call and speak with a staff member as needed, this is a cost-effective, first-line option for symptom management for those experiencing cancer-related symptoms. We believe this text messaging tool can have system wide use and benefit throughout the VHA.
1. Bruera E, Dev R. Overview of managing common non-pain symptoms in palliative care. https://www.uptodate.com/contents/overview-of-managing-common-non-pain-symptoms-in-palliative-care. Updated June 12, 2019. Accessed July 18, 2019.
2. Pirschel C. The crucial role of symptom management in cancer care. https://voice.ons.org/news-and-views/the-crucial-role-of-symptom-management-in-cancer-care. Published December 14, 2017. Accessed July 18, 2019.
3. Adam R, Burton CD, Bond CM, de Bruin M, Murchie P. Can patient-reported measurements of pain be used to improve cancer pain management? A systematic review and meta-analysis. BMJ Support Palliat Care. 2017;7(4):373-382.
4. Basch E, Deal AM, Kris MG, et al. Symptom monitoring with patient-reported outcomes during routine cancer treatment: a randomized controlled trial. J Clin Oncol. 2016;34(6):557-565.
5. Berry DL, Blonquist TM, Patel RA, Halpenny B, McReynolds J. Exposure to a patient-centered, Web-based intervention for managing cancer symptom and quality of life issues: Impact on symptom distress. J Med Internet Res. 2015;3(7):e136.
6. Kolb NA, Smith AG, Singleton JR, et al. Chemotherapy-related neuropathic symptom management: a randomized trial of an automated symptom-monitoring system paired with nurse practitioner follow-up. Support Care Cancer. 2018;26(5):1607-1615
7. Kamdar MM, Centi AJ, Fischer N, Jetwani K. A randomized controlled trial of a novel artificial-intelligence based smartphone application to optimize the management of cancer-related pain. Presented at: 2018 Palliative and Supportive Care in Oncology Symposium; November 16-17, 2018; San Diego, CA.
8. Mooney KH, Beck SL, Wong B, et al. Automated home monitoring and management of patient-reported symptoms during chemotherapy: results of the symptom care at home RCT. Cancer Med. 2017;6(3):537-546.
9. Spoelstra SL, Given CW, Sikorskii A, et al. Proof of concept of a mobile health short message service text message intervention that promotes adherence to oral anticancer agent medications: a randomized controlled trial. Telemed J E Health. 2016;22(6):497-506.
10. Fridriksdottir N, Gunnarsdottir S, Zoëga S, Ingadottir B, Hafsteinsdottir EJG. Effects of web-based interventions on cancer patients’ symptoms: review of randomized trials. Support Care Cancer. 2018;26(2):3370-351.
11. Kim AR, Park HA. Web-based self-management support intervention for cancer survivors: a systematic review and meta-analysis. Stud Health Technol Inform. 2015;216:142-147.
12. Girgis A, Durcinoska I, Levesque JV, et al; PROMPT-Care Program Group. eHealth system for collecting and utilizing patient reported outcome measures for personalized treatment and care (PROMPT-Care) among cancer patients: mixed methods approach to evaluate feasibility and acceptability. J Med Internet Res. 2017;19(10):e330.
13. Moradian S, Krzyzanowska MK, Maguire R, et al. Usability evaluation of a mobile phone-based system for remote monitoring and management of chemotherapy-related side effects in cancer patients: Mixed methods study. JMIR Cancer. 2018;4(2): e10932.
14. Voruganti T, Grunfeld E, Jamieson T, et al. My team of care study: a pilot randomized controlled trial of a web-based communication tool for collaborative care in patients with advanced cancer. J Med Internet Res. 2017;19(7):e219.
15. The Health Foundation. Overview of Florence simple telehealth text messaging system. https://www.health.org.uk/article/overview-of-the-florence-simple-telehealth-text-messaging-system. Accessed July 31, 2019.
16. Bragg DD, Edis H, Clark S, Parsons SL, Perumpalath B…Maxwell-Armstrong CA. Development of a telehealth monitoring service after colorectal surgery: a feasibility study. 2017;9(9):193-199.
17. O’Connell P. Annie-the VA’s self-care game changer. http://www.simple.uk.net/home/blog/blogcontent/annie-thevasself-caregamechanger. Published April 21, 2016. Accessed August 2, 2019.
18. Kataria L, Sundahl, C, Skalina L, et al. Text message reminders and intensive education improves positive airway pressure compliance and cognition in veterans with traumatic brain injury and obstructive sleep apnea: ANNIE pilot study (P1.097). Neurology, 2018; 90(suppl 15):P1.097.
Cancer and cancer-related treatment can cause a myriad of adverse effects.1,2 Early identification and management of these symptoms is paramount to the success of cancer treatment completion; however, clinic and telephonic strategies for addressing symptoms often result in delays in care.1 New strategies for patient engagement in the management of cancer and treatment-related symptoms are needed.
The use of online self-management tools can result in improvement in symptoms, reduce cancer symptom distress, improve quality-of-life, and improve medication adherence.3-9 A meta-analysis concluded that online interventions showed promise, but optimizing interventions would require additional research.10 Another meta-analysis found that online self-management was effective in managing several symptoms.11 An e-health method of collecting patient self-reported symptoms has been found to be acceptable to patients and feasible for use.12-14 We postulated that a mobile text messaging strategy may be an effective modality for augmenting symptom management for cancer patients in real time.
In the US Departmant of Veterans Affairs (VA), “Annie,” a self-care tool utilizing a text-messaging system has been implemented. Annie was developed modeling “Flo,” a messaging system in the United Kingdom that has been used for case management of chronic obstructive pulmonary disease, heart failure, stress incontinence, asthma, as a medication reminder tool, and to provide support for weight loss or post-operatively.15-17 Using Annie in the US, veterans have the ability to receive and track health information. Use of the Annie program has demonstrated improved continuous positive airway pressure monitor utilization in veterans with traumatic brain injury.18 Other uses within the Veterans Health Administration (VHA) include assisting patients with anger management, liver disease, anxiety, asthma, diabetes, HIV, hypertension, weight loss, and smoking cessation.
Methods
The Hematology/Oncology division of the Minneapolis VA Healthcare System (MVAHCS) is a tertiary care facility that administers about 260 new chemotherapy regimens annually. The MVAHCS interdisciplinary hematology/oncology group initiated a quality improvement project to determine the feasibility, acceptability, and experience of tailoring the Annie tool for self-management of cancer symptoms. The group consisted of 2 physicians, 3 advanced practice registered nurses, 1 physician assistant, 2 registered nurses, and 2 Annie program team members.
We first created a symptom management pilot protocol as a result of multidisciplinary team discussions. Examples of discussion points for consideration included, but were not limited to, timing of texts, amount of information to ask for and provide, what potential symptoms to consider, and which patient population to pilot first.
The initial protocol was agreed upon and is as follows: Patients were sent text messages twice daily Monday through Friday, and asked to rate 2 symptoms per day, using a severity scale of 0 to 4 (absent, mild, moderate, severe, or disabling): nausea/vomiting, mouth sores, fatigue (Figure 1), trouble breathing, appetite, constipation, diarrhea (Figure 2), numbness/tingling, pain. In addition, patients were asked whether they had had a fever or not. Based on their response to the symptom inquiries, the patient received an automated text response. The text may have provided positive affirmation that they were doing well, given them advice for home management, referred them to an educational hyperlink, asked them to call a direct number to the clinic, or instructed them to report directly to the emergency department (ED). Patients could input a particular symptom on any day, even if they were not specifically asked about that symptom on that day. Patients also were instructed to text, only if it was not an inconvenience to them, as we wanted the intervention to be helpful and not a burden.
Results
Through screening new patient consults or those referred for chemotherapy education, 15 male veterans enrolled in the symptom monitoring program over an 8 month period. There were additional patients who were not offered the program or chose not to participate; often due to not having texting capabilities on their phone or not liking the texting feature. The majority of those who participated in the program (n = 14) were enrolled at the start of Cycle 1; the other patient was enrolled at the start of Cycle 2. Patients were enrolled an average of 89 days (range 8-204). Average response rate was 84.2% (range 30-100%).
Although symptoms were not reviewed in real time, we reviewed responses to determine the utilization of the instructions given for the program. No veteran had 0 symptoms reported. There were numerous occurrences of a score of 1 or 2. Many of these patients had baseline symptoms due to their underlying cancer. A score of 3 or 4 on the system prompted the patient to call the clinic or go to the ED. Seven patients (some with multiple occurrences) were prompted to call; only 4 of these made the follow-up call to the clinic. All were offered a same day visit, but each declined. Only 1 patient reported a symptom on a day not prompted for that symptom. Symptoms that were reported are listed in order of frequency: fatigue, appetite loss, numbness, pain, mouth sore, and breathing difficulty. There were no visits to the ED.
Program Evaluation
An evaluation was conducted 30 to 60 days after program enrollment. We elicited feedback to determine who was reading and responding to the text message: the patient, a family member, or a caregiver; whether they found the prompts helpful and took action; how they felt about the number of texts; if they felt the program was helpful; and any other feedback that would improve the program. In general, the patients (8) answered the texts independently. In 4 cases, the spouse answered the texts, and 3 patients answered the texts together with their spouses. Most patients (11) found the amount of texting to be “just right.” However, 3 found it to be too many texts and 1 didn’t find the amount of texting to be enough.
Three veterans did not have enough symptoms to feel the program was of benefit to them, but they did feel it would have been helpful if they had been more symptomatic. One veteran recalled taking loperamide as needed, as a result of prompting. No veterans felt as though the texting feature was difficult to use; and overall, were very positive about the program. Several appreciated receiving messages that validated when they were doing well, and they felt empowered by self-management. One of the spouses was a registered nurse and found the information too basic to be of use.
Discussion
Initial evaluation of the program via survey found no technology challenges. Patients have been very positive about the program including ease of use, appreciation of messages that validated when they were doing well, empowerment of self-management, and some utilization of the texting advice for symptom management. Educational hyperlinks for constipation, fatigue, diarrhea, and nausea/vomiting were added after this evaluation, and patients felt that these additions provided a higher level of education.
Staff time for this intervention was minimal. A nurse navigator offered the texting program to the patient during chemotherapy education, along with some instructions, which generally took about 5 minutes. One of the Annie program staff enrolled the patient. From that point forward, this was a self-management tool, beyond checking to ensure that the patient was successful in starting the program and evaluating use for the purposes of this quality improvement project. This self-management tool did not replace any other mechanism that a patient would normally have in our department for seeking help for symptoms. The MVAHSC typical process for symptom management is to have patients call a 24/7 nurse line. If the triage nurse feels the symptoms are related to the patient’s cancer or cancer treatment, they are referred to the physician assistant who is assigned to take those calls and has the option to see the patient the same day. Patients could continue to call the nurse line or speak with providers at the next appointment at their discretion.
Conclusion
Although Annie has the option of using either text messaging or a mobile application, this project only utilized text messaging. The study by Basch and colleagues was the closest randomized trial we could identify to compare to our quality improvement intervention.5 The 2 main, distinct differences were that Basch and colleagues utilized online monitoring; and nurses were utilized to screen and intervene on responses, as appropriate.
The ability of our program to text patients without the use of an application or tablet, may enable more patients to participate due to ease of use. There would be no increased in expected workload for clinical staff, and may lead to decreased call burden. Since our program is automated, while still providing patients with the option to call and speak with a staff member as needed, this is a cost-effective, first-line option for symptom management for those experiencing cancer-related symptoms. We believe this text messaging tool can have system wide use and benefit throughout the VHA.
Cancer and cancer-related treatment can cause a myriad of adverse effects.1,2 Early identification and management of these symptoms is paramount to the success of cancer treatment completion; however, clinic and telephonic strategies for addressing symptoms often result in delays in care.1 New strategies for patient engagement in the management of cancer and treatment-related symptoms are needed.
The use of online self-management tools can result in improvement in symptoms, reduce cancer symptom distress, improve quality-of-life, and improve medication adherence.3-9 A meta-analysis concluded that online interventions showed promise, but optimizing interventions would require additional research.10 Another meta-analysis found that online self-management was effective in managing several symptoms.11 An e-health method of collecting patient self-reported symptoms has been found to be acceptable to patients and feasible for use.12-14 We postulated that a mobile text messaging strategy may be an effective modality for augmenting symptom management for cancer patients in real time.
In the US Departmant of Veterans Affairs (VA), “Annie,” a self-care tool utilizing a text-messaging system has been implemented. Annie was developed modeling “Flo,” a messaging system in the United Kingdom that has been used for case management of chronic obstructive pulmonary disease, heart failure, stress incontinence, asthma, as a medication reminder tool, and to provide support for weight loss or post-operatively.15-17 Using Annie in the US, veterans have the ability to receive and track health information. Use of the Annie program has demonstrated improved continuous positive airway pressure monitor utilization in veterans with traumatic brain injury.18 Other uses within the Veterans Health Administration (VHA) include assisting patients with anger management, liver disease, anxiety, asthma, diabetes, HIV, hypertension, weight loss, and smoking cessation.
Methods
The Hematology/Oncology division of the Minneapolis VA Healthcare System (MVAHCS) is a tertiary care facility that administers about 260 new chemotherapy regimens annually. The MVAHCS interdisciplinary hematology/oncology group initiated a quality improvement project to determine the feasibility, acceptability, and experience of tailoring the Annie tool for self-management of cancer symptoms. The group consisted of 2 physicians, 3 advanced practice registered nurses, 1 physician assistant, 2 registered nurses, and 2 Annie program team members.
We first created a symptom management pilot protocol as a result of multidisciplinary team discussions. Examples of discussion points for consideration included, but were not limited to, timing of texts, amount of information to ask for and provide, what potential symptoms to consider, and which patient population to pilot first.
The initial protocol was agreed upon and is as follows: Patients were sent text messages twice daily Monday through Friday, and asked to rate 2 symptoms per day, using a severity scale of 0 to 4 (absent, mild, moderate, severe, or disabling): nausea/vomiting, mouth sores, fatigue (Figure 1), trouble breathing, appetite, constipation, diarrhea (Figure 2), numbness/tingling, pain. In addition, patients were asked whether they had had a fever or not. Based on their response to the symptom inquiries, the patient received an automated text response. The text may have provided positive affirmation that they were doing well, given them advice for home management, referred them to an educational hyperlink, asked them to call a direct number to the clinic, or instructed them to report directly to the emergency department (ED). Patients could input a particular symptom on any day, even if they were not specifically asked about that symptom on that day. Patients also were instructed to text, only if it was not an inconvenience to them, as we wanted the intervention to be helpful and not a burden.
Results
Through screening new patient consults or those referred for chemotherapy education, 15 male veterans enrolled in the symptom monitoring program over an 8 month period. There were additional patients who were not offered the program or chose not to participate; often due to not having texting capabilities on their phone or not liking the texting feature. The majority of those who participated in the program (n = 14) were enrolled at the start of Cycle 1; the other patient was enrolled at the start of Cycle 2. Patients were enrolled an average of 89 days (range 8-204). Average response rate was 84.2% (range 30-100%).
Although symptoms were not reviewed in real time, we reviewed responses to determine the utilization of the instructions given for the program. No veteran had 0 symptoms reported. There were numerous occurrences of a score of 1 or 2. Many of these patients had baseline symptoms due to their underlying cancer. A score of 3 or 4 on the system prompted the patient to call the clinic or go to the ED. Seven patients (some with multiple occurrences) were prompted to call; only 4 of these made the follow-up call to the clinic. All were offered a same day visit, but each declined. Only 1 patient reported a symptom on a day not prompted for that symptom. Symptoms that were reported are listed in order of frequency: fatigue, appetite loss, numbness, pain, mouth sore, and breathing difficulty. There were no visits to the ED.
Program Evaluation
An evaluation was conducted 30 to 60 days after program enrollment. We elicited feedback to determine who was reading and responding to the text message: the patient, a family member, or a caregiver; whether they found the prompts helpful and took action; how they felt about the number of texts; if they felt the program was helpful; and any other feedback that would improve the program. In general, the patients (8) answered the texts independently. In 4 cases, the spouse answered the texts, and 3 patients answered the texts together with their spouses. Most patients (11) found the amount of texting to be “just right.” However, 3 found it to be too many texts and 1 didn’t find the amount of texting to be enough.
Three veterans did not have enough symptoms to feel the program was of benefit to them, but they did feel it would have been helpful if they had been more symptomatic. One veteran recalled taking loperamide as needed, as a result of prompting. No veterans felt as though the texting feature was difficult to use; and overall, were very positive about the program. Several appreciated receiving messages that validated when they were doing well, and they felt empowered by self-management. One of the spouses was a registered nurse and found the information too basic to be of use.
Discussion
Initial evaluation of the program via survey found no technology challenges. Patients have been very positive about the program including ease of use, appreciation of messages that validated when they were doing well, empowerment of self-management, and some utilization of the texting advice for symptom management. Educational hyperlinks for constipation, fatigue, diarrhea, and nausea/vomiting were added after this evaluation, and patients felt that these additions provided a higher level of education.
Staff time for this intervention was minimal. A nurse navigator offered the texting program to the patient during chemotherapy education, along with some instructions, which generally took about 5 minutes. One of the Annie program staff enrolled the patient. From that point forward, this was a self-management tool, beyond checking to ensure that the patient was successful in starting the program and evaluating use for the purposes of this quality improvement project. This self-management tool did not replace any other mechanism that a patient would normally have in our department for seeking help for symptoms. The MVAHSC typical process for symptom management is to have patients call a 24/7 nurse line. If the triage nurse feels the symptoms are related to the patient’s cancer or cancer treatment, they are referred to the physician assistant who is assigned to take those calls and has the option to see the patient the same day. Patients could continue to call the nurse line or speak with providers at the next appointment at their discretion.
Conclusion
Although Annie has the option of using either text messaging or a mobile application, this project only utilized text messaging. The study by Basch and colleagues was the closest randomized trial we could identify to compare to our quality improvement intervention.5 The 2 main, distinct differences were that Basch and colleagues utilized online monitoring; and nurses were utilized to screen and intervene on responses, as appropriate.
The ability of our program to text patients without the use of an application or tablet, may enable more patients to participate due to ease of use. There would be no increased in expected workload for clinical staff, and may lead to decreased call burden. Since our program is automated, while still providing patients with the option to call and speak with a staff member as needed, this is a cost-effective, first-line option for symptom management for those experiencing cancer-related symptoms. We believe this text messaging tool can have system wide use and benefit throughout the VHA.
1. Bruera E, Dev R. Overview of managing common non-pain symptoms in palliative care. https://www.uptodate.com/contents/overview-of-managing-common-non-pain-symptoms-in-palliative-care. Updated June 12, 2019. Accessed July 18, 2019.
2. Pirschel C. The crucial role of symptom management in cancer care. https://voice.ons.org/news-and-views/the-crucial-role-of-symptom-management-in-cancer-care. Published December 14, 2017. Accessed July 18, 2019.
3. Adam R, Burton CD, Bond CM, de Bruin M, Murchie P. Can patient-reported measurements of pain be used to improve cancer pain management? A systematic review and meta-analysis. BMJ Support Palliat Care. 2017;7(4):373-382.
4. Basch E, Deal AM, Kris MG, et al. Symptom monitoring with patient-reported outcomes during routine cancer treatment: a randomized controlled trial. J Clin Oncol. 2016;34(6):557-565.
5. Berry DL, Blonquist TM, Patel RA, Halpenny B, McReynolds J. Exposure to a patient-centered, Web-based intervention for managing cancer symptom and quality of life issues: Impact on symptom distress. J Med Internet Res. 2015;3(7):e136.
6. Kolb NA, Smith AG, Singleton JR, et al. Chemotherapy-related neuropathic symptom management: a randomized trial of an automated symptom-monitoring system paired with nurse practitioner follow-up. Support Care Cancer. 2018;26(5):1607-1615
7. Kamdar MM, Centi AJ, Fischer N, Jetwani K. A randomized controlled trial of a novel artificial-intelligence based smartphone application to optimize the management of cancer-related pain. Presented at: 2018 Palliative and Supportive Care in Oncology Symposium; November 16-17, 2018; San Diego, CA.
8. Mooney KH, Beck SL, Wong B, et al. Automated home monitoring and management of patient-reported symptoms during chemotherapy: results of the symptom care at home RCT. Cancer Med. 2017;6(3):537-546.
9. Spoelstra SL, Given CW, Sikorskii A, et al. Proof of concept of a mobile health short message service text message intervention that promotes adherence to oral anticancer agent medications: a randomized controlled trial. Telemed J E Health. 2016;22(6):497-506.
10. Fridriksdottir N, Gunnarsdottir S, Zoëga S, Ingadottir B, Hafsteinsdottir EJG. Effects of web-based interventions on cancer patients’ symptoms: review of randomized trials. Support Care Cancer. 2018;26(2):3370-351.
11. Kim AR, Park HA. Web-based self-management support intervention for cancer survivors: a systematic review and meta-analysis. Stud Health Technol Inform. 2015;216:142-147.
12. Girgis A, Durcinoska I, Levesque JV, et al; PROMPT-Care Program Group. eHealth system for collecting and utilizing patient reported outcome measures for personalized treatment and care (PROMPT-Care) among cancer patients: mixed methods approach to evaluate feasibility and acceptability. J Med Internet Res. 2017;19(10):e330.
13. Moradian S, Krzyzanowska MK, Maguire R, et al. Usability evaluation of a mobile phone-based system for remote monitoring and management of chemotherapy-related side effects in cancer patients: Mixed methods study. JMIR Cancer. 2018;4(2): e10932.
14. Voruganti T, Grunfeld E, Jamieson T, et al. My team of care study: a pilot randomized controlled trial of a web-based communication tool for collaborative care in patients with advanced cancer. J Med Internet Res. 2017;19(7):e219.
15. The Health Foundation. Overview of Florence simple telehealth text messaging system. https://www.health.org.uk/article/overview-of-the-florence-simple-telehealth-text-messaging-system. Accessed July 31, 2019.
16. Bragg DD, Edis H, Clark S, Parsons SL, Perumpalath B…Maxwell-Armstrong CA. Development of a telehealth monitoring service after colorectal surgery: a feasibility study. 2017;9(9):193-199.
17. O’Connell P. Annie-the VA’s self-care game changer. http://www.simple.uk.net/home/blog/blogcontent/annie-thevasself-caregamechanger. Published April 21, 2016. Accessed August 2, 2019.
18. Kataria L, Sundahl, C, Skalina L, et al. Text message reminders and intensive education improves positive airway pressure compliance and cognition in veterans with traumatic brain injury and obstructive sleep apnea: ANNIE pilot study (P1.097). Neurology, 2018; 90(suppl 15):P1.097.
1. Bruera E, Dev R. Overview of managing common non-pain symptoms in palliative care. https://www.uptodate.com/contents/overview-of-managing-common-non-pain-symptoms-in-palliative-care. Updated June 12, 2019. Accessed July 18, 2019.
2. Pirschel C. The crucial role of symptom management in cancer care. https://voice.ons.org/news-and-views/the-crucial-role-of-symptom-management-in-cancer-care. Published December 14, 2017. Accessed July 18, 2019.
3. Adam R, Burton CD, Bond CM, de Bruin M, Murchie P. Can patient-reported measurements of pain be used to improve cancer pain management? A systematic review and meta-analysis. BMJ Support Palliat Care. 2017;7(4):373-382.
4. Basch E, Deal AM, Kris MG, et al. Symptom monitoring with patient-reported outcomes during routine cancer treatment: a randomized controlled trial. J Clin Oncol. 2016;34(6):557-565.
5. Berry DL, Blonquist TM, Patel RA, Halpenny B, McReynolds J. Exposure to a patient-centered, Web-based intervention for managing cancer symptom and quality of life issues: Impact on symptom distress. J Med Internet Res. 2015;3(7):e136.
6. Kolb NA, Smith AG, Singleton JR, et al. Chemotherapy-related neuropathic symptom management: a randomized trial of an automated symptom-monitoring system paired with nurse practitioner follow-up. Support Care Cancer. 2018;26(5):1607-1615
7. Kamdar MM, Centi AJ, Fischer N, Jetwani K. A randomized controlled trial of a novel artificial-intelligence based smartphone application to optimize the management of cancer-related pain. Presented at: 2018 Palliative and Supportive Care in Oncology Symposium; November 16-17, 2018; San Diego, CA.
8. Mooney KH, Beck SL, Wong B, et al. Automated home monitoring and management of patient-reported symptoms during chemotherapy: results of the symptom care at home RCT. Cancer Med. 2017;6(3):537-546.
9. Spoelstra SL, Given CW, Sikorskii A, et al. Proof of concept of a mobile health short message service text message intervention that promotes adherence to oral anticancer agent medications: a randomized controlled trial. Telemed J E Health. 2016;22(6):497-506.
10. Fridriksdottir N, Gunnarsdottir S, Zoëga S, Ingadottir B, Hafsteinsdottir EJG. Effects of web-based interventions on cancer patients’ symptoms: review of randomized trials. Support Care Cancer. 2018;26(2):3370-351.
11. Kim AR, Park HA. Web-based self-management support intervention for cancer survivors: a systematic review and meta-analysis. Stud Health Technol Inform. 2015;216:142-147.
12. Girgis A, Durcinoska I, Levesque JV, et al; PROMPT-Care Program Group. eHealth system for collecting and utilizing patient reported outcome measures for personalized treatment and care (PROMPT-Care) among cancer patients: mixed methods approach to evaluate feasibility and acceptability. J Med Internet Res. 2017;19(10):e330.
13. Moradian S, Krzyzanowska MK, Maguire R, et al. Usability evaluation of a mobile phone-based system for remote monitoring and management of chemotherapy-related side effects in cancer patients: Mixed methods study. JMIR Cancer. 2018;4(2): e10932.
14. Voruganti T, Grunfeld E, Jamieson T, et al. My team of care study: a pilot randomized controlled trial of a web-based communication tool for collaborative care in patients with advanced cancer. J Med Internet Res. 2017;19(7):e219.
15. The Health Foundation. Overview of Florence simple telehealth text messaging system. https://www.health.org.uk/article/overview-of-the-florence-simple-telehealth-text-messaging-system. Accessed July 31, 2019.
16. Bragg DD, Edis H, Clark S, Parsons SL, Perumpalath B…Maxwell-Armstrong CA. Development of a telehealth monitoring service after colorectal surgery: a feasibility study. 2017;9(9):193-199.
17. O’Connell P. Annie-the VA’s self-care game changer. http://www.simple.uk.net/home/blog/blogcontent/annie-thevasself-caregamechanger. Published April 21, 2016. Accessed August 2, 2019.
18. Kataria L, Sundahl, C, Skalina L, et al. Text message reminders and intensive education improves positive airway pressure compliance and cognition in veterans with traumatic brain injury and obstructive sleep apnea: ANNIE pilot study (P1.097). Neurology, 2018; 90(suppl 15):P1.097.
Tales From VA Anesthesiology
The patient grabbed my attention as I glanced through our clinic schedule. It was his age: He was 99 years old and scheduled for eye surgery. The plastic surgery resident’s note read: “Patient understands that this would involve surgery under general anesthesia and is agreeable to moving forward...Extremely high risk of anesthesia emphasized.”
I reviewed the patient’s history. At baseline, he had severe pulmonary hypertension, severe aortic stenosis (AS), diastolic heart failure, chronic atrial fibrillation, chronic kidney disease (estimated glomerular filtration rate of 26 mL/min [normal is > 60 mL/min]), anemia (hematocrit 26%), and a standing do not resuscitate (DNR) order. His maximal daily exercise was walking slowly across a room, primarily limited by joint pain. Recent geropsychiatry notes indicated mild cognitive impairment. The anesthesia record from an urgent hip fracture repair 7 months before under general anesthesia was unremarkable.
I phoned the attending plastic surgeon. Our conversation was as follows:
“Hi, I’m about to see a 99-year-old patient with a DNR who is scheduled for resection of an eyelid tumor. His medical history makes me nervous. Are you sure this is a good idea?”“Hmmm, 99-year-old…okay, that’s right,” he responded. “He has an invasive squamous that could become a big problem. The actual procedure is under 10 minutes. Waiting for the pathology report will be the longest part of the procedure.”
“Can it be done under local?” I asked.
“Yes,” he replied.
“Okay, I’ll talk to him and call you back.”
I found the patient in the waiting room, flanked by his 2 daughters and invited them into the clinic room. After introductions, I began asking whether they had any questions about the anesthesia. By midsentence a daughter was prompting him to discuss what happened “last time.” He described a history of posttraumatic stress disorder (PTSD) stemming from his hip surgery, which he blamed squarely on the anesthesia. His emotion was evident in the gathering pauses. “I hate that I am so emotional since they kept me awake during my surgery.”
Through the fog of multiple accounts, it became clear that he was traumatized by the loss of control during the administration of and emergence from the anesthesia.
“They told me it was only oxygen,” he said. “They lied. There was a taste to it…I was awake and skinned alive…They said I was a monster when I woke up thrashing.” He went on, explaining that in the recovery room “there were 2 people bothering me, man-handling me, asking me questions.”
One of his daughters showed me pictures of bruises on his face from ripping off the mask and pulling out the breathing tube. They were visibly upset by the memory of his postoperative combativeness and paranoia. The note written by the orthopedic surgery resident on the day after surgery stated succinctly, “Doing well, had some delirium from anesthesia overnight.” Subsequent geropsychiatry home visits attested to intrusive thoughts, flashbacks, and nightmares from his time as a combat soldier in World War II, 65 years in the past.
“It took me months…months to recover,” he said.
He was in the mood to reminisce, however, perhaps a willful distraction. He had the floor for at least 30 minutes, during which I spoke about 5 sentences. With every sad story he told there was a happy, humorous one, such as meeting his future wife while on leave in New Zealand during the war, recalled down to exact dates. And another story:
There we were in New Caledonia. All our supplies went out to replace what sank on [USS] Coolidge, including a lot of food. Well, there were deer on the island. So we took out a truck and a rifle and wouldn’t you know we came upon a roadblock in the form of a big steer. We figured it looked enough like a deer. My buddy shot it dead with one shot. We dressed it and loaded it into the jeep. Hardly before we even got back to the mess hall, the officers’ cook came sniffing around. He and our captain agreed it was easily the biggest deer they’d ever seen and appropriated it to the officers’ mess. Next day the CO [commanding officer] of the whole outfit came by and announced it was the best tasting ‘venison’ he’d ever had. I heard the farmer got paid a pretty penny for that steer. I didn’t get a damn bite.
He delivered this last bit with relish.
When the conversation returned to anesthesia, I read them the record of his hip fracture repair. I explained that on the face of it, the report seemed uneventful. One daughter asked astute questions about his awareness. I explained that although awareness during general anesthesia is possible, it seemed from the record, he’d had plenty of anesthesia during the case and that there is always less at the beginning and end, the periods that apparently had caused him distress. I also explained that most studies report the incidence of true awareness as at most 1 out of thousands of events and that he had none of the established risk factors for it, such as female gender, young age, chronic substance abuse, cardiac and obstetric surgery, and history of awareness.1
The other daughter wondered why he was so agitated afterward. I recited data on the frequency of postoperative delirium in elderly patients but explained that the range is wide, depending on the study and population, from about 1% in elderly patients undergoing ambulatory surgery to 65% for open aortic surgery.2,3 I added that their father had 2 of the strongest risk factors for delirium, advanced age and cognitive impairment.3 Only after airing each question about the hip surgery in detail were they ready to discuss the eye surgery.
He started that conversation with the right question: “Do I really need it?”
I quoted my surgical colleague’s concern. I told him that, should he opt to undergo the surgery, I was confident that this time around his experience would be different from the last.
“If you’re okay with it, all you need is some numbing medicine from the surgeon; you won’t need any anesthesia from me.”
I walked step-by-step through what they could expect on the day of surgery. Maintaining control was of obvious importance to him. He felt comfortable going forward. His daughters intuited that less would be more for a quick recovery.
We then addressed the DNR directive. I acknowledged his absolute right to self-determination and explained that the need for resuscitation is, at times, a consequence of the surgery and anesthesia. I reassured them that our plan made resuscitation and intubation highly unlikely. They also asked to use any interventions necessary to restart his heart if it should stop beating. I documented their decision in my notes and communicated it to the surgical team. We had talked for 90 minutes.
I met the patient and his daughters on the day of surgery in the preoperative holding area. I inserted an IV, applied electrocardiography leads, and affixed a pulse oximeter and a noninvasive blood pressure cuff. In the operating room (OR) we took time to place his 99-year-old joints into, as he said, the “least worst” position. He tolerated the injection of the local by the surgeon perfectly well. We were in the OR for 3 hours, during which he taught me a fair amount about boating and outboard engines among other things. Pathology reported clean margins. He was discharged home soon after and had an uneventful recovery.
Patient-First Approach
A core competency of the Accreditation Council for Graduate Medical Education for an anesthesia residency is the Interpersonal and Communication Skills program. A comprehensive discussion of communication is far beyond the scope here. But not surprisingly, deficient communication between physicians and patients can cause emotional distress, significant dissatisfaction among family members, and negative patient judgment of how well we communicate.4-6 These observations are particularly true in our increasingly elderly surgical population, in which both surgeons and anesthesiologists often feel unequal to the task of discussing concepts such as code status.7,8
In our practice and in residency training, the preoperative clinic often is the location where patient/provider communication occurs. Here we consider the latest American College of Cardiology/American Heart Association guidelines, examine airways, review electrocardiograms, and formulate plans agreeable to and understood by our anxious patients and their families. The potent anxiolytic effect of a preoperative visit by an anesthesiologist is well established.9 Anxiety about surgery is a risk factor for impaired decision making before surgery.10 And surgery is traumatic—as many as 7.6% of postoperative patients experience symptoms consistent with PTSD attributable to the surgery, placing it on a par with being mugged (8.0%).11,12
The patient in this case presented several communication challenges even absent his revelation of prior traumatic experience with anesthesia. He was elderly, anxious, and had multiple comorbidities. He had mild cognitive impairment and required a code status discussion. There also were the clinical challenges—navigating a 99-year-old with severe aortic stenosis and a right ventricular systolic pressure > 90 mm Hg through a general anesthetic gave me a sinking feeling.
He was fortunate that the procedure could be done with local anesthesia, mitigating his risk of cognitive dysfunction, including delirium. He also was fortunate in that his anesthesiologist and surgeon had created a collaborative, patient-first approach and that his US Department of Veterans Affairs (VA) clinic had the time, space, and staffing to accommodate an unexpected 90-minute visit. A big investment in communication, mainly my keeping quiet, made the intraoperative management simple. Such is life in an integrated health care system without financial incentives for high-volume care—and another reminder that VA physicians are blessed to guide patients through some of the most vulnerable and distressing moments of their lives.
Postscript
During the preparation of this manuscript, the patient passed away at the age of 100. His obituary was consistent with what I had learned about him and his family during our 2 encounters: a long successful career in local industry; extensive involvement in his community; an avid sportsman; and nearly 30 grandchildren, great-grandchildren, and great-great grandchildren. But there was one more detail that never came up during my extensive discussion with him and his daughters: He was awarded the Purple Heart for his service in World War II.
1. Ghoneim MM, Block RI, Haffarnan M, Mathews MJ. Awareness during anesthesia: risk factors, causes and sequelae: a review of reported cases in the literature. Anesth Analg. 2009;108(2):527-535.
2. Aya AGM, Pouchain PH, Thomas H, Ripart J, Cuvillon P. Incidence of postoperative delirium in elderly ambulatory patients: a prospective evaluation using the FAM-CAM instrument. J Clin Anesth. 2019;53:35-38.
3. Raats JW, Steunenberg SL, de Lange DC, van der Laan L. Risk factors of post-operative delirium after elective vascular surgery in the elderly: a systematic review. Int J Surg. 2016;35:1-6.
4. Roter DL, Hall JA, Kern DE, Barker LR, Cole KA, Roca RP. Improving physicians’ interviewing skills and reducing patients’ emotional distress: a randomized clinical trial. Arch Intern Med. 1995;155(17):1877-1884.
5. Wright AA, Keating NL, Ayanian JZ, et al. Family perspectives on aggressive cancer care near the end of life. JAMA. 2016;315(3):284-292.
6. Hall JA, Roter DL, Rand CS. Communication of affect between patient and physician. J Health Soc Behav. 1981;22(1):18-30.
7. Cooper Z, Meyers M, Keating NL, Gu X, Lipsitz SR, Rogers SO. Resident education and management of end-of-life care: the resident’s perspective. J Surg Educ. 2010;67(2):79-84.
8. Hickey TR, Cooper Z, Urman RD, Hepner DL, Bader AM. An agenda for improving perioperative code status discussion. A A Case Rep. 2016;6(12):411-415.
9. Egbert LD, Battit GE, Turndorf H, Beecher HK. The value of the preoperative visit by an anesthetist. JAMA. 1963;185(7):553-555.
10. Ankuda CK, Block SD, Cooper Z, et al. Measuring critical deficits in shared decision making before elective surgery. Patient Educ Couns. 2014;94(3):328-333.
11. Whitlock EL, Rodebaugh TL, Hassett AL, et al. Psychological sequelae of surgery in a prospective cohort of patients from three intraoperative awareness prevention trials. Anesth Analg. 2015;120(1):87-95.
12. Breslau N, Kessler RC, Chilcoat HD, Schultz LR, Davis GC, Andreski P. Trauma and posttraumatic stress disorder in the community: the 1996 Detroit Area Survey of Trauma. Arch Gen Psychiatry. 1998;55(7):626-632.
The patient grabbed my attention as I glanced through our clinic schedule. It was his age: He was 99 years old and scheduled for eye surgery. The plastic surgery resident’s note read: “Patient understands that this would involve surgery under general anesthesia and is agreeable to moving forward...Extremely high risk of anesthesia emphasized.”
I reviewed the patient’s history. At baseline, he had severe pulmonary hypertension, severe aortic stenosis (AS), diastolic heart failure, chronic atrial fibrillation, chronic kidney disease (estimated glomerular filtration rate of 26 mL/min [normal is > 60 mL/min]), anemia (hematocrit 26%), and a standing do not resuscitate (DNR) order. His maximal daily exercise was walking slowly across a room, primarily limited by joint pain. Recent geropsychiatry notes indicated mild cognitive impairment. The anesthesia record from an urgent hip fracture repair 7 months before under general anesthesia was unremarkable.
I phoned the attending plastic surgeon. Our conversation was as follows:
“Hi, I’m about to see a 99-year-old patient with a DNR who is scheduled for resection of an eyelid tumor. His medical history makes me nervous. Are you sure this is a good idea?”“Hmmm, 99-year-old…okay, that’s right,” he responded. “He has an invasive squamous that could become a big problem. The actual procedure is under 10 minutes. Waiting for the pathology report will be the longest part of the procedure.”
“Can it be done under local?” I asked.
“Yes,” he replied.
“Okay, I’ll talk to him and call you back.”
I found the patient in the waiting room, flanked by his 2 daughters and invited them into the clinic room. After introductions, I began asking whether they had any questions about the anesthesia. By midsentence a daughter was prompting him to discuss what happened “last time.” He described a history of posttraumatic stress disorder (PTSD) stemming from his hip surgery, which he blamed squarely on the anesthesia. His emotion was evident in the gathering pauses. “I hate that I am so emotional since they kept me awake during my surgery.”
Through the fog of multiple accounts, it became clear that he was traumatized by the loss of control during the administration of and emergence from the anesthesia.
“They told me it was only oxygen,” he said. “They lied. There was a taste to it…I was awake and skinned alive…They said I was a monster when I woke up thrashing.” He went on, explaining that in the recovery room “there were 2 people bothering me, man-handling me, asking me questions.”
One of his daughters showed me pictures of bruises on his face from ripping off the mask and pulling out the breathing tube. They were visibly upset by the memory of his postoperative combativeness and paranoia. The note written by the orthopedic surgery resident on the day after surgery stated succinctly, “Doing well, had some delirium from anesthesia overnight.” Subsequent geropsychiatry home visits attested to intrusive thoughts, flashbacks, and nightmares from his time as a combat soldier in World War II, 65 years in the past.
“It took me months…months to recover,” he said.
He was in the mood to reminisce, however, perhaps a willful distraction. He had the floor for at least 30 minutes, during which I spoke about 5 sentences. With every sad story he told there was a happy, humorous one, such as meeting his future wife while on leave in New Zealand during the war, recalled down to exact dates. And another story:
There we were in New Caledonia. All our supplies went out to replace what sank on [USS] Coolidge, including a lot of food. Well, there were deer on the island. So we took out a truck and a rifle and wouldn’t you know we came upon a roadblock in the form of a big steer. We figured it looked enough like a deer. My buddy shot it dead with one shot. We dressed it and loaded it into the jeep. Hardly before we even got back to the mess hall, the officers’ cook came sniffing around. He and our captain agreed it was easily the biggest deer they’d ever seen and appropriated it to the officers’ mess. Next day the CO [commanding officer] of the whole outfit came by and announced it was the best tasting ‘venison’ he’d ever had. I heard the farmer got paid a pretty penny for that steer. I didn’t get a damn bite.
He delivered this last bit with relish.
When the conversation returned to anesthesia, I read them the record of his hip fracture repair. I explained that on the face of it, the report seemed uneventful. One daughter asked astute questions about his awareness. I explained that although awareness during general anesthesia is possible, it seemed from the record, he’d had plenty of anesthesia during the case and that there is always less at the beginning and end, the periods that apparently had caused him distress. I also explained that most studies report the incidence of true awareness as at most 1 out of thousands of events and that he had none of the established risk factors for it, such as female gender, young age, chronic substance abuse, cardiac and obstetric surgery, and history of awareness.1
The other daughter wondered why he was so agitated afterward. I recited data on the frequency of postoperative delirium in elderly patients but explained that the range is wide, depending on the study and population, from about 1% in elderly patients undergoing ambulatory surgery to 65% for open aortic surgery.2,3 I added that their father had 2 of the strongest risk factors for delirium, advanced age and cognitive impairment.3 Only after airing each question about the hip surgery in detail were they ready to discuss the eye surgery.
He started that conversation with the right question: “Do I really need it?”
I quoted my surgical colleague’s concern. I told him that, should he opt to undergo the surgery, I was confident that this time around his experience would be different from the last.
“If you’re okay with it, all you need is some numbing medicine from the surgeon; you won’t need any anesthesia from me.”
I walked step-by-step through what they could expect on the day of surgery. Maintaining control was of obvious importance to him. He felt comfortable going forward. His daughters intuited that less would be more for a quick recovery.
We then addressed the DNR directive. I acknowledged his absolute right to self-determination and explained that the need for resuscitation is, at times, a consequence of the surgery and anesthesia. I reassured them that our plan made resuscitation and intubation highly unlikely. They also asked to use any interventions necessary to restart his heart if it should stop beating. I documented their decision in my notes and communicated it to the surgical team. We had talked for 90 minutes.
I met the patient and his daughters on the day of surgery in the preoperative holding area. I inserted an IV, applied electrocardiography leads, and affixed a pulse oximeter and a noninvasive blood pressure cuff. In the operating room (OR) we took time to place his 99-year-old joints into, as he said, the “least worst” position. He tolerated the injection of the local by the surgeon perfectly well. We were in the OR for 3 hours, during which he taught me a fair amount about boating and outboard engines among other things. Pathology reported clean margins. He was discharged home soon after and had an uneventful recovery.
Patient-First Approach
A core competency of the Accreditation Council for Graduate Medical Education for an anesthesia residency is the Interpersonal and Communication Skills program. A comprehensive discussion of communication is far beyond the scope here. But not surprisingly, deficient communication between physicians and patients can cause emotional distress, significant dissatisfaction among family members, and negative patient judgment of how well we communicate.4-6 These observations are particularly true in our increasingly elderly surgical population, in which both surgeons and anesthesiologists often feel unequal to the task of discussing concepts such as code status.7,8
In our practice and in residency training, the preoperative clinic often is the location where patient/provider communication occurs. Here we consider the latest American College of Cardiology/American Heart Association guidelines, examine airways, review electrocardiograms, and formulate plans agreeable to and understood by our anxious patients and their families. The potent anxiolytic effect of a preoperative visit by an anesthesiologist is well established.9 Anxiety about surgery is a risk factor for impaired decision making before surgery.10 And surgery is traumatic—as many as 7.6% of postoperative patients experience symptoms consistent with PTSD attributable to the surgery, placing it on a par with being mugged (8.0%).11,12
The patient in this case presented several communication challenges even absent his revelation of prior traumatic experience with anesthesia. He was elderly, anxious, and had multiple comorbidities. He had mild cognitive impairment and required a code status discussion. There also were the clinical challenges—navigating a 99-year-old with severe aortic stenosis and a right ventricular systolic pressure > 90 mm Hg through a general anesthetic gave me a sinking feeling.
He was fortunate that the procedure could be done with local anesthesia, mitigating his risk of cognitive dysfunction, including delirium. He also was fortunate in that his anesthesiologist and surgeon had created a collaborative, patient-first approach and that his US Department of Veterans Affairs (VA) clinic had the time, space, and staffing to accommodate an unexpected 90-minute visit. A big investment in communication, mainly my keeping quiet, made the intraoperative management simple. Such is life in an integrated health care system without financial incentives for high-volume care—and another reminder that VA physicians are blessed to guide patients through some of the most vulnerable and distressing moments of their lives.
Postscript
During the preparation of this manuscript, the patient passed away at the age of 100. His obituary was consistent with what I had learned about him and his family during our 2 encounters: a long successful career in local industry; extensive involvement in his community; an avid sportsman; and nearly 30 grandchildren, great-grandchildren, and great-great grandchildren. But there was one more detail that never came up during my extensive discussion with him and his daughters: He was awarded the Purple Heart for his service in World War II.
The patient grabbed my attention as I glanced through our clinic schedule. It was his age: He was 99 years old and scheduled for eye surgery. The plastic surgery resident’s note read: “Patient understands that this would involve surgery under general anesthesia and is agreeable to moving forward...Extremely high risk of anesthesia emphasized.”
I reviewed the patient’s history. At baseline, he had severe pulmonary hypertension, severe aortic stenosis (AS), diastolic heart failure, chronic atrial fibrillation, chronic kidney disease (estimated glomerular filtration rate of 26 mL/min [normal is > 60 mL/min]), anemia (hematocrit 26%), and a standing do not resuscitate (DNR) order. His maximal daily exercise was walking slowly across a room, primarily limited by joint pain. Recent geropsychiatry notes indicated mild cognitive impairment. The anesthesia record from an urgent hip fracture repair 7 months before under general anesthesia was unremarkable.
I phoned the attending plastic surgeon. Our conversation was as follows:
“Hi, I’m about to see a 99-year-old patient with a DNR who is scheduled for resection of an eyelid tumor. His medical history makes me nervous. Are you sure this is a good idea?”“Hmmm, 99-year-old…okay, that’s right,” he responded. “He has an invasive squamous that could become a big problem. The actual procedure is under 10 minutes. Waiting for the pathology report will be the longest part of the procedure.”
“Can it be done under local?” I asked.
“Yes,” he replied.
“Okay, I’ll talk to him and call you back.”
I found the patient in the waiting room, flanked by his 2 daughters and invited them into the clinic room. After introductions, I began asking whether they had any questions about the anesthesia. By midsentence a daughter was prompting him to discuss what happened “last time.” He described a history of posttraumatic stress disorder (PTSD) stemming from his hip surgery, which he blamed squarely on the anesthesia. His emotion was evident in the gathering pauses. “I hate that I am so emotional since they kept me awake during my surgery.”
Through the fog of multiple accounts, it became clear that he was traumatized by the loss of control during the administration of and emergence from the anesthesia.
“They told me it was only oxygen,” he said. “They lied. There was a taste to it…I was awake and skinned alive…They said I was a monster when I woke up thrashing.” He went on, explaining that in the recovery room “there were 2 people bothering me, man-handling me, asking me questions.”
One of his daughters showed me pictures of bruises on his face from ripping off the mask and pulling out the breathing tube. They were visibly upset by the memory of his postoperative combativeness and paranoia. The note written by the orthopedic surgery resident on the day after surgery stated succinctly, “Doing well, had some delirium from anesthesia overnight.” Subsequent geropsychiatry home visits attested to intrusive thoughts, flashbacks, and nightmares from his time as a combat soldier in World War II, 65 years in the past.
“It took me months…months to recover,” he said.
He was in the mood to reminisce, however, perhaps a willful distraction. He had the floor for at least 30 minutes, during which I spoke about 5 sentences. With every sad story he told there was a happy, humorous one, such as meeting his future wife while on leave in New Zealand during the war, recalled down to exact dates. And another story:
There we were in New Caledonia. All our supplies went out to replace what sank on [USS] Coolidge, including a lot of food. Well, there were deer on the island. So we took out a truck and a rifle and wouldn’t you know we came upon a roadblock in the form of a big steer. We figured it looked enough like a deer. My buddy shot it dead with one shot. We dressed it and loaded it into the jeep. Hardly before we even got back to the mess hall, the officers’ cook came sniffing around. He and our captain agreed it was easily the biggest deer they’d ever seen and appropriated it to the officers’ mess. Next day the CO [commanding officer] of the whole outfit came by and announced it was the best tasting ‘venison’ he’d ever had. I heard the farmer got paid a pretty penny for that steer. I didn’t get a damn bite.
He delivered this last bit with relish.
When the conversation returned to anesthesia, I read them the record of his hip fracture repair. I explained that on the face of it, the report seemed uneventful. One daughter asked astute questions about his awareness. I explained that although awareness during general anesthesia is possible, it seemed from the record, he’d had plenty of anesthesia during the case and that there is always less at the beginning and end, the periods that apparently had caused him distress. I also explained that most studies report the incidence of true awareness as at most 1 out of thousands of events and that he had none of the established risk factors for it, such as female gender, young age, chronic substance abuse, cardiac and obstetric surgery, and history of awareness.1
The other daughter wondered why he was so agitated afterward. I recited data on the frequency of postoperative delirium in elderly patients but explained that the range is wide, depending on the study and population, from about 1% in elderly patients undergoing ambulatory surgery to 65% for open aortic surgery.2,3 I added that their father had 2 of the strongest risk factors for delirium, advanced age and cognitive impairment.3 Only after airing each question about the hip surgery in detail were they ready to discuss the eye surgery.
He started that conversation with the right question: “Do I really need it?”
I quoted my surgical colleague’s concern. I told him that, should he opt to undergo the surgery, I was confident that this time around his experience would be different from the last.
“If you’re okay with it, all you need is some numbing medicine from the surgeon; you won’t need any anesthesia from me.”
I walked step-by-step through what they could expect on the day of surgery. Maintaining control was of obvious importance to him. He felt comfortable going forward. His daughters intuited that less would be more for a quick recovery.
We then addressed the DNR directive. I acknowledged his absolute right to self-determination and explained that the need for resuscitation is, at times, a consequence of the surgery and anesthesia. I reassured them that our plan made resuscitation and intubation highly unlikely. They also asked to use any interventions necessary to restart his heart if it should stop beating. I documented their decision in my notes and communicated it to the surgical team. We had talked for 90 minutes.
I met the patient and his daughters on the day of surgery in the preoperative holding area. I inserted an IV, applied electrocardiography leads, and affixed a pulse oximeter and a noninvasive blood pressure cuff. In the operating room (OR) we took time to place his 99-year-old joints into, as he said, the “least worst” position. He tolerated the injection of the local by the surgeon perfectly well. We were in the OR for 3 hours, during which he taught me a fair amount about boating and outboard engines among other things. Pathology reported clean margins. He was discharged home soon after and had an uneventful recovery.
Patient-First Approach
A core competency of the Accreditation Council for Graduate Medical Education for an anesthesia residency is the Interpersonal and Communication Skills program. A comprehensive discussion of communication is far beyond the scope here. But not surprisingly, deficient communication between physicians and patients can cause emotional distress, significant dissatisfaction among family members, and negative patient judgment of how well we communicate.4-6 These observations are particularly true in our increasingly elderly surgical population, in which both surgeons and anesthesiologists often feel unequal to the task of discussing concepts such as code status.7,8
In our practice and in residency training, the preoperative clinic often is the location where patient/provider communication occurs. Here we consider the latest American College of Cardiology/American Heart Association guidelines, examine airways, review electrocardiograms, and formulate plans agreeable to and understood by our anxious patients and their families. The potent anxiolytic effect of a preoperative visit by an anesthesiologist is well established.9 Anxiety about surgery is a risk factor for impaired decision making before surgery.10 And surgery is traumatic—as many as 7.6% of postoperative patients experience symptoms consistent with PTSD attributable to the surgery, placing it on a par with being mugged (8.0%).11,12
The patient in this case presented several communication challenges even absent his revelation of prior traumatic experience with anesthesia. He was elderly, anxious, and had multiple comorbidities. He had mild cognitive impairment and required a code status discussion. There also were the clinical challenges—navigating a 99-year-old with severe aortic stenosis and a right ventricular systolic pressure > 90 mm Hg through a general anesthetic gave me a sinking feeling.
He was fortunate that the procedure could be done with local anesthesia, mitigating his risk of cognitive dysfunction, including delirium. He also was fortunate in that his anesthesiologist and surgeon had created a collaborative, patient-first approach and that his US Department of Veterans Affairs (VA) clinic had the time, space, and staffing to accommodate an unexpected 90-minute visit. A big investment in communication, mainly my keeping quiet, made the intraoperative management simple. Such is life in an integrated health care system without financial incentives for high-volume care—and another reminder that VA physicians are blessed to guide patients through some of the most vulnerable and distressing moments of their lives.
Postscript
During the preparation of this manuscript, the patient passed away at the age of 100. His obituary was consistent with what I had learned about him and his family during our 2 encounters: a long successful career in local industry; extensive involvement in his community; an avid sportsman; and nearly 30 grandchildren, great-grandchildren, and great-great grandchildren. But there was one more detail that never came up during my extensive discussion with him and his daughters: He was awarded the Purple Heart for his service in World War II.
1. Ghoneim MM, Block RI, Haffarnan M, Mathews MJ. Awareness during anesthesia: risk factors, causes and sequelae: a review of reported cases in the literature. Anesth Analg. 2009;108(2):527-535.
2. Aya AGM, Pouchain PH, Thomas H, Ripart J, Cuvillon P. Incidence of postoperative delirium in elderly ambulatory patients: a prospective evaluation using the FAM-CAM instrument. J Clin Anesth. 2019;53:35-38.
3. Raats JW, Steunenberg SL, de Lange DC, van der Laan L. Risk factors of post-operative delirium after elective vascular surgery in the elderly: a systematic review. Int J Surg. 2016;35:1-6.
4. Roter DL, Hall JA, Kern DE, Barker LR, Cole KA, Roca RP. Improving physicians’ interviewing skills and reducing patients’ emotional distress: a randomized clinical trial. Arch Intern Med. 1995;155(17):1877-1884.
5. Wright AA, Keating NL, Ayanian JZ, et al. Family perspectives on aggressive cancer care near the end of life. JAMA. 2016;315(3):284-292.
6. Hall JA, Roter DL, Rand CS. Communication of affect between patient and physician. J Health Soc Behav. 1981;22(1):18-30.
7. Cooper Z, Meyers M, Keating NL, Gu X, Lipsitz SR, Rogers SO. Resident education and management of end-of-life care: the resident’s perspective. J Surg Educ. 2010;67(2):79-84.
8. Hickey TR, Cooper Z, Urman RD, Hepner DL, Bader AM. An agenda for improving perioperative code status discussion. A A Case Rep. 2016;6(12):411-415.
9. Egbert LD, Battit GE, Turndorf H, Beecher HK. The value of the preoperative visit by an anesthetist. JAMA. 1963;185(7):553-555.
10. Ankuda CK, Block SD, Cooper Z, et al. Measuring critical deficits in shared decision making before elective surgery. Patient Educ Couns. 2014;94(3):328-333.
11. Whitlock EL, Rodebaugh TL, Hassett AL, et al. Psychological sequelae of surgery in a prospective cohort of patients from three intraoperative awareness prevention trials. Anesth Analg. 2015;120(1):87-95.
12. Breslau N, Kessler RC, Chilcoat HD, Schultz LR, Davis GC, Andreski P. Trauma and posttraumatic stress disorder in the community: the 1996 Detroit Area Survey of Trauma. Arch Gen Psychiatry. 1998;55(7):626-632.
1. Ghoneim MM, Block RI, Haffarnan M, Mathews MJ. Awareness during anesthesia: risk factors, causes and sequelae: a review of reported cases in the literature. Anesth Analg. 2009;108(2):527-535.
2. Aya AGM, Pouchain PH, Thomas H, Ripart J, Cuvillon P. Incidence of postoperative delirium in elderly ambulatory patients: a prospective evaluation using the FAM-CAM instrument. J Clin Anesth. 2019;53:35-38.
3. Raats JW, Steunenberg SL, de Lange DC, van der Laan L. Risk factors of post-operative delirium after elective vascular surgery in the elderly: a systematic review. Int J Surg. 2016;35:1-6.
4. Roter DL, Hall JA, Kern DE, Barker LR, Cole KA, Roca RP. Improving physicians’ interviewing skills and reducing patients’ emotional distress: a randomized clinical trial. Arch Intern Med. 1995;155(17):1877-1884.
5. Wright AA, Keating NL, Ayanian JZ, et al. Family perspectives on aggressive cancer care near the end of life. JAMA. 2016;315(3):284-292.
6. Hall JA, Roter DL, Rand CS. Communication of affect between patient and physician. J Health Soc Behav. 1981;22(1):18-30.
7. Cooper Z, Meyers M, Keating NL, Gu X, Lipsitz SR, Rogers SO. Resident education and management of end-of-life care: the resident’s perspective. J Surg Educ. 2010;67(2):79-84.
8. Hickey TR, Cooper Z, Urman RD, Hepner DL, Bader AM. An agenda for improving perioperative code status discussion. A A Case Rep. 2016;6(12):411-415.
9. Egbert LD, Battit GE, Turndorf H, Beecher HK. The value of the preoperative visit by an anesthetist. JAMA. 1963;185(7):553-555.
10. Ankuda CK, Block SD, Cooper Z, et al. Measuring critical deficits in shared decision making before elective surgery. Patient Educ Couns. 2014;94(3):328-333.
11. Whitlock EL, Rodebaugh TL, Hassett AL, et al. Psychological sequelae of surgery in a prospective cohort of patients from three intraoperative awareness prevention trials. Anesth Analg. 2015;120(1):87-95.
12. Breslau N, Kessler RC, Chilcoat HD, Schultz LR, Davis GC, Andreski P. Trauma and posttraumatic stress disorder in the community: the 1996 Detroit Area Survey of Trauma. Arch Gen Psychiatry. 1998;55(7):626-632.
A Novel Pharmaceutical Care Model for High-Risk Patients
Nonadherence is a significant problem that has a negative impact on both patients and public health. Patients with multiple diseases often have complicated medication regimens, which can be difficult for them to manage. Unfortunately, nonadherence in these high-risk patients can have drastic consequences, including disease progression, hospitalization, and death, resulting in billions of dollars in unnecessary costs nationwide.1,2 The Wheel Model of Pharmaceutical Care (Figure) is a novel care model developed at the Gallup Indian Medical Center (GIMC) in New Mexico to address these problems by positioning pharmacy as a proactive service. The Wheel Model of Pharmaceutical Care was designed to improve adherence and patient outcomes and to encourage communication among the patient, pharmacists, prescribers, and other health care team members.
Pharmacists are central to managing patients’ medication therapies and coordinating communication among the health care providers (HCPs).1,3 Medication therapy management (MTM), a required component of Medicare Part D plans, helps ensure appropriate drug use and reduce the risk of adverse events.3 Since pharmacists receive prescriptions from all of the patient’s HCPs, patients may see pharmacists more often than they see any other HCP. GIMC is currently piloting a new clinic, the Medication Optimization, Synchronization, and Adherence Improvement Clinic (MOSAIC), that was created to implement the Wheel Model of Pharmaceutical Care. MOSAIC aims to provide proactive pharmacy services and continuous MTM to high-risk patients and will enable the effectiveness of this new pharmaceutical care model to be assessed.
Methods
Studies have identified certain populations who are at an increased risk for nonadherence: the elderly, patients with complex or extensive medication regimens, patients with multiple chronic medical conditions, substance misusers, certain ethnicities, patients of lower socioeconomic status, patients with limited literacy, and the homeless.2,4 Federal regulations require that Medicare Part D plans target beneficiaries who meet specific criteria for MTM programs. Under these rules, plans must target beneficiaries with ≥ 3 chronic diseases and ≥ 8 chronic medications, although plans also may include patients with fewer medications and diseases.3 Although the Wheel Model of Pharmaceutical Care is postulated to be an accurate model for the ideal care of all patients, initial implementation should be targeted toward populations who are likely to benefit the most from intervention. For these reasons, elderly Native American patients who have ≥ 2 chronic diseases and who take ≥ 5 chronic medications were targeted for initial enrollment in MOSAIC at GIMC.
Overview
In MOSAIC, pharmacists act as the hub of the pharmaceutical care wheel. Pharmacists work to ensure optimization of the patient’s comprehensive, integrated care plan—the rim of the wheel. As a part of this optimization process, MOSAIC pharmacists facilitate synchronization of the patient’s prescriptions to a monthly or quarterly target fill date. The patient’s current medication therapy is organized, and pharmacists track which medications are due to be filled instead of depending on the patient to request each prescription refill. This process effectively changes pharmacy from a requested service to a provided service.
Pharmacists also monitor the air in the tire to promote adherence. This is accomplished by providing efficient monthly or quarterly telephone or in-person consultations, which helps the patient better understand his or her comprehensive, integrated care plan. MOSAIC eliminates the possibility of nonadherence due to running out of refills. Specialized packaging, such as pill boxes or blister packs, can also improve adherence for certain patients.
MOSAIC ensures that pharmacists stay connected with the spokes, which represent a patient’s numerous prescribers, and close communication loops. Pharmacists can make prescribers aware of potential gaps or overlaps in treatment and assist them in the optimization and development of the patient’s comprehensive, integrated care plan. Pharmacists also make sure that the patient’s medication profile is current and accurate in the electronic health record (EHR). Any pertinent information discovered during MOSAIC encounters, such as abnormal laboratory results or changes in medications or disease, is documented in an EHR note. The patient’s prescribers are made aware of this information by tagging them as additional signers to the note in the EHR.
Keeping patients—the tires—healthy will ensure smooth operation of the vehicle and have a positive impact on public health. MOSAIC is expected to not only improve individual patient outcomes, but also decrease health care costs for patients and society due to nonadherence, suboptimal regimens, stockpiled home medications, and preventable hospital admissions.
Traditionally, pharmacy has been a requested service: A patient requests each of their prescriptions to be refilled, and the pharmacy fills the prescription. Ideally, pharmacy must become a provided service, with pharmacists keeping track of when a patient’s medications are due to be filled and actively looking for medication therapy optimization opportunities. This is accomplished by synchronizing the patient’s medications to the same monthly or quarterly fill date; screening for any potentially inappropriate medications, including high-risk medications in elderly patients, duplications, and omissions; verifying any medication changes with the patient each fill; and then providing all needed medications to the patient at a scheduled time.
To facilitate this process, custom software was developed for MOSAIC. In addition, a collaborative practice agreement (CPA) was drafted that allowed MOSAIC pharmacists to make certain medication therapy optimizations on behalf of the patient’s primary care provider. As part of this CPA, pharmacists also may order and act on certain laboratory tests, which helps to monitor disease progression, ensure safe medication use, and meet Government Performance and Results Act (GPRA) measures. As a novel model of pharmaceutical care, the effects of this approach are not yet known; however, research suggests that increased communication among HCPs and patient-centered approaches to care are beneficial to patient outcomes, adherence, and public health.1,5
Investigated Outcomes
As patients continue to enroll in MOSAIC, the effectiveness of the clinic will be evaluated. Specifically, quality of life, patient and HCP satisfaction with the program, adherence metrics, hospitalization rates, and all-cause mortality will be assessed for patients enrolled in MOSAIC as well as similar patients who are not enrolled in MOSAIC. Also, pharmacists will log all recommended medication therapy interventions so that the optimization component of MOSAIC may be quantified. GPRA measures and the financial implications of the interventions made by MOSAIC will also be evaluated.
Discussion
There are a number of factors, such as MTM services and interprofessional care teams, that research has shown to independently improve patient outcomes, adherence, or public health. By synthesizing these factors, a completely new approach—the Wheel Model of Pharmaceutical Care—was developed. This model presents a radical departure from traditional, requested-service practices and posits pharmacy as a provided service instead. Although the ideas of MTM and interprofessional care teams are not new, there has never been a practical way to truly integrate community pharmacists into the patient care team or to ensure adequate communication among all of the patient’s HCPs. The Wheel Model of Pharmaceutical Care includes public health as one of its core components and provides a framework for pharmacies to meaningfully impact health outcomes for patients.
The Wheel Model of Pharmaceutical Care was designed to minimize the likelihood of nonadherence. Despite this, patients might willfully choose to be nonadherent, forget to take their medications, or neglect to pick up their medications. Additionally, in health care systems where patients must pay for their medications, prescription drug costs might be a barrier to adherence.
When nonadherence is suspected, the Wheel Model of Pharmaceutical Care directs pharmacists in MOSAIC to take action. First, the underlying cause of the nonadherence must be determined. For example, if a patient is nonadherent because of an adverse drug reaction, a therapy change may be indicated. If a patient is nonadherent due to apathy toward their health or therapy, the patient may benefit from education about their condition and treatment options; thus, the patient can make shared, informed decisions and feel more actively involved with his or her health. If a patients is nonadherent due to forgetfulness, adherence packaging dispense methods should be considered as an alternative to traditional vials. Depending on the services offered by a given pharmacy, adherence packaging options may include blister packs, pill boxes, or strips prepared by robotic dispensing systems. The use of medication reminders, whether in the form of a smartphone application or a simple alarm clock, should be discussed with the patient. If the patient does not pick up their medications on time, a pharmacist can contact the patient to determine why the medications were not picked up and to assess any nonadherence. In this case, mail order pharmacy services, if available, should be offered to patients as a more convenient option.
The medication regimen optimization component of MOSAIC helps reduce the workload of primary care providers and allows pharmacists to act autonomously based on clinical judgment, within the scope of the CPA. This can prevent delays in care caused by no refills remaining on a prescription. The laboratory monitoring component allows pharmacists to track diseases and take action if necessary, which should have a favorable impact on GPRA measures. Medication optimizations can reduce wasted resources by identifying cost-saving formulary alternatives, potentially inappropriate medications, and suboptimal doses.
Since many Indian Health Service beneficiaries do not have private insurance and therefore do not generate third-party reimbursements for services and care provided by GIMC, keeping patients healthy and out of the hospital is a top priority. As more patients are enrolled in MOSAIC, the program is expected to have a favorable impact on pharmacy workload and workflow as well. Prescriptions are anticipated and filled in advance, which decreases the amount of patients calling and presenting to the pharmacy for same-day refill requests. Scheduling when MOSAIC patients’ medications are to be filled and dispensed creates a predictable workload that allows the pharmacy staff to be managed more efficiently.
Conclusion
Adherence is the responsibility of the patient, but the Wheel Model of Pharmaceutical Care aims to provide pharmacists with a framework to monitor and encourage adherence in their patients. By taking this patient-centered approach, MOSAIC is expected to improve outcomes and decrease hospitalizations for high-risk patients who simply need a little extra help with their medications.
1. Bosworth HB, Granger BB, Mendys P, et al. Medication adherence: a call for action. Am Heart J. 2011;162(3):412-424.
2. Vlasnik JJ, Aliotta SL, DeLor B. Medication adherence: factors influencing compliance with prescribed medication plans. Case Manager. 2005;16(2):47-51.
3. Drug utilization management, quality assurance, and medication therapy management programs (MTMPs). Fed Regist. 2012;77(71):2207-22175. To be codified at 42 CFR § 423.153.
4. Thiruchselvam T, Naglie G, Moineddin R, et al. Risk factors for medication nonadherence in older adults with cognitive impairment who live alone. Int J Geriatr Psychiatry. 2012;27(12):1275-1282.
5. Liddy C, Blazkho V, Mill K. Challenges of self-management when living with multiple chronic conditions: systematic review of the qualitative literature. Can Fam Physician. 2014;60(12):1123-1133.
Nonadherence is a significant problem that has a negative impact on both patients and public health. Patients with multiple diseases often have complicated medication regimens, which can be difficult for them to manage. Unfortunately, nonadherence in these high-risk patients can have drastic consequences, including disease progression, hospitalization, and death, resulting in billions of dollars in unnecessary costs nationwide.1,2 The Wheel Model of Pharmaceutical Care (Figure) is a novel care model developed at the Gallup Indian Medical Center (GIMC) in New Mexico to address these problems by positioning pharmacy as a proactive service. The Wheel Model of Pharmaceutical Care was designed to improve adherence and patient outcomes and to encourage communication among the patient, pharmacists, prescribers, and other health care team members.
Pharmacists are central to managing patients’ medication therapies and coordinating communication among the health care providers (HCPs).1,3 Medication therapy management (MTM), a required component of Medicare Part D plans, helps ensure appropriate drug use and reduce the risk of adverse events.3 Since pharmacists receive prescriptions from all of the patient’s HCPs, patients may see pharmacists more often than they see any other HCP. GIMC is currently piloting a new clinic, the Medication Optimization, Synchronization, and Adherence Improvement Clinic (MOSAIC), that was created to implement the Wheel Model of Pharmaceutical Care. MOSAIC aims to provide proactive pharmacy services and continuous MTM to high-risk patients and will enable the effectiveness of this new pharmaceutical care model to be assessed.
Methods
Studies have identified certain populations who are at an increased risk for nonadherence: the elderly, patients with complex or extensive medication regimens, patients with multiple chronic medical conditions, substance misusers, certain ethnicities, patients of lower socioeconomic status, patients with limited literacy, and the homeless.2,4 Federal regulations require that Medicare Part D plans target beneficiaries who meet specific criteria for MTM programs. Under these rules, plans must target beneficiaries with ≥ 3 chronic diseases and ≥ 8 chronic medications, although plans also may include patients with fewer medications and diseases.3 Although the Wheel Model of Pharmaceutical Care is postulated to be an accurate model for the ideal care of all patients, initial implementation should be targeted toward populations who are likely to benefit the most from intervention. For these reasons, elderly Native American patients who have ≥ 2 chronic diseases and who take ≥ 5 chronic medications were targeted for initial enrollment in MOSAIC at GIMC.
Overview
In MOSAIC, pharmacists act as the hub of the pharmaceutical care wheel. Pharmacists work to ensure optimization of the patient’s comprehensive, integrated care plan—the rim of the wheel. As a part of this optimization process, MOSAIC pharmacists facilitate synchronization of the patient’s prescriptions to a monthly or quarterly target fill date. The patient’s current medication therapy is organized, and pharmacists track which medications are due to be filled instead of depending on the patient to request each prescription refill. This process effectively changes pharmacy from a requested service to a provided service.
Pharmacists also monitor the air in the tire to promote adherence. This is accomplished by providing efficient monthly or quarterly telephone or in-person consultations, which helps the patient better understand his or her comprehensive, integrated care plan. MOSAIC eliminates the possibility of nonadherence due to running out of refills. Specialized packaging, such as pill boxes or blister packs, can also improve adherence for certain patients.
MOSAIC ensures that pharmacists stay connected with the spokes, which represent a patient’s numerous prescribers, and close communication loops. Pharmacists can make prescribers aware of potential gaps or overlaps in treatment and assist them in the optimization and development of the patient’s comprehensive, integrated care plan. Pharmacists also make sure that the patient’s medication profile is current and accurate in the electronic health record (EHR). Any pertinent information discovered during MOSAIC encounters, such as abnormal laboratory results or changes in medications or disease, is documented in an EHR note. The patient’s prescribers are made aware of this information by tagging them as additional signers to the note in the EHR.
Keeping patients—the tires—healthy will ensure smooth operation of the vehicle and have a positive impact on public health. MOSAIC is expected to not only improve individual patient outcomes, but also decrease health care costs for patients and society due to nonadherence, suboptimal regimens, stockpiled home medications, and preventable hospital admissions.
Traditionally, pharmacy has been a requested service: A patient requests each of their prescriptions to be refilled, and the pharmacy fills the prescription. Ideally, pharmacy must become a provided service, with pharmacists keeping track of when a patient’s medications are due to be filled and actively looking for medication therapy optimization opportunities. This is accomplished by synchronizing the patient’s medications to the same monthly or quarterly fill date; screening for any potentially inappropriate medications, including high-risk medications in elderly patients, duplications, and omissions; verifying any medication changes with the patient each fill; and then providing all needed medications to the patient at a scheduled time.
To facilitate this process, custom software was developed for MOSAIC. In addition, a collaborative practice agreement (CPA) was drafted that allowed MOSAIC pharmacists to make certain medication therapy optimizations on behalf of the patient’s primary care provider. As part of this CPA, pharmacists also may order and act on certain laboratory tests, which helps to monitor disease progression, ensure safe medication use, and meet Government Performance and Results Act (GPRA) measures. As a novel model of pharmaceutical care, the effects of this approach are not yet known; however, research suggests that increased communication among HCPs and patient-centered approaches to care are beneficial to patient outcomes, adherence, and public health.1,5
Investigated Outcomes
As patients continue to enroll in MOSAIC, the effectiveness of the clinic will be evaluated. Specifically, quality of life, patient and HCP satisfaction with the program, adherence metrics, hospitalization rates, and all-cause mortality will be assessed for patients enrolled in MOSAIC as well as similar patients who are not enrolled in MOSAIC. Also, pharmacists will log all recommended medication therapy interventions so that the optimization component of MOSAIC may be quantified. GPRA measures and the financial implications of the interventions made by MOSAIC will also be evaluated.
Discussion
There are a number of factors, such as MTM services and interprofessional care teams, that research has shown to independently improve patient outcomes, adherence, or public health. By synthesizing these factors, a completely new approach—the Wheel Model of Pharmaceutical Care—was developed. This model presents a radical departure from traditional, requested-service practices and posits pharmacy as a provided service instead. Although the ideas of MTM and interprofessional care teams are not new, there has never been a practical way to truly integrate community pharmacists into the patient care team or to ensure adequate communication among all of the patient’s HCPs. The Wheel Model of Pharmaceutical Care includes public health as one of its core components and provides a framework for pharmacies to meaningfully impact health outcomes for patients.
The Wheel Model of Pharmaceutical Care was designed to minimize the likelihood of nonadherence. Despite this, patients might willfully choose to be nonadherent, forget to take their medications, or neglect to pick up their medications. Additionally, in health care systems where patients must pay for their medications, prescription drug costs might be a barrier to adherence.
When nonadherence is suspected, the Wheel Model of Pharmaceutical Care directs pharmacists in MOSAIC to take action. First, the underlying cause of the nonadherence must be determined. For example, if a patient is nonadherent because of an adverse drug reaction, a therapy change may be indicated. If a patient is nonadherent due to apathy toward their health or therapy, the patient may benefit from education about their condition and treatment options; thus, the patient can make shared, informed decisions and feel more actively involved with his or her health. If a patients is nonadherent due to forgetfulness, adherence packaging dispense methods should be considered as an alternative to traditional vials. Depending on the services offered by a given pharmacy, adherence packaging options may include blister packs, pill boxes, or strips prepared by robotic dispensing systems. The use of medication reminders, whether in the form of a smartphone application or a simple alarm clock, should be discussed with the patient. If the patient does not pick up their medications on time, a pharmacist can contact the patient to determine why the medications were not picked up and to assess any nonadherence. In this case, mail order pharmacy services, if available, should be offered to patients as a more convenient option.
The medication regimen optimization component of MOSAIC helps reduce the workload of primary care providers and allows pharmacists to act autonomously based on clinical judgment, within the scope of the CPA. This can prevent delays in care caused by no refills remaining on a prescription. The laboratory monitoring component allows pharmacists to track diseases and take action if necessary, which should have a favorable impact on GPRA measures. Medication optimizations can reduce wasted resources by identifying cost-saving formulary alternatives, potentially inappropriate medications, and suboptimal doses.
Since many Indian Health Service beneficiaries do not have private insurance and therefore do not generate third-party reimbursements for services and care provided by GIMC, keeping patients healthy and out of the hospital is a top priority. As more patients are enrolled in MOSAIC, the program is expected to have a favorable impact on pharmacy workload and workflow as well. Prescriptions are anticipated and filled in advance, which decreases the amount of patients calling and presenting to the pharmacy for same-day refill requests. Scheduling when MOSAIC patients’ medications are to be filled and dispensed creates a predictable workload that allows the pharmacy staff to be managed more efficiently.
Conclusion
Adherence is the responsibility of the patient, but the Wheel Model of Pharmaceutical Care aims to provide pharmacists with a framework to monitor and encourage adherence in their patients. By taking this patient-centered approach, MOSAIC is expected to improve outcomes and decrease hospitalizations for high-risk patients who simply need a little extra help with their medications.
Nonadherence is a significant problem that has a negative impact on both patients and public health. Patients with multiple diseases often have complicated medication regimens, which can be difficult for them to manage. Unfortunately, nonadherence in these high-risk patients can have drastic consequences, including disease progression, hospitalization, and death, resulting in billions of dollars in unnecessary costs nationwide.1,2 The Wheel Model of Pharmaceutical Care (Figure) is a novel care model developed at the Gallup Indian Medical Center (GIMC) in New Mexico to address these problems by positioning pharmacy as a proactive service. The Wheel Model of Pharmaceutical Care was designed to improve adherence and patient outcomes and to encourage communication among the patient, pharmacists, prescribers, and other health care team members.
Pharmacists are central to managing patients’ medication therapies and coordinating communication among the health care providers (HCPs).1,3 Medication therapy management (MTM), a required component of Medicare Part D plans, helps ensure appropriate drug use and reduce the risk of adverse events.3 Since pharmacists receive prescriptions from all of the patient’s HCPs, patients may see pharmacists more often than they see any other HCP. GIMC is currently piloting a new clinic, the Medication Optimization, Synchronization, and Adherence Improvement Clinic (MOSAIC), that was created to implement the Wheel Model of Pharmaceutical Care. MOSAIC aims to provide proactive pharmacy services and continuous MTM to high-risk patients and will enable the effectiveness of this new pharmaceutical care model to be assessed.
Methods
Studies have identified certain populations who are at an increased risk for nonadherence: the elderly, patients with complex or extensive medication regimens, patients with multiple chronic medical conditions, substance misusers, certain ethnicities, patients of lower socioeconomic status, patients with limited literacy, and the homeless.2,4 Federal regulations require that Medicare Part D plans target beneficiaries who meet specific criteria for MTM programs. Under these rules, plans must target beneficiaries with ≥ 3 chronic diseases and ≥ 8 chronic medications, although plans also may include patients with fewer medications and diseases.3 Although the Wheel Model of Pharmaceutical Care is postulated to be an accurate model for the ideal care of all patients, initial implementation should be targeted toward populations who are likely to benefit the most from intervention. For these reasons, elderly Native American patients who have ≥ 2 chronic diseases and who take ≥ 5 chronic medications were targeted for initial enrollment in MOSAIC at GIMC.
Overview
In MOSAIC, pharmacists act as the hub of the pharmaceutical care wheel. Pharmacists work to ensure optimization of the patient’s comprehensive, integrated care plan—the rim of the wheel. As a part of this optimization process, MOSAIC pharmacists facilitate synchronization of the patient’s prescriptions to a monthly or quarterly target fill date. The patient’s current medication therapy is organized, and pharmacists track which medications are due to be filled instead of depending on the patient to request each prescription refill. This process effectively changes pharmacy from a requested service to a provided service.
Pharmacists also monitor the air in the tire to promote adherence. This is accomplished by providing efficient monthly or quarterly telephone or in-person consultations, which helps the patient better understand his or her comprehensive, integrated care plan. MOSAIC eliminates the possibility of nonadherence due to running out of refills. Specialized packaging, such as pill boxes or blister packs, can also improve adherence for certain patients.
MOSAIC ensures that pharmacists stay connected with the spokes, which represent a patient’s numerous prescribers, and close communication loops. Pharmacists can make prescribers aware of potential gaps or overlaps in treatment and assist them in the optimization and development of the patient’s comprehensive, integrated care plan. Pharmacists also make sure that the patient’s medication profile is current and accurate in the electronic health record (EHR). Any pertinent information discovered during MOSAIC encounters, such as abnormal laboratory results or changes in medications or disease, is documented in an EHR note. The patient’s prescribers are made aware of this information by tagging them as additional signers to the note in the EHR.
Keeping patients—the tires—healthy will ensure smooth operation of the vehicle and have a positive impact on public health. MOSAIC is expected to not only improve individual patient outcomes, but also decrease health care costs for patients and society due to nonadherence, suboptimal regimens, stockpiled home medications, and preventable hospital admissions.
Traditionally, pharmacy has been a requested service: A patient requests each of their prescriptions to be refilled, and the pharmacy fills the prescription. Ideally, pharmacy must become a provided service, with pharmacists keeping track of when a patient’s medications are due to be filled and actively looking for medication therapy optimization opportunities. This is accomplished by synchronizing the patient’s medications to the same monthly or quarterly fill date; screening for any potentially inappropriate medications, including high-risk medications in elderly patients, duplications, and omissions; verifying any medication changes with the patient each fill; and then providing all needed medications to the patient at a scheduled time.
To facilitate this process, custom software was developed for MOSAIC. In addition, a collaborative practice agreement (CPA) was drafted that allowed MOSAIC pharmacists to make certain medication therapy optimizations on behalf of the patient’s primary care provider. As part of this CPA, pharmacists also may order and act on certain laboratory tests, which helps to monitor disease progression, ensure safe medication use, and meet Government Performance and Results Act (GPRA) measures. As a novel model of pharmaceutical care, the effects of this approach are not yet known; however, research suggests that increased communication among HCPs and patient-centered approaches to care are beneficial to patient outcomes, adherence, and public health.1,5
Investigated Outcomes
As patients continue to enroll in MOSAIC, the effectiveness of the clinic will be evaluated. Specifically, quality of life, patient and HCP satisfaction with the program, adherence metrics, hospitalization rates, and all-cause mortality will be assessed for patients enrolled in MOSAIC as well as similar patients who are not enrolled in MOSAIC. Also, pharmacists will log all recommended medication therapy interventions so that the optimization component of MOSAIC may be quantified. GPRA measures and the financial implications of the interventions made by MOSAIC will also be evaluated.
Discussion
There are a number of factors, such as MTM services and interprofessional care teams, that research has shown to independently improve patient outcomes, adherence, or public health. By synthesizing these factors, a completely new approach—the Wheel Model of Pharmaceutical Care—was developed. This model presents a radical departure from traditional, requested-service practices and posits pharmacy as a provided service instead. Although the ideas of MTM and interprofessional care teams are not new, there has never been a practical way to truly integrate community pharmacists into the patient care team or to ensure adequate communication among all of the patient’s HCPs. The Wheel Model of Pharmaceutical Care includes public health as one of its core components and provides a framework for pharmacies to meaningfully impact health outcomes for patients.
The Wheel Model of Pharmaceutical Care was designed to minimize the likelihood of nonadherence. Despite this, patients might willfully choose to be nonadherent, forget to take their medications, or neglect to pick up their medications. Additionally, in health care systems where patients must pay for their medications, prescription drug costs might be a barrier to adherence.
When nonadherence is suspected, the Wheel Model of Pharmaceutical Care directs pharmacists in MOSAIC to take action. First, the underlying cause of the nonadherence must be determined. For example, if a patient is nonadherent because of an adverse drug reaction, a therapy change may be indicated. If a patient is nonadherent due to apathy toward their health or therapy, the patient may benefit from education about their condition and treatment options; thus, the patient can make shared, informed decisions and feel more actively involved with his or her health. If a patients is nonadherent due to forgetfulness, adherence packaging dispense methods should be considered as an alternative to traditional vials. Depending on the services offered by a given pharmacy, adherence packaging options may include blister packs, pill boxes, or strips prepared by robotic dispensing systems. The use of medication reminders, whether in the form of a smartphone application or a simple alarm clock, should be discussed with the patient. If the patient does not pick up their medications on time, a pharmacist can contact the patient to determine why the medications were not picked up and to assess any nonadherence. In this case, mail order pharmacy services, if available, should be offered to patients as a more convenient option.
The medication regimen optimization component of MOSAIC helps reduce the workload of primary care providers and allows pharmacists to act autonomously based on clinical judgment, within the scope of the CPA. This can prevent delays in care caused by no refills remaining on a prescription. The laboratory monitoring component allows pharmacists to track diseases and take action if necessary, which should have a favorable impact on GPRA measures. Medication optimizations can reduce wasted resources by identifying cost-saving formulary alternatives, potentially inappropriate medications, and suboptimal doses.
Since many Indian Health Service beneficiaries do not have private insurance and therefore do not generate third-party reimbursements for services and care provided by GIMC, keeping patients healthy and out of the hospital is a top priority. As more patients are enrolled in MOSAIC, the program is expected to have a favorable impact on pharmacy workload and workflow as well. Prescriptions are anticipated and filled in advance, which decreases the amount of patients calling and presenting to the pharmacy for same-day refill requests. Scheduling when MOSAIC patients’ medications are to be filled and dispensed creates a predictable workload that allows the pharmacy staff to be managed more efficiently.
Conclusion
Adherence is the responsibility of the patient, but the Wheel Model of Pharmaceutical Care aims to provide pharmacists with a framework to monitor and encourage adherence in their patients. By taking this patient-centered approach, MOSAIC is expected to improve outcomes and decrease hospitalizations for high-risk patients who simply need a little extra help with their medications.
1. Bosworth HB, Granger BB, Mendys P, et al. Medication adherence: a call for action. Am Heart J. 2011;162(3):412-424.
2. Vlasnik JJ, Aliotta SL, DeLor B. Medication adherence: factors influencing compliance with prescribed medication plans. Case Manager. 2005;16(2):47-51.
3. Drug utilization management, quality assurance, and medication therapy management programs (MTMPs). Fed Regist. 2012;77(71):2207-22175. To be codified at 42 CFR § 423.153.
4. Thiruchselvam T, Naglie G, Moineddin R, et al. Risk factors for medication nonadherence in older adults with cognitive impairment who live alone. Int J Geriatr Psychiatry. 2012;27(12):1275-1282.
5. Liddy C, Blazkho V, Mill K. Challenges of self-management when living with multiple chronic conditions: systematic review of the qualitative literature. Can Fam Physician. 2014;60(12):1123-1133.
1. Bosworth HB, Granger BB, Mendys P, et al. Medication adherence: a call for action. Am Heart J. 2011;162(3):412-424.
2. Vlasnik JJ, Aliotta SL, DeLor B. Medication adherence: factors influencing compliance with prescribed medication plans. Case Manager. 2005;16(2):47-51.
3. Drug utilization management, quality assurance, and medication therapy management programs (MTMPs). Fed Regist. 2012;77(71):2207-22175. To be codified at 42 CFR § 423.153.
4. Thiruchselvam T, Naglie G, Moineddin R, et al. Risk factors for medication nonadherence in older adults with cognitive impairment who live alone. Int J Geriatr Psychiatry. 2012;27(12):1275-1282.
5. Liddy C, Blazkho V, Mill K. Challenges of self-management when living with multiple chronic conditions: systematic review of the qualitative literature. Can Fam Physician. 2014;60(12):1123-1133.
Racial, ethnic minorities often don’t practice sun protective behaviors
Despite higher rates of skin cancer morbidity and mortality among racial and ethnic minorities, affected adults often are not recognizing their risks or taking preventive measures, said Costner McKenzie, BA, and Roopal V. Kundu, MD of Northwestern University, Chicago.
In a multivariable logistic regression analysis, Mr. Costner and Dr. Kundu sampled data of 33,672 adults included in the 2015 National Health Interview Survey. Data from the 2010 U.S. Census Bureau also were used to develop sample weights representative of the U.S. population. There was a survey of a smaller sample of adults who were determined to have sun-sensitive skin. The findings were published in the Journal of the American Academy of Dermatology.
Sun sensitivity was determined by skin reaction to 1 hour of unprotected sun exposure. Those who self-reported severe sunburn with blisters or moderate sunburn with peeling were determined to be sun sensitive.
The sample surveyed comprised 3,665 women (41%) and 5,287 men (59%). Of these, 82% were white non-Hispanic, 3% black non-Hispanic, 3% Asian non-Hispanic, 11% Hispanic, and 1% other non-Hispanic.
Mr. McKenzie and Dr. Kundu found that (adjusted odds ratio [aOR], 0.43, 0.54, and 0.70, respectively). Non-Hispanic blacks and Hispanics also were less likely to use sunscreen greater than SPF 15 (a0R, 0.39 and 0.64, respectively). Non-Hispanic blacks, non-Hispanic Asians, and Hispanics were less likely to have ever had a total body skin examination (aOR, 0.29, 0.21, and 0.39, respectively).
Yet these same three groups were more likely to wear long sleeves outside (non-Hispanic blacks aOR, 1.96, non-Hispanic Asians aOR, 2.09, and Hispanics aOR, 2.29). In addition, non-Hispanic Asians and Hispanics were more likely to shelter in the shade on warm, sunny days (aOR, 1.63 and 1.85, respectively).
Citing recent literature, the authors noted that although skin cancer is the most commonly diagnosed cancer, it is not typically thought of as a disease that afflicts minority populations, especially among minorities themselves, who do not generally recognize their own risk (Arch Dermatol. 2009;145[2]:207-8). In fact, morbidity and mortality from skin cancer actually are greater in racial and ethnic minorities (J Am Acad Dermatol. 2016;75[5]:983-91; J Am Acad Dermatol. 2006;55[5]:741-60), despite greater incidence of skin cancer among white adults.
“This study highlights the impact of race and ethnicity on sun protective behaviors,” said Mr. McKenzie and Dr. Kundu. Cultural beliefs, stigma, personal preferences, as well as a lack of “knowledge-based interventions” specifically intended for minorities could be responsible for the observed differences between population groups, they speculated.
The primary limitations of the study were its cross-sectional design and the use of self-reported data, the authors noted.
Additional research is needed to fully examine the reasons behind these differences as well as to identify appropriate interventions that promote sun protection, they added.
There was no external funding and the authors had no conflicts of interest to disclose.
SOURCE: McKenzie C and Kundu RV. J Am Acad Dermatol. 2019 Jun 19. doi: 10.1016/j.jaad.2019.06.1306.
Despite higher rates of skin cancer morbidity and mortality among racial and ethnic minorities, affected adults often are not recognizing their risks or taking preventive measures, said Costner McKenzie, BA, and Roopal V. Kundu, MD of Northwestern University, Chicago.
In a multivariable logistic regression analysis, Mr. Costner and Dr. Kundu sampled data of 33,672 adults included in the 2015 National Health Interview Survey. Data from the 2010 U.S. Census Bureau also were used to develop sample weights representative of the U.S. population. There was a survey of a smaller sample of adults who were determined to have sun-sensitive skin. The findings were published in the Journal of the American Academy of Dermatology.
Sun sensitivity was determined by skin reaction to 1 hour of unprotected sun exposure. Those who self-reported severe sunburn with blisters or moderate sunburn with peeling were determined to be sun sensitive.
The sample surveyed comprised 3,665 women (41%) and 5,287 men (59%). Of these, 82% were white non-Hispanic, 3% black non-Hispanic, 3% Asian non-Hispanic, 11% Hispanic, and 1% other non-Hispanic.
Mr. McKenzie and Dr. Kundu found that (adjusted odds ratio [aOR], 0.43, 0.54, and 0.70, respectively). Non-Hispanic blacks and Hispanics also were less likely to use sunscreen greater than SPF 15 (a0R, 0.39 and 0.64, respectively). Non-Hispanic blacks, non-Hispanic Asians, and Hispanics were less likely to have ever had a total body skin examination (aOR, 0.29, 0.21, and 0.39, respectively).
Yet these same three groups were more likely to wear long sleeves outside (non-Hispanic blacks aOR, 1.96, non-Hispanic Asians aOR, 2.09, and Hispanics aOR, 2.29). In addition, non-Hispanic Asians and Hispanics were more likely to shelter in the shade on warm, sunny days (aOR, 1.63 and 1.85, respectively).
Citing recent literature, the authors noted that although skin cancer is the most commonly diagnosed cancer, it is not typically thought of as a disease that afflicts minority populations, especially among minorities themselves, who do not generally recognize their own risk (Arch Dermatol. 2009;145[2]:207-8). In fact, morbidity and mortality from skin cancer actually are greater in racial and ethnic minorities (J Am Acad Dermatol. 2016;75[5]:983-91; J Am Acad Dermatol. 2006;55[5]:741-60), despite greater incidence of skin cancer among white adults.
“This study highlights the impact of race and ethnicity on sun protective behaviors,” said Mr. McKenzie and Dr. Kundu. Cultural beliefs, stigma, personal preferences, as well as a lack of “knowledge-based interventions” specifically intended for minorities could be responsible for the observed differences between population groups, they speculated.
The primary limitations of the study were its cross-sectional design and the use of self-reported data, the authors noted.
Additional research is needed to fully examine the reasons behind these differences as well as to identify appropriate interventions that promote sun protection, they added.
There was no external funding and the authors had no conflicts of interest to disclose.
SOURCE: McKenzie C and Kundu RV. J Am Acad Dermatol. 2019 Jun 19. doi: 10.1016/j.jaad.2019.06.1306.
Despite higher rates of skin cancer morbidity and mortality among racial and ethnic minorities, affected adults often are not recognizing their risks or taking preventive measures, said Costner McKenzie, BA, and Roopal V. Kundu, MD of Northwestern University, Chicago.
In a multivariable logistic regression analysis, Mr. Costner and Dr. Kundu sampled data of 33,672 adults included in the 2015 National Health Interview Survey. Data from the 2010 U.S. Census Bureau also were used to develop sample weights representative of the U.S. population. There was a survey of a smaller sample of adults who were determined to have sun-sensitive skin. The findings were published in the Journal of the American Academy of Dermatology.
Sun sensitivity was determined by skin reaction to 1 hour of unprotected sun exposure. Those who self-reported severe sunburn with blisters or moderate sunburn with peeling were determined to be sun sensitive.
The sample surveyed comprised 3,665 women (41%) and 5,287 men (59%). Of these, 82% were white non-Hispanic, 3% black non-Hispanic, 3% Asian non-Hispanic, 11% Hispanic, and 1% other non-Hispanic.
Mr. McKenzie and Dr. Kundu found that (adjusted odds ratio [aOR], 0.43, 0.54, and 0.70, respectively). Non-Hispanic blacks and Hispanics also were less likely to use sunscreen greater than SPF 15 (a0R, 0.39 and 0.64, respectively). Non-Hispanic blacks, non-Hispanic Asians, and Hispanics were less likely to have ever had a total body skin examination (aOR, 0.29, 0.21, and 0.39, respectively).
Yet these same three groups were more likely to wear long sleeves outside (non-Hispanic blacks aOR, 1.96, non-Hispanic Asians aOR, 2.09, and Hispanics aOR, 2.29). In addition, non-Hispanic Asians and Hispanics were more likely to shelter in the shade on warm, sunny days (aOR, 1.63 and 1.85, respectively).
Citing recent literature, the authors noted that although skin cancer is the most commonly diagnosed cancer, it is not typically thought of as a disease that afflicts minority populations, especially among minorities themselves, who do not generally recognize their own risk (Arch Dermatol. 2009;145[2]:207-8). In fact, morbidity and mortality from skin cancer actually are greater in racial and ethnic minorities (J Am Acad Dermatol. 2016;75[5]:983-91; J Am Acad Dermatol. 2006;55[5]:741-60), despite greater incidence of skin cancer among white adults.
“This study highlights the impact of race and ethnicity on sun protective behaviors,” said Mr. McKenzie and Dr. Kundu. Cultural beliefs, stigma, personal preferences, as well as a lack of “knowledge-based interventions” specifically intended for minorities could be responsible for the observed differences between population groups, they speculated.
The primary limitations of the study were its cross-sectional design and the use of self-reported data, the authors noted.
Additional research is needed to fully examine the reasons behind these differences as well as to identify appropriate interventions that promote sun protection, they added.
There was no external funding and the authors had no conflicts of interest to disclose.
SOURCE: McKenzie C and Kundu RV. J Am Acad Dermatol. 2019 Jun 19. doi: 10.1016/j.jaad.2019.06.1306.
FROM THE JOURNAL OF THE AMERICAN ACADEMY OF DERMATOLOGY
Occupational Hazard: Disruptive Behavior in Patients
While private or other public health care organizations can refuse to care for patients who have displayed disruptive behavior (DB), the VA Response to Disruptive Behavior of Patients law (38 CFR §17.107) prohibits the Veterans Health Administration (VHA) of the Department of Veterans Affairs (VA) from refusing care to veterans who display DB.1 The VHA defines DB as any behavior that is intimidating, threatening, or dangerous or that has, or could, jeopardize the health or safety of patients, VHA staff, or others.2
VA Response to DB Law
The VA Response to Disruptive Behavior of Patients requires the VHA to provide alternative care options that minimize risk while ensuring services; for example, providing care at a different location and/or time when additional staff are available to assist and monitor the patient. This can provide a unique opportunity to capture data on DB and the results of alternative forms of caring for this population.
The reason public health care organizations refuse care to persons who display DB is clear: DBs hinder business operations, are financially taxing, and put health care workers at risk.3-10 “In 2009, the VHA spent close to $5.5 million on workers’ compensation and medical expenditures for 425 incidents–or about $130,000 per DB incident (Hodgson M, Drummond D, Van Male L. Unpublished data, 2010).” In another study, 106 of 762 nurses in 1 hospital system reported an assault by a patient, and 30 required medical attention, which resulted in a total cost of $94,156.8 From 2002 to 2013, incidents of serious workplace violence requiring days off for an injured worker to recover on average were 4 times more common in health care than in other industries.6-11 Incidents of patient violence and aggression toward staff transcend specialization; however, hospital nurses and staff from the emergency, rehabilitation and gerontology departments, psychiatric unit, and home-based services are more susceptible and vulnerable to DB incidents than are other types of employees.8,10-19
Data reported by health care staff suggest that patients rather than staff members or visitors initiate > 70% of serious physical attacks against health care workers.9,13,20-23 A 2015 study of VHA health care providers (HCPs) found that > 60% had experienced some form of DB, verbal abuse being the most prevalent, followed by sexual abuse and physical abuse.20 Of 72,000 VHA staff responding to a nationwide survey, 13% experienced, on average, ≥ 1 assault by a veteran (eg, something was thrown at them; they were pushed, kicked, slapped; or were threatened or injured by a weapon).8,21
To meet its legal obligations and deliver empathetic care, the VHA documents and analyzes data on all patients who exhibit DB. A local DB Committee (DBC) reviews the data, whether it occurs in an inpatient or outpatient setting, such as community-based outpatient clinics. Once a DB incident is reported, the DBC begins an evidence-based risk evaluation, including the option of contacting the persons who displayed or experienced the DB. Goals are to (1) prevent future DB incidents; (2) detect vulnerabilities in the environment; and (3) collaborate with HCPs and patients to provide optimal care while improving the patient/provider interactions.
Effects of Disruptive Behavior
DB has negative consequences for both patients and health care workers and results in poor evaluations of care from both groups.27-32 Aside from interfering with safe medical care, DB also impacts care for other patients by delaying access to care and increasing appointment wait times due to employee absenteeism and staff shortages.3,4,20,32,33 For HCPs, patient violence is associated with unwillingness to provide care, briefer treatment periods, and decreases in occupational satisfaction, performance, and commitment
Harmful health effects experienced by HCPs who have been victims of DB include fear, mood disorders, anxiety, all symptoms of psychological distress and posttraumatic stress disorder (PTSD).10,22,30,34-36 In a study of the impact on productivity of PTSD triggered by job-related DB, PTSD symptoms were associated with withdrawal from or minimizing encounters with patients, job turnover, and troubles with thinking
Reporting Disruptive Behavior
The literature suggests that consistent and effective DB reporting is pivotal to improving the outcome and quality of care for those displaying DB.37-39 To provide high-quality health services to veterans who display DB, the VHA must promote the management and reporting of DB. Without knowledge of the full spectrum of DB events at VHA facilities, efforts to prevent or manage DB and ensure safety may have limited impact.7,37 Reports can be used for clinical decision making to optimize staff training in delivery of quality care while assuring staff safety. More than 80% of DB incidents occur during interactions with patients, thus this is a clinical issue that can affect the outcome of patient care.8,21
Documented DB reports are used to analyze the degree, frequency, and nature of incidents, which might reveal risk factors and develop preventive efforts and training for specific hazards.8,39 Some have argued that implementing a standardized DB reporting system is a crucial first step toward minimizing hazards and improving health care.38,40,41
When DB incidents were recorded through a hospital electronic reporting system and discussed in meetings, staff reported: (1) increased awareness of DB; (2) improved ability to manage DB incidents; and (3) amplified reporting of incidents.38,41,42 These findings support similar results from studies of an intervention implemented at VA Community Living Centers (CLCs) from 2013 to 2017: Staff Training in Assisted Living Residences (STAR-VA).4,12,19 The aim of STAR-VA was to minimize challenging dementia-related DB in CLCs. The intervention initially was established to train direct-care, assisted-living staff to provide better care to older patients displaying DB. Data revealed that documentation of DBs was, the first step to ensuring staff and patient safety.18,40
VHA Reporting System
In 2013, the VA Office of Inspector General (OIG) found no standardized documentation of DB events across the VA health care system.42 Instead, DB events were documented in multiple records in various locations, including administrative and progress notes in the electronic health record (EHR), police reports, e-mails, or letters submitted to DBC chairs.42 This situation reduced administrators’ ability to consider all relevant information and render appropriate decisions in DB cases.42 In 2015, based on OIG recommendations, the VHA implemented the Disruptive Behavior Reporting System (DBRS) nationwide, which allowed all VHA staff to report DB events. The DBRS was designed to address factors likely to impede reporting and management of DB, namely, complexity of and lack of access to a central reporting system.43,44 The DBRS is currently the primary VHA tool to document DB events.
The DBRS consists of 32 questions in 5 sections relating to the (1) location and time of DB event; (2) reporter; (3) disrupter; (4) DB event details; and (5) the person who experienced (experiencer) the event. The system also provides a list of the types of DB, such as inappropriate communication, bullying and/or intimidation, verbal or written threat of physical harm, physical violence, sexual harassment, sexual assault, and property damage. The DBRS has the potential to provide useful data on DB and DB reporting, such as the typical staff entering data and the number and/or types of DB occurring.
The DBRS complements the preexisting VHA policies and committees for care of veterans who display DB.1-3,14,21,24,25 The VHA Workplace Violence Prevention Program (WVPP) required facilities to submit data on DB events through a Workplace Behavioral Risk report. Data for the report were obtained from police reports, patient safety reports, DBC records, and notes in the EHR. Following implementations of DBRS, the number of DB events per year became a part of facility performance standards.
VHA is creating novel approaches to handling DB that allow health care workers to render care in a safe and effective manner guided by documented information. For example, DBCs can recommend the use of Category I Patient Record Flags (PRFs) following documented DB, which informs staff of the potential risk of DB and provides guidance on protective methods to use when meeting with the patient.2,21,24 A survey of 140 VA hospital chiefs of staff indicated that DBC procedures were related to a decrease in the rates of assaults.1 Additionally, VA provides training for staff in techniques to promote personal safety, such as identifying signs that precede DB, using verbal deescalation, and practicing therapeutic containment.
Resistance to Reporting
Many health care employees and employers are reticent to report DBs.22,31,43,45-48 Studies suggest health care organizations can cultivate a culture that is resistant to reporting DB.49,50 This complicates the ability of the health care system to design and maintain safety protocols and safer treatment plans.3,41,51 Worldwide, < 30% of DBs are reported.47 One barrier may be that supervisors may not wish to acknowledge DBs on their units or may not provide sufficient staff time for training or reporting.31,46,47 HCPs may worry that a DB report will stigmatize patients, especially those who are elderly or have cognitive impairment, brain injury, psychological illness, or developmental disability. Patients with cognitive conditions are reportedly 20% more likely to be violent toward caregivers and providers.31 A dementia diagnosis, for example, is associated with a high likelihood for DB.30,52 More than 80% of DB events displayed by patients with dementia may go unreported.26,31,50,52
Some clinicians may attribute DB to physiologic conditions that need to be treated, not reported. However, employers can face various legal liabilities if steps are not taken to protect employees.47,51 Federal and state statutes require that organizations provide a healthy and safe employment environment for workers. This requires that employers institute reasonable protective measures, such as procedures to intervene, policies on addressing DB incidents, and/or training to minimize or deescalate DB.51,53 Also, employees may sue employers if security measures are inadequate or deficient in properly investigating current and past evidence of DB or identifying vulnerabilities in the workplace. Unwillingness to investigate DB and safety-related workplace concerns have contributed to increased workplace violence and legal liability.52,53 The mission of caring and trust is consistent with assuring a safe environment.
Training and Empathetic Care
To combat cultural resistance to reporting DBs, more and perhaps different contextual approaches to education and training may be needed that address ethical dilemmas and concerns of providers. The success of training relies on administrators supporting staff in reporting DB. Training must address providers’ conflicting beliefs and assist with identifying strategies to provide the best possible care for patients who display DB.1,38 HCPs are less likely to document a DB if they feel that administrators are creating documentation that will have negative consequences for a patient. Thus, leadership is responsible for ensuring that misconceptions are dispelled through training and other efforts and information on how reported DB data will be used is communicated through strategic channels.
Education and training must consider empathic care that attempts to understand why patients behave as they do through the information gathered.55 Empathy in health care is multifaceted: It involves comprehending a patient’s viewpoint, circumstances, and feelings and the capacity to analyze whether one is comprehending these accurately in order to demonstrate supportive care.54,55
Improving patient and staff interaction once a problematic behavior is identified is the aim of empathic care. Increasing empathic care can improve compassionate, patient-centered interactions that begin once the patient seeks care. This approach has proven to decrease DB by patients with dementia and improve their care, lessen staff problems during interactions, and increase staff morale.20 Experts call for the adoption of an interpersonal approach to patient encounters, and there is evidence that creating organizational change by moving toward compassionate care can lead to a positive impact for patients.54,55
Future Studies
There are growth opportunities in utilization of the DBRS. Analysis of the DBRS database by the VA Central Office (VACO) showed that the system is underutilized by facilities across the VA system.56 In response to this current underutilization, VACO is taking steps to close these gaps through increasing training to staff and promotion of the use of the DBRS. A 2015 pilot study of VHA providers showed that > 70% of providers had experienced a DB as defined by VHA, but only 34% of them reported their most recently experienced DB within the past 12 months.20 Thus, DBRS use must be studied within the context that patient-perpetrated DB is underreported in health care organizations.5,9,29,41,43,57,58 Studies addressing national DBRS utilization patterns and the cost associated with implementing the DBRS also are needed. One study suggests that there is an association between measures of facility complexity and staff perceptions of safety, which should be considered in analyzing DBRS usage.57 Studies addressing the role of the DBRS and misconceptions that the tool may represent a punitive tool also are needed. VHA should consider how the attribution “disruptive behavior” assigns a negative connotation and leads HCPs to avoid using the DBRS. Additionally, DB reporting may increase when HCPs understand that DB reporting is part of the comprehensive, consultative strategy to provide the best care to patients.
Conclusion
Accurate reporting of DB events enables the development of strategies for multidisciplinary teams to work together to minimize hazards and to provide interventions that provide for the safe delivery of health care to all patients. Improving reporting ensures there is an accurate representation of how disruptive events impact care provided within a facility—and what types of variables may be associated with increased risk for these types of events.
Additionally, ensuring that reporting is maximized also provides the VHA with opportunities for DBCs to offer evidence-based risk assessment of violence and consultation to staff members who may benefit from improved competencies in working with patients who display DB. These potential improvements are consistent with the VHA I CARE values and will provide data that can inform recommendations for health care in other agencies/health care organizations.
Acknowledgments
This work was supported by the Center of Innovation on Disability and Rehabilitation Research (CINDRR) of the Health Services Research and Development Service, Office of Research and Development, Department of Veterans Affairs.
1. Hodgson MJ, Mohr DC, Drummond DJ, Bell M, Van Male L. Managing disruptive patients in health care: necessary solutions to a difficult problem. Am J Ind Med. 2012;55(11):1009-1017.
2. US Department of Veterans Affairs, Veterans Health Administration. VHA Directive 2010-053. Patient Record Flags. https://www.va.gov/vhapublications/ViewPublication.asp?pub_ID=2341 Published December 3, 2010. Accessed March 29, 2019.
3. US Department of Veterans Affairs, Veterans Health Administration. VHA Directive 2012-026. Sexual Assaults and Other Defined Public Safety Incidents in VHA Facilities. https://www.va.gov/vhapublications/ViewPublication.asp?pub_ID=2797. Published September 27, 2012. Accessed March 29, 2019.
4. Curyto KJ, McCurry SM, Luci K, Karlin BE, Teri L, Karel MJ. Managing challenging behaviors of dementia in veterans: identifying and changing activators and consequences using STAR-VA. J Gerontol Nurs. 2017;43(2):33-43.
5. Speroni KG, Fitch T, Dawson E, Dugan L, Atherton M. Incidence and cost of nurse workplace violence perpetrated by hospital patients or patient visitors. J Emerg Nurs. 2014;40(3):218-228.
6. Phillips JP. Workplace violence against health care workers in the United States. NEJM. 2016;374(17):1661-1669.
7. Janocha JA, Smith RT. Workplace safety and health in the health care and social assistance industry, 2003–07. https://www.bls.gov/opub/mlr/cwc/workplace-safety-and-health-in-the-health-care-and-social-assistance-industry-2003-07.pdf. Published August 30, 2010. Accessed February 19, 2019.
8. US Department of Labor, Occupational Safety and Health Administration. Workplace violence in healthcare: understanding the challenge. https://www.osha.gov/Publications/OSHA3826.pdf. Published December 2015. Accessed February 19, 2019.
9. US Department of Labor, Occupational Safety and Health Administration. Prevention of Workplace Violence in Healthcare and Social Assistance. Occupational Safety and Health Administration, https://www.govinfo.gov/content/pkg/FR-2016-12-07/pdf/2016-29197.pdf. Accessed January 20, 2017.
10. Gerberich SG, Church TR, McGovern PM, et al. An epidemiological study of the magnitude and consequences of work related violence: the Minnesota Nurses’ Study. Occup Environ Med. 2004;61(6):495-503.
11. Sherman MF, Gershon RRM, Samar SM, Pearson JM, Canton AN, Damsky MR. Safety factors predictive of job satisfaction and job retention among home healthcare aides. J Occup Environ Med. 2008;50(12):1430-1441.
12. Karel MJ, Teri L, McConnell E, Visnic S, Karlin BE. Effectiveness of expanded implementation of STAR-VA for managing dementia-related behaviors among veterans. Gerontologist. 2016;56(1):126-134.
13. US Department of Labor, Bureau of Labor Statistics. Nonfatal occupational injuries and illnesses requiring days away from work. https://www.bls.gov/news.release/archives/osh2_11192015.htm. Published November 19, 2015.
14. Beech B, Leather P. Workplace violence in the health care sector: A review of staff training and integration of training evaluation models. Aggression Violent Behav. 2006;11(1):27-43.
15. Campbell CL, McCoy S, Burg MA, Hoffman N. Enhancing home care staff safety through reducing client aggression and violence in noninstitutional care settings: a systematic review. Home Health Care Manage Pract. 2014;26(1):3-10.
16. Gallant-Roman MA. Strategies and tools to reduce workplace violence. AAOHNJ. 2008;56(11):449-454.
17. Weinberger LE, Sreenivasan S, Smee DE, McGuire J, Garrick T. Balancing safety against obstruction to health care access: an examination of behavioral flags in the VA health care system. J Threat Assess Manage. 2018;5(1):35-41.
18. Elbogen EB, Johnson SC, Wagner HR, et al. Protective factors and risk modification of violence in Iraq and Afghanistan war veterans. J Clin Psychiatry. 2012;73(6):e767-e773.
19. Karlin BE, Visnic S, McGee JS, Teri L. Results from the multisite implementation of STAR-VA: a multicomponent psychosocial intervention for managing challenging dementia-related behaviors of veterans. Psychol Serv. 2014;11(2):200-208.
20. Semeah LM, Campbell CL, Cowper DC, Peet AC. Serving our homeless veterans: patient perpetrated violence as a barrier to health care access. J Pub Nonprofit Aff. 2017;3(2):223-234.
21. Hodgson MJ, Reed R, Craig T, et al. Violence in healthcare facilities: lessons from the Veterans Health Administration. J Occup Environ Med. 2004;46(11):1158-1165.
22. Farrell GA, Bobrowski C, Bobrowski P. Scoping workplace aggression in nursing: findings from an Australian study. J Adv Nurs. 2006;55(6):778-787.
23. Barling J, Rogers AG, Kelloway EK. Behind closed doors: in-home workers’ experience of sexual harassment and workplace violence. J Occup Health Psychol. 2001;6(3):255-269.
24. Pompeii LA, Schoenfisch AL, Lipscomb HJ, Dement JM, Smith CD, Upadhyaya M. Physical assault, physical threat, and verbal abuse perpetrated against hospital workers by patients or visitors in six U.S. hospitals. Am J Ind Med. 2015;58(11):1194-1204.
25. Sippel LM, Mota NP, Kachadourian LK, et al. The burden of hostility in U.S. veterans: results from the National Health and Resilience in Veterans Study. Psychiatry Res. 2016;243(suppl C):421-430.
26. Campbell C. Patient Violence and Aggression in Non-Institutional Health Care Settings: Predictors of Reporting By Healthcare Providers [doctoral dissertation]. Orlando: University of Central Florida; 2016.
27. Galinsky T, Feng HA, Streit J, et al. Risk factors associated with patient assaults of home healthcare workers. Rehabil Nurs. 2010;35(5):206-215.
28. Campbell CL. Incident reporting by health-care workers in noninstitutional care settings. Trauma, Violence Abuse. 2017;18(4):445-456.
29. Arnetz JE, Arnetz BB. Violence towards health care staff and possible effects on the quality of patient care. Soc Sci Med. 2001;52(3):417-427.
30. Gates D, Fitzwater E, Succop P. Relationships of stressors, strain, and anger to caregiver assaults. Issues Ment Health Nurs. 2003;24(8):775-793.
31. Brillhart B, Kruse B, Heard L. Safety concerns for rehabilitation nurses in home care. Rehabil Nurs. 2004;29(6):227-229.
32. Taylor H. Patient violence against clinicians: managing the risk. Innov Clin Neurosci. 2013;10(3):40-42.
33. US Department of Veterans Affairs, Office of Public and Intergovernmental Affairs. The Joint Commission releases results of surveys of the VA health care system. https://www.va.gov/opa/pressrel/pressrelease.cfm?id=2808. Updated August 5, 2014. Accessed February 19, 2019.
34. Büssing A, Höge T. Aggression and violence against home care workers. J Occup Health Psychol. 2004;9(3):206-219.
35. Geiger-Brown J, Muntaner C, McPhaul K, Lipscomb J, Trinkoff A. Abuse and violence during home care work as predictor of worker depression. Home Health Care Serv Q. 2007;26(1):59-77.
36. Gates DM, Gillespie GL, Succop P. Violence against nurses and its impact on stress and productivity. Nurs Econ. 2011;29(2):59-66.
37. Petterson IL, Arnetz BB. Psychosocial stressors and well-being in health care workers: the impact of an intervention program. Soc Sci Med. 1998;47(11):1763-1772.
38. Arnetz JE, Arnetz BB. Implementation and evaluation of a practical intervention programme for dealing with violence towards health care workers. J Adv Nurs. 2000;31(3):668-680.
39. Arnetz JE, Hamblin L, Russell J, et al. Preventing patient-to-worker violence in hospitals: outcome of a randomized controlled intervention. J Occup Environ Med. 2017;59(1):18-27.
40. Elbogen EB, Tomkins AJ, Pothuloori AP, Scalora MJ. Documentation of violence risk information in psychiatric hospital patient charts: an empirical examination. J Am Acad Psychiatry Law. 2003;31(1):58-64.
41. Winsvold Prang I, Jelson-Jorgensen LP. Should I report? A qualitative study of barriers to incident reporting among nurses working in nursing homes. Geriatr Nurs. 2014;35(6):441-447.
42. US Department of Veterans Affairs, Office of Inspector General. Healthcare inspection: management of disruptive patient behavior at VA medical facilities. Report No. 11-02585-129. https://www.va.gov/oig/pubs/VAOIG-11-02585-129.pdf. Published Mrach 7, 2013. Accessed February 21, 2019.
43. Lipscomb J, London M. Not Part of the Job: How to Take a Stand Against Violence in the Work Setting. Silver Spring, MD: American Nurses Association; 2015.
44. May DD, Grubbs LM. The extent, nature, and precipitating factors of nurse assault among three groups of registered nurses in a regional medical center. J Emerg Nurs. 2002;28(1):11-17.
45. Wharton TC, Ford BK. What is known about dementia care recipient violence and aggression against caregivers? J Gerontol Soc Work. 2014;57(5):460-477.
46. Brennan C, Worrall-Davies A, McMillan D, Gilbody S, House A. The hospital anxiety and depression scale: a diagnostic meta-analysis of case-finding ability. J Psychosom Res. 2010;69(4):371-378.
47. McPhaul K, Lipscomb J, Johnson J. Assessing risk for violence on home health visits. Home Healthc Nurse. 2010;28(5):278-289.
48. McPhaul KM, London M, Murrett K, Flannery K, Rosen J, Lipscomb J. Environmental evaluation for workplace violence in healthcare and social services. J Safety Res. 2008;39(2):237-250.
49. Kelly JA, Somlai AM, DiFranceisco WJ, et al. Bridging the gap between the science and service of HIV prevention: transferring effective research-based HIV prevention interventions to community AIDS service providers. Am J Public Health. 2000;90(7):1082-1088.
50. Pawlin S. Reporting violence. Emerg Nurse. 2008;16(4):16-21.
51. Brakel SJ. Legal liability and workplace violence. J Am Acad Psychiatry Law. 1998;26(4):553-562.
52. Neuman JH, Baron RA. Workplace violence and workplace aggression: evidence concerning specific forms, potential causes, and preferred targets. J Manage. 1998;24(3):391-419.53. Ferns T, Chojnacka I. Angels and swingers, matrons and sinners: nursing stereotypes. Br J Nurs. 2005;14(19):1028-1032.
54. Mercer SW, Reynolds WJ. Empathy and quality of care. Br J Gen Pract 2002;52(suppl):S9-S12.
55. Lee TH. An Epidemic of Empathy in Healthcare: How to Deliver Compassionate, Connected Patient Care That Creates a Competitive Advantage. Columbus, OH: McGraw-Hill Education; 2015.
56. US Department of Veterans Affairs, Veterans Health Administrastion. Veterans Health Administration workplace violence prevention program (WVPP): disruptive behavior reporting system utilization report. Published 2017. https://vaww.portal2.va.gov/sites/wvpp/Shared%20Documents/DBRS%20Utilization%20Reports/FY2017%20DBRS%20Quarterly%20Utilization%20Report%20(Quarter%201).pdf. [Source not verified.]
57. Campbell CL, Burg, MA, Gammonley D. Measures for incident reporting of patient violence and aggression towards healthcare providers: a systematic review. Aggression Violent Behav. 2015;25(part B):314-322.
58. Carney PT, West P, Neily J, Mills PD, Bagian JP. The effect of facility complexity on perceptions of safety climate in the operating room: size matters. Am J Med Qual. 2010;25(6):457-461.
While private or other public health care organizations can refuse to care for patients who have displayed disruptive behavior (DB), the VA Response to Disruptive Behavior of Patients law (38 CFR §17.107) prohibits the Veterans Health Administration (VHA) of the Department of Veterans Affairs (VA) from refusing care to veterans who display DB.1 The VHA defines DB as any behavior that is intimidating, threatening, or dangerous or that has, or could, jeopardize the health or safety of patients, VHA staff, or others.2
VA Response to DB Law
The VA Response to Disruptive Behavior of Patients requires the VHA to provide alternative care options that minimize risk while ensuring services; for example, providing care at a different location and/or time when additional staff are available to assist and monitor the patient. This can provide a unique opportunity to capture data on DB and the results of alternative forms of caring for this population.
The reason public health care organizations refuse care to persons who display DB is clear: DBs hinder business operations, are financially taxing, and put health care workers at risk.3-10 “In 2009, the VHA spent close to $5.5 million on workers’ compensation and medical expenditures for 425 incidents–or about $130,000 per DB incident (Hodgson M, Drummond D, Van Male L. Unpublished data, 2010).” In another study, 106 of 762 nurses in 1 hospital system reported an assault by a patient, and 30 required medical attention, which resulted in a total cost of $94,156.8 From 2002 to 2013, incidents of serious workplace violence requiring days off for an injured worker to recover on average were 4 times more common in health care than in other industries.6-11 Incidents of patient violence and aggression toward staff transcend specialization; however, hospital nurses and staff from the emergency, rehabilitation and gerontology departments, psychiatric unit, and home-based services are more susceptible and vulnerable to DB incidents than are other types of employees.8,10-19
Data reported by health care staff suggest that patients rather than staff members or visitors initiate > 70% of serious physical attacks against health care workers.9,13,20-23 A 2015 study of VHA health care providers (HCPs) found that > 60% had experienced some form of DB, verbal abuse being the most prevalent, followed by sexual abuse and physical abuse.20 Of 72,000 VHA staff responding to a nationwide survey, 13% experienced, on average, ≥ 1 assault by a veteran (eg, something was thrown at them; they were pushed, kicked, slapped; or were threatened or injured by a weapon).8,21
To meet its legal obligations and deliver empathetic care, the VHA documents and analyzes data on all patients who exhibit DB. A local DB Committee (DBC) reviews the data, whether it occurs in an inpatient or outpatient setting, such as community-based outpatient clinics. Once a DB incident is reported, the DBC begins an evidence-based risk evaluation, including the option of contacting the persons who displayed or experienced the DB. Goals are to (1) prevent future DB incidents; (2) detect vulnerabilities in the environment; and (3) collaborate with HCPs and patients to provide optimal care while improving the patient/provider interactions.
Effects of Disruptive Behavior
DB has negative consequences for both patients and health care workers and results in poor evaluations of care from both groups.27-32 Aside from interfering with safe medical care, DB also impacts care for other patients by delaying access to care and increasing appointment wait times due to employee absenteeism and staff shortages.3,4,20,32,33 For HCPs, patient violence is associated with unwillingness to provide care, briefer treatment periods, and decreases in occupational satisfaction, performance, and commitment
Harmful health effects experienced by HCPs who have been victims of DB include fear, mood disorders, anxiety, all symptoms of psychological distress and posttraumatic stress disorder (PTSD).10,22,30,34-36 In a study of the impact on productivity of PTSD triggered by job-related DB, PTSD symptoms were associated with withdrawal from or minimizing encounters with patients, job turnover, and troubles with thinking
Reporting Disruptive Behavior
The literature suggests that consistent and effective DB reporting is pivotal to improving the outcome and quality of care for those displaying DB.37-39 To provide high-quality health services to veterans who display DB, the VHA must promote the management and reporting of DB. Without knowledge of the full spectrum of DB events at VHA facilities, efforts to prevent or manage DB and ensure safety may have limited impact.7,37 Reports can be used for clinical decision making to optimize staff training in delivery of quality care while assuring staff safety. More than 80% of DB incidents occur during interactions with patients, thus this is a clinical issue that can affect the outcome of patient care.8,21
Documented DB reports are used to analyze the degree, frequency, and nature of incidents, which might reveal risk factors and develop preventive efforts and training for specific hazards.8,39 Some have argued that implementing a standardized DB reporting system is a crucial first step toward minimizing hazards and improving health care.38,40,41
When DB incidents were recorded through a hospital electronic reporting system and discussed in meetings, staff reported: (1) increased awareness of DB; (2) improved ability to manage DB incidents; and (3) amplified reporting of incidents.38,41,42 These findings support similar results from studies of an intervention implemented at VA Community Living Centers (CLCs) from 2013 to 2017: Staff Training in Assisted Living Residences (STAR-VA).4,12,19 The aim of STAR-VA was to minimize challenging dementia-related DB in CLCs. The intervention initially was established to train direct-care, assisted-living staff to provide better care to older patients displaying DB. Data revealed that documentation of DBs was, the first step to ensuring staff and patient safety.18,40
VHA Reporting System
In 2013, the VA Office of Inspector General (OIG) found no standardized documentation of DB events across the VA health care system.42 Instead, DB events were documented in multiple records in various locations, including administrative and progress notes in the electronic health record (EHR), police reports, e-mails, or letters submitted to DBC chairs.42 This situation reduced administrators’ ability to consider all relevant information and render appropriate decisions in DB cases.42 In 2015, based on OIG recommendations, the VHA implemented the Disruptive Behavior Reporting System (DBRS) nationwide, which allowed all VHA staff to report DB events. The DBRS was designed to address factors likely to impede reporting and management of DB, namely, complexity of and lack of access to a central reporting system.43,44 The DBRS is currently the primary VHA tool to document DB events.
The DBRS consists of 32 questions in 5 sections relating to the (1) location and time of DB event; (2) reporter; (3) disrupter; (4) DB event details; and (5) the person who experienced (experiencer) the event. The system also provides a list of the types of DB, such as inappropriate communication, bullying and/or intimidation, verbal or written threat of physical harm, physical violence, sexual harassment, sexual assault, and property damage. The DBRS has the potential to provide useful data on DB and DB reporting, such as the typical staff entering data and the number and/or types of DB occurring.
The DBRS complements the preexisting VHA policies and committees for care of veterans who display DB.1-3,14,21,24,25 The VHA Workplace Violence Prevention Program (WVPP) required facilities to submit data on DB events through a Workplace Behavioral Risk report. Data for the report were obtained from police reports, patient safety reports, DBC records, and notes in the EHR. Following implementations of DBRS, the number of DB events per year became a part of facility performance standards.
VHA is creating novel approaches to handling DB that allow health care workers to render care in a safe and effective manner guided by documented information. For example, DBCs can recommend the use of Category I Patient Record Flags (PRFs) following documented DB, which informs staff of the potential risk of DB and provides guidance on protective methods to use when meeting with the patient.2,21,24 A survey of 140 VA hospital chiefs of staff indicated that DBC procedures were related to a decrease in the rates of assaults.1 Additionally, VA provides training for staff in techniques to promote personal safety, such as identifying signs that precede DB, using verbal deescalation, and practicing therapeutic containment.
Resistance to Reporting
Many health care employees and employers are reticent to report DBs.22,31,43,45-48 Studies suggest health care organizations can cultivate a culture that is resistant to reporting DB.49,50 This complicates the ability of the health care system to design and maintain safety protocols and safer treatment plans.3,41,51 Worldwide, < 30% of DBs are reported.47 One barrier may be that supervisors may not wish to acknowledge DBs on their units or may not provide sufficient staff time for training or reporting.31,46,47 HCPs may worry that a DB report will stigmatize patients, especially those who are elderly or have cognitive impairment, brain injury, psychological illness, or developmental disability. Patients with cognitive conditions are reportedly 20% more likely to be violent toward caregivers and providers.31 A dementia diagnosis, for example, is associated with a high likelihood for DB.30,52 More than 80% of DB events displayed by patients with dementia may go unreported.26,31,50,52
Some clinicians may attribute DB to physiologic conditions that need to be treated, not reported. However, employers can face various legal liabilities if steps are not taken to protect employees.47,51 Federal and state statutes require that organizations provide a healthy and safe employment environment for workers. This requires that employers institute reasonable protective measures, such as procedures to intervene, policies on addressing DB incidents, and/or training to minimize or deescalate DB.51,53 Also, employees may sue employers if security measures are inadequate or deficient in properly investigating current and past evidence of DB or identifying vulnerabilities in the workplace. Unwillingness to investigate DB and safety-related workplace concerns have contributed to increased workplace violence and legal liability.52,53 The mission of caring and trust is consistent with assuring a safe environment.
Training and Empathetic Care
To combat cultural resistance to reporting DBs, more and perhaps different contextual approaches to education and training may be needed that address ethical dilemmas and concerns of providers. The success of training relies on administrators supporting staff in reporting DB. Training must address providers’ conflicting beliefs and assist with identifying strategies to provide the best possible care for patients who display DB.1,38 HCPs are less likely to document a DB if they feel that administrators are creating documentation that will have negative consequences for a patient. Thus, leadership is responsible for ensuring that misconceptions are dispelled through training and other efforts and information on how reported DB data will be used is communicated through strategic channels.
Education and training must consider empathic care that attempts to understand why patients behave as they do through the information gathered.55 Empathy in health care is multifaceted: It involves comprehending a patient’s viewpoint, circumstances, and feelings and the capacity to analyze whether one is comprehending these accurately in order to demonstrate supportive care.54,55
Improving patient and staff interaction once a problematic behavior is identified is the aim of empathic care. Increasing empathic care can improve compassionate, patient-centered interactions that begin once the patient seeks care. This approach has proven to decrease DB by patients with dementia and improve their care, lessen staff problems during interactions, and increase staff morale.20 Experts call for the adoption of an interpersonal approach to patient encounters, and there is evidence that creating organizational change by moving toward compassionate care can lead to a positive impact for patients.54,55
Future Studies
There are growth opportunities in utilization of the DBRS. Analysis of the DBRS database by the VA Central Office (VACO) showed that the system is underutilized by facilities across the VA system.56 In response to this current underutilization, VACO is taking steps to close these gaps through increasing training to staff and promotion of the use of the DBRS. A 2015 pilot study of VHA providers showed that > 70% of providers had experienced a DB as defined by VHA, but only 34% of them reported their most recently experienced DB within the past 12 months.20 Thus, DBRS use must be studied within the context that patient-perpetrated DB is underreported in health care organizations.5,9,29,41,43,57,58 Studies addressing national DBRS utilization patterns and the cost associated with implementing the DBRS also are needed. One study suggests that there is an association between measures of facility complexity and staff perceptions of safety, which should be considered in analyzing DBRS usage.57 Studies addressing the role of the DBRS and misconceptions that the tool may represent a punitive tool also are needed. VHA should consider how the attribution “disruptive behavior” assigns a negative connotation and leads HCPs to avoid using the DBRS. Additionally, DB reporting may increase when HCPs understand that DB reporting is part of the comprehensive, consultative strategy to provide the best care to patients.
Conclusion
Accurate reporting of DB events enables the development of strategies for multidisciplinary teams to work together to minimize hazards and to provide interventions that provide for the safe delivery of health care to all patients. Improving reporting ensures there is an accurate representation of how disruptive events impact care provided within a facility—and what types of variables may be associated with increased risk for these types of events.
Additionally, ensuring that reporting is maximized also provides the VHA with opportunities for DBCs to offer evidence-based risk assessment of violence and consultation to staff members who may benefit from improved competencies in working with patients who display DB. These potential improvements are consistent with the VHA I CARE values and will provide data that can inform recommendations for health care in other agencies/health care organizations.
Acknowledgments
This work was supported by the Center of Innovation on Disability and Rehabilitation Research (CINDRR) of the Health Services Research and Development Service, Office of Research and Development, Department of Veterans Affairs.
While private or other public health care organizations can refuse to care for patients who have displayed disruptive behavior (DB), the VA Response to Disruptive Behavior of Patients law (38 CFR §17.107) prohibits the Veterans Health Administration (VHA) of the Department of Veterans Affairs (VA) from refusing care to veterans who display DB.1 The VHA defines DB as any behavior that is intimidating, threatening, or dangerous or that has, or could, jeopardize the health or safety of patients, VHA staff, or others.2
VA Response to DB Law
The VA Response to Disruptive Behavior of Patients requires the VHA to provide alternative care options that minimize risk while ensuring services; for example, providing care at a different location and/or time when additional staff are available to assist and monitor the patient. This can provide a unique opportunity to capture data on DB and the results of alternative forms of caring for this population.
The reason public health care organizations refuse care to persons who display DB is clear: DBs hinder business operations, are financially taxing, and put health care workers at risk.3-10 “In 2009, the VHA spent close to $5.5 million on workers’ compensation and medical expenditures for 425 incidents–or about $130,000 per DB incident (Hodgson M, Drummond D, Van Male L. Unpublished data, 2010).” In another study, 106 of 762 nurses in 1 hospital system reported an assault by a patient, and 30 required medical attention, which resulted in a total cost of $94,156.8 From 2002 to 2013, incidents of serious workplace violence requiring days off for an injured worker to recover on average were 4 times more common in health care than in other industries.6-11 Incidents of patient violence and aggression toward staff transcend specialization; however, hospital nurses and staff from the emergency, rehabilitation and gerontology departments, psychiatric unit, and home-based services are more susceptible and vulnerable to DB incidents than are other types of employees.8,10-19
Data reported by health care staff suggest that patients rather than staff members or visitors initiate > 70% of serious physical attacks against health care workers.9,13,20-23 A 2015 study of VHA health care providers (HCPs) found that > 60% had experienced some form of DB, verbal abuse being the most prevalent, followed by sexual abuse and physical abuse.20 Of 72,000 VHA staff responding to a nationwide survey, 13% experienced, on average, ≥ 1 assault by a veteran (eg, something was thrown at them; they were pushed, kicked, slapped; or were threatened or injured by a weapon).8,21
To meet its legal obligations and deliver empathetic care, the VHA documents and analyzes data on all patients who exhibit DB. A local DB Committee (DBC) reviews the data, whether it occurs in an inpatient or outpatient setting, such as community-based outpatient clinics. Once a DB incident is reported, the DBC begins an evidence-based risk evaluation, including the option of contacting the persons who displayed or experienced the DB. Goals are to (1) prevent future DB incidents; (2) detect vulnerabilities in the environment; and (3) collaborate with HCPs and patients to provide optimal care while improving the patient/provider interactions.
Effects of Disruptive Behavior
DB has negative consequences for both patients and health care workers and results in poor evaluations of care from both groups.27-32 Aside from interfering with safe medical care, DB also impacts care for other patients by delaying access to care and increasing appointment wait times due to employee absenteeism and staff shortages.3,4,20,32,33 For HCPs, patient violence is associated with unwillingness to provide care, briefer treatment periods, and decreases in occupational satisfaction, performance, and commitment
Harmful health effects experienced by HCPs who have been victims of DB include fear, mood disorders, anxiety, all symptoms of psychological distress and posttraumatic stress disorder (PTSD).10,22,30,34-36 In a study of the impact on productivity of PTSD triggered by job-related DB, PTSD symptoms were associated with withdrawal from or minimizing encounters with patients, job turnover, and troubles with thinking
Reporting Disruptive Behavior
The literature suggests that consistent and effective DB reporting is pivotal to improving the outcome and quality of care for those displaying DB.37-39 To provide high-quality health services to veterans who display DB, the VHA must promote the management and reporting of DB. Without knowledge of the full spectrum of DB events at VHA facilities, efforts to prevent or manage DB and ensure safety may have limited impact.7,37 Reports can be used for clinical decision making to optimize staff training in delivery of quality care while assuring staff safety. More than 80% of DB incidents occur during interactions with patients, thus this is a clinical issue that can affect the outcome of patient care.8,21
Documented DB reports are used to analyze the degree, frequency, and nature of incidents, which might reveal risk factors and develop preventive efforts and training for specific hazards.8,39 Some have argued that implementing a standardized DB reporting system is a crucial first step toward minimizing hazards and improving health care.38,40,41
When DB incidents were recorded through a hospital electronic reporting system and discussed in meetings, staff reported: (1) increased awareness of DB; (2) improved ability to manage DB incidents; and (3) amplified reporting of incidents.38,41,42 These findings support similar results from studies of an intervention implemented at VA Community Living Centers (CLCs) from 2013 to 2017: Staff Training in Assisted Living Residences (STAR-VA).4,12,19 The aim of STAR-VA was to minimize challenging dementia-related DB in CLCs. The intervention initially was established to train direct-care, assisted-living staff to provide better care to older patients displaying DB. Data revealed that documentation of DBs was, the first step to ensuring staff and patient safety.18,40
VHA Reporting System
In 2013, the VA Office of Inspector General (OIG) found no standardized documentation of DB events across the VA health care system.42 Instead, DB events were documented in multiple records in various locations, including administrative and progress notes in the electronic health record (EHR), police reports, e-mails, or letters submitted to DBC chairs.42 This situation reduced administrators’ ability to consider all relevant information and render appropriate decisions in DB cases.42 In 2015, based on OIG recommendations, the VHA implemented the Disruptive Behavior Reporting System (DBRS) nationwide, which allowed all VHA staff to report DB events. The DBRS was designed to address factors likely to impede reporting and management of DB, namely, complexity of and lack of access to a central reporting system.43,44 The DBRS is currently the primary VHA tool to document DB events.
The DBRS consists of 32 questions in 5 sections relating to the (1) location and time of DB event; (2) reporter; (3) disrupter; (4) DB event details; and (5) the person who experienced (experiencer) the event. The system also provides a list of the types of DB, such as inappropriate communication, bullying and/or intimidation, verbal or written threat of physical harm, physical violence, sexual harassment, sexual assault, and property damage. The DBRS has the potential to provide useful data on DB and DB reporting, such as the typical staff entering data and the number and/or types of DB occurring.
The DBRS complements the preexisting VHA policies and committees for care of veterans who display DB.1-3,14,21,24,25 The VHA Workplace Violence Prevention Program (WVPP) required facilities to submit data on DB events through a Workplace Behavioral Risk report. Data for the report were obtained from police reports, patient safety reports, DBC records, and notes in the EHR. Following implementations of DBRS, the number of DB events per year became a part of facility performance standards.
VHA is creating novel approaches to handling DB that allow health care workers to render care in a safe and effective manner guided by documented information. For example, DBCs can recommend the use of Category I Patient Record Flags (PRFs) following documented DB, which informs staff of the potential risk of DB and provides guidance on protective methods to use when meeting with the patient.2,21,24 A survey of 140 VA hospital chiefs of staff indicated that DBC procedures were related to a decrease in the rates of assaults.1 Additionally, VA provides training for staff in techniques to promote personal safety, such as identifying signs that precede DB, using verbal deescalation, and practicing therapeutic containment.
Resistance to Reporting
Many health care employees and employers are reticent to report DBs.22,31,43,45-48 Studies suggest health care organizations can cultivate a culture that is resistant to reporting DB.49,50 This complicates the ability of the health care system to design and maintain safety protocols and safer treatment plans.3,41,51 Worldwide, < 30% of DBs are reported.47 One barrier may be that supervisors may not wish to acknowledge DBs on their units or may not provide sufficient staff time for training or reporting.31,46,47 HCPs may worry that a DB report will stigmatize patients, especially those who are elderly or have cognitive impairment, brain injury, psychological illness, or developmental disability. Patients with cognitive conditions are reportedly 20% more likely to be violent toward caregivers and providers.31 A dementia diagnosis, for example, is associated with a high likelihood for DB.30,52 More than 80% of DB events displayed by patients with dementia may go unreported.26,31,50,52
Some clinicians may attribute DB to physiologic conditions that need to be treated, not reported. However, employers can face various legal liabilities if steps are not taken to protect employees.47,51 Federal and state statutes require that organizations provide a healthy and safe employment environment for workers. This requires that employers institute reasonable protective measures, such as procedures to intervene, policies on addressing DB incidents, and/or training to minimize or deescalate DB.51,53 Also, employees may sue employers if security measures are inadequate or deficient in properly investigating current and past evidence of DB or identifying vulnerabilities in the workplace. Unwillingness to investigate DB and safety-related workplace concerns have contributed to increased workplace violence and legal liability.52,53 The mission of caring and trust is consistent with assuring a safe environment.
Training and Empathetic Care
To combat cultural resistance to reporting DBs, more and perhaps different contextual approaches to education and training may be needed that address ethical dilemmas and concerns of providers. The success of training relies on administrators supporting staff in reporting DB. Training must address providers’ conflicting beliefs and assist with identifying strategies to provide the best possible care for patients who display DB.1,38 HCPs are less likely to document a DB if they feel that administrators are creating documentation that will have negative consequences for a patient. Thus, leadership is responsible for ensuring that misconceptions are dispelled through training and other efforts and information on how reported DB data will be used is communicated through strategic channels.
Education and training must consider empathic care that attempts to understand why patients behave as they do through the information gathered.55 Empathy in health care is multifaceted: It involves comprehending a patient’s viewpoint, circumstances, and feelings and the capacity to analyze whether one is comprehending these accurately in order to demonstrate supportive care.54,55
Improving patient and staff interaction once a problematic behavior is identified is the aim of empathic care. Increasing empathic care can improve compassionate, patient-centered interactions that begin once the patient seeks care. This approach has proven to decrease DB by patients with dementia and improve their care, lessen staff problems during interactions, and increase staff morale.20 Experts call for the adoption of an interpersonal approach to patient encounters, and there is evidence that creating organizational change by moving toward compassionate care can lead to a positive impact for patients.54,55
Future Studies
There are growth opportunities in utilization of the DBRS. Analysis of the DBRS database by the VA Central Office (VACO) showed that the system is underutilized by facilities across the VA system.56 In response to this current underutilization, VACO is taking steps to close these gaps through increasing training to staff and promotion of the use of the DBRS. A 2015 pilot study of VHA providers showed that > 70% of providers had experienced a DB as defined by VHA, but only 34% of them reported their most recently experienced DB within the past 12 months.20 Thus, DBRS use must be studied within the context that patient-perpetrated DB is underreported in health care organizations.5,9,29,41,43,57,58 Studies addressing national DBRS utilization patterns and the cost associated with implementing the DBRS also are needed. One study suggests that there is an association between measures of facility complexity and staff perceptions of safety, which should be considered in analyzing DBRS usage.57 Studies addressing the role of the DBRS and misconceptions that the tool may represent a punitive tool also are needed. VHA should consider how the attribution “disruptive behavior” assigns a negative connotation and leads HCPs to avoid using the DBRS. Additionally, DB reporting may increase when HCPs understand that DB reporting is part of the comprehensive, consultative strategy to provide the best care to patients.
Conclusion
Accurate reporting of DB events enables the development of strategies for multidisciplinary teams to work together to minimize hazards and to provide interventions that provide for the safe delivery of health care to all patients. Improving reporting ensures there is an accurate representation of how disruptive events impact care provided within a facility—and what types of variables may be associated with increased risk for these types of events.
Additionally, ensuring that reporting is maximized also provides the VHA with opportunities for DBCs to offer evidence-based risk assessment of violence and consultation to staff members who may benefit from improved competencies in working with patients who display DB. These potential improvements are consistent with the VHA I CARE values and will provide data that can inform recommendations for health care in other agencies/health care organizations.
Acknowledgments
This work was supported by the Center of Innovation on Disability and Rehabilitation Research (CINDRR) of the Health Services Research and Development Service, Office of Research and Development, Department of Veterans Affairs.
1. Hodgson MJ, Mohr DC, Drummond DJ, Bell M, Van Male L. Managing disruptive patients in health care: necessary solutions to a difficult problem. Am J Ind Med. 2012;55(11):1009-1017.
2. US Department of Veterans Affairs, Veterans Health Administration. VHA Directive 2010-053. Patient Record Flags. https://www.va.gov/vhapublications/ViewPublication.asp?pub_ID=2341 Published December 3, 2010. Accessed March 29, 2019.
3. US Department of Veterans Affairs, Veterans Health Administration. VHA Directive 2012-026. Sexual Assaults and Other Defined Public Safety Incidents in VHA Facilities. https://www.va.gov/vhapublications/ViewPublication.asp?pub_ID=2797. Published September 27, 2012. Accessed March 29, 2019.
4. Curyto KJ, McCurry SM, Luci K, Karlin BE, Teri L, Karel MJ. Managing challenging behaviors of dementia in veterans: identifying and changing activators and consequences using STAR-VA. J Gerontol Nurs. 2017;43(2):33-43.
5. Speroni KG, Fitch T, Dawson E, Dugan L, Atherton M. Incidence and cost of nurse workplace violence perpetrated by hospital patients or patient visitors. J Emerg Nurs. 2014;40(3):218-228.
6. Phillips JP. Workplace violence against health care workers in the United States. NEJM. 2016;374(17):1661-1669.
7. Janocha JA, Smith RT. Workplace safety and health in the health care and social assistance industry, 2003–07. https://www.bls.gov/opub/mlr/cwc/workplace-safety-and-health-in-the-health-care-and-social-assistance-industry-2003-07.pdf. Published August 30, 2010. Accessed February 19, 2019.
8. US Department of Labor, Occupational Safety and Health Administration. Workplace violence in healthcare: understanding the challenge. https://www.osha.gov/Publications/OSHA3826.pdf. Published December 2015. Accessed February 19, 2019.
9. US Department of Labor, Occupational Safety and Health Administration. Prevention of Workplace Violence in Healthcare and Social Assistance. Occupational Safety and Health Administration, https://www.govinfo.gov/content/pkg/FR-2016-12-07/pdf/2016-29197.pdf. Accessed January 20, 2017.
10. Gerberich SG, Church TR, McGovern PM, et al. An epidemiological study of the magnitude and consequences of work related violence: the Minnesota Nurses’ Study. Occup Environ Med. 2004;61(6):495-503.
11. Sherman MF, Gershon RRM, Samar SM, Pearson JM, Canton AN, Damsky MR. Safety factors predictive of job satisfaction and job retention among home healthcare aides. J Occup Environ Med. 2008;50(12):1430-1441.
12. Karel MJ, Teri L, McConnell E, Visnic S, Karlin BE. Effectiveness of expanded implementation of STAR-VA for managing dementia-related behaviors among veterans. Gerontologist. 2016;56(1):126-134.
13. US Department of Labor, Bureau of Labor Statistics. Nonfatal occupational injuries and illnesses requiring days away from work. https://www.bls.gov/news.release/archives/osh2_11192015.htm. Published November 19, 2015.
14. Beech B, Leather P. Workplace violence in the health care sector: A review of staff training and integration of training evaluation models. Aggression Violent Behav. 2006;11(1):27-43.
15. Campbell CL, McCoy S, Burg MA, Hoffman N. Enhancing home care staff safety through reducing client aggression and violence in noninstitutional care settings: a systematic review. Home Health Care Manage Pract. 2014;26(1):3-10.
16. Gallant-Roman MA. Strategies and tools to reduce workplace violence. AAOHNJ. 2008;56(11):449-454.
17. Weinberger LE, Sreenivasan S, Smee DE, McGuire J, Garrick T. Balancing safety against obstruction to health care access: an examination of behavioral flags in the VA health care system. J Threat Assess Manage. 2018;5(1):35-41.
18. Elbogen EB, Johnson SC, Wagner HR, et al. Protective factors and risk modification of violence in Iraq and Afghanistan war veterans. J Clin Psychiatry. 2012;73(6):e767-e773.
19. Karlin BE, Visnic S, McGee JS, Teri L. Results from the multisite implementation of STAR-VA: a multicomponent psychosocial intervention for managing challenging dementia-related behaviors of veterans. Psychol Serv. 2014;11(2):200-208.
20. Semeah LM, Campbell CL, Cowper DC, Peet AC. Serving our homeless veterans: patient perpetrated violence as a barrier to health care access. J Pub Nonprofit Aff. 2017;3(2):223-234.
21. Hodgson MJ, Reed R, Craig T, et al. Violence in healthcare facilities: lessons from the Veterans Health Administration. J Occup Environ Med. 2004;46(11):1158-1165.
22. Farrell GA, Bobrowski C, Bobrowski P. Scoping workplace aggression in nursing: findings from an Australian study. J Adv Nurs. 2006;55(6):778-787.
23. Barling J, Rogers AG, Kelloway EK. Behind closed doors: in-home workers’ experience of sexual harassment and workplace violence. J Occup Health Psychol. 2001;6(3):255-269.
24. Pompeii LA, Schoenfisch AL, Lipscomb HJ, Dement JM, Smith CD, Upadhyaya M. Physical assault, physical threat, and verbal abuse perpetrated against hospital workers by patients or visitors in six U.S. hospitals. Am J Ind Med. 2015;58(11):1194-1204.
25. Sippel LM, Mota NP, Kachadourian LK, et al. The burden of hostility in U.S. veterans: results from the National Health and Resilience in Veterans Study. Psychiatry Res. 2016;243(suppl C):421-430.
26. Campbell C. Patient Violence and Aggression in Non-Institutional Health Care Settings: Predictors of Reporting By Healthcare Providers [doctoral dissertation]. Orlando: University of Central Florida; 2016.
27. Galinsky T, Feng HA, Streit J, et al. Risk factors associated with patient assaults of home healthcare workers. Rehabil Nurs. 2010;35(5):206-215.
28. Campbell CL. Incident reporting by health-care workers in noninstitutional care settings. Trauma, Violence Abuse. 2017;18(4):445-456.
29. Arnetz JE, Arnetz BB. Violence towards health care staff and possible effects on the quality of patient care. Soc Sci Med. 2001;52(3):417-427.
30. Gates D, Fitzwater E, Succop P. Relationships of stressors, strain, and anger to caregiver assaults. Issues Ment Health Nurs. 2003;24(8):775-793.
31. Brillhart B, Kruse B, Heard L. Safety concerns for rehabilitation nurses in home care. Rehabil Nurs. 2004;29(6):227-229.
32. Taylor H. Patient violence against clinicians: managing the risk. Innov Clin Neurosci. 2013;10(3):40-42.
33. US Department of Veterans Affairs, Office of Public and Intergovernmental Affairs. The Joint Commission releases results of surveys of the VA health care system. https://www.va.gov/opa/pressrel/pressrelease.cfm?id=2808. Updated August 5, 2014. Accessed February 19, 2019.
34. Büssing A, Höge T. Aggression and violence against home care workers. J Occup Health Psychol. 2004;9(3):206-219.
35. Geiger-Brown J, Muntaner C, McPhaul K, Lipscomb J, Trinkoff A. Abuse and violence during home care work as predictor of worker depression. Home Health Care Serv Q. 2007;26(1):59-77.
36. Gates DM, Gillespie GL, Succop P. Violence against nurses and its impact on stress and productivity. Nurs Econ. 2011;29(2):59-66.
37. Petterson IL, Arnetz BB. Psychosocial stressors and well-being in health care workers: the impact of an intervention program. Soc Sci Med. 1998;47(11):1763-1772.
38. Arnetz JE, Arnetz BB. Implementation and evaluation of a practical intervention programme for dealing with violence towards health care workers. J Adv Nurs. 2000;31(3):668-680.
39. Arnetz JE, Hamblin L, Russell J, et al. Preventing patient-to-worker violence in hospitals: outcome of a randomized controlled intervention. J Occup Environ Med. 2017;59(1):18-27.
40. Elbogen EB, Tomkins AJ, Pothuloori AP, Scalora MJ. Documentation of violence risk information in psychiatric hospital patient charts: an empirical examination. J Am Acad Psychiatry Law. 2003;31(1):58-64.
41. Winsvold Prang I, Jelson-Jorgensen LP. Should I report? A qualitative study of barriers to incident reporting among nurses working in nursing homes. Geriatr Nurs. 2014;35(6):441-447.
42. US Department of Veterans Affairs, Office of Inspector General. Healthcare inspection: management of disruptive patient behavior at VA medical facilities. Report No. 11-02585-129. https://www.va.gov/oig/pubs/VAOIG-11-02585-129.pdf. Published Mrach 7, 2013. Accessed February 21, 2019.
43. Lipscomb J, London M. Not Part of the Job: How to Take a Stand Against Violence in the Work Setting. Silver Spring, MD: American Nurses Association; 2015.
44. May DD, Grubbs LM. The extent, nature, and precipitating factors of nurse assault among three groups of registered nurses in a regional medical center. J Emerg Nurs. 2002;28(1):11-17.
45. Wharton TC, Ford BK. What is known about dementia care recipient violence and aggression against caregivers? J Gerontol Soc Work. 2014;57(5):460-477.
46. Brennan C, Worrall-Davies A, McMillan D, Gilbody S, House A. The hospital anxiety and depression scale: a diagnostic meta-analysis of case-finding ability. J Psychosom Res. 2010;69(4):371-378.
47. McPhaul K, Lipscomb J, Johnson J. Assessing risk for violence on home health visits. Home Healthc Nurse. 2010;28(5):278-289.
48. McPhaul KM, London M, Murrett K, Flannery K, Rosen J, Lipscomb J. Environmental evaluation for workplace violence in healthcare and social services. J Safety Res. 2008;39(2):237-250.
49. Kelly JA, Somlai AM, DiFranceisco WJ, et al. Bridging the gap between the science and service of HIV prevention: transferring effective research-based HIV prevention interventions to community AIDS service providers. Am J Public Health. 2000;90(7):1082-1088.
50. Pawlin S. Reporting violence. Emerg Nurse. 2008;16(4):16-21.
51. Brakel SJ. Legal liability and workplace violence. J Am Acad Psychiatry Law. 1998;26(4):553-562.
52. Neuman JH, Baron RA. Workplace violence and workplace aggression: evidence concerning specific forms, potential causes, and preferred targets. J Manage. 1998;24(3):391-419.53. Ferns T, Chojnacka I. Angels and swingers, matrons and sinners: nursing stereotypes. Br J Nurs. 2005;14(19):1028-1032.
54. Mercer SW, Reynolds WJ. Empathy and quality of care. Br J Gen Pract 2002;52(suppl):S9-S12.
55. Lee TH. An Epidemic of Empathy in Healthcare: How to Deliver Compassionate, Connected Patient Care That Creates a Competitive Advantage. Columbus, OH: McGraw-Hill Education; 2015.
56. US Department of Veterans Affairs, Veterans Health Administrastion. Veterans Health Administration workplace violence prevention program (WVPP): disruptive behavior reporting system utilization report. Published 2017. https://vaww.portal2.va.gov/sites/wvpp/Shared%20Documents/DBRS%20Utilization%20Reports/FY2017%20DBRS%20Quarterly%20Utilization%20Report%20(Quarter%201).pdf. [Source not verified.]
57. Campbell CL, Burg, MA, Gammonley D. Measures for incident reporting of patient violence and aggression towards healthcare providers: a systematic review. Aggression Violent Behav. 2015;25(part B):314-322.
58. Carney PT, West P, Neily J, Mills PD, Bagian JP. The effect of facility complexity on perceptions of safety climate in the operating room: size matters. Am J Med Qual. 2010;25(6):457-461.
1. Hodgson MJ, Mohr DC, Drummond DJ, Bell M, Van Male L. Managing disruptive patients in health care: necessary solutions to a difficult problem. Am J Ind Med. 2012;55(11):1009-1017.
2. US Department of Veterans Affairs, Veterans Health Administration. VHA Directive 2010-053. Patient Record Flags. https://www.va.gov/vhapublications/ViewPublication.asp?pub_ID=2341 Published December 3, 2010. Accessed March 29, 2019.
3. US Department of Veterans Affairs, Veterans Health Administration. VHA Directive 2012-026. Sexual Assaults and Other Defined Public Safety Incidents in VHA Facilities. https://www.va.gov/vhapublications/ViewPublication.asp?pub_ID=2797. Published September 27, 2012. Accessed March 29, 2019.
4. Curyto KJ, McCurry SM, Luci K, Karlin BE, Teri L, Karel MJ. Managing challenging behaviors of dementia in veterans: identifying and changing activators and consequences using STAR-VA. J Gerontol Nurs. 2017;43(2):33-43.
5. Speroni KG, Fitch T, Dawson E, Dugan L, Atherton M. Incidence and cost of nurse workplace violence perpetrated by hospital patients or patient visitors. J Emerg Nurs. 2014;40(3):218-228.
6. Phillips JP. Workplace violence against health care workers in the United States. NEJM. 2016;374(17):1661-1669.
7. Janocha JA, Smith RT. Workplace safety and health in the health care and social assistance industry, 2003–07. https://www.bls.gov/opub/mlr/cwc/workplace-safety-and-health-in-the-health-care-and-social-assistance-industry-2003-07.pdf. Published August 30, 2010. Accessed February 19, 2019.
8. US Department of Labor, Occupational Safety and Health Administration. Workplace violence in healthcare: understanding the challenge. https://www.osha.gov/Publications/OSHA3826.pdf. Published December 2015. Accessed February 19, 2019.
9. US Department of Labor, Occupational Safety and Health Administration. Prevention of Workplace Violence in Healthcare and Social Assistance. Occupational Safety and Health Administration, https://www.govinfo.gov/content/pkg/FR-2016-12-07/pdf/2016-29197.pdf. Accessed January 20, 2017.
10. Gerberich SG, Church TR, McGovern PM, et al. An epidemiological study of the magnitude and consequences of work related violence: the Minnesota Nurses’ Study. Occup Environ Med. 2004;61(6):495-503.
11. Sherman MF, Gershon RRM, Samar SM, Pearson JM, Canton AN, Damsky MR. Safety factors predictive of job satisfaction and job retention among home healthcare aides. J Occup Environ Med. 2008;50(12):1430-1441.
12. Karel MJ, Teri L, McConnell E, Visnic S, Karlin BE. Effectiveness of expanded implementation of STAR-VA for managing dementia-related behaviors among veterans. Gerontologist. 2016;56(1):126-134.
13. US Department of Labor, Bureau of Labor Statistics. Nonfatal occupational injuries and illnesses requiring days away from work. https://www.bls.gov/news.release/archives/osh2_11192015.htm. Published November 19, 2015.
14. Beech B, Leather P. Workplace violence in the health care sector: A review of staff training and integration of training evaluation models. Aggression Violent Behav. 2006;11(1):27-43.
15. Campbell CL, McCoy S, Burg MA, Hoffman N. Enhancing home care staff safety through reducing client aggression and violence in noninstitutional care settings: a systematic review. Home Health Care Manage Pract. 2014;26(1):3-10.
16. Gallant-Roman MA. Strategies and tools to reduce workplace violence. AAOHNJ. 2008;56(11):449-454.
17. Weinberger LE, Sreenivasan S, Smee DE, McGuire J, Garrick T. Balancing safety against obstruction to health care access: an examination of behavioral flags in the VA health care system. J Threat Assess Manage. 2018;5(1):35-41.
18. Elbogen EB, Johnson SC, Wagner HR, et al. Protective factors and risk modification of violence in Iraq and Afghanistan war veterans. J Clin Psychiatry. 2012;73(6):e767-e773.
19. Karlin BE, Visnic S, McGee JS, Teri L. Results from the multisite implementation of STAR-VA: a multicomponent psychosocial intervention for managing challenging dementia-related behaviors of veterans. Psychol Serv. 2014;11(2):200-208.
20. Semeah LM, Campbell CL, Cowper DC, Peet AC. Serving our homeless veterans: patient perpetrated violence as a barrier to health care access. J Pub Nonprofit Aff. 2017;3(2):223-234.
21. Hodgson MJ, Reed R, Craig T, et al. Violence in healthcare facilities: lessons from the Veterans Health Administration. J Occup Environ Med. 2004;46(11):1158-1165.
22. Farrell GA, Bobrowski C, Bobrowski P. Scoping workplace aggression in nursing: findings from an Australian study. J Adv Nurs. 2006;55(6):778-787.
23. Barling J, Rogers AG, Kelloway EK. Behind closed doors: in-home workers’ experience of sexual harassment and workplace violence. J Occup Health Psychol. 2001;6(3):255-269.
24. Pompeii LA, Schoenfisch AL, Lipscomb HJ, Dement JM, Smith CD, Upadhyaya M. Physical assault, physical threat, and verbal abuse perpetrated against hospital workers by patients or visitors in six U.S. hospitals. Am J Ind Med. 2015;58(11):1194-1204.
25. Sippel LM, Mota NP, Kachadourian LK, et al. The burden of hostility in U.S. veterans: results from the National Health and Resilience in Veterans Study. Psychiatry Res. 2016;243(suppl C):421-430.
26. Campbell C. Patient Violence and Aggression in Non-Institutional Health Care Settings: Predictors of Reporting By Healthcare Providers [doctoral dissertation]. Orlando: University of Central Florida; 2016.
27. Galinsky T, Feng HA, Streit J, et al. Risk factors associated with patient assaults of home healthcare workers. Rehabil Nurs. 2010;35(5):206-215.
28. Campbell CL. Incident reporting by health-care workers in noninstitutional care settings. Trauma, Violence Abuse. 2017;18(4):445-456.
29. Arnetz JE, Arnetz BB. Violence towards health care staff and possible effects on the quality of patient care. Soc Sci Med. 2001;52(3):417-427.
30. Gates D, Fitzwater E, Succop P. Relationships of stressors, strain, and anger to caregiver assaults. Issues Ment Health Nurs. 2003;24(8):775-793.
31. Brillhart B, Kruse B, Heard L. Safety concerns for rehabilitation nurses in home care. Rehabil Nurs. 2004;29(6):227-229.
32. Taylor H. Patient violence against clinicians: managing the risk. Innov Clin Neurosci. 2013;10(3):40-42.
33. US Department of Veterans Affairs, Office of Public and Intergovernmental Affairs. The Joint Commission releases results of surveys of the VA health care system. https://www.va.gov/opa/pressrel/pressrelease.cfm?id=2808. Updated August 5, 2014. Accessed February 19, 2019.
34. Büssing A, Höge T. Aggression and violence against home care workers. J Occup Health Psychol. 2004;9(3):206-219.
35. Geiger-Brown J, Muntaner C, McPhaul K, Lipscomb J, Trinkoff A. Abuse and violence during home care work as predictor of worker depression. Home Health Care Serv Q. 2007;26(1):59-77.
36. Gates DM, Gillespie GL, Succop P. Violence against nurses and its impact on stress and productivity. Nurs Econ. 2011;29(2):59-66.
37. Petterson IL, Arnetz BB. Psychosocial stressors and well-being in health care workers: the impact of an intervention program. Soc Sci Med. 1998;47(11):1763-1772.
38. Arnetz JE, Arnetz BB. Implementation and evaluation of a practical intervention programme for dealing with violence towards health care workers. J Adv Nurs. 2000;31(3):668-680.
39. Arnetz JE, Hamblin L, Russell J, et al. Preventing patient-to-worker violence in hospitals: outcome of a randomized controlled intervention. J Occup Environ Med. 2017;59(1):18-27.
40. Elbogen EB, Tomkins AJ, Pothuloori AP, Scalora MJ. Documentation of violence risk information in psychiatric hospital patient charts: an empirical examination. J Am Acad Psychiatry Law. 2003;31(1):58-64.
41. Winsvold Prang I, Jelson-Jorgensen LP. Should I report? A qualitative study of barriers to incident reporting among nurses working in nursing homes. Geriatr Nurs. 2014;35(6):441-447.
42. US Department of Veterans Affairs, Office of Inspector General. Healthcare inspection: management of disruptive patient behavior at VA medical facilities. Report No. 11-02585-129. https://www.va.gov/oig/pubs/VAOIG-11-02585-129.pdf. Published Mrach 7, 2013. Accessed February 21, 2019.
43. Lipscomb J, London M. Not Part of the Job: How to Take a Stand Against Violence in the Work Setting. Silver Spring, MD: American Nurses Association; 2015.
44. May DD, Grubbs LM. The extent, nature, and precipitating factors of nurse assault among three groups of registered nurses in a regional medical center. J Emerg Nurs. 2002;28(1):11-17.
45. Wharton TC, Ford BK. What is known about dementia care recipient violence and aggression against caregivers? J Gerontol Soc Work. 2014;57(5):460-477.
46. Brennan C, Worrall-Davies A, McMillan D, Gilbody S, House A. The hospital anxiety and depression scale: a diagnostic meta-analysis of case-finding ability. J Psychosom Res. 2010;69(4):371-378.
47. McPhaul K, Lipscomb J, Johnson J. Assessing risk for violence on home health visits. Home Healthc Nurse. 2010;28(5):278-289.
48. McPhaul KM, London M, Murrett K, Flannery K, Rosen J, Lipscomb J. Environmental evaluation for workplace violence in healthcare and social services. J Safety Res. 2008;39(2):237-250.
49. Kelly JA, Somlai AM, DiFranceisco WJ, et al. Bridging the gap between the science and service of HIV prevention: transferring effective research-based HIV prevention interventions to community AIDS service providers. Am J Public Health. 2000;90(7):1082-1088.
50. Pawlin S. Reporting violence. Emerg Nurse. 2008;16(4):16-21.
51. Brakel SJ. Legal liability and workplace violence. J Am Acad Psychiatry Law. 1998;26(4):553-562.
52. Neuman JH, Baron RA. Workplace violence and workplace aggression: evidence concerning specific forms, potential causes, and preferred targets. J Manage. 1998;24(3):391-419.53. Ferns T, Chojnacka I. Angels and swingers, matrons and sinners: nursing stereotypes. Br J Nurs. 2005;14(19):1028-1032.
54. Mercer SW, Reynolds WJ. Empathy and quality of care. Br J Gen Pract 2002;52(suppl):S9-S12.
55. Lee TH. An Epidemic of Empathy in Healthcare: How to Deliver Compassionate, Connected Patient Care That Creates a Competitive Advantage. Columbus, OH: McGraw-Hill Education; 2015.
56. US Department of Veterans Affairs, Veterans Health Administrastion. Veterans Health Administration workplace violence prevention program (WVPP): disruptive behavior reporting system utilization report. Published 2017. https://vaww.portal2.va.gov/sites/wvpp/Shared%20Documents/DBRS%20Utilization%20Reports/FY2017%20DBRS%20Quarterly%20Utilization%20Report%20(Quarter%201).pdf. [Source not verified.]
57. Campbell CL, Burg, MA, Gammonley D. Measures for incident reporting of patient violence and aggression towards healthcare providers: a systematic review. Aggression Violent Behav. 2015;25(part B):314-322.
58. Carney PT, West P, Neily J, Mills PD, Bagian JP. The effect of facility complexity on perceptions of safety climate in the operating room: size matters. Am J Med Qual. 2010;25(6):457-461.
Why Am I Being Treated Like a Female Breast Cancer Patient? (FULL)
Patient Perspective
Breast cancer has been one of my life’s greatest blessings. Its highs and lows, prospects, and disappointments have only strengthened my faith and turned me more to God.
In March 2012, I had a bad cold, and while I was coughing and grabbing my chest, I discovered a small knot in my left breast, and for whatever reason, I suspected it was cancer. I immediately woke my wife. She, groggy and in usual humor exclaimed, “Oh great! You have breast cancer! Well guess what? I have prostate cancer…now go back to sleep!” I laughed at the prospect of her having prostate cancer. It certainly would’ve changed a few dynamics in our relationship.
Two weeks later my fears were confirmed. I was told that I needed to have a mastectomy of my left breast. I wanted nothing but to have this poison removed. Yesterday would not have been too soon.
My surgery was scheduled a month later; it was a long wait. And it soon became clear that as I recovered from the impending mastectomy, I also would be in line for open-heart surgery.
The mastectomy was a textbook procedure with no complications. My surgeon apprehensively warned me that follow-up visits would be at the Women’s Health Center. I must admit, it was awkward every time I went. Realistically though, I cared more about my health than about others’ perceptions.
While I prepared for my cardiac surgery, the blood test revealed triglyceride levels that were through the roof. In fact, the cardiac surgeon described them as “industrial strength.” After an exhaustive review, it was determined that my adjuvant therapy with tamoxifen was the culprit! I immediately stopped taking it, and within days my levels returned to normal. I was now left to fight any future bouts of cancer with just my body’s own defenses.
It probably seems strange, but if I had not found the breast lump, the problems with my heart would have gone undetected. I most likely would’ve died. Had the cancer not been a part of my life, I wouldn’t have been able to keep on living.
In the middle of March 2016, during preliminary testing for surgery to remove a skin tag, my chest X-ray displayed abnormalities. The workup showed that my breast cancer had returned. Worse yet, it had metastasized to my lungs. It had gone into my lymph nodes and lower spine.
The fight was on. A treatment plan was outlined; 12 weeks of chemotherapy infusions was a reasonable plan of attack. A second opinion was not necessarily an opportunity to find a differing plan, but as in my case, it was comforting affirmation of a good plan. I remember wondering if the rest of my life was going to be a mix of hospital visits, blood transfusions, chemotherapies, and injections.
While fear of the unknown works on one’s psyche, I made a decision to focus on my faith and God. My cancer experiences are probably no worse or different from the experiences of most other patients. I do believe that my perception of how cancer affected me psychologically is a different story. I know and trust that I am in the capable and knowledgeable hands of my doctor.
While the experience of good health care is remarkable, living with cancer does not end with medical care. I am blessed to have a partner who loves me infinitely. I cannot imagine my life without her.
I am grateful my cancer has allowed me to remain alive. The prospect of death does not shake me. I plan on living my life to the fullest.
Oncologist Perspective
Yes, men do get breast cancer! Unlike female breast cancer (FBC), male breast cancer (MBC) makes up about 1% of all cases in the U.S. The lifetime risk of a man developing breast cancer is about 1 in 1,000 vs 1 in 8 women.1 Little is known about MBC because its rarity renders prospective randomized trials problematic. As a result, the management of breast cancer in males from diagnosis to treatment is based on research on FBC. Patients with MBC have higher mortality, and the incidence is rising 1.1% per year; by comparison both trends are decreasing for females with breast cancer.2,3
Males are usually older and present with an advanced stage of the disease at the time of the diagnosis. Most MBC is ER+/PR+ and HER2−.4 Comparison data of 1,123 male veterans with 5,320 females revealed that the mean age at diagnosis was 70 years for MBC and 57 years for FBC, respectively (P < .01); 95% of patients with MBC and 72% of patients with FBC were aged > 50 years (P < .01). Patients with MBC were more likely to present with stage III or IV disease (40% vs 24%, respectively). Eighty percent of patients with MBC had ER+/PR+ tumors. Mortality was 31.6% in males vs 14.9% in females.
Given the high prevalence of ER/PR positivity, MBC usually is considered to have a better prognosis, but that does not explain the high mortality. Unlike FBC, delay in diagnosis due to lack of MBC awareness and no screening guidelines for MBC, older age at diagnosis, and comorbidities have been considered the etiology of higher mortality in MBC, but there has to be more than that. I believe that the differences in MBC biology and pathology also have to be contributing factors to MBC mortality.
As a VA oncologist, I have treated a number of patients with MBC. Surprisingly, my experience treating these patients has been different from treating FBC. In 2011, when I first met Mr. Lewis, he laughed and questioned his diagnosis—how could he have breast cancer if males don’t have breasts, and none of his family member had any type of cancer. Prior to his cancer diagnoses, he had gone through multiple cardiac stents and had a history of hypertriglyceridemia. His cancer workup and treatment plan were the same as that of females with breast cancer, and he questioned me again, “Why am I being treated like a female breast cancer patient?”
Unlike females with breast cancer, he had to have a complete mastectomy given the small breast tissue. His final diagnosis was stage IIA invasive ductal carcinoma of the left breast.
Because of Mr. Lewis’ cardiac history and recent stent placement, I was hesitant to give him first-line adjuvant anthracycline. The Oncotype DX test is highly recommended and easily done for FBC, but I had to go through great difficulty to order this test for him. The Oncotype Dx RS score for him was 17 (a so-called low score) with distant recurrence risk of 11%. I interpreted the test the same way as I would for a patient with FBC. We were happy that he did not have to be exposed to toxic chemotherapies.
Because of the lack of data for aromatase inhibitors (AIs) use in males, adjuvant tamoxifen was given but had to be stopped after a month because of hypertriglyceridemia > 8,000 mg/dL and cholesterol > 700 mg/dL. Tamoxifen as well as an AI was deemed not to be the right adjuvant treatment for him. There were no data on adjuvant fulvestrant; not even for females in 2012. Mr. Lewis was among the unlucky 11% and presented with stage IV disease in his lungs and bones 4 years after the initial diagnosis. He has not had a great response to taxanes and now is being treated with fulvestrant. He remains positive and hopeful, he told me only God—not medical science—has the power to take back the gift of life.
My experience with Mr. Lewis and others has underscored that MBC is not the same disease as FBC. I am hopeful we will see more clinical trials to further identify MBC biology and genomics.
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Author disclosures
The authors report no actual or potential conflicts of interest with regard to this article.
Disclaimer
The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the U.S. Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.
1. American Cancer Society. Cancer facts and figures 2014. Atlanta, GA: American Cancer Society; 2014.
2. Anderson WF, Jatoi I, Tse J, Rosenberg PS. Male breast cancer: a population-based comparison with female breast cancer. J Clin Oncol. 2010;28(2):232-239.
3. Howlander N, Noone AM, Krapcho M, et al. eds. SEER cancer statistics review, 1975-2009: fast stats. http://seer.cancer.gov/csr/1975_2009_pops09. Updated April 2012. Accessed January 20, 2018.
4. Ly D, Forman D, Ferlay J, Brinton LA, Cook MB. An international comparison of male and female breast cancer incidence rates. Int J Cancer. 2013;132(8):1918-1926.
Patient Perspective
Breast cancer has been one of my life’s greatest blessings. Its highs and lows, prospects, and disappointments have only strengthened my faith and turned me more to God.
In March 2012, I had a bad cold, and while I was coughing and grabbing my chest, I discovered a small knot in my left breast, and for whatever reason, I suspected it was cancer. I immediately woke my wife. She, groggy and in usual humor exclaimed, “Oh great! You have breast cancer! Well guess what? I have prostate cancer…now go back to sleep!” I laughed at the prospect of her having prostate cancer. It certainly would’ve changed a few dynamics in our relationship.
Two weeks later my fears were confirmed. I was told that I needed to have a mastectomy of my left breast. I wanted nothing but to have this poison removed. Yesterday would not have been too soon.
My surgery was scheduled a month later; it was a long wait. And it soon became clear that as I recovered from the impending mastectomy, I also would be in line for open-heart surgery.
The mastectomy was a textbook procedure with no complications. My surgeon apprehensively warned me that follow-up visits would be at the Women’s Health Center. I must admit, it was awkward every time I went. Realistically though, I cared more about my health than about others’ perceptions.
While I prepared for my cardiac surgery, the blood test revealed triglyceride levels that were through the roof. In fact, the cardiac surgeon described them as “industrial strength.” After an exhaustive review, it was determined that my adjuvant therapy with tamoxifen was the culprit! I immediately stopped taking it, and within days my levels returned to normal. I was now left to fight any future bouts of cancer with just my body’s own defenses.
It probably seems strange, but if I had not found the breast lump, the problems with my heart would have gone undetected. I most likely would’ve died. Had the cancer not been a part of my life, I wouldn’t have been able to keep on living.
In the middle of March 2016, during preliminary testing for surgery to remove a skin tag, my chest X-ray displayed abnormalities. The workup showed that my breast cancer had returned. Worse yet, it had metastasized to my lungs. It had gone into my lymph nodes and lower spine.
The fight was on. A treatment plan was outlined; 12 weeks of chemotherapy infusions was a reasonable plan of attack. A second opinion was not necessarily an opportunity to find a differing plan, but as in my case, it was comforting affirmation of a good plan. I remember wondering if the rest of my life was going to be a mix of hospital visits, blood transfusions, chemotherapies, and injections.
While fear of the unknown works on one’s psyche, I made a decision to focus on my faith and God. My cancer experiences are probably no worse or different from the experiences of most other patients. I do believe that my perception of how cancer affected me psychologically is a different story. I know and trust that I am in the capable and knowledgeable hands of my doctor.
While the experience of good health care is remarkable, living with cancer does not end with medical care. I am blessed to have a partner who loves me infinitely. I cannot imagine my life without her.
I am grateful my cancer has allowed me to remain alive. The prospect of death does not shake me. I plan on living my life to the fullest.
Oncologist Perspective
Yes, men do get breast cancer! Unlike female breast cancer (FBC), male breast cancer (MBC) makes up about 1% of all cases in the U.S. The lifetime risk of a man developing breast cancer is about 1 in 1,000 vs 1 in 8 women.1 Little is known about MBC because its rarity renders prospective randomized trials problematic. As a result, the management of breast cancer in males from diagnosis to treatment is based on research on FBC. Patients with MBC have higher mortality, and the incidence is rising 1.1% per year; by comparison both trends are decreasing for females with breast cancer.2,3
Males are usually older and present with an advanced stage of the disease at the time of the diagnosis. Most MBC is ER+/PR+ and HER2−.4 Comparison data of 1,123 male veterans with 5,320 females revealed that the mean age at diagnosis was 70 years for MBC and 57 years for FBC, respectively (P < .01); 95% of patients with MBC and 72% of patients with FBC were aged > 50 years (P < .01). Patients with MBC were more likely to present with stage III or IV disease (40% vs 24%, respectively). Eighty percent of patients with MBC had ER+/PR+ tumors. Mortality was 31.6% in males vs 14.9% in females.
Given the high prevalence of ER/PR positivity, MBC usually is considered to have a better prognosis, but that does not explain the high mortality. Unlike FBC, delay in diagnosis due to lack of MBC awareness and no screening guidelines for MBC, older age at diagnosis, and comorbidities have been considered the etiology of higher mortality in MBC, but there has to be more than that. I believe that the differences in MBC biology and pathology also have to be contributing factors to MBC mortality.
As a VA oncologist, I have treated a number of patients with MBC. Surprisingly, my experience treating these patients has been different from treating FBC. In 2011, when I first met Mr. Lewis, he laughed and questioned his diagnosis—how could he have breast cancer if males don’t have breasts, and none of his family member had any type of cancer. Prior to his cancer diagnoses, he had gone through multiple cardiac stents and had a history of hypertriglyceridemia. His cancer workup and treatment plan were the same as that of females with breast cancer, and he questioned me again, “Why am I being treated like a female breast cancer patient?”
Unlike females with breast cancer, he had to have a complete mastectomy given the small breast tissue. His final diagnosis was stage IIA invasive ductal carcinoma of the left breast.
Because of Mr. Lewis’ cardiac history and recent stent placement, I was hesitant to give him first-line adjuvant anthracycline. The Oncotype DX test is highly recommended and easily done for FBC, but I had to go through great difficulty to order this test for him. The Oncotype Dx RS score for him was 17 (a so-called low score) with distant recurrence risk of 11%. I interpreted the test the same way as I would for a patient with FBC. We were happy that he did not have to be exposed to toxic chemotherapies.
Because of the lack of data for aromatase inhibitors (AIs) use in males, adjuvant tamoxifen was given but had to be stopped after a month because of hypertriglyceridemia > 8,000 mg/dL and cholesterol > 700 mg/dL. Tamoxifen as well as an AI was deemed not to be the right adjuvant treatment for him. There were no data on adjuvant fulvestrant; not even for females in 2012. Mr. Lewis was among the unlucky 11% and presented with stage IV disease in his lungs and bones 4 years after the initial diagnosis. He has not had a great response to taxanes and now is being treated with fulvestrant. He remains positive and hopeful, he told me only God—not medical science—has the power to take back the gift of life.
My experience with Mr. Lewis and others has underscored that MBC is not the same disease as FBC. I am hopeful we will see more clinical trials to further identify MBC biology and genomics.
Click here to read the digital edition.
Author disclosures
The authors report no actual or potential conflicts of interest with regard to this article.
Disclaimer
The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the U.S. Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.
Patient Perspective
Breast cancer has been one of my life’s greatest blessings. Its highs and lows, prospects, and disappointments have only strengthened my faith and turned me more to God.
In March 2012, I had a bad cold, and while I was coughing and grabbing my chest, I discovered a small knot in my left breast, and for whatever reason, I suspected it was cancer. I immediately woke my wife. She, groggy and in usual humor exclaimed, “Oh great! You have breast cancer! Well guess what? I have prostate cancer…now go back to sleep!” I laughed at the prospect of her having prostate cancer. It certainly would’ve changed a few dynamics in our relationship.
Two weeks later my fears were confirmed. I was told that I needed to have a mastectomy of my left breast. I wanted nothing but to have this poison removed. Yesterday would not have been too soon.
My surgery was scheduled a month later; it was a long wait. And it soon became clear that as I recovered from the impending mastectomy, I also would be in line for open-heart surgery.
The mastectomy was a textbook procedure with no complications. My surgeon apprehensively warned me that follow-up visits would be at the Women’s Health Center. I must admit, it was awkward every time I went. Realistically though, I cared more about my health than about others’ perceptions.
While I prepared for my cardiac surgery, the blood test revealed triglyceride levels that were through the roof. In fact, the cardiac surgeon described them as “industrial strength.” After an exhaustive review, it was determined that my adjuvant therapy with tamoxifen was the culprit! I immediately stopped taking it, and within days my levels returned to normal. I was now left to fight any future bouts of cancer with just my body’s own defenses.
It probably seems strange, but if I had not found the breast lump, the problems with my heart would have gone undetected. I most likely would’ve died. Had the cancer not been a part of my life, I wouldn’t have been able to keep on living.
In the middle of March 2016, during preliminary testing for surgery to remove a skin tag, my chest X-ray displayed abnormalities. The workup showed that my breast cancer had returned. Worse yet, it had metastasized to my lungs. It had gone into my lymph nodes and lower spine.
The fight was on. A treatment plan was outlined; 12 weeks of chemotherapy infusions was a reasonable plan of attack. A second opinion was not necessarily an opportunity to find a differing plan, but as in my case, it was comforting affirmation of a good plan. I remember wondering if the rest of my life was going to be a mix of hospital visits, blood transfusions, chemotherapies, and injections.
While fear of the unknown works on one’s psyche, I made a decision to focus on my faith and God. My cancer experiences are probably no worse or different from the experiences of most other patients. I do believe that my perception of how cancer affected me psychologically is a different story. I know and trust that I am in the capable and knowledgeable hands of my doctor.
While the experience of good health care is remarkable, living with cancer does not end with medical care. I am blessed to have a partner who loves me infinitely. I cannot imagine my life without her.
I am grateful my cancer has allowed me to remain alive. The prospect of death does not shake me. I plan on living my life to the fullest.
Oncologist Perspective
Yes, men do get breast cancer! Unlike female breast cancer (FBC), male breast cancer (MBC) makes up about 1% of all cases in the U.S. The lifetime risk of a man developing breast cancer is about 1 in 1,000 vs 1 in 8 women.1 Little is known about MBC because its rarity renders prospective randomized trials problematic. As a result, the management of breast cancer in males from diagnosis to treatment is based on research on FBC. Patients with MBC have higher mortality, and the incidence is rising 1.1% per year; by comparison both trends are decreasing for females with breast cancer.2,3
Males are usually older and present with an advanced stage of the disease at the time of the diagnosis. Most MBC is ER+/PR+ and HER2−.4 Comparison data of 1,123 male veterans with 5,320 females revealed that the mean age at diagnosis was 70 years for MBC and 57 years for FBC, respectively (P < .01); 95% of patients with MBC and 72% of patients with FBC were aged > 50 years (P < .01). Patients with MBC were more likely to present with stage III or IV disease (40% vs 24%, respectively). Eighty percent of patients with MBC had ER+/PR+ tumors. Mortality was 31.6% in males vs 14.9% in females.
Given the high prevalence of ER/PR positivity, MBC usually is considered to have a better prognosis, but that does not explain the high mortality. Unlike FBC, delay in diagnosis due to lack of MBC awareness and no screening guidelines for MBC, older age at diagnosis, and comorbidities have been considered the etiology of higher mortality in MBC, but there has to be more than that. I believe that the differences in MBC biology and pathology also have to be contributing factors to MBC mortality.
As a VA oncologist, I have treated a number of patients with MBC. Surprisingly, my experience treating these patients has been different from treating FBC. In 2011, when I first met Mr. Lewis, he laughed and questioned his diagnosis—how could he have breast cancer if males don’t have breasts, and none of his family member had any type of cancer. Prior to his cancer diagnoses, he had gone through multiple cardiac stents and had a history of hypertriglyceridemia. His cancer workup and treatment plan were the same as that of females with breast cancer, and he questioned me again, “Why am I being treated like a female breast cancer patient?”
Unlike females with breast cancer, he had to have a complete mastectomy given the small breast tissue. His final diagnosis was stage IIA invasive ductal carcinoma of the left breast.
Because of Mr. Lewis’ cardiac history and recent stent placement, I was hesitant to give him first-line adjuvant anthracycline. The Oncotype DX test is highly recommended and easily done for FBC, but I had to go through great difficulty to order this test for him. The Oncotype Dx RS score for him was 17 (a so-called low score) with distant recurrence risk of 11%. I interpreted the test the same way as I would for a patient with FBC. We were happy that he did not have to be exposed to toxic chemotherapies.
Because of the lack of data for aromatase inhibitors (AIs) use in males, adjuvant tamoxifen was given but had to be stopped after a month because of hypertriglyceridemia > 8,000 mg/dL and cholesterol > 700 mg/dL. Tamoxifen as well as an AI was deemed not to be the right adjuvant treatment for him. There were no data on adjuvant fulvestrant; not even for females in 2012. Mr. Lewis was among the unlucky 11% and presented with stage IV disease in his lungs and bones 4 years after the initial diagnosis. He has not had a great response to taxanes and now is being treated with fulvestrant. He remains positive and hopeful, he told me only God—not medical science—has the power to take back the gift of life.
My experience with Mr. Lewis and others has underscored that MBC is not the same disease as FBC. I am hopeful we will see more clinical trials to further identify MBC biology and genomics.
Click here to read the digital edition.
Author disclosures
The authors report no actual or potential conflicts of interest with regard to this article.
Disclaimer
The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the U.S. Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review the complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.
1. American Cancer Society. Cancer facts and figures 2014. Atlanta, GA: American Cancer Society; 2014.
2. Anderson WF, Jatoi I, Tse J, Rosenberg PS. Male breast cancer: a population-based comparison with female breast cancer. J Clin Oncol. 2010;28(2):232-239.
3. Howlander N, Noone AM, Krapcho M, et al. eds. SEER cancer statistics review, 1975-2009: fast stats. http://seer.cancer.gov/csr/1975_2009_pops09. Updated April 2012. Accessed January 20, 2018.
4. Ly D, Forman D, Ferlay J, Brinton LA, Cook MB. An international comparison of male and female breast cancer incidence rates. Int J Cancer. 2013;132(8):1918-1926.
1. American Cancer Society. Cancer facts and figures 2014. Atlanta, GA: American Cancer Society; 2014.
2. Anderson WF, Jatoi I, Tse J, Rosenberg PS. Male breast cancer: a population-based comparison with female breast cancer. J Clin Oncol. 2010;28(2):232-239.
3. Howlander N, Noone AM, Krapcho M, et al. eds. SEER cancer statistics review, 1975-2009: fast stats. http://seer.cancer.gov/csr/1975_2009_pops09. Updated April 2012. Accessed January 20, 2018.
4. Ly D, Forman D, Ferlay J, Brinton LA, Cook MB. An international comparison of male and female breast cancer incidence rates. Int J Cancer. 2013;132(8):1918-1926.
Evaluating Management and Change in Glycemic Control After Discontinuation of Metformin in Patients With Elevated Serum Creatinine (FULL)
According to the American Diabetes Association (ADA), about 29 million Americans have diabetes mellitus (DM). Uncontrolled DM causes various microvascular and macrovascular complications and leads to significant mortality. In 2011, DM was the seventh leading cause of death.1 The ADA recommends setting a hemoglobin A1c (HbA1c) goal of < 7% to prevent microvascular and macrovascular complications.1
The treatment cost of DM continues to rise and accounts for about $245 billion annually.1 Given its effectiveness, low cost, and low adverse-event (AE) profile, metformin has been the cornerstone of therapy in DM over the past 20 years. The ADA recommends metformin as first-line therapy in type 2 DM (T2DM). In 2014, 14.4 million Americans were dispensed a metformin-containing product.2 Metformin exerts its effect mainly by decreasing hepatic glucose production and increasing insulin sensitivity. Study results suggest gluconeogenesis may be decreased up to 75% in these patients.3 Metformin is effective in reducing the level of HbA1c by an average of 1.5%.3
Background
Metformin-induced lactic acidosis is a rare concern in patients with renal impairment (0.03 case/1,000 patient-years).4 Much of this concern stems from the high incidence of lactic acidosis associated with the medication phenformin, which was approved in the 1950s but taken off the market because of its high incidence of lactic acidosis in patients with a serum creatinine (SCr) level > 1.4 mg/dL.
Although phenformin and metformin are both biguanide class medications, they vastly differ. Increased phenformin levels in the blood are correlated with decreased glucose oxidation and increased lactate production. Conversely, metformin may enhance glucose oxidation, and there seems to be no correlation between metformin levels with lactate levels. Lactic acidosis occurred 10 to 20 times more often with phenformin than it does with metformin.5 In studies in which patients with an estimated glomerular filtration rate (eGFR) of 30 to 60 mL/min/1.73 m2 continued to use metformin, lactic acidosis was rare, even in the presence of comorbid conditions that may promote lactic acidosis, such as chronic obstructive pulmonary disease, congestive heart failure, and liver disease.6 In 2012, the National Kidney Foundation (NKF) suggested an eGFR cutoff be considered when prescribing metformin.7
When the present study was initiated, metformin was contraindicated in patients with renal dysfunction (SCr levels ≥ 1.5 mg/dL in males≥ 1.4 mg/dL in females).5 The estimated incidence of renal dysfunction in patients with T2DM is 12%. Under this labeling, metformin use is prohibited in at least 2.5 million people. Study results have shown that, when package insert guidelines were disregarded and metformin was given against renal recommendations, the rate of AEs was not increased, and patients benefited clinically.8 Data suggest that the rate of lactic acidosis may be increased in patients with advanced kidney disease.8
In April 2016, the FDA started requiring that manufacturers update their labeling to indicate metformin may be used safely in cases of mild-to-moderate renal impairment. The FDA also changed a recommendation: now, before starting metformin, health care professionals should obtain the patient’s eGFR, which provides a more accurate determination of kidney function by taking into account age, sex, and race. Metformin is contraindicated in patients with an eGFR < 30 mL/min/1.73 m2 and is not recommended to be initiated in patients with an eGFR of 30 to 45 mL/min/1.73 m2. The suggestion for patients already using metformin is to obtain eGFR at least annually. In addition, when eGFR drops to between 30 and 45 mL/min/1.73 m2, the risks and benefits of continuing metformin should be weighed on a patient-specific basis.2,4
Methods
The authors retrospectively reviewed the charts of 199 randomly selected patients at Huntington VAMC in West Virginia who had metformin discontinued because of elevated SCr (defined as ≥ 1.5 mg/dL) between September 1, 2009 and September 1, 2014. Clinician notes written at time of discontinuation were assessed for other reasons for discontinuation, and patients thus identified were excluded. Change in glycemic control was assessed by comparing first HbA1c level 60 to 365 days after discontinuation of metformin with the most recent HbA1c level before discontinuation. Other data analyzed included age, time to next recorded SCr level, reinitiation of metformin (yes or no), and change in diabetic medication regimen. Class of medication initiated was recorded but not dose or insulin type. Subgroup analysis was performed on patients initiated on insulin after discontinuation of metformin. Evaluations were made of most recent HbA1c level at time of discontinuation of metformin, first HbA1c level after discontinuation, and HbA1c level 1 year after discontinuation in patients on insulin.
The primary endpoint of the study was change in HbA1c after discontinuation of metformin. This was studied to justify the value of metformin in T2DM and to evaluate whether patients could remain on metformin with mild-to-moderate renal impairment without AEs. Secondary endpoints were time to next recorded SCr level after discontinuation of metformin, reinitiation of metformin (yes or no), when next recorded SCr level was < 1.5 mg/dL, change in medication regimen after discontinuation of metformin, and incidence of lactic acidosis. Study inclusion criteria were male sex, age between 18 and 89 years, discontinuation of metformin because of elevated SCr, and documented repeat HbA1c level 60 to 365 days after discontinuation of metformin. Exclusion criteria were insulin therapy at time of discontinuation of metformin and type 1 DM diagnosis. A 2-sided t test was used to compare change in HbA1c level.
Results
Of the 199 patients who had metformin discontinued because of elevated SCr (> 1.5 mg/dL), 106 were excluded for reasons listed in Figure 1; the other 93 met the study inclusion criteria and had their cases analyzed for change in glycemic control after discontinuation of metformin. 
The mean age was 68.2 years and mean weight was 97.36 kg for the included patients. Four were African American, and 89 were white. At time of discontinuation of metformin, mean SCr level was 1.65 mg/dL, and mean eGFR was 43.25 mL/min/1.73 m2 (Table). Mean (SD) HbA1c level was 7.2% (1.1%) before discontinuation of metformin and 7.7% (1.5%) after discontinuation (P < .05) (Figure 2). Subgroup analysis of patients initiated on insulin after discontinuation of metformin (n = 47) revealed mean (SD) HbA1c levels of 7.5% (1.1%) before discontinuation and 8.3% (1.3%) after discontinuation (P < .05). One year or more after discontinuation of metformin in patients in whom insulin was initiated, mean HbA1c level decreased to the prediscontinuation (baseline) level of 7.5% (P = .91).
Twenty of these patients initiated on insulin had improved renal function and would have met the criteria for restarting metformin. After discontinuation of metformin, mean (SD) time to next recorded SCr level was 95.7 (89.9) days. Of the 93 study patients, 43 met the criterion for reinitiating metformin (rechecked SCr level, < 1.5 mg/dL), but in only 9 (21%) of these patients was metformin restarted.
Discussion
Overall, a decline in glycemic control was found in patients who had metformin discontinued. This anticipated decline prompted clinicians to replace metformin with other oral medications as well as insulin. Despite DM medication regimen changes, mean HbA1c level increased significantly after discontinuation of metformin. The initial decline in glycemic control after starting insulin could be attributable either to clinician preference in insulin initiation—starting at lower doses to avoid hypoglycemia—or to a delay in initiating insulin, as opposed to initiating insulin at time of discontinuation of metformin. Subgroup analysis of the large number of patients who started on insulin after discontinuation of metformin (51%) revealed the same HbA1c levels before and 1 year after discontinuation. This finding is clinically relevant because many patients showed a decline in glycemic control for a year, despite initiation of insulin therapy.
In 43 (46%) of the 93 patients studied, SCr level improved to < 1.5 mg/dL after discontinuation of metformin. Of the patients in the subgroup started on insulin, 20 had improved renal function. This finding suggests that many of the patients who were initiated on insulin showed an improvement in renal function and potentially could have had metformin reinitiated. If these patients had continued or restarted metformin, insulin therapy may have been avoided or delayed. Overall, many opportunities to resume metformin were missed; only 9 of the 43 patients with improved SCr levels (< 1.5 mg/dL) on recheck were restarted on metformin. Many clinicians seemed hesitant to restart metformin even after kidney function improved. In addition, mean time to next recorded SCr level after discontinuation of metformin was 95.7 days. If SCr levels are more closely monitored after discontinuation of metformin, metformin possibly could be restarted sooner, leading to improved glycemic control and prevention of both microvascular and macrovascular complications.
In its 2012 update, the NKF suggested that it may be reasonable to consider using an eGFR cutoff when prescribing metformin. Clearance of metformin is reduced by 75% when eGFR is under 60 mL/min/1.73m2 but declines no further until eGFR is < 30 mL/min/1.73 m2.7 A systematic review of 65 articles found that, overall, levels of metformin remained in the therapeutic range, and lactate concentrations did not increase significantly in mild-to-moderate renal impairment (eGFR, 30-60 mL/min/1.73 m2).8 This finding corresponds to the updated 2016 FDA recommendations regarding use of metformin in mild-to-moderate renal impairment.
In the present study, patients who had metformin discontinued earlier, under strict package labeling, may have been able to continue metformin with use of eGFR under the revised labeling. Thirty-seven patients had an eGFR > 45 mL/min/1.73 m2 at the time of discontinuation of metformin, 49 had an eGFR between 45 and 30 mL/min/1.73 m2, and 7 had an eGFR < 30 mL/min/1.73 m2. Only 7 (8%) of the 93 patients would have had a contraindication to continuing metformin on the basis of current FDA recommendations. Forty-nine patients (53%) could have continued metformin if the benefit outweighed the risk, and 37 (39%) could have continued metformin given an eGFR > 45 mL/min/1.73 m2. The earlier labeling required initial discontinuation of metformin in these patients, but new FDA recommendations would allow more of them with mild-to-moderate renal impairment to benefit from treatment with metformin.
Limitations
This study had a few limitations. Its design was retrospective, and its narrow demographics may not permit generalizability to other patient populations. In addition, the study evaluated initiation of new medications at time of discontinuation of metformin but not dosage adjustments of current medications. Insulin type and dosage were not evaluated—only whether insulin was initiated. Further, follow-up time was limited; change in long-term glycemic control requires more study. Another limitation was that adherence could not be assessed.
Conclusion
After discontinuation of metformin, there was a statistically significant increase in HbA1c level. Insulin was initiated in 51% of patients after discontinuation of metformin. Subgroup analysis of the patients who started insulin after discontinuation of metformin revealed the same HbA1c levels before and 1 year after discontinuation with a loss of glycemic control throughout the year. Of the 47 patients who were initiated on insulin, 20 had their SCr level decrease to < 1.5 mg/dL and could have been restarted on metformin. This finding indicates that many patients may have been able to delay time to insulin initiation and maintain the same glycemic control if metformin could have been continued. With more study, long-term change in glycemic control after discontinuation of metformin can be determined. In many patients, metformin is needed for adequate glycemic control. The revised FDA labeling allows many patients with mild-to-moderate kidney disease to benefit from treatment with metformin.
Click here to read the digital edition.
1. American Diabetes Association. Statistics about diabetes. http://www.diabetes.org/diabetesbasics/statistics/#sthash.3vJD53aO.dpuf. Accessed August 31, 2017.
2. U.S. Food and Drug Administration. FDA drug safety communication: FDA revises warnings regarding use of the diabetes medicine metformin in certain patients with reduced kidney function. https://www.fda.gov/Drugs/DrugSafety/ucm493244.htm. Published April 8, 2016. Updated April 5, 2017. Accessed August 31, 2017.
3. Fowler MJ. Diabetes treatment, part 2: oral agents for glycemic management. Clin Diabetes. 2007;25(4):131-134.
4. Metformin [package insert]. Princeton, NJ: Bristol-Myers Squibb Company; 2015.
5. Lipska KJ, Baily CJ, Inzucchi SE. Use of metformin in the setting of mild-to-moderate renal insufficiency. Diabetes Care. 2011;34(6):1431-1437.
6. Triplitt CL, Reasner CA. Chapter 83. Diabetes mellitus. In: DiPiro JT, Talbert RL, Yee GC, Matzke GR, Wells BG, Posey L. eds. Pharmacotherapy: A Pathophysiologic Approach. 8th ed. New York, NY: McGraw-Hill; 2011:chap 83.
7. National Kidney Foundation. KDOQI clinical practice guideline for diabetes and CKD: 2012 update. Am J Kidney Dis. 2012;60(5):850-886.
8. Inzucchi SE, Lipska KJ, Mayo H, Bailey CJ, McGuire DK. Metformin in patients with type 2 diabetes and kidney disease. JAMA. 2014;312(24):2668-2675.
According to the American Diabetes Association (ADA), about 29 million Americans have diabetes mellitus (DM). Uncontrolled DM causes various microvascular and macrovascular complications and leads to significant mortality. In 2011, DM was the seventh leading cause of death.1 The ADA recommends setting a hemoglobin A1c (HbA1c) goal of < 7% to prevent microvascular and macrovascular complications.1
The treatment cost of DM continues to rise and accounts for about $245 billion annually.1 Given its effectiveness, low cost, and low adverse-event (AE) profile, metformin has been the cornerstone of therapy in DM over the past 20 years. The ADA recommends metformin as first-line therapy in type 2 DM (T2DM). In 2014, 14.4 million Americans were dispensed a metformin-containing product.2 Metformin exerts its effect mainly by decreasing hepatic glucose production and increasing insulin sensitivity. Study results suggest gluconeogenesis may be decreased up to 75% in these patients.3 Metformin is effective in reducing the level of HbA1c by an average of 1.5%.3
Background
Metformin-induced lactic acidosis is a rare concern in patients with renal impairment (0.03 case/1,000 patient-years).4 Much of this concern stems from the high incidence of lactic acidosis associated with the medication phenformin, which was approved in the 1950s but taken off the market because of its high incidence of lactic acidosis in patients with a serum creatinine (SCr) level > 1.4 mg/dL.
Although phenformin and metformin are both biguanide class medications, they vastly differ. Increased phenformin levels in the blood are correlated with decreased glucose oxidation and increased lactate production. Conversely, metformin may enhance glucose oxidation, and there seems to be no correlation between metformin levels with lactate levels. Lactic acidosis occurred 10 to 20 times more often with phenformin than it does with metformin.5 In studies in which patients with an estimated glomerular filtration rate (eGFR) of 30 to 60 mL/min/1.73 m2 continued to use metformin, lactic acidosis was rare, even in the presence of comorbid conditions that may promote lactic acidosis, such as chronic obstructive pulmonary disease, congestive heart failure, and liver disease.6 In 2012, the National Kidney Foundation (NKF) suggested an eGFR cutoff be considered when prescribing metformin.7
When the present study was initiated, metformin was contraindicated in patients with renal dysfunction (SCr levels ≥ 1.5 mg/dL in males≥ 1.4 mg/dL in females).5 The estimated incidence of renal dysfunction in patients with T2DM is 12%. Under this labeling, metformin use is prohibited in at least 2.5 million people. Study results have shown that, when package insert guidelines were disregarded and metformin was given against renal recommendations, the rate of AEs was not increased, and patients benefited clinically.8 Data suggest that the rate of lactic acidosis may be increased in patients with advanced kidney disease.8
In April 2016, the FDA started requiring that manufacturers update their labeling to indicate metformin may be used safely in cases of mild-to-moderate renal impairment. The FDA also changed a recommendation: now, before starting metformin, health care professionals should obtain the patient’s eGFR, which provides a more accurate determination of kidney function by taking into account age, sex, and race. Metformin is contraindicated in patients with an eGFR < 30 mL/min/1.73 m2 and is not recommended to be initiated in patients with an eGFR of 30 to 45 mL/min/1.73 m2. The suggestion for patients already using metformin is to obtain eGFR at least annually. In addition, when eGFR drops to between 30 and 45 mL/min/1.73 m2, the risks and benefits of continuing metformin should be weighed on a patient-specific basis.2,4
Methods
The authors retrospectively reviewed the charts of 199 randomly selected patients at Huntington VAMC in West Virginia who had metformin discontinued because of elevated SCr (defined as ≥ 1.5 mg/dL) between September 1, 2009 and September 1, 2014. Clinician notes written at time of discontinuation were assessed for other reasons for discontinuation, and patients thus identified were excluded. Change in glycemic control was assessed by comparing first HbA1c level 60 to 365 days after discontinuation of metformin with the most recent HbA1c level before discontinuation. Other data analyzed included age, time to next recorded SCr level, reinitiation of metformin (yes or no), and change in diabetic medication regimen. Class of medication initiated was recorded but not dose or insulin type. Subgroup analysis was performed on patients initiated on insulin after discontinuation of metformin. Evaluations were made of most recent HbA1c level at time of discontinuation of metformin, first HbA1c level after discontinuation, and HbA1c level 1 year after discontinuation in patients on insulin.
The primary endpoint of the study was change in HbA1c after discontinuation of metformin. This was studied to justify the value of metformin in T2DM and to evaluate whether patients could remain on metformin with mild-to-moderate renal impairment without AEs. Secondary endpoints were time to next recorded SCr level after discontinuation of metformin, reinitiation of metformin (yes or no), when next recorded SCr level was < 1.5 mg/dL, change in medication regimen after discontinuation of metformin, and incidence of lactic acidosis. Study inclusion criteria were male sex, age between 18 and 89 years, discontinuation of metformin because of elevated SCr, and documented repeat HbA1c level 60 to 365 days after discontinuation of metformin. Exclusion criteria were insulin therapy at time of discontinuation of metformin and type 1 DM diagnosis. A 2-sided t test was used to compare change in HbA1c level.
Results
Of the 199 patients who had metformin discontinued because of elevated SCr (> 1.5 mg/dL), 106 were excluded for reasons listed in Figure 1; the other 93 met the study inclusion criteria and had their cases analyzed for change in glycemic control after discontinuation of metformin.
The mean age was 68.2 years and mean weight was 97.36 kg for the included patients. Four were African American, and 89 were white. At time of discontinuation of metformin, mean SCr level was 1.65 mg/dL, and mean eGFR was 43.25 mL/min/1.73 m2 (Table). Mean (SD) HbA1c level was 7.2% (1.1%) before discontinuation of metformin and 7.7% (1.5%) after discontinuation (P < .05) (Figure 2). Subgroup analysis of patients initiated on insulin after discontinuation of metformin (n = 47) revealed mean (SD) HbA1c levels of 7.5% (1.1%) before discontinuation and 8.3% (1.3%) after discontinuation (P < .05). One year or more after discontinuation of metformin in patients in whom insulin was initiated, mean HbA1c level decreased to the prediscontinuation (baseline) level of 7.5% (P = .91).
Twenty of these patients initiated on insulin had improved renal function and would have met the criteria for restarting metformin. After discontinuation of metformin, mean (SD) time to next recorded SCr level was 95.7 (89.9) days. Of the 93 study patients, 43 met the criterion for reinitiating metformin (rechecked SCr level, < 1.5 mg/dL), but in only 9 (21%) of these patients was metformin restarted.
Discussion
Overall, a decline in glycemic control was found in patients who had metformin discontinued. This anticipated decline prompted clinicians to replace metformin with other oral medications as well as insulin. Despite DM medication regimen changes, mean HbA1c level increased significantly after discontinuation of metformin. The initial decline in glycemic control after starting insulin could be attributable either to clinician preference in insulin initiation—starting at lower doses to avoid hypoglycemia—or to a delay in initiating insulin, as opposed to initiating insulin at time of discontinuation of metformin. Subgroup analysis of the large number of patients who started on insulin after discontinuation of metformin (51%) revealed the same HbA1c levels before and 1 year after discontinuation. This finding is clinically relevant because many patients showed a decline in glycemic control for a year, despite initiation of insulin therapy.
In 43 (46%) of the 93 patients studied, SCr level improved to < 1.5 mg/dL after discontinuation of metformin. Of the patients in the subgroup started on insulin, 20 had improved renal function. This finding suggests that many of the patients who were initiated on insulin showed an improvement in renal function and potentially could have had metformin reinitiated. If these patients had continued or restarted metformin, insulin therapy may have been avoided or delayed. Overall, many opportunities to resume metformin were missed; only 9 of the 43 patients with improved SCr levels (< 1.5 mg/dL) on recheck were restarted on metformin. Many clinicians seemed hesitant to restart metformin even after kidney function improved. In addition, mean time to next recorded SCr level after discontinuation of metformin was 95.7 days. If SCr levels are more closely monitored after discontinuation of metformin, metformin possibly could be restarted sooner, leading to improved glycemic control and prevention of both microvascular and macrovascular complications.
In its 2012 update, the NKF suggested that it may be reasonable to consider using an eGFR cutoff when prescribing metformin. Clearance of metformin is reduced by 75% when eGFR is under 60 mL/min/1.73m2 but declines no further until eGFR is < 30 mL/min/1.73 m2.7 A systematic review of 65 articles found that, overall, levels of metformin remained in the therapeutic range, and lactate concentrations did not increase significantly in mild-to-moderate renal impairment (eGFR, 30-60 mL/min/1.73 m2).8 This finding corresponds to the updated 2016 FDA recommendations regarding use of metformin in mild-to-moderate renal impairment.
In the present study, patients who had metformin discontinued earlier, under strict package labeling, may have been able to continue metformin with use of eGFR under the revised labeling. Thirty-seven patients had an eGFR > 45 mL/min/1.73 m2 at the time of discontinuation of metformin, 49 had an eGFR between 45 and 30 mL/min/1.73 m2, and 7 had an eGFR < 30 mL/min/1.73 m2. Only 7 (8%) of the 93 patients would have had a contraindication to continuing metformin on the basis of current FDA recommendations. Forty-nine patients (53%) could have continued metformin if the benefit outweighed the risk, and 37 (39%) could have continued metformin given an eGFR > 45 mL/min/1.73 m2. The earlier labeling required initial discontinuation of metformin in these patients, but new FDA recommendations would allow more of them with mild-to-moderate renal impairment to benefit from treatment with metformin.
Limitations
This study had a few limitations. Its design was retrospective, and its narrow demographics may not permit generalizability to other patient populations. In addition, the study evaluated initiation of new medications at time of discontinuation of metformin but not dosage adjustments of current medications. Insulin type and dosage were not evaluated—only whether insulin was initiated. Further, follow-up time was limited; change in long-term glycemic control requires more study. Another limitation was that adherence could not be assessed.
Conclusion
After discontinuation of metformin, there was a statistically significant increase in HbA1c level. Insulin was initiated in 51% of patients after discontinuation of metformin. Subgroup analysis of the patients who started insulin after discontinuation of metformin revealed the same HbA1c levels before and 1 year after discontinuation with a loss of glycemic control throughout the year. Of the 47 patients who were initiated on insulin, 20 had their SCr level decrease to < 1.5 mg/dL and could have been restarted on metformin. This finding indicates that many patients may have been able to delay time to insulin initiation and maintain the same glycemic control if metformin could have been continued. With more study, long-term change in glycemic control after discontinuation of metformin can be determined. In many patients, metformin is needed for adequate glycemic control. The revised FDA labeling allows many patients with mild-to-moderate kidney disease to benefit from treatment with metformin.
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According to the American Diabetes Association (ADA), about 29 million Americans have diabetes mellitus (DM). Uncontrolled DM causes various microvascular and macrovascular complications and leads to significant mortality. In 2011, DM was the seventh leading cause of death.1 The ADA recommends setting a hemoglobin A1c (HbA1c) goal of < 7% to prevent microvascular and macrovascular complications.1
The treatment cost of DM continues to rise and accounts for about $245 billion annually.1 Given its effectiveness, low cost, and low adverse-event (AE) profile, metformin has been the cornerstone of therapy in DM over the past 20 years. The ADA recommends metformin as first-line therapy in type 2 DM (T2DM). In 2014, 14.4 million Americans were dispensed a metformin-containing product.2 Metformin exerts its effect mainly by decreasing hepatic glucose production and increasing insulin sensitivity. Study results suggest gluconeogenesis may be decreased up to 75% in these patients.3 Metformin is effective in reducing the level of HbA1c by an average of 1.5%.3
Background
Metformin-induced lactic acidosis is a rare concern in patients with renal impairment (0.03 case/1,000 patient-years).4 Much of this concern stems from the high incidence of lactic acidosis associated with the medication phenformin, which was approved in the 1950s but taken off the market because of its high incidence of lactic acidosis in patients with a serum creatinine (SCr) level > 1.4 mg/dL.
Although phenformin and metformin are both biguanide class medications, they vastly differ. Increased phenformin levels in the blood are correlated with decreased glucose oxidation and increased lactate production. Conversely, metformin may enhance glucose oxidation, and there seems to be no correlation between metformin levels with lactate levels. Lactic acidosis occurred 10 to 20 times more often with phenformin than it does with metformin.5 In studies in which patients with an estimated glomerular filtration rate (eGFR) of 30 to 60 mL/min/1.73 m2 continued to use metformin, lactic acidosis was rare, even in the presence of comorbid conditions that may promote lactic acidosis, such as chronic obstructive pulmonary disease, congestive heart failure, and liver disease.6 In 2012, the National Kidney Foundation (NKF) suggested an eGFR cutoff be considered when prescribing metformin.7
When the present study was initiated, metformin was contraindicated in patients with renal dysfunction (SCr levels ≥ 1.5 mg/dL in males≥ 1.4 mg/dL in females).5 The estimated incidence of renal dysfunction in patients with T2DM is 12%. Under this labeling, metformin use is prohibited in at least 2.5 million people. Study results have shown that, when package insert guidelines were disregarded and metformin was given against renal recommendations, the rate of AEs was not increased, and patients benefited clinically.8 Data suggest that the rate of lactic acidosis may be increased in patients with advanced kidney disease.8
In April 2016, the FDA started requiring that manufacturers update their labeling to indicate metformin may be used safely in cases of mild-to-moderate renal impairment. The FDA also changed a recommendation: now, before starting metformin, health care professionals should obtain the patient’s eGFR, which provides a more accurate determination of kidney function by taking into account age, sex, and race. Metformin is contraindicated in patients with an eGFR < 30 mL/min/1.73 m2 and is not recommended to be initiated in patients with an eGFR of 30 to 45 mL/min/1.73 m2. The suggestion for patients already using metformin is to obtain eGFR at least annually. In addition, when eGFR drops to between 30 and 45 mL/min/1.73 m2, the risks and benefits of continuing metformin should be weighed on a patient-specific basis.2,4
Methods
The authors retrospectively reviewed the charts of 199 randomly selected patients at Huntington VAMC in West Virginia who had metformin discontinued because of elevated SCr (defined as ≥ 1.5 mg/dL) between September 1, 2009 and September 1, 2014. Clinician notes written at time of discontinuation were assessed for other reasons for discontinuation, and patients thus identified were excluded. Change in glycemic control was assessed by comparing first HbA1c level 60 to 365 days after discontinuation of metformin with the most recent HbA1c level before discontinuation. Other data analyzed included age, time to next recorded SCr level, reinitiation of metformin (yes or no), and change in diabetic medication regimen. Class of medication initiated was recorded but not dose or insulin type. Subgroup analysis was performed on patients initiated on insulin after discontinuation of metformin. Evaluations were made of most recent HbA1c level at time of discontinuation of metformin, first HbA1c level after discontinuation, and HbA1c level 1 year after discontinuation in patients on insulin.
The primary endpoint of the study was change in HbA1c after discontinuation of metformin. This was studied to justify the value of metformin in T2DM and to evaluate whether patients could remain on metformin with mild-to-moderate renal impairment without AEs. Secondary endpoints were time to next recorded SCr level after discontinuation of metformin, reinitiation of metformin (yes or no), when next recorded SCr level was < 1.5 mg/dL, change in medication regimen after discontinuation of metformin, and incidence of lactic acidosis. Study inclusion criteria were male sex, age between 18 and 89 years, discontinuation of metformin because of elevated SCr, and documented repeat HbA1c level 60 to 365 days after discontinuation of metformin. Exclusion criteria were insulin therapy at time of discontinuation of metformin and type 1 DM diagnosis. A 2-sided t test was used to compare change in HbA1c level.
Results
Of the 199 patients who had metformin discontinued because of elevated SCr (> 1.5 mg/dL), 106 were excluded for reasons listed in Figure 1; the other 93 met the study inclusion criteria and had their cases analyzed for change in glycemic control after discontinuation of metformin.
The mean age was 68.2 years and mean weight was 97.36 kg for the included patients. Four were African American, and 89 were white. At time of discontinuation of metformin, mean SCr level was 1.65 mg/dL, and mean eGFR was 43.25 mL/min/1.73 m2 (Table). Mean (SD) HbA1c level was 7.2% (1.1%) before discontinuation of metformin and 7.7% (1.5%) after discontinuation (P < .05) (Figure 2). Subgroup analysis of patients initiated on insulin after discontinuation of metformin (n = 47) revealed mean (SD) HbA1c levels of 7.5% (1.1%) before discontinuation and 8.3% (1.3%) after discontinuation (P < .05). One year or more after discontinuation of metformin in patients in whom insulin was initiated, mean HbA1c level decreased to the prediscontinuation (baseline) level of 7.5% (P = .91).
Twenty of these patients initiated on insulin had improved renal function and would have met the criteria for restarting metformin. After discontinuation of metformin, mean (SD) time to next recorded SCr level was 95.7 (89.9) days. Of the 93 study patients, 43 met the criterion for reinitiating metformin (rechecked SCr level, < 1.5 mg/dL), but in only 9 (21%) of these patients was metformin restarted.
Discussion
Overall, a decline in glycemic control was found in patients who had metformin discontinued. This anticipated decline prompted clinicians to replace metformin with other oral medications as well as insulin. Despite DM medication regimen changes, mean HbA1c level increased significantly after discontinuation of metformin. The initial decline in glycemic control after starting insulin could be attributable either to clinician preference in insulin initiation—starting at lower doses to avoid hypoglycemia—or to a delay in initiating insulin, as opposed to initiating insulin at time of discontinuation of metformin. Subgroup analysis of the large number of patients who started on insulin after discontinuation of metformin (51%) revealed the same HbA1c levels before and 1 year after discontinuation. This finding is clinically relevant because many patients showed a decline in glycemic control for a year, despite initiation of insulin therapy.
In 43 (46%) of the 93 patients studied, SCr level improved to < 1.5 mg/dL after discontinuation of metformin. Of the patients in the subgroup started on insulin, 20 had improved renal function. This finding suggests that many of the patients who were initiated on insulin showed an improvement in renal function and potentially could have had metformin reinitiated. If these patients had continued or restarted metformin, insulin therapy may have been avoided or delayed. Overall, many opportunities to resume metformin were missed; only 9 of the 43 patients with improved SCr levels (< 1.5 mg/dL) on recheck were restarted on metformin. Many clinicians seemed hesitant to restart metformin even after kidney function improved. In addition, mean time to next recorded SCr level after discontinuation of metformin was 95.7 days. If SCr levels are more closely monitored after discontinuation of metformin, metformin possibly could be restarted sooner, leading to improved glycemic control and prevention of both microvascular and macrovascular complications.
In its 2012 update, the NKF suggested that it may be reasonable to consider using an eGFR cutoff when prescribing metformin. Clearance of metformin is reduced by 75% when eGFR is under 60 mL/min/1.73m2 but declines no further until eGFR is < 30 mL/min/1.73 m2.7 A systematic review of 65 articles found that, overall, levels of metformin remained in the therapeutic range, and lactate concentrations did not increase significantly in mild-to-moderate renal impairment (eGFR, 30-60 mL/min/1.73 m2).8 This finding corresponds to the updated 2016 FDA recommendations regarding use of metformin in mild-to-moderate renal impairment.
In the present study, patients who had metformin discontinued earlier, under strict package labeling, may have been able to continue metformin with use of eGFR under the revised labeling. Thirty-seven patients had an eGFR > 45 mL/min/1.73 m2 at the time of discontinuation of metformin, 49 had an eGFR between 45 and 30 mL/min/1.73 m2, and 7 had an eGFR < 30 mL/min/1.73 m2. Only 7 (8%) of the 93 patients would have had a contraindication to continuing metformin on the basis of current FDA recommendations. Forty-nine patients (53%) could have continued metformin if the benefit outweighed the risk, and 37 (39%) could have continued metformin given an eGFR > 45 mL/min/1.73 m2. The earlier labeling required initial discontinuation of metformin in these patients, but new FDA recommendations would allow more of them with mild-to-moderate renal impairment to benefit from treatment with metformin.
Limitations
This study had a few limitations. Its design was retrospective, and its narrow demographics may not permit generalizability to other patient populations. In addition, the study evaluated initiation of new medications at time of discontinuation of metformin but not dosage adjustments of current medications. Insulin type and dosage were not evaluated—only whether insulin was initiated. Further, follow-up time was limited; change in long-term glycemic control requires more study. Another limitation was that adherence could not be assessed.
Conclusion
After discontinuation of metformin, there was a statistically significant increase in HbA1c level. Insulin was initiated in 51% of patients after discontinuation of metformin. Subgroup analysis of the patients who started insulin after discontinuation of metformin revealed the same HbA1c levels before and 1 year after discontinuation with a loss of glycemic control throughout the year. Of the 47 patients who were initiated on insulin, 20 had their SCr level decrease to < 1.5 mg/dL and could have been restarted on metformin. This finding indicates that many patients may have been able to delay time to insulin initiation and maintain the same glycemic control if metformin could have been continued. With more study, long-term change in glycemic control after discontinuation of metformin can be determined. In many patients, metformin is needed for adequate glycemic control. The revised FDA labeling allows many patients with mild-to-moderate kidney disease to benefit from treatment with metformin.
Click here to read the digital edition.
1. American Diabetes Association. Statistics about diabetes. http://www.diabetes.org/diabetesbasics/statistics/#sthash.3vJD53aO.dpuf. Accessed August 31, 2017.
2. U.S. Food and Drug Administration. FDA drug safety communication: FDA revises warnings regarding use of the diabetes medicine metformin in certain patients with reduced kidney function. https://www.fda.gov/Drugs/DrugSafety/ucm493244.htm. Published April 8, 2016. Updated April 5, 2017. Accessed August 31, 2017.
3. Fowler MJ. Diabetes treatment, part 2: oral agents for glycemic management. Clin Diabetes. 2007;25(4):131-134.
4. Metformin [package insert]. Princeton, NJ: Bristol-Myers Squibb Company; 2015.
5. Lipska KJ, Baily CJ, Inzucchi SE. Use of metformin in the setting of mild-to-moderate renal insufficiency. Diabetes Care. 2011;34(6):1431-1437.
6. Triplitt CL, Reasner CA. Chapter 83. Diabetes mellitus. In: DiPiro JT, Talbert RL, Yee GC, Matzke GR, Wells BG, Posey L. eds. Pharmacotherapy: A Pathophysiologic Approach. 8th ed. New York, NY: McGraw-Hill; 2011:chap 83.
7. National Kidney Foundation. KDOQI clinical practice guideline for diabetes and CKD: 2012 update. Am J Kidney Dis. 2012;60(5):850-886.
8. Inzucchi SE, Lipska KJ, Mayo H, Bailey CJ, McGuire DK. Metformin in patients with type 2 diabetes and kidney disease. JAMA. 2014;312(24):2668-2675.
1. American Diabetes Association. Statistics about diabetes. http://www.diabetes.org/diabetesbasics/statistics/#sthash.3vJD53aO.dpuf. Accessed August 31, 2017.
2. U.S. Food and Drug Administration. FDA drug safety communication: FDA revises warnings regarding use of the diabetes medicine metformin in certain patients with reduced kidney function. https://www.fda.gov/Drugs/DrugSafety/ucm493244.htm. Published April 8, 2016. Updated April 5, 2017. Accessed August 31, 2017.
3. Fowler MJ. Diabetes treatment, part 2: oral agents for glycemic management. Clin Diabetes. 2007;25(4):131-134.
4. Metformin [package insert]. Princeton, NJ: Bristol-Myers Squibb Company; 2015.
5. Lipska KJ, Baily CJ, Inzucchi SE. Use of metformin in the setting of mild-to-moderate renal insufficiency. Diabetes Care. 2011;34(6):1431-1437.
6. Triplitt CL, Reasner CA. Chapter 83. Diabetes mellitus. In: DiPiro JT, Talbert RL, Yee GC, Matzke GR, Wells BG, Posey L. eds. Pharmacotherapy: A Pathophysiologic Approach. 8th ed. New York, NY: McGraw-Hill; 2011:chap 83.
7. National Kidney Foundation. KDOQI clinical practice guideline for diabetes and CKD: 2012 update. Am J Kidney Dis. 2012;60(5):850-886.
8. Inzucchi SE, Lipska KJ, Mayo H, Bailey CJ, McGuire DK. Metformin in patients with type 2 diabetes and kidney disease. JAMA. 2014;312(24):2668-2675.
Concentrated Insulins: A Review and Recommendations (FULL)
For a long time, 500 U/mL (U-500) insulin was the only concentrated insulin available on the market. With many diabetes mellitus (DM) patients requiring larger doses, additional 200 U/mL (U-200) and 300 U/mL (U-300) concentrations became available. As clinical guidelines lack specific recommendations for optimal use of U-200 and U-300 insulins, clinical discretion is warranted in identifying patients for whom use of these insulins is appropriate. U-500 insulin is recommended in cases that require ≥ 200 U/d or > 2 U/kg/d. Given the ongoing DM and obesity epidemics, increased use of concentrated insulins is likely. Clinicians must stay well informed about the characteristics and benefits of concentrated insulins to remain confident recommending, prescribing, and adjusting these medications.
U-200 Insulin Lispro
Pharmacokinetics/Pharmacodynamics
The amino acid structure of U-200 insulin lispro is different from that of endogenous insulin. In U-200 lispro, lysine replaces a proline at position B28, and proline replaces a lysine at position B29.
In a euglycemic clamp study of patients without DM, a 20-U dose of U-200 lispro and a single 20-U dose of U-100 lispro were found to have similar mean area under the glucose infusion rate curves, mean area under the serum insulin concentration-time curves from time 0 to infinity, mean peak serum insulin levels, and time to maximum glucose-lowering effects.1 For both U-200 lispro and U-100 lispro, time to maximum effect was 1 hour.2
Even numbers are marked on the dial of the pen. Odd numbers are not marked, but longer lines appear in their place. U-200 lispro should not be mixed with any other insulin, whereas U-100 lispro can be mixed with neutral protamine Hagedorn insulin.
Safety/Efficacy
There has been 1 bioequivalence study of euglycemic patients without type 2 DM (T2DM) but no studies of the safety or efficacy of U-200 lispro in patients with DM.3,4 U-100 lispro converts 1:1 to U-200 lispro (eg, 60 U of U-100 lispro converts to 60 U of U-200 lispro).1 The volume of U-200 lispro would be smaller than that of U-100 lispro.
Economic Analysis
There are no published U-200 lispro economic analyses.
Dosing
U-200 lispro should be converted from other bolus insulins in a 1:1 ratio.1
Recommendations
Definitive recommendations await efficacy trials comparing use of U-200 lispro and other bolus insulins in patients with DM. Currently, U-200 lispro may be considered for patients with DM who require high doses of bolus insulin and who may benefit from smaller volumes of lispro.
U-200 Insulin Degludec
Pharmacokinetics/Pharmacodynamics
The basal insulin degludec (Tresiba) is available in U-100 and U-200 concentrations in a pen. After subcutaneous injection, degludec forms gradually dissociating multihexamer chains, which account for its flat and stable PK/PD profile. U-100 degludec and U-200 degludec have similar duration of action (≥ 42 hours) and time to steady state (2-3 days).5,6 A patient who misses a regularly scheduled dose should allow at least 8 hours between injections. Taking degludec at variable times does not decrease efficacy as long as this 8-hour minimum interval is observed.7
Safety/Efficacy
During its development, degludec was evaluated in more than 5,000 patients across 11 therapeutic trials.8 The key studies that led to the approval of degludec used insulin glargine as a comparator. In a 52-week study of 1,030 insulin-naïve patients with T2DM, degludec was noninferior to glargine in hemoglobin A1c (HbA1c) reduction (1.06% vs 1.19%). Overall hypoglycemia rates were similar, though there were fewer nocturnal hypoglycemia episodes with degludec than with glargine (0.25 vs 0.39 per patient-year of exposure; P = .38).9
The BEGIN Basal-Bolus trial series evaluated use of degludec combined with bolus insulin aspart in insulin-experienced patients with T2DM (n = 992) and type 1 DM (T1DM) (n = 629) over 52 weeks.10,11 Both trials found noninferiority in A1c reduction: 1.1% (degludec) and 1.18% (glargine) in patients with T2DM and 0.4% (degludec) and 0.39% (glargine) in those with T1DM.10,11 Significantly fewer episodes of overall hypoglycemia (11.09 vs 13.63 per patient-year) and nocturnal hypoglycemia (1.39 vs 1.84 per patient-year) were found with degludec in patients with T2DM.5 Overall hypoglycemia rates were similar, though there was a 25% lower rate of nocturnal hypoglycemia with degludec in patients with T1DM.11
A meta-analysis of 7 phase 3a trials that compared degludec with glargine revealed significantly lower rates of overall, nocturnal, and severe hypoglycemia with degludec in insulin-naïve patients.12 The analysis confirmed findings of significantly lower rates of overall and nocturnal hypoglycemia with degludec in the overall T2DM population and significantly lower rates of nocturnal hypoglycemia in the T1DM population.12
In the DEVOTE trial, which included 7,637 T2DM patients at high risk for a cardiovascular event, degludec and glargine were compared on the composite primary outcome of death with a cardiovascular cause, nonfatal myocardial infarction, or nonfatal stroke. After a median of 1.99 years, the primary outcome occurred in 8.5% of degludec patients and 9.3% of glargine patients (hazard ratio, 0.91; 95% confidence interval, 0.78-1.06; P < .001 for noninferiority). Mean HbA1c level was 7.5 in both groups; severe hypoglycemia occurred more often in the glargine group (odds ratio, 0.73; P < .001 for superiority).13 Findings from the randomized, crossover SWITCH 1 and SWITCH 2 trials confirmed lower rates of symptomatic hypoglycemia with degludec compared with glargine in patients with T1DM and T2DM, respectively.14,15 No statistically significant differences in weight gain were observed in the clinical trials comparing degludec and glargine.
Economic Analysis
Weatherall and colleagues used a budget impact model to evaluate the costs of degludec and glargine for commercially insured patients with DM in the U.S.16 Three treatment groups were analyzed: basal/bolus combination in T1DM and T2DM, and basal/oral combination in T2DM. Although degludec cost more, overall cost was reduced in T1DM because of reduced insulin usage and fewer hypoglycemic episodes in T2DM with basal/oral combination therapy. The authors acknowledged the many assumptions needed and the potential oversimplification of their model.16 In other countries, economic analyses had similar findings.17-19
Dosing
Degludec converts 1:1 to other basal insulins. Recommended starting doses for U-200 degludec are 10 U once daily for insulin-naïve adults with T2DM and one-third to one-half the total weight-based daily insulin dose for insulin-naïve adults with T1DM.4
Recommendations
For some patients, lower PD variability may make degludec a desirable alternative. As degludec retains its efficacy with variable dosing times, it may be ideal for patients who have difficulty with a once-daily dosing schedule. It is important to inform patients that the degludec pen allows for 2-U increments. Given the lower frequency of nocturnal hypoglycemic events with degludec compared with glargine, degludec is an appropriate basal insulin option for patients with nocturnal hypoglycemia. In addition, U-200 degludec may be considered for DM patients who require high doses of basal insulin and who may benefit from smaller volumes of degludec.
U-300 Isulin Glargine
Pharmacokinetics/Pharmacodynamics
U-300 glargine is a concentrated basal insulin. There are notable differences between its U-100 and U-300 concentrations. For U-300 glargine dosed at 0.4 U/kg, duration of action is 24 hours; for U-300 glargine dosed at 0.6 U/kg or higher, longer duration is expected.20 Steady state is reached after 5 days.21 The U-300 glargine pen contains 1.5 mL, less than the 3 mL in the U-100 pen. U-300 glargine typically is administered in 1 injection once daily if the dose is < 80 U; 2 injections are required if the dose is > 80 U.
Safety/Efficacy
In the EDITION trials, which compared U-300 and U-100 glargine in patients with T1DM and T2DM, the primary endpoint was 6-month HbA1c reduction.22-24 Comparable HbA1c reductions were found in all of the studies. In EDITION 1, in which 2,474 patients with T2DM were taking concomitant bolus insulin with or without metformin, 11% more U-300 glargine than U-100 glargine was needed to achieve similar results.22 In EDITION 4, in which bolus insulin was used in combination in 546 patients with T1DM, 17.5% more U-300 glargine than U-100 glargine was needed to achieve similar glycemic goals.25
Economic Analysis
Compared with other insulins, U-300 glargine has limited published data and economic analyses. Using a cost-utility model to compare U-300 with U-100 glargine in Spanish patients with T2DM, and reporting results in euros per quality-adjusted life years, Monero and colleagues concluded that the hypoglycemia reduction and possible time-of-dose flexibility found with U-300 glargine may contribute to its cost-effectiveness.26
Dosing
U-300 glargine should be converted in a 1:1 ratio from U-100 glargine or detemir. The U-300 glargine dose should be reduced by 20% when switching from NPH insulin.21
Recommendations
A meta-analysis of the EDITION trials 1 to 3 revealed a lower incidence of daytime and nocturnal hypoglycemia with use of U-300 glargine over U-100 glargine and a beneficial shorter hold time after injection of U-300 glargine (5 seconds) compared with U-100 glargine (10 seconds).27 There was statistically lower weight gain with U-300 glargine compared with U-100 glargine however weight gain was < 1 kg in both groups.27 These characteristics of U-300 glargine may prove advantageous for individual patients.
U-500 Insulin
Pharmacokinetics/Pharmacodynamics
U-500 insulin (Humulin R) has been available in a vial since 1997, but other formulations have been used therapeutically since 1952.28 The U-500 KwikPen device, recently added to the market, has improved the vial and syringe dosing. The new U-500 BD (Becton, Dickinson, Franklin Lakes, NJ) syringes allow doses up to 250 U, and the U-500 KwikPens provide up to 300 U per injection.29 When it was first introduced, U-500 insulin had no dedicated delivery device and dose conversion was required to deliver the appropriate dose using an allergy or TB syringe. As a consequence, confusion often resulted between prescribers, pharmacists and patients.30,31 U-500 insulin acts as basal and bolus insulins do. Onset of action is ~15 minutes, time to peak is 4 to 8 hours, and duration of action is ≤ 21 hours.32 As its onset of action is similar to that of U-100, U-500 should be injected 30 minutes before meals.
A single-site, randomized, double-blind, crossover euglycemic clamp study that compared equivalent doses of U-500 and U-100 in healthy obese patients found the formulations had similar overall exposures and effects—the only differences were that U-500 had an extended time to peak and a prolonged post-peak effect. The longer post-peak effect contributes to longer duration of action and allows for fewer daily injections.33
Safety/Efficacy
In the Humulin R U-500 Initiation trial, both of these algorithms improved glycemic control and were associated with a low incidence of severe hypoglycemia. In addition, the associated weight gains were similar. Last, the rate of nonsevere hypoglycemia was slightly lower for the 3-times-daily than for the 2-times-daily regimen.34 A real-world outcome analysis of U-500 initiation confirmed the benefits of switching from U-100 to U-500. A clinically significant improvement in glycemic control was found in all the study participants. Dose and frequency of administration, however, were not reported.35
According to a secondary analysis in the Humulin R U-500 Initiation trial, baseline U-100 total daily dose did not yield a difference in efficacy or safety between the 2-times-a-day and 3-times-a-day arms—allowing use of a simpler 2-times-a-day schedule without regard to baseline total daily dose.28,36 The 2-times-a-day regimen is preferred in clinical practice given that the 2 regimens are equivalent in safety and efficacy and that the 2-times-a-day regimen is simpler, allows for easier titrations, improves patient perceptions of the effect of insulin on daily life function and psychological health, lowers daily injection burden, and maximizes adherence.37
Economic Analysis
A retrospective database analysis revealed lower overall cost and lower pharmacy cost associated with U-500 in comparison with high-dose U-100, as well as reduced hypoglycemia-specific costs or resource utilization, even though U-500 was associated with a slightly higher incidence of hypoglycemia.28 However, the fact that hypoglycemia was reported with a billing code (ICD-9) implies the hypoglycemic event was severe enough to require medical attention. Given these findings, 2-times-a-day U-500 seems more cost-effective than high-dose U-100.
Dosing
The U-500 Humulin R package insert recommends converting a dose to U-500 on the basis of most recent HbA1c level. U-500 can be dosed 2 times daily (60%, 40%) or 3 times daily (40%, 30%, 30%). If HbA1c is > 8%, then the starting total daily dose (TDD) of U-500 is 100% of the U-100 TDD. If HbA1c is ≤ 8%, then the starting TDD of U-500 is 80% of the final U-100 TDD (20% reduction). Dose adjustments may range from 5% to 10% depending on subsequent blood glucose readings.32
Recommendations
U-500 is a safe and effective monotherapy alternative for patients who require high doses of U-100. Initial conversion from U-100 is based on HbA1c level. The total daily dose of U-500 is then divided by 2 (60%, 40%) or 3 (40%, 30%, 30%). The 2-times-a-day regimen enhances adherence and thus may be preferred.
Discussion
It has been suggested that large volumes or depots of insulin approaching 100 units impedes absorption and are more painful compared with smaller volume injections.37 For patients with DM who require higher doses of insulin, concentrated insulins offer the advantage of smaller volumes. Also smaller volumes are a substantial benefit in addressing the growing epidemic of DM and the progressive nature of insulin resistance. Furthermore, concentrated insulins are available in pens. Compared with syringes and vials, pens are associated with a lower risk of dosing errors. The major advantages to the use of concentrated insulins include patient acceptability and the potential for decreased volumes and frequency of injections.
Potential disadvantages also exist for the use of concentrated insulins. Depending on insurance coverage, concentrated insulins may be more expensive than U-100 insulin options. Additionally, thorough counseling and education are of paramount importance when concentrated insulins are initiated or switched in patients with DM. The dosing errors that occur with concentrated insulins could increase the risk of hypoglycemia. Pharmacists should provide detailed counseling to DM patients initiating or switching concentrated insulins. It is important to implement or revise institution and clinic safe practices for concentrated insulins to avoid errors in prescribing, distributing, administering, and monitoring these medications.
Conclusion
Concentrated insulins provide expanded treatment options for patients with DM. Clinicians must stay well informed about concentrated insulin characteristics and dosing strategies to optimize DM treatment. As more evidence becomes available, standardized recommendations can be developed to guide clinicians in the appropriate use of concentrated insulins.
Click here to read the digital edition.
1. Humalog [package insert]. Indianapolis, IN: Eli Lilly & Co; 2015.
2. de la Peña A, Seger M, Soon D, et al. Bioequivalence and comparative pharmacodynamics of insulin lispro 200 U/mL relative to insulin lispro (Humalog®) 100 U/mL. Clin Pharmacol Drug Dev. 2016;5(1):69-75.
3. VA Pharmacy Benefits Management Services, Medical Advisory Panel, VISN Phar macist Executives. Insulin Lispro 200units/mL (Humalog) KwikPen abbreviated review. https://www.pbm.va.gov/PBM/clinicalguidance/abbreviatedre views/Insu lin_Lispro_200units_per_mL_Abbreviated_Review.pdf. Published February 2016. Accessed August 22, 2017.
4. Painter NA, Sisson E. An overview of concentrated insulin products. Diabetes Spectr. 2016;29(3):136-140.
5. Korsatko S, Deller S, Koehler G, et al. A comparison of the steady-state pharmacokinetic and pharmacodynamic profiles of 100 and 200 U/mL formulations of ultra-long-acting insulin degludec. Clin Drug Investig. 2013;33(7):515-521.
6. Goldman-Levine JD, Patel DK, Schnee DM. Insulin degludec: a novel basal insulin analogue. Ann Pharmacother. 2013;47(2):269-277.
7. Meneghini L, Atkin SL, Gouch SC, et al; NN1250-3668 (BEGIN FLEX) Trial Investigators. The efficacy and safety of insulin degludec given in variable once-daily dosing intervals compared with insulin glargine and insulin degludec dosed at the same time daily: a 26-week, randomized, open-label, parallel-group, treat-to-target trial in individuals with type 2 diabetes. Diabetes Care. 2013;36(4):858-864.
8. Rendell M. United States experience of insulin degludec alone or in combination for type 1 and type 2 diabetes. Drug Des Dev Ther. 2017;11:1209-1220.
9. Zinman B, Philis-Tsimikas A, Caropi B, et al; NN1250-3579 (BEGIN Once Long) Trial Investigators. Insulin degludec versus insulin glargine in insulin-naive patients with type 2 diabetes: a 1-year, randomized, treat-to-target trial (BEGIN Once Long). Diabetes Care. 2012;35(12):2464-2471.
10. Garber AJ, King AB, Del Prato S, et al; NN1250-3582 (BEGIN BB T2D) Trial Investigators. Insulin degludec, an ultra-longacting basal insulin, versus insulin glargine in basal-bolus treatment with mealtime insulin aspart in type 2 diabetes (BEGIN Basal-Bolus Type 2): a phase 3, randomised, open-label, treat-to-target non-inferiority trial. Lancet. 2012;379(9825):1498-1507.
11. Heller S, Buse J, Fisher M, et al; BEGIN Basal-Bolus Type 1 Trial Investigators. Insulin degludec, an ultra-longacting basal insulin, versus insulin glargine in basal-bolus treatment with mealtime insulin aspart in type 1 diabetes (BEGIN Basal-Bolus Type 1): a phase 3, randomised, open-label, treat-to-target non-inferiority trial. Lancet. 2012;379(9825):1489-1497.
12. Ratner RE, Gough SC, Mathieu C, et al. Hypoglycaemia risk with insulin degludec compared with insulin glargine in type 2 and type 1 diabetes: a pre-planned meta-analysis of phase 3 trials. Diabetes Obes Metab. 2013;15(2):175-184.
13. Marso SP, McGuire DK, Zinman B, et al; DEVOTE Study Group. Efficacy and safety of degludec versus glargine in type 2 diabetes. N Engl J Med. 2017;377(8):723-732.
14. Lane W, Bailey TS, Gerety G, et al; Group Information; SWITCH 1. Effect of insulin degludec vs insulin glargine U100 on hypoglycemia in patients with type 1 diabetes: the SWITCH 1 randomized clinical trial. JAMA. 2017;318(1):33-44.
15. Wysham C, Bhargava A, Chaykin L, et al. Effect of insulin degludec vs insulin glargine U100 on hypoglycemia in patients with type 2 diabetes: the SWITCH 2 randomized clinical trial. JAMA. 2017;318(1):45-56.
16. Weatherall J, Bludek L, Buchs S. Budget impact of treating commercially insured type 1 and type 2 diabetes patients in the United States with insulin degludec compared to insulin glargine. Curr Med Res Opin. 2017;33(2):231-238.
17. Mezquita-Raya P, Darbà J, Ascanio M, Ramírez de Arellano A. Cost-effectiveness analysis of insulin degludec compared with insulin glargine u100 for the management of type 1 and type 2 diabetes mellitus—from the Spanish National Health System perspective. Expert Rev Pharmacoecon Outcomes Res. 2017:1-9. [Epub ahead of print.]
18. Landstedt-Hallin L, Gundgaard J, Ericsson Å, Ellfors-Zetterlund S. Cost-effectiveness of switching to insulin degludec from other basal insulins: evidence from Swedish real-world data. Curr Med Res Opin. 2017;33(4):647-655.
19. Pollock RF, Tikkanen CK. A short-term cost-utility analysis of insulin degludec versus insulin glargine U100 in patients with type 1 or type 2 diabetes in Denmark. J Med Econ. 2017;20(3):213-220.
20. Becker RH, Dahmen R, Bergmann K, Lehmann A, Jax T, Heise T. New insulin glargine 300 units · mL-1 provides a more even activity profile and prolonged glycemic control at steady state compared with insulin glargine 100 units · mL-1. Diabetes Care. 2015;38(4):637-643.
21. Toujeo [package insert]. Bridgewater, NJ: Sanofi-Aventis; 2015.
22. Riddle MC, Bolli GB, Ziemen M, Muehlen-Bartmer I, Bizet F, Home PD; EDITION 1 Study Investigators. New insulin glargine 300 units/mL versus glargine 100 units/mL in people with type 2 diabetes using basal and mealtime insulin: glucose control and hypoglycemia in a 6-month randomized controlled trial (EDITION 1). Diabetes Care. 2014;37(10):2755-2762.
23. Yki-Järvinen H, Bergenstal R, Ziemen M, et al; EDITION 2 Study Investigators. New insulin glargine 300 units/mL versus glargine 100 units/mL in people with type 2 diabetes using oral agents and basal insulin: glucose control and hypoglycemia in a 6-month randomized controlled trial (EDITION 2). Diabetes Care. 2014;37(12):3235-3243.
24. Bolli GB, Riddle MC, Bergenstal RM, et al; on behalf of the EDITION 3 Study Investigators. New insulin glargine 300 U/ml compared with glargine 100 U/ml in insulin-naïve people with type 2 diabetes on oral glucose-lowering drugs: a randomized controlled trial (EDITION 3). Diabetes Obes Metab. 2015;17(4):386-394.
25. Home PD, Bergenstal RM, Bolli GB, et al. New insulin glargine 300 units/mL versus glargine 100 units/mL in people with type 1 diabetes: a randomized, phase 3a, open-label clinical trial (EDITION 4). Diabetes Care. 2015;38(12):2217-2225.
26. Monero S, Delgado M, Rubio M, Gasche D, Fournier M. Cost-utility evaluation of insulin glargine 300 (GLA-300) versus insulin glargine 100 (GLA-100) in patients with type 2 diabetes mellitus (T2DM). Poster presented at: International Society for Pharmacoeconomics and Outcomes Research (ISPOR) 19th Annual European Congress; October 29-November 2, 2016; Vienna, Austria.
27. Ritzel R, Roussel R, Bolli GB, et al. Patient-level meta-analysis of the EDITION 1, 2 and 3 studies: glycaemic control and hypoglycaemia with new insulin glargine 300 U/ml versus glargine 100 U/ml in people with type 2 diabetes. Diabetes Obes Metab. 2015;17(9):859-867.
28. Eby EL, Wang P, Curtis BH, et al. Cost, healthcare resource utilization, and adherence of individuals with diabetes using U-500 or U-100 insulin: a retrospective database analysis. J Med Econ. 2013;16(4):529-538.
29. Lilly USA. Pharmacy tips about Humulin R U-500 KwikPen syringe and vial. http://www.humulin.com/pharmacy-tips.aspx#about-the-u500-syringe_and_vial. Accessed
30. Meneghini, L. New insulin preparations: a primer for the clinician. Cleve Clin J Med. 2016;83(5 suppl 1):S27-S33.
31. Humulin R U-500 KwikPen [package insert]. Indianapolis, IN: Eli Lilly & Co; 2016.
32. de la Peña A, Riddle M, Morrow LA, et al. Pharmacokinetics and pharmacodynamics of high-dose human regular U-500 insulin versus human regular U-100 insulin in healthy obese subjects. Diabetes Care. 2011;34(12):2496-2501.
33. Hood RC, Arakaki RF, Wysham C, Li YG, Settles JA, Jackson JA. Two treatment approaches for human regular U-500 insulin in patients with type 2 diabetes not achieving adequate glycemic control on high-dose U-100 insulin therapy with or without oral agents: a randomized, titration-to-target clinical trial. Endocr Pract. 2015;21(7):782-793.
34. Eby EL, Curtis BH, Gelwicks SC, et al. Initiation of human regular U-500 insulin use is associated with improved glycemic control: a real-world US cohort study. BMJ Open Diabetes Res Care. 2015;3(1):e000074.
35. Wysham C, Hood RC, Warren ML, Wang T, Morwick TM, Jackson JA. Effect of total daily dose on efficacy, dosing, and safety of 2 dose titration regimens of human regular U500 insulin in severely insulin-resistant patients with type 2 diabetes. Endocr Pract. 2016;22(6):653-665.
36. Kabul S, Hood RC, Duan R, DeLozier AM, Settles J. Patient-reported outcomes in transition from high-dose U-100 insulin to human regular U-500 insulin in severely insulin-resistant patients with type 2 diabetes: analysis of a randomized clinical trial. Health Qual Life Outcomes. 2016;14(1):139.
37. Hirsch IB. Lipodystrophy: metabolic and clinical aspects. https://www.endo crine.org/~/media/endosociety/files/education/lypodystrophy-files/hirsch_tdeg-2013_lrc_final.pdf?la=en. Accessed September 7, 2017.
For a long time, 500 U/mL (U-500) insulin was the only concentrated insulin available on the market. With many diabetes mellitus (DM) patients requiring larger doses, additional 200 U/mL (U-200) and 300 U/mL (U-300) concentrations became available. As clinical guidelines lack specific recommendations for optimal use of U-200 and U-300 insulins, clinical discretion is warranted in identifying patients for whom use of these insulins is appropriate. U-500 insulin is recommended in cases that require ≥ 200 U/d or > 2 U/kg/d. Given the ongoing DM and obesity epidemics, increased use of concentrated insulins is likely. Clinicians must stay well informed about the characteristics and benefits of concentrated insulins to remain confident recommending, prescribing, and adjusting these medications.
U-200 Insulin Lispro
Pharmacokinetics/Pharmacodynamics
The amino acid structure of U-200 insulin lispro is different from that of endogenous insulin. In U-200 lispro, lysine replaces a proline at position B28, and proline replaces a lysine at position B29.
In a euglycemic clamp study of patients without DM, a 20-U dose of U-200 lispro and a single 20-U dose of U-100 lispro were found to have similar mean area under the glucose infusion rate curves, mean area under the serum insulin concentration-time curves from time 0 to infinity, mean peak serum insulin levels, and time to maximum glucose-lowering effects.1 For both U-200 lispro and U-100 lispro, time to maximum effect was 1 hour.2
Even numbers are marked on the dial of the pen. Odd numbers are not marked, but longer lines appear in their place. U-200 lispro should not be mixed with any other insulin, whereas U-100 lispro can be mixed with neutral protamine Hagedorn insulin.
Safety/Efficacy
There has been 1 bioequivalence study of euglycemic patients without type 2 DM (T2DM) but no studies of the safety or efficacy of U-200 lispro in patients with DM.3,4 U-100 lispro converts 1:1 to U-200 lispro (eg, 60 U of U-100 lispro converts to 60 U of U-200 lispro).1 The volume of U-200 lispro would be smaller than that of U-100 lispro.
Economic Analysis
There are no published U-200 lispro economic analyses.
Dosing
U-200 lispro should be converted from other bolus insulins in a 1:1 ratio.1
Recommendations
Definitive recommendations await efficacy trials comparing use of U-200 lispro and other bolus insulins in patients with DM. Currently, U-200 lispro may be considered for patients with DM who require high doses of bolus insulin and who may benefit from smaller volumes of lispro.
U-200 Insulin Degludec
Pharmacokinetics/Pharmacodynamics
The basal insulin degludec (Tresiba) is available in U-100 and U-200 concentrations in a pen. After subcutaneous injection, degludec forms gradually dissociating multihexamer chains, which account for its flat and stable PK/PD profile. U-100 degludec and U-200 degludec have similar duration of action (≥ 42 hours) and time to steady state (2-3 days).5,6 A patient who misses a regularly scheduled dose should allow at least 8 hours between injections. Taking degludec at variable times does not decrease efficacy as long as this 8-hour minimum interval is observed.7
Safety/Efficacy
During its development, degludec was evaluated in more than 5,000 patients across 11 therapeutic trials.8 The key studies that led to the approval of degludec used insulin glargine as a comparator. In a 52-week study of 1,030 insulin-naïve patients with T2DM, degludec was noninferior to glargine in hemoglobin A1c (HbA1c) reduction (1.06% vs 1.19%). Overall hypoglycemia rates were similar, though there were fewer nocturnal hypoglycemia episodes with degludec than with glargine (0.25 vs 0.39 per patient-year of exposure; P = .38).9
The BEGIN Basal-Bolus trial series evaluated use of degludec combined with bolus insulin aspart in insulin-experienced patients with T2DM (n = 992) and type 1 DM (T1DM) (n = 629) over 52 weeks.10,11 Both trials found noninferiority in A1c reduction: 1.1% (degludec) and 1.18% (glargine) in patients with T2DM and 0.4% (degludec) and 0.39% (glargine) in those with T1DM.10,11 Significantly fewer episodes of overall hypoglycemia (11.09 vs 13.63 per patient-year) and nocturnal hypoglycemia (1.39 vs 1.84 per patient-year) were found with degludec in patients with T2DM.5 Overall hypoglycemia rates were similar, though there was a 25% lower rate of nocturnal hypoglycemia with degludec in patients with T1DM.11
A meta-analysis of 7 phase 3a trials that compared degludec with glargine revealed significantly lower rates of overall, nocturnal, and severe hypoglycemia with degludec in insulin-naïve patients.12 The analysis confirmed findings of significantly lower rates of overall and nocturnal hypoglycemia with degludec in the overall T2DM population and significantly lower rates of nocturnal hypoglycemia in the T1DM population.12
In the DEVOTE trial, which included 7,637 T2DM patients at high risk for a cardiovascular event, degludec and glargine were compared on the composite primary outcome of death with a cardiovascular cause, nonfatal myocardial infarction, or nonfatal stroke. After a median of 1.99 years, the primary outcome occurred in 8.5% of degludec patients and 9.3% of glargine patients (hazard ratio, 0.91; 95% confidence interval, 0.78-1.06; P < .001 for noninferiority). Mean HbA1c level was 7.5 in both groups; severe hypoglycemia occurred more often in the glargine group (odds ratio, 0.73; P < .001 for superiority).13 Findings from the randomized, crossover SWITCH 1 and SWITCH 2 trials confirmed lower rates of symptomatic hypoglycemia with degludec compared with glargine in patients with T1DM and T2DM, respectively.14,15 No statistically significant differences in weight gain were observed in the clinical trials comparing degludec and glargine.
Economic Analysis
Weatherall and colleagues used a budget impact model to evaluate the costs of degludec and glargine for commercially insured patients with DM in the U.S.16 Three treatment groups were analyzed: basal/bolus combination in T1DM and T2DM, and basal/oral combination in T2DM. Although degludec cost more, overall cost was reduced in T1DM because of reduced insulin usage and fewer hypoglycemic episodes in T2DM with basal/oral combination therapy. The authors acknowledged the many assumptions needed and the potential oversimplification of their model.16 In other countries, economic analyses had similar findings.17-19
Dosing
Degludec converts 1:1 to other basal insulins. Recommended starting doses for U-200 degludec are 10 U once daily for insulin-naïve adults with T2DM and one-third to one-half the total weight-based daily insulin dose for insulin-naïve adults with T1DM.4
Recommendations
For some patients, lower PD variability may make degludec a desirable alternative. As degludec retains its efficacy with variable dosing times, it may be ideal for patients who have difficulty with a once-daily dosing schedule. It is important to inform patients that the degludec pen allows for 2-U increments. Given the lower frequency of nocturnal hypoglycemic events with degludec compared with glargine, degludec is an appropriate basal insulin option for patients with nocturnal hypoglycemia. In addition, U-200 degludec may be considered for DM patients who require high doses of basal insulin and who may benefit from smaller volumes of degludec.
U-300 Isulin Glargine
Pharmacokinetics/Pharmacodynamics
U-300 glargine is a concentrated basal insulin. There are notable differences between its U-100 and U-300 concentrations. For U-300 glargine dosed at 0.4 U/kg, duration of action is 24 hours; for U-300 glargine dosed at 0.6 U/kg or higher, longer duration is expected.20 Steady state is reached after 5 days.21 The U-300 glargine pen contains 1.5 mL, less than the 3 mL in the U-100 pen. U-300 glargine typically is administered in 1 injection once daily if the dose is < 80 U; 2 injections are required if the dose is > 80 U.
Safety/Efficacy
In the EDITION trials, which compared U-300 and U-100 glargine in patients with T1DM and T2DM, the primary endpoint was 6-month HbA1c reduction.22-24 Comparable HbA1c reductions were found in all of the studies. In EDITION 1, in which 2,474 patients with T2DM were taking concomitant bolus insulin with or without metformin, 11% more U-300 glargine than U-100 glargine was needed to achieve similar results.22 In EDITION 4, in which bolus insulin was used in combination in 546 patients with T1DM, 17.5% more U-300 glargine than U-100 glargine was needed to achieve similar glycemic goals.25
Economic Analysis
Compared with other insulins, U-300 glargine has limited published data and economic analyses. Using a cost-utility model to compare U-300 with U-100 glargine in Spanish patients with T2DM, and reporting results in euros per quality-adjusted life years, Monero and colleagues concluded that the hypoglycemia reduction and possible time-of-dose flexibility found with U-300 glargine may contribute to its cost-effectiveness.26
Dosing
U-300 glargine should be converted in a 1:1 ratio from U-100 glargine or detemir. The U-300 glargine dose should be reduced by 20% when switching from NPH insulin.21
Recommendations
A meta-analysis of the EDITION trials 1 to 3 revealed a lower incidence of daytime and nocturnal hypoglycemia with use of U-300 glargine over U-100 glargine and a beneficial shorter hold time after injection of U-300 glargine (5 seconds) compared with U-100 glargine (10 seconds).27 There was statistically lower weight gain with U-300 glargine compared with U-100 glargine however weight gain was < 1 kg in both groups.27 These characteristics of U-300 glargine may prove advantageous for individual patients.
U-500 Insulin
Pharmacokinetics/Pharmacodynamics
U-500 insulin (Humulin R) has been available in a vial since 1997, but other formulations have been used therapeutically since 1952.28 The U-500 KwikPen device, recently added to the market, has improved the vial and syringe dosing. The new U-500 BD (Becton, Dickinson, Franklin Lakes, NJ) syringes allow doses up to 250 U, and the U-500 KwikPens provide up to 300 U per injection.29 When it was first introduced, U-500 insulin had no dedicated delivery device and dose conversion was required to deliver the appropriate dose using an allergy or TB syringe. As a consequence, confusion often resulted between prescribers, pharmacists and patients.30,31 U-500 insulin acts as basal and bolus insulins do. Onset of action is ~15 minutes, time to peak is 4 to 8 hours, and duration of action is ≤ 21 hours.32 As its onset of action is similar to that of U-100, U-500 should be injected 30 minutes before meals.
A single-site, randomized, double-blind, crossover euglycemic clamp study that compared equivalent doses of U-500 and U-100 in healthy obese patients found the formulations had similar overall exposures and effects—the only differences were that U-500 had an extended time to peak and a prolonged post-peak effect. The longer post-peak effect contributes to longer duration of action and allows for fewer daily injections.33
Safety/Efficacy
In the Humulin R U-500 Initiation trial, both of these algorithms improved glycemic control and were associated with a low incidence of severe hypoglycemia. In addition, the associated weight gains were similar. Last, the rate of nonsevere hypoglycemia was slightly lower for the 3-times-daily than for the 2-times-daily regimen.34 A real-world outcome analysis of U-500 initiation confirmed the benefits of switching from U-100 to U-500. A clinically significant improvement in glycemic control was found in all the study participants. Dose and frequency of administration, however, were not reported.35
According to a secondary analysis in the Humulin R U-500 Initiation trial, baseline U-100 total daily dose did not yield a difference in efficacy or safety between the 2-times-a-day and 3-times-a-day arms—allowing use of a simpler 2-times-a-day schedule without regard to baseline total daily dose.28,36 The 2-times-a-day regimen is preferred in clinical practice given that the 2 regimens are equivalent in safety and efficacy and that the 2-times-a-day regimen is simpler, allows for easier titrations, improves patient perceptions of the effect of insulin on daily life function and psychological health, lowers daily injection burden, and maximizes adherence.37
Economic Analysis
A retrospective database analysis revealed lower overall cost and lower pharmacy cost associated with U-500 in comparison with high-dose U-100, as well as reduced hypoglycemia-specific costs or resource utilization, even though U-500 was associated with a slightly higher incidence of hypoglycemia.28 However, the fact that hypoglycemia was reported with a billing code (ICD-9) implies the hypoglycemic event was severe enough to require medical attention. Given these findings, 2-times-a-day U-500 seems more cost-effective than high-dose U-100.
Dosing
The U-500 Humulin R package insert recommends converting a dose to U-500 on the basis of most recent HbA1c level. U-500 can be dosed 2 times daily (60%, 40%) or 3 times daily (40%, 30%, 30%). If HbA1c is > 8%, then the starting total daily dose (TDD) of U-500 is 100% of the U-100 TDD. If HbA1c is ≤ 8%, then the starting TDD of U-500 is 80% of the final U-100 TDD (20% reduction). Dose adjustments may range from 5% to 10% depending on subsequent blood glucose readings.32
Recommendations
U-500 is a safe and effective monotherapy alternative for patients who require high doses of U-100. Initial conversion from U-100 is based on HbA1c level. The total daily dose of U-500 is then divided by 2 (60%, 40%) or 3 (40%, 30%, 30%). The 2-times-a-day regimen enhances adherence and thus may be preferred.
Discussion
It has been suggested that large volumes or depots of insulin approaching 100 units impedes absorption and are more painful compared with smaller volume injections.37 For patients with DM who require higher doses of insulin, concentrated insulins offer the advantage of smaller volumes. Also smaller volumes are a substantial benefit in addressing the growing epidemic of DM and the progressive nature of insulin resistance. Furthermore, concentrated insulins are available in pens. Compared with syringes and vials, pens are associated with a lower risk of dosing errors. The major advantages to the use of concentrated insulins include patient acceptability and the potential for decreased volumes and frequency of injections.
Potential disadvantages also exist for the use of concentrated insulins. Depending on insurance coverage, concentrated insulins may be more expensive than U-100 insulin options. Additionally, thorough counseling and education are of paramount importance when concentrated insulins are initiated or switched in patients with DM. The dosing errors that occur with concentrated insulins could increase the risk of hypoglycemia. Pharmacists should provide detailed counseling to DM patients initiating or switching concentrated insulins. It is important to implement or revise institution and clinic safe practices for concentrated insulins to avoid errors in prescribing, distributing, administering, and monitoring these medications.
Conclusion
Concentrated insulins provide expanded treatment options for patients with DM. Clinicians must stay well informed about concentrated insulin characteristics and dosing strategies to optimize DM treatment. As more evidence becomes available, standardized recommendations can be developed to guide clinicians in the appropriate use of concentrated insulins.
Click here to read the digital edition.
For a long time, 500 U/mL (U-500) insulin was the only concentrated insulin available on the market. With many diabetes mellitus (DM) patients requiring larger doses, additional 200 U/mL (U-200) and 300 U/mL (U-300) concentrations became available. As clinical guidelines lack specific recommendations for optimal use of U-200 and U-300 insulins, clinical discretion is warranted in identifying patients for whom use of these insulins is appropriate. U-500 insulin is recommended in cases that require ≥ 200 U/d or > 2 U/kg/d. Given the ongoing DM and obesity epidemics, increased use of concentrated insulins is likely. Clinicians must stay well informed about the characteristics and benefits of concentrated insulins to remain confident recommending, prescribing, and adjusting these medications.
U-200 Insulin Lispro
Pharmacokinetics/Pharmacodynamics
The amino acid structure of U-200 insulin lispro is different from that of endogenous insulin. In U-200 lispro, lysine replaces a proline at position B28, and proline replaces a lysine at position B29.
In a euglycemic clamp study of patients without DM, a 20-U dose of U-200 lispro and a single 20-U dose of U-100 lispro were found to have similar mean area under the glucose infusion rate curves, mean area under the serum insulin concentration-time curves from time 0 to infinity, mean peak serum insulin levels, and time to maximum glucose-lowering effects.1 For both U-200 lispro and U-100 lispro, time to maximum effect was 1 hour.2
Even numbers are marked on the dial of the pen. Odd numbers are not marked, but longer lines appear in their place. U-200 lispro should not be mixed with any other insulin, whereas U-100 lispro can be mixed with neutral protamine Hagedorn insulin.
Safety/Efficacy
There has been 1 bioequivalence study of euglycemic patients without type 2 DM (T2DM) but no studies of the safety or efficacy of U-200 lispro in patients with DM.3,4 U-100 lispro converts 1:1 to U-200 lispro (eg, 60 U of U-100 lispro converts to 60 U of U-200 lispro).1 The volume of U-200 lispro would be smaller than that of U-100 lispro.
Economic Analysis
There are no published U-200 lispro economic analyses.
Dosing
U-200 lispro should be converted from other bolus insulins in a 1:1 ratio.1
Recommendations
Definitive recommendations await efficacy trials comparing use of U-200 lispro and other bolus insulins in patients with DM. Currently, U-200 lispro may be considered for patients with DM who require high doses of bolus insulin and who may benefit from smaller volumes of lispro.
U-200 Insulin Degludec
Pharmacokinetics/Pharmacodynamics
The basal insulin degludec (Tresiba) is available in U-100 and U-200 concentrations in a pen. After subcutaneous injection, degludec forms gradually dissociating multihexamer chains, which account for its flat and stable PK/PD profile. U-100 degludec and U-200 degludec have similar duration of action (≥ 42 hours) and time to steady state (2-3 days).5,6 A patient who misses a regularly scheduled dose should allow at least 8 hours between injections. Taking degludec at variable times does not decrease efficacy as long as this 8-hour minimum interval is observed.7
Safety/Efficacy
During its development, degludec was evaluated in more than 5,000 patients across 11 therapeutic trials.8 The key studies that led to the approval of degludec used insulin glargine as a comparator. In a 52-week study of 1,030 insulin-naïve patients with T2DM, degludec was noninferior to glargine in hemoglobin A1c (HbA1c) reduction (1.06% vs 1.19%). Overall hypoglycemia rates were similar, though there were fewer nocturnal hypoglycemia episodes with degludec than with glargine (0.25 vs 0.39 per patient-year of exposure; P = .38).9
The BEGIN Basal-Bolus trial series evaluated use of degludec combined with bolus insulin aspart in insulin-experienced patients with T2DM (n = 992) and type 1 DM (T1DM) (n = 629) over 52 weeks.10,11 Both trials found noninferiority in A1c reduction: 1.1% (degludec) and 1.18% (glargine) in patients with T2DM and 0.4% (degludec) and 0.39% (glargine) in those with T1DM.10,11 Significantly fewer episodes of overall hypoglycemia (11.09 vs 13.63 per patient-year) and nocturnal hypoglycemia (1.39 vs 1.84 per patient-year) were found with degludec in patients with T2DM.5 Overall hypoglycemia rates were similar, though there was a 25% lower rate of nocturnal hypoglycemia with degludec in patients with T1DM.11
A meta-analysis of 7 phase 3a trials that compared degludec with glargine revealed significantly lower rates of overall, nocturnal, and severe hypoglycemia with degludec in insulin-naïve patients.12 The analysis confirmed findings of significantly lower rates of overall and nocturnal hypoglycemia with degludec in the overall T2DM population and significantly lower rates of nocturnal hypoglycemia in the T1DM population.12
In the DEVOTE trial, which included 7,637 T2DM patients at high risk for a cardiovascular event, degludec and glargine were compared on the composite primary outcome of death with a cardiovascular cause, nonfatal myocardial infarction, or nonfatal stroke. After a median of 1.99 years, the primary outcome occurred in 8.5% of degludec patients and 9.3% of glargine patients (hazard ratio, 0.91; 95% confidence interval, 0.78-1.06; P < .001 for noninferiority). Mean HbA1c level was 7.5 in both groups; severe hypoglycemia occurred more often in the glargine group (odds ratio, 0.73; P < .001 for superiority).13 Findings from the randomized, crossover SWITCH 1 and SWITCH 2 trials confirmed lower rates of symptomatic hypoglycemia with degludec compared with glargine in patients with T1DM and T2DM, respectively.14,15 No statistically significant differences in weight gain were observed in the clinical trials comparing degludec and glargine.
Economic Analysis
Weatherall and colleagues used a budget impact model to evaluate the costs of degludec and glargine for commercially insured patients with DM in the U.S.16 Three treatment groups were analyzed: basal/bolus combination in T1DM and T2DM, and basal/oral combination in T2DM. Although degludec cost more, overall cost was reduced in T1DM because of reduced insulin usage and fewer hypoglycemic episodes in T2DM with basal/oral combination therapy. The authors acknowledged the many assumptions needed and the potential oversimplification of their model.16 In other countries, economic analyses had similar findings.17-19
Dosing
Degludec converts 1:1 to other basal insulins. Recommended starting doses for U-200 degludec are 10 U once daily for insulin-naïve adults with T2DM and one-third to one-half the total weight-based daily insulin dose for insulin-naïve adults with T1DM.4
Recommendations
For some patients, lower PD variability may make degludec a desirable alternative. As degludec retains its efficacy with variable dosing times, it may be ideal for patients who have difficulty with a once-daily dosing schedule. It is important to inform patients that the degludec pen allows for 2-U increments. Given the lower frequency of nocturnal hypoglycemic events with degludec compared with glargine, degludec is an appropriate basal insulin option for patients with nocturnal hypoglycemia. In addition, U-200 degludec may be considered for DM patients who require high doses of basal insulin and who may benefit from smaller volumes of degludec.
U-300 Isulin Glargine
Pharmacokinetics/Pharmacodynamics
U-300 glargine is a concentrated basal insulin. There are notable differences between its U-100 and U-300 concentrations. For U-300 glargine dosed at 0.4 U/kg, duration of action is 24 hours; for U-300 glargine dosed at 0.6 U/kg or higher, longer duration is expected.20 Steady state is reached after 5 days.21 The U-300 glargine pen contains 1.5 mL, less than the 3 mL in the U-100 pen. U-300 glargine typically is administered in 1 injection once daily if the dose is < 80 U; 2 injections are required if the dose is > 80 U.
Safety/Efficacy
In the EDITION trials, which compared U-300 and U-100 glargine in patients with T1DM and T2DM, the primary endpoint was 6-month HbA1c reduction.22-24 Comparable HbA1c reductions were found in all of the studies. In EDITION 1, in which 2,474 patients with T2DM were taking concomitant bolus insulin with or without metformin, 11% more U-300 glargine than U-100 glargine was needed to achieve similar results.22 In EDITION 4, in which bolus insulin was used in combination in 546 patients with T1DM, 17.5% more U-300 glargine than U-100 glargine was needed to achieve similar glycemic goals.25
Economic Analysis
Compared with other insulins, U-300 glargine has limited published data and economic analyses. Using a cost-utility model to compare U-300 with U-100 glargine in Spanish patients with T2DM, and reporting results in euros per quality-adjusted life years, Monero and colleagues concluded that the hypoglycemia reduction and possible time-of-dose flexibility found with U-300 glargine may contribute to its cost-effectiveness.26
Dosing
U-300 glargine should be converted in a 1:1 ratio from U-100 glargine or detemir. The U-300 glargine dose should be reduced by 20% when switching from NPH insulin.21
Recommendations
A meta-analysis of the EDITION trials 1 to 3 revealed a lower incidence of daytime and nocturnal hypoglycemia with use of U-300 glargine over U-100 glargine and a beneficial shorter hold time after injection of U-300 glargine (5 seconds) compared with U-100 glargine (10 seconds).27 There was statistically lower weight gain with U-300 glargine compared with U-100 glargine however weight gain was < 1 kg in both groups.27 These characteristics of U-300 glargine may prove advantageous for individual patients.
U-500 Insulin
Pharmacokinetics/Pharmacodynamics
U-500 insulin (Humulin R) has been available in a vial since 1997, but other formulations have been used therapeutically since 1952.28 The U-500 KwikPen device, recently added to the market, has improved the vial and syringe dosing. The new U-500 BD (Becton, Dickinson, Franklin Lakes, NJ) syringes allow doses up to 250 U, and the U-500 KwikPens provide up to 300 U per injection.29 When it was first introduced, U-500 insulin had no dedicated delivery device and dose conversion was required to deliver the appropriate dose using an allergy or TB syringe. As a consequence, confusion often resulted between prescribers, pharmacists and patients.30,31 U-500 insulin acts as basal and bolus insulins do. Onset of action is ~15 minutes, time to peak is 4 to 8 hours, and duration of action is ≤ 21 hours.32 As its onset of action is similar to that of U-100, U-500 should be injected 30 minutes before meals.
A single-site, randomized, double-blind, crossover euglycemic clamp study that compared equivalent doses of U-500 and U-100 in healthy obese patients found the formulations had similar overall exposures and effects—the only differences were that U-500 had an extended time to peak and a prolonged post-peak effect. The longer post-peak effect contributes to longer duration of action and allows for fewer daily injections.33
Safety/Efficacy
In the Humulin R U-500 Initiation trial, both of these algorithms improved glycemic control and were associated with a low incidence of severe hypoglycemia. In addition, the associated weight gains were similar. Last, the rate of nonsevere hypoglycemia was slightly lower for the 3-times-daily than for the 2-times-daily regimen.34 A real-world outcome analysis of U-500 initiation confirmed the benefits of switching from U-100 to U-500. A clinically significant improvement in glycemic control was found in all the study participants. Dose and frequency of administration, however, were not reported.35
According to a secondary analysis in the Humulin R U-500 Initiation trial, baseline U-100 total daily dose did not yield a difference in efficacy or safety between the 2-times-a-day and 3-times-a-day arms—allowing use of a simpler 2-times-a-day schedule without regard to baseline total daily dose.28,36 The 2-times-a-day regimen is preferred in clinical practice given that the 2 regimens are equivalent in safety and efficacy and that the 2-times-a-day regimen is simpler, allows for easier titrations, improves patient perceptions of the effect of insulin on daily life function and psychological health, lowers daily injection burden, and maximizes adherence.37
Economic Analysis
A retrospective database analysis revealed lower overall cost and lower pharmacy cost associated with U-500 in comparison with high-dose U-100, as well as reduced hypoglycemia-specific costs or resource utilization, even though U-500 was associated with a slightly higher incidence of hypoglycemia.28 However, the fact that hypoglycemia was reported with a billing code (ICD-9) implies the hypoglycemic event was severe enough to require medical attention. Given these findings, 2-times-a-day U-500 seems more cost-effective than high-dose U-100.
Dosing
The U-500 Humulin R package insert recommends converting a dose to U-500 on the basis of most recent HbA1c level. U-500 can be dosed 2 times daily (60%, 40%) or 3 times daily (40%, 30%, 30%). If HbA1c is > 8%, then the starting total daily dose (TDD) of U-500 is 100% of the U-100 TDD. If HbA1c is ≤ 8%, then the starting TDD of U-500 is 80% of the final U-100 TDD (20% reduction). Dose adjustments may range from 5% to 10% depending on subsequent blood glucose readings.32
Recommendations
U-500 is a safe and effective monotherapy alternative for patients who require high doses of U-100. Initial conversion from U-100 is based on HbA1c level. The total daily dose of U-500 is then divided by 2 (60%, 40%) or 3 (40%, 30%, 30%). The 2-times-a-day regimen enhances adherence and thus may be preferred.
Discussion
It has been suggested that large volumes or depots of insulin approaching 100 units impedes absorption and are more painful compared with smaller volume injections.37 For patients with DM who require higher doses of insulin, concentrated insulins offer the advantage of smaller volumes. Also smaller volumes are a substantial benefit in addressing the growing epidemic of DM and the progressive nature of insulin resistance. Furthermore, concentrated insulins are available in pens. Compared with syringes and vials, pens are associated with a lower risk of dosing errors. The major advantages to the use of concentrated insulins include patient acceptability and the potential for decreased volumes and frequency of injections.
Potential disadvantages also exist for the use of concentrated insulins. Depending on insurance coverage, concentrated insulins may be more expensive than U-100 insulin options. Additionally, thorough counseling and education are of paramount importance when concentrated insulins are initiated or switched in patients with DM. The dosing errors that occur with concentrated insulins could increase the risk of hypoglycemia. Pharmacists should provide detailed counseling to DM patients initiating or switching concentrated insulins. It is important to implement or revise institution and clinic safe practices for concentrated insulins to avoid errors in prescribing, distributing, administering, and monitoring these medications.
Conclusion
Concentrated insulins provide expanded treatment options for patients with DM. Clinicians must stay well informed about concentrated insulin characteristics and dosing strategies to optimize DM treatment. As more evidence becomes available, standardized recommendations can be developed to guide clinicians in the appropriate use of concentrated insulins.
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1. Humalog [package insert]. Indianapolis, IN: Eli Lilly & Co; 2015.
2. de la Peña A, Seger M, Soon D, et al. Bioequivalence and comparative pharmacodynamics of insulin lispro 200 U/mL relative to insulin lispro (Humalog®) 100 U/mL. Clin Pharmacol Drug Dev. 2016;5(1):69-75.
3. VA Pharmacy Benefits Management Services, Medical Advisory Panel, VISN Phar macist Executives. Insulin Lispro 200units/mL (Humalog) KwikPen abbreviated review. https://www.pbm.va.gov/PBM/clinicalguidance/abbreviatedre views/Insu lin_Lispro_200units_per_mL_Abbreviated_Review.pdf. Published February 2016. Accessed August 22, 2017.
4. Painter NA, Sisson E. An overview of concentrated insulin products. Diabetes Spectr. 2016;29(3):136-140.
5. Korsatko S, Deller S, Koehler G, et al. A comparison of the steady-state pharmacokinetic and pharmacodynamic profiles of 100 and 200 U/mL formulations of ultra-long-acting insulin degludec. Clin Drug Investig. 2013;33(7):515-521.
6. Goldman-Levine JD, Patel DK, Schnee DM. Insulin degludec: a novel basal insulin analogue. Ann Pharmacother. 2013;47(2):269-277.
7. Meneghini L, Atkin SL, Gouch SC, et al; NN1250-3668 (BEGIN FLEX) Trial Investigators. The efficacy and safety of insulin degludec given in variable once-daily dosing intervals compared with insulin glargine and insulin degludec dosed at the same time daily: a 26-week, randomized, open-label, parallel-group, treat-to-target trial in individuals with type 2 diabetes. Diabetes Care. 2013;36(4):858-864.
8. Rendell M. United States experience of insulin degludec alone or in combination for type 1 and type 2 diabetes. Drug Des Dev Ther. 2017;11:1209-1220.
9. Zinman B, Philis-Tsimikas A, Caropi B, et al; NN1250-3579 (BEGIN Once Long) Trial Investigators. Insulin degludec versus insulin glargine in insulin-naive patients with type 2 diabetes: a 1-year, randomized, treat-to-target trial (BEGIN Once Long). Diabetes Care. 2012;35(12):2464-2471.
10. Garber AJ, King AB, Del Prato S, et al; NN1250-3582 (BEGIN BB T2D) Trial Investigators. Insulin degludec, an ultra-longacting basal insulin, versus insulin glargine in basal-bolus treatment with mealtime insulin aspart in type 2 diabetes (BEGIN Basal-Bolus Type 2): a phase 3, randomised, open-label, treat-to-target non-inferiority trial. Lancet. 2012;379(9825):1498-1507.
11. Heller S, Buse J, Fisher M, et al; BEGIN Basal-Bolus Type 1 Trial Investigators. Insulin degludec, an ultra-longacting basal insulin, versus insulin glargine in basal-bolus treatment with mealtime insulin aspart in type 1 diabetes (BEGIN Basal-Bolus Type 1): a phase 3, randomised, open-label, treat-to-target non-inferiority trial. Lancet. 2012;379(9825):1489-1497.
12. Ratner RE, Gough SC, Mathieu C, et al. Hypoglycaemia risk with insulin degludec compared with insulin glargine in type 2 and type 1 diabetes: a pre-planned meta-analysis of phase 3 trials. Diabetes Obes Metab. 2013;15(2):175-184.
13. Marso SP, McGuire DK, Zinman B, et al; DEVOTE Study Group. Efficacy and safety of degludec versus glargine in type 2 diabetes. N Engl J Med. 2017;377(8):723-732.
14. Lane W, Bailey TS, Gerety G, et al; Group Information; SWITCH 1. Effect of insulin degludec vs insulin glargine U100 on hypoglycemia in patients with type 1 diabetes: the SWITCH 1 randomized clinical trial. JAMA. 2017;318(1):33-44.
15. Wysham C, Bhargava A, Chaykin L, et al. Effect of insulin degludec vs insulin glargine U100 on hypoglycemia in patients with type 2 diabetes: the SWITCH 2 randomized clinical trial. JAMA. 2017;318(1):45-56.
16. Weatherall J, Bludek L, Buchs S. Budget impact of treating commercially insured type 1 and type 2 diabetes patients in the United States with insulin degludec compared to insulin glargine. Curr Med Res Opin. 2017;33(2):231-238.
17. Mezquita-Raya P, Darbà J, Ascanio M, Ramírez de Arellano A. Cost-effectiveness analysis of insulin degludec compared with insulin glargine u100 for the management of type 1 and type 2 diabetes mellitus—from the Spanish National Health System perspective. Expert Rev Pharmacoecon Outcomes Res. 2017:1-9. [Epub ahead of print.]
18. Landstedt-Hallin L, Gundgaard J, Ericsson Å, Ellfors-Zetterlund S. Cost-effectiveness of switching to insulin degludec from other basal insulins: evidence from Swedish real-world data. Curr Med Res Opin. 2017;33(4):647-655.
19. Pollock RF, Tikkanen CK. A short-term cost-utility analysis of insulin degludec versus insulin glargine U100 in patients with type 1 or type 2 diabetes in Denmark. J Med Econ. 2017;20(3):213-220.
20. Becker RH, Dahmen R, Bergmann K, Lehmann A, Jax T, Heise T. New insulin glargine 300 units · mL-1 provides a more even activity profile and prolonged glycemic control at steady state compared with insulin glargine 100 units · mL-1. Diabetes Care. 2015;38(4):637-643.
21. Toujeo [package insert]. Bridgewater, NJ: Sanofi-Aventis; 2015.
22. Riddle MC, Bolli GB, Ziemen M, Muehlen-Bartmer I, Bizet F, Home PD; EDITION 1 Study Investigators. New insulin glargine 300 units/mL versus glargine 100 units/mL in people with type 2 diabetes using basal and mealtime insulin: glucose control and hypoglycemia in a 6-month randomized controlled trial (EDITION 1). Diabetes Care. 2014;37(10):2755-2762.
23. Yki-Järvinen H, Bergenstal R, Ziemen M, et al; EDITION 2 Study Investigators. New insulin glargine 300 units/mL versus glargine 100 units/mL in people with type 2 diabetes using oral agents and basal insulin: glucose control and hypoglycemia in a 6-month randomized controlled trial (EDITION 2). Diabetes Care. 2014;37(12):3235-3243.
24. Bolli GB, Riddle MC, Bergenstal RM, et al; on behalf of the EDITION 3 Study Investigators. New insulin glargine 300 U/ml compared with glargine 100 U/ml in insulin-naïve people with type 2 diabetes on oral glucose-lowering drugs: a randomized controlled trial (EDITION 3). Diabetes Obes Metab. 2015;17(4):386-394.
25. Home PD, Bergenstal RM, Bolli GB, et al. New insulin glargine 300 units/mL versus glargine 100 units/mL in people with type 1 diabetes: a randomized, phase 3a, open-label clinical trial (EDITION 4). Diabetes Care. 2015;38(12):2217-2225.
26. Monero S, Delgado M, Rubio M, Gasche D, Fournier M. Cost-utility evaluation of insulin glargine 300 (GLA-300) versus insulin glargine 100 (GLA-100) in patients with type 2 diabetes mellitus (T2DM). Poster presented at: International Society for Pharmacoeconomics and Outcomes Research (ISPOR) 19th Annual European Congress; October 29-November 2, 2016; Vienna, Austria.
27. Ritzel R, Roussel R, Bolli GB, et al. Patient-level meta-analysis of the EDITION 1, 2 and 3 studies: glycaemic control and hypoglycaemia with new insulin glargine 300 U/ml versus glargine 100 U/ml in people with type 2 diabetes. Diabetes Obes Metab. 2015;17(9):859-867.
28. Eby EL, Wang P, Curtis BH, et al. Cost, healthcare resource utilization, and adherence of individuals with diabetes using U-500 or U-100 insulin: a retrospective database analysis. J Med Econ. 2013;16(4):529-538.
29. Lilly USA. Pharmacy tips about Humulin R U-500 KwikPen syringe and vial. http://www.humulin.com/pharmacy-tips.aspx#about-the-u500-syringe_and_vial. Accessed
30. Meneghini, L. New insulin preparations: a primer for the clinician. Cleve Clin J Med. 2016;83(5 suppl 1):S27-S33.
31. Humulin R U-500 KwikPen [package insert]. Indianapolis, IN: Eli Lilly & Co; 2016.
32. de la Peña A, Riddle M, Morrow LA, et al. Pharmacokinetics and pharmacodynamics of high-dose human regular U-500 insulin versus human regular U-100 insulin in healthy obese subjects. Diabetes Care. 2011;34(12):2496-2501.
33. Hood RC, Arakaki RF, Wysham C, Li YG, Settles JA, Jackson JA. Two treatment approaches for human regular U-500 insulin in patients with type 2 diabetes not achieving adequate glycemic control on high-dose U-100 insulin therapy with or without oral agents: a randomized, titration-to-target clinical trial. Endocr Pract. 2015;21(7):782-793.
34. Eby EL, Curtis BH, Gelwicks SC, et al. Initiation of human regular U-500 insulin use is associated with improved glycemic control: a real-world US cohort study. BMJ Open Diabetes Res Care. 2015;3(1):e000074.
35. Wysham C, Hood RC, Warren ML, Wang T, Morwick TM, Jackson JA. Effect of total daily dose on efficacy, dosing, and safety of 2 dose titration regimens of human regular U500 insulin in severely insulin-resistant patients with type 2 diabetes. Endocr Pract. 2016;22(6):653-665.
36. Kabul S, Hood RC, Duan R, DeLozier AM, Settles J. Patient-reported outcomes in transition from high-dose U-100 insulin to human regular U-500 insulin in severely insulin-resistant patients with type 2 diabetes: analysis of a randomized clinical trial. Health Qual Life Outcomes. 2016;14(1):139.
37. Hirsch IB. Lipodystrophy: metabolic and clinical aspects. https://www.endo crine.org/~/media/endosociety/files/education/lypodystrophy-files/hirsch_tdeg-2013_lrc_final.pdf?la=en. Accessed September 7, 2017.
1. Humalog [package insert]. Indianapolis, IN: Eli Lilly & Co; 2015.
2. de la Peña A, Seger M, Soon D, et al. Bioequivalence and comparative pharmacodynamics of insulin lispro 200 U/mL relative to insulin lispro (Humalog®) 100 U/mL. Clin Pharmacol Drug Dev. 2016;5(1):69-75.
3. VA Pharmacy Benefits Management Services, Medical Advisory Panel, VISN Phar macist Executives. Insulin Lispro 200units/mL (Humalog) KwikPen abbreviated review. https://www.pbm.va.gov/PBM/clinicalguidance/abbreviatedre views/Insu lin_Lispro_200units_per_mL_Abbreviated_Review.pdf. Published February 2016. Accessed August 22, 2017.
4. Painter NA, Sisson E. An overview of concentrated insulin products. Diabetes Spectr. 2016;29(3):136-140.
5. Korsatko S, Deller S, Koehler G, et al. A comparison of the steady-state pharmacokinetic and pharmacodynamic profiles of 100 and 200 U/mL formulations of ultra-long-acting insulin degludec. Clin Drug Investig. 2013;33(7):515-521.
6. Goldman-Levine JD, Patel DK, Schnee DM. Insulin degludec: a novel basal insulin analogue. Ann Pharmacother. 2013;47(2):269-277.
7. Meneghini L, Atkin SL, Gouch SC, et al; NN1250-3668 (BEGIN FLEX) Trial Investigators. The efficacy and safety of insulin degludec given in variable once-daily dosing intervals compared with insulin glargine and insulin degludec dosed at the same time daily: a 26-week, randomized, open-label, parallel-group, treat-to-target trial in individuals with type 2 diabetes. Diabetes Care. 2013;36(4):858-864.
8. Rendell M. United States experience of insulin degludec alone or in combination for type 1 and type 2 diabetes. Drug Des Dev Ther. 2017;11:1209-1220.
9. Zinman B, Philis-Tsimikas A, Caropi B, et al; NN1250-3579 (BEGIN Once Long) Trial Investigators. Insulin degludec versus insulin glargine in insulin-naive patients with type 2 diabetes: a 1-year, randomized, treat-to-target trial (BEGIN Once Long). Diabetes Care. 2012;35(12):2464-2471.
10. Garber AJ, King AB, Del Prato S, et al; NN1250-3582 (BEGIN BB T2D) Trial Investigators. Insulin degludec, an ultra-longacting basal insulin, versus insulin glargine in basal-bolus treatment with mealtime insulin aspart in type 2 diabetes (BEGIN Basal-Bolus Type 2): a phase 3, randomised, open-label, treat-to-target non-inferiority trial. Lancet. 2012;379(9825):1498-1507.
11. Heller S, Buse J, Fisher M, et al; BEGIN Basal-Bolus Type 1 Trial Investigators. Insulin degludec, an ultra-longacting basal insulin, versus insulin glargine in basal-bolus treatment with mealtime insulin aspart in type 1 diabetes (BEGIN Basal-Bolus Type 1): a phase 3, randomised, open-label, treat-to-target non-inferiority trial. Lancet. 2012;379(9825):1489-1497.
12. Ratner RE, Gough SC, Mathieu C, et al. Hypoglycaemia risk with insulin degludec compared with insulin glargine in type 2 and type 1 diabetes: a pre-planned meta-analysis of phase 3 trials. Diabetes Obes Metab. 2013;15(2):175-184.
13. Marso SP, McGuire DK, Zinman B, et al; DEVOTE Study Group. Efficacy and safety of degludec versus glargine in type 2 diabetes. N Engl J Med. 2017;377(8):723-732.
14. Lane W, Bailey TS, Gerety G, et al; Group Information; SWITCH 1. Effect of insulin degludec vs insulin glargine U100 on hypoglycemia in patients with type 1 diabetes: the SWITCH 1 randomized clinical trial. JAMA. 2017;318(1):33-44.
15. Wysham C, Bhargava A, Chaykin L, et al. Effect of insulin degludec vs insulin glargine U100 on hypoglycemia in patients with type 2 diabetes: the SWITCH 2 randomized clinical trial. JAMA. 2017;318(1):45-56.
16. Weatherall J, Bludek L, Buchs S. Budget impact of treating commercially insured type 1 and type 2 diabetes patients in the United States with insulin degludec compared to insulin glargine. Curr Med Res Opin. 2017;33(2):231-238.
17. Mezquita-Raya P, Darbà J, Ascanio M, Ramírez de Arellano A. Cost-effectiveness analysis of insulin degludec compared with insulin glargine u100 for the management of type 1 and type 2 diabetes mellitus—from the Spanish National Health System perspective. Expert Rev Pharmacoecon Outcomes Res. 2017:1-9. [Epub ahead of print.]
18. Landstedt-Hallin L, Gundgaard J, Ericsson Å, Ellfors-Zetterlund S. Cost-effectiveness of switching to insulin degludec from other basal insulins: evidence from Swedish real-world data. Curr Med Res Opin. 2017;33(4):647-655.
19. Pollock RF, Tikkanen CK. A short-term cost-utility analysis of insulin degludec versus insulin glargine U100 in patients with type 1 or type 2 diabetes in Denmark. J Med Econ. 2017;20(3):213-220.
20. Becker RH, Dahmen R, Bergmann K, Lehmann A, Jax T, Heise T. New insulin glargine 300 units · mL-1 provides a more even activity profile and prolonged glycemic control at steady state compared with insulin glargine 100 units · mL-1. Diabetes Care. 2015;38(4):637-643.
21. Toujeo [package insert]. Bridgewater, NJ: Sanofi-Aventis; 2015.
22. Riddle MC, Bolli GB, Ziemen M, Muehlen-Bartmer I, Bizet F, Home PD; EDITION 1 Study Investigators. New insulin glargine 300 units/mL versus glargine 100 units/mL in people with type 2 diabetes using basal and mealtime insulin: glucose control and hypoglycemia in a 6-month randomized controlled trial (EDITION 1). Diabetes Care. 2014;37(10):2755-2762.
23. Yki-Järvinen H, Bergenstal R, Ziemen M, et al; EDITION 2 Study Investigators. New insulin glargine 300 units/mL versus glargine 100 units/mL in people with type 2 diabetes using oral agents and basal insulin: glucose control and hypoglycemia in a 6-month randomized controlled trial (EDITION 2). Diabetes Care. 2014;37(12):3235-3243.
24. Bolli GB, Riddle MC, Bergenstal RM, et al; on behalf of the EDITION 3 Study Investigators. New insulin glargine 300 U/ml compared with glargine 100 U/ml in insulin-naïve people with type 2 diabetes on oral glucose-lowering drugs: a randomized controlled trial (EDITION 3). Diabetes Obes Metab. 2015;17(4):386-394.
25. Home PD, Bergenstal RM, Bolli GB, et al. New insulin glargine 300 units/mL versus glargine 100 units/mL in people with type 1 diabetes: a randomized, phase 3a, open-label clinical trial (EDITION 4). Diabetes Care. 2015;38(12):2217-2225.
26. Monero S, Delgado M, Rubio M, Gasche D, Fournier M. Cost-utility evaluation of insulin glargine 300 (GLA-300) versus insulin glargine 100 (GLA-100) in patients with type 2 diabetes mellitus (T2DM). Poster presented at: International Society for Pharmacoeconomics and Outcomes Research (ISPOR) 19th Annual European Congress; October 29-November 2, 2016; Vienna, Austria.
27. Ritzel R, Roussel R, Bolli GB, et al. Patient-level meta-analysis of the EDITION 1, 2 and 3 studies: glycaemic control and hypoglycaemia with new insulin glargine 300 U/ml versus glargine 100 U/ml in people with type 2 diabetes. Diabetes Obes Metab. 2015;17(9):859-867.
28. Eby EL, Wang P, Curtis BH, et al. Cost, healthcare resource utilization, and adherence of individuals with diabetes using U-500 or U-100 insulin: a retrospective database analysis. J Med Econ. 2013;16(4):529-538.
29. Lilly USA. Pharmacy tips about Humulin R U-500 KwikPen syringe and vial. http://www.humulin.com/pharmacy-tips.aspx#about-the-u500-syringe_and_vial. Accessed
30. Meneghini, L. New insulin preparations: a primer for the clinician. Cleve Clin J Med. 2016;83(5 suppl 1):S27-S33.
31. Humulin R U-500 KwikPen [package insert]. Indianapolis, IN: Eli Lilly & Co; 2016.
32. de la Peña A, Riddle M, Morrow LA, et al. Pharmacokinetics and pharmacodynamics of high-dose human regular U-500 insulin versus human regular U-100 insulin in healthy obese subjects. Diabetes Care. 2011;34(12):2496-2501.
33. Hood RC, Arakaki RF, Wysham C, Li YG, Settles JA, Jackson JA. Two treatment approaches for human regular U-500 insulin in patients with type 2 diabetes not achieving adequate glycemic control on high-dose U-100 insulin therapy with or without oral agents: a randomized, titration-to-target clinical trial. Endocr Pract. 2015;21(7):782-793.
34. Eby EL, Curtis BH, Gelwicks SC, et al. Initiation of human regular U-500 insulin use is associated with improved glycemic control: a real-world US cohort study. BMJ Open Diabetes Res Care. 2015;3(1):e000074.
35. Wysham C, Hood RC, Warren ML, Wang T, Morwick TM, Jackson JA. Effect of total daily dose on efficacy, dosing, and safety of 2 dose titration regimens of human regular U500 insulin in severely insulin-resistant patients with type 2 diabetes. Endocr Pract. 2016;22(6):653-665.
36. Kabul S, Hood RC, Duan R, DeLozier AM, Settles J. Patient-reported outcomes in transition from high-dose U-100 insulin to human regular U-500 insulin in severely insulin-resistant patients with type 2 diabetes: analysis of a randomized clinical trial. Health Qual Life Outcomes. 2016;14(1):139.
37. Hirsch IB. Lipodystrophy: metabolic and clinical aspects. https://www.endo crine.org/~/media/endosociety/files/education/lypodystrophy-files/hirsch_tdeg-2013_lrc_final.pdf?la=en. Accessed September 7, 2017.
Serious Mental Illness and Its Impact on Diabetes Care in a VA Nurse/Pharmacist-Managed Population (FULL)
Diabetes mellitus (DM) is considered one of the most psychologically and behaviorally demanding chronic medical conditions. Patients with DM and serious mental illness (SMI), including schizophrenia, schizoaffective disorder, major depressive disorder (MDD), and bipolar disorder, are more likely to have poor adherence to medications as well as poor adherence to diet and lifestyle recommendations, which can lead to poor glycemic control, decreased quality of life, and increased health care expenses.1-4 Up to 27% of patients with DM have a depression diagnosis, and up to 60% of patients with DM experience depressive symptoms.5 Additionally, 1 in 4 patients with schizophrenia have a DM diagnosis.6 Serious mental illness can compromise DM self-management and glycemic control, which increases the risk of DM-related complications.7
These factors combine to make DM self-management essential for optimal glycemic control and prevention of DM-related complications. The American Diabetes Association recommends coordinated management of DM and SMI to achieve DM treatment targets.8 Interventions involving collaborative care teams have assisted in managing patients with concurrent SMI and DM. Collaborative interventions have reduced all-cause mortality, increased the number of patients reaching hemoglobin A1c (HbA1c) targets, increased overall improvement in HbA1c, increased rates of depression remission, and increased medication adherence.9-12
Background
Collaborative interventions have improved glycemic control in patients with concurrent SMI and DM. A study by Desai and colleagues examined the relationship between psychiatric disorders and the quality of DM care in a national sample of veterans.7 Data were collected using chart-abstracted quality data from administrative database records for a sample of veterans with DM who had at least 3 outpatient visits in the previous year (n = 38,020). About 25% of the sample had a diagnosed psychiatric disorder, 91.5% of veterans completed an HbA1c test, and most veterans with a psychiatric disorder completed the 5 quality indicators for DM care (foot inspection, HbA1c determination, pedal pulses examination, foot sensory examination, and retina examination). Veterans with psychiatric disorders did not have a poorer quality of care for secondary prevention of DM compared with that of other veterans.7
In the PROSPECT study (Prevention of Suicide in Primary Care Elderly: Collaborative Trial), a primary care-based depression management program assessed a collaborative intervention to improve care in patients with depression and DM.9 Fifteen depression care managers, including trained social workers, registered nurses (RNs), and psychologists, collaborated with primary care physicians (PCPs) to assist in recognizing depression, offer guideline-based treatment recommendations, and provide algorithm-based care, monitoring, and follow-up. After a median follow-up of 52 months, patients with depression and DM in the intervention group were less likely to die during the 5-year follow-up period compared with those in usual care (adjusted hazard ratio, 0.49 [95% confidence interval (CI), 0.24-0.98]). The study authors concluded that integrated depression care management significantly reduced all-cause mortality in patients with depression and DM.9
A single-blind, randomized, controlled trial conducted by Katon and colleagues examined patients with poorly controlled DM, coronary artery disease (CAD), or both, and concurrent depression in 14 primary care clinics (n = 214).10 The intervention consisted of nurse care managers who were trained RNs with experience in DM education and supervised by PCPs, providing guideline-based, collaborative care over 12 months to improve glycemic control, blood pressure (BP), and lipid control. The nurse care managers followed up with patients every 2 to3 weeks at office visits, and the intervention was compared with usual care by a physician. At 12 months, patients in the intervention group had significant improvement in HbA1c, low-density lipoprotein cholesterol, systolic BP, and depression compared with that of those under usual care. At 12 months, the HbA1c in the patients in the intervention group was significantly improved with an overall percentage change of 0.81 compared with 0.23 in the usual care group (estimated between-group difference, -0.56 [95% CI, -0.85 to -0.27]). The study authors concluded that integrated management and proactive follow-up of medical and psychological illnesses improved both medical outcomes and depression in patients with DM, CAD, or both.10
Another study by Bogner and colleagues investigated an integrated care intervention for patients with depression and DM to improve adherence to antidepressant and antidiabetic medications, glycemic control, and depression remission.11 Two trained research coordinators (a bachelor’s level and a master’s level) administered all intervention activities. The integrated care managers collaborated with physicians, offering education and guideline-based treatment recommendations to patients to monitor medication adherence and clinical status. The intervention supplemented regular primary care follow-up visits and was compared with usual care. At 12 weeks, patients in the integrated care group were more likely to achieve an HbA1c < 7% (60.9% vs 35.7%; P < .001) and remission of depression (58.7% vs 30.7%; P < .001) compared with those in usual care. There also was a significant improvement in adherence to DM and antidepressant medications in the intervention group compared with those in usual care during the study period.11
A systematic review and meta-analysis by Huang and colleagues assessed randomized controlled trials of collaborative care for diabetic patients with depression.12 Trials that reported depression treatment response, depression remission, HbA1c values, and adherence to antidepressant and/or hypoglycemic medications were included. A total of 8 trials randomized 2,238 patients with concurrent depression and DM and compared collaborative care with usual care. Collaborative care was associated with a significant increase in depression treatment response, reduction in HbA1c, and significant improvement in adherence rates for antidepressant and hypoglycemic medications compared with that of usual care. A reduction in HbA1c favored the collaborative care group; however, this reduction was not significant (mean difference, -0.13 [95% CI, -0.46 to 0.19]; P = .08 for heterogeneity; I2 = 51%). The study authors concluded that a collaborative care model significantly improved depression outcomes and adherence to medications in patients with concurrent DM and depression and recommended continued collaborative care for this population.12
Methods
The current study examines a novel service involving the collaboration of a registered nurse-certified DM educator (RN CDE) and clinical pharmacy specialist (PharmD) to improve access to care and maximize DM outcomes. The Louis Stokes Cleveland VAMC defines the PharmD scope of practice. One of the pharmacist’s clinical obligations includes serving as a preceptor for the RN CDE, a collaboration that has not been investigated in previous studies. A primary care provider (PCP) refers veterans to the RN CDE/PharmD clinic, with HbA1c ≥ 8%.
The RN CDE/PharmD clinic tends to receive referrals for the most challenging veterans who may have very elevated HbA1c readings, complex multidrug regimens, or basal/bolus insulin regimens. The RN CDE sees veterans in individual appointments and takes manual BP readings, checks point-of-care glucose/HbA1c readings, downloads home glucometer results into the electronic medical record (EMR), and provides education on DM management specific to the veteran’s individual needs. Because there is no established treatment algorithm for the RN CDE to follow, all medication changes are determined by a preceptor in real time.
When the RN CDE clinic was established, the RN CDE presented veterans to their PCP who determined the veteran’s plan of care. However, this plan was frustrating for the RN CDE because the PCP was not always readily available, causing delays in the workflow of the RN CDE clinic. Since the PharmD has a scope of practice and is more frequently available to discuss veteran cases, RN CDE/PharmD collaboration was initiated. Based on information gathered during the appointment, medication additions, titrations, and changes are precepted with the PharmD. Veterans can be seen in clinic every 2 to 4 weeks, allowing for continued medication adjustments if warranted until their HbA1c target is achieved. Veterans are discharged to their PCP once their HbA1c is at target.
Within the primary care clinic, this service was compared with usual care by a PCP and was associated with a clinically significant reduction in HbA1c by 2.5% compared with usual care after 1 year (P < .001).13 The same study population was investigated to determine whether there was a difference in glycemic control between veterans with SMI compared with veterans without SMI (non-SMI) to provide insight and better support for veterans with SMI and their DM care.
A retrospective review of the veterans referred to the RN CDE/PharmD clinic from January 1, 2011 to December 31, 2014 was performed with institutional review board approval. Veterans were identified using a pharmacy-generated list searching for clinic note titles from the Computerized Patient Record System (CPRS).
The primary objective of this study was to determine the percentage change in mean HbA1c in veterans with SMI compared with that of veterans without SMI after referral to the RN CDE/PharmD clinic. The following secondary objectives also were investigated: the difference in the percentage of veterans with glycemic relapse after the intervention in veterans with SMI compared with that of veterans without SMI, and the difference in time to glycemic relapse between veterans with SMI compared with that of veterans without SMI. Serious mental illness was defined as schizophrenia, schizoaffective disorder, bipolar disorder, and MDD and identified in CPRS using ICD-9 and ICD-10 codes, medicine progress notes, and psychiatry progress notes. Glycemic relapse was defined as > 1% increase in HbA1c from the lowest HbA1c within 1 year of being followed by the RN CDE/PharmD clinic (nadir).
Veterans were included in the study if they were aged ≥ 18 years, referred to the RN CDE/PharmD clinic from January 1, 2011 to December 31, 2014, had at least 2 clinic visits, an HbA1c > 8% at the date of the first clinic visit, and at least 1 HbA1c test at baseline and 1 HbA1c test at least 2 months after referral to the clinic. Veterans were excluded from the study if they met the following criteria: diagnosed with SMI during the study period, followed by the RN CDE or PharmD in other primary care clinics prior to referral, followed by the PharmD clinic within 365 days after the initial RN CDE/PharmD clinic visit, referred to or followed by endocrinology, or veterans enrolled in a VA DM research trial. Veterans continued to be enrolled until target enrollment was met.
Medical records were reviewed to capture the following information: demographics (age and gender), type of SMI, date diagnosed with SMI, number of mental health-related visits, antidepressant and antipsychotic use, HbA1c prior to referral to the RN CDE/PharmD clinic (initial HbA1c) and date, HbA1c nadir and date, and highest postnadir HbA1c (glycemic relapse) and date, number of clinic visits, time followed by the clinic, and reason for glycemic relapse.
A total sample size of 100 veterans was needed to determine a medium effect size of 0.25 for between-group treatment effect on veterans with SMI compared with that of veterans without SMI, using a 2-group by 2 time-point repeated measures analysis of variance (ANOVA) with a power of 80% and alpha of 0.05. Of the 100 veterans, 50 veterans in each group were necessary to meet power. The percentage change in mean HbA1c from the initial time point to nadir was analyzed using a 2-time point by 2-group repeated measures ANOVA analysis. The secondary objectives were analyzed using descriptive statistics, a repeated measures ANOVA test to determine the percentage change in mean HbA1c from nadir to relapse, and an independent samples Student t test to analyze time to glycemic relapse.
Results
Discussion
Collaborative interventions have improved glycemic control in patients with concurrent SMI and DM. Although there was not a significant difference in mean HbA1c from the initial HbA1c to the nadir HbA1c between study groups, this study provided valuable insight for the RN CDE/PharmD clinic. The mean HbA1c decreased over time in both study groups, demonstrating that the collaborative intervention was effective in improving glycemic control in veterans with SMI and veterans without SMI. The mean HbA1c decrease in the SMI group was slightly higher compared with that of the non-SMI group, but the difference was not significant. The decrease in mean HbA1c also demonstrated that the RN CDE/PharmD interventions were effective in each group. Contrary to this study’s hypothesis that veterans with SMI would have worse glycemic control compared with that of veterans without SMI, this study demonstrated that there was no difference in glycemic control between groups.
Veterans in the SMI group had a significantly greater percentage increase in mean HbA1c postnadir, indicating that their glycemic control worsened postnadir compared with that of the non-SMI group. If veterans with SMI relapsed, they tended to relapse to a greater extent compared with veterans without SMI, as indicated by a larger percentage increase in mean HbA1c. Time to relapse was shorter in veterans with SMI compared with that of veterans without SMI, but the difference was not significant. Using the information gathered, if veterans with SMI relapsed, they tended to relapse sooner and with a greater percentage increase in HbA1c compared with that of veterans without SMI.
Limitations
As a retrospective study, data collection was limited to the information found in the veteran’s EMR: Data collected were dependent on accurate and comprehensive documentation in the veteran’s problem list and progress notes. Additionally, the time between HbA1c tests was not analyzed when determining the differences in mean HbA1c. These data may be helpful in identifying reasons for glycemic relapse. Glycemic relapse depended on the number of HbA1c tests that the veteran completed. Time to glycemic relapse may occur sooner in veterans who completed more frequent HbA1c testing.
Conclusion
There was a significant decrease in mean HbA1c for the entire group over time. In comparing the percentage change in mean HbA1c between groups, there was not a significant difference in the decrease in mean HbA1c from initial to nadir HbA1c in veterans with SMI compared with that of veterans without SMI. However, veterans with SMI had a significantly larger increase in HbA1c postnadir compared with that of veterans without SMI, indicating that support would likely be needed after the veteran achieves his or her HbA1c target. Strategies such as extending the follow-up time in the RN CDE/PharmD clinic, expanding collaborative services with behavioral medicine and psychiatry, additional shared medical appointments or support groups for veterans with DM and SMI, and health literacy assessments may need to be adapted to assist in maintaining glycemic control in veterans with concurrent SMI and DM.
Click here to read the digital edition.
1. Razzano LA, Cook JA, Yost C, et al. Factors associated with co-occurring medical conditions among adults with serious mental disorders. Schizophr Res. 2015;161(2-3):458-464.
2. Lustman PJ, Clouse RE. Depression in diabetic patients: the relationship between mood and glycemic control. J Diabetes Complications. 2005;19(2):113-122.
3. Lustman PJ, Griffith LS, Freeland KE, Clouse RE. The course of major depression in diabetes. Gen Hosp Psychiatry. 1997;19(2):138-143.
4. Cox DJ, Gonder-Fredrick L. Major developments in behavioral diabetes research. J Consult Clin Psychol. 1992;60(4):628-638.
5. Gavard JA, Lustman PJ, Clouse RE. Prevalence of depression in adults with diabetes. An epidemiological evaluation. Diabetes Care. 1993;16(8):1167-1178.
6. Dixon L, Weiden P, Delahanty J, et al. Prevalence and correlates of diabetes in national schizophrenia samples. Schizophr Bull. 2000;26(4):903-912.
7. Desai MM, Rosenheck RA, Druss BG, Perlin JB. Mental disorders and quality of diabetes care in the veterans health administration. Am J Psychiatry. 2002;159(9):1584-1590.
8. American Diabetes Association. Standards of medical care in diabetes—2017. Diabetes Care. 2017;40(suppl 1):S25-S43.
9. Bogner HR, Morales KH, Post EP, Bruce ML. Diabetes, depression, and death: a randomized controlled trial of a depression treatment program for older adults based in primary care (PROSPECT). Diabetes Care. 2007;30(12):3005-3010.
10. Katon WJ, Lin EHB, Von Korff M, et al. Collaborative care for patients with depression and chronic illnesses. N Engl J Med. 2010;363(27):2611-2620.
11. Bogner HR, Morales KH, de Vries HF, Cappola AR. Integrated management of type 2 diabetes mellitus and depression treatment to improve medication adherence: a randomized controlled trial. Ann Fam Med. 2012;10(1):15-22.
12. Huang Y, Wei X, Wu T, Chen R, Guo A. Collaborative care for patients with depression and diabetes mellitus: a systematic review and meta-analysis. BMC Psychiatry. 2013;13(260):1-11.
13. James A, Leciejewski K, Pascuzzi K. Effect on diabetes care by a nurse certified diabetes educator with pharmacist support within a primary care clinic in a Veterans Affairs hospital. Abstract presented at: Ohio College of Clinical Pharmacy Spring Meeting; May 29, 2015; Cleveland, Ohio.
Diabetes mellitus (DM) is considered one of the most psychologically and behaviorally demanding chronic medical conditions. Patients with DM and serious mental illness (SMI), including schizophrenia, schizoaffective disorder, major depressive disorder (MDD), and bipolar disorder, are more likely to have poor adherence to medications as well as poor adherence to diet and lifestyle recommendations, which can lead to poor glycemic control, decreased quality of life, and increased health care expenses.1-4 Up to 27% of patients with DM have a depression diagnosis, and up to 60% of patients with DM experience depressive symptoms.5 Additionally, 1 in 4 patients with schizophrenia have a DM diagnosis.6 Serious mental illness can compromise DM self-management and glycemic control, which increases the risk of DM-related complications.7
These factors combine to make DM self-management essential for optimal glycemic control and prevention of DM-related complications. The American Diabetes Association recommends coordinated management of DM and SMI to achieve DM treatment targets.8 Interventions involving collaborative care teams have assisted in managing patients with concurrent SMI and DM. Collaborative interventions have reduced all-cause mortality, increased the number of patients reaching hemoglobin A1c (HbA1c) targets, increased overall improvement in HbA1c, increased rates of depression remission, and increased medication adherence.9-12
Background
Collaborative interventions have improved glycemic control in patients with concurrent SMI and DM. A study by Desai and colleagues examined the relationship between psychiatric disorders and the quality of DM care in a national sample of veterans.7 Data were collected using chart-abstracted quality data from administrative database records for a sample of veterans with DM who had at least 3 outpatient visits in the previous year (n = 38,020). About 25% of the sample had a diagnosed psychiatric disorder, 91.5% of veterans completed an HbA1c test, and most veterans with a psychiatric disorder completed the 5 quality indicators for DM care (foot inspection, HbA1c determination, pedal pulses examination, foot sensory examination, and retina examination). Veterans with psychiatric disorders did not have a poorer quality of care for secondary prevention of DM compared with that of other veterans.7
In the PROSPECT study (Prevention of Suicide in Primary Care Elderly: Collaborative Trial), a primary care-based depression management program assessed a collaborative intervention to improve care in patients with depression and DM.9 Fifteen depression care managers, including trained social workers, registered nurses (RNs), and psychologists, collaborated with primary care physicians (PCPs) to assist in recognizing depression, offer guideline-based treatment recommendations, and provide algorithm-based care, monitoring, and follow-up. After a median follow-up of 52 months, patients with depression and DM in the intervention group were less likely to die during the 5-year follow-up period compared with those in usual care (adjusted hazard ratio, 0.49 [95% confidence interval (CI), 0.24-0.98]). The study authors concluded that integrated depression care management significantly reduced all-cause mortality in patients with depression and DM.9
A single-blind, randomized, controlled trial conducted by Katon and colleagues examined patients with poorly controlled DM, coronary artery disease (CAD), or both, and concurrent depression in 14 primary care clinics (n = 214).10 The intervention consisted of nurse care managers who were trained RNs with experience in DM education and supervised by PCPs, providing guideline-based, collaborative care over 12 months to improve glycemic control, blood pressure (BP), and lipid control. The nurse care managers followed up with patients every 2 to3 weeks at office visits, and the intervention was compared with usual care by a physician. At 12 months, patients in the intervention group had significant improvement in HbA1c, low-density lipoprotein cholesterol, systolic BP, and depression compared with that of those under usual care. At 12 months, the HbA1c in the patients in the intervention group was significantly improved with an overall percentage change of 0.81 compared with 0.23 in the usual care group (estimated between-group difference, -0.56 [95% CI, -0.85 to -0.27]). The study authors concluded that integrated management and proactive follow-up of medical and psychological illnesses improved both medical outcomes and depression in patients with DM, CAD, or both.10
Another study by Bogner and colleagues investigated an integrated care intervention for patients with depression and DM to improve adherence to antidepressant and antidiabetic medications, glycemic control, and depression remission.11 Two trained research coordinators (a bachelor’s level and a master’s level) administered all intervention activities. The integrated care managers collaborated with physicians, offering education and guideline-based treatment recommendations to patients to monitor medication adherence and clinical status. The intervention supplemented regular primary care follow-up visits and was compared with usual care. At 12 weeks, patients in the integrated care group were more likely to achieve an HbA1c < 7% (60.9% vs 35.7%; P < .001) and remission of depression (58.7% vs 30.7%; P < .001) compared with those in usual care. There also was a significant improvement in adherence to DM and antidepressant medications in the intervention group compared with those in usual care during the study period.11
A systematic review and meta-analysis by Huang and colleagues assessed randomized controlled trials of collaborative care for diabetic patients with depression.12 Trials that reported depression treatment response, depression remission, HbA1c values, and adherence to antidepressant and/or hypoglycemic medications were included. A total of 8 trials randomized 2,238 patients with concurrent depression and DM and compared collaborative care with usual care. Collaborative care was associated with a significant increase in depression treatment response, reduction in HbA1c, and significant improvement in adherence rates for antidepressant and hypoglycemic medications compared with that of usual care. A reduction in HbA1c favored the collaborative care group; however, this reduction was not significant (mean difference, -0.13 [95% CI, -0.46 to 0.19]; P = .08 for heterogeneity; I2 = 51%). The study authors concluded that a collaborative care model significantly improved depression outcomes and adherence to medications in patients with concurrent DM and depression and recommended continued collaborative care for this population.12
Methods
The current study examines a novel service involving the collaboration of a registered nurse-certified DM educator (RN CDE) and clinical pharmacy specialist (PharmD) to improve access to care and maximize DM outcomes. The Louis Stokes Cleveland VAMC defines the PharmD scope of practice. One of the pharmacist’s clinical obligations includes serving as a preceptor for the RN CDE, a collaboration that has not been investigated in previous studies. A primary care provider (PCP) refers veterans to the RN CDE/PharmD clinic, with HbA1c ≥ 8%.
The RN CDE/PharmD clinic tends to receive referrals for the most challenging veterans who may have very elevated HbA1c readings, complex multidrug regimens, or basal/bolus insulin regimens. The RN CDE sees veterans in individual appointments and takes manual BP readings, checks point-of-care glucose/HbA1c readings, downloads home glucometer results into the electronic medical record (EMR), and provides education on DM management specific to the veteran’s individual needs. Because there is no established treatment algorithm for the RN CDE to follow, all medication changes are determined by a preceptor in real time.
When the RN CDE clinic was established, the RN CDE presented veterans to their PCP who determined the veteran’s plan of care. However, this plan was frustrating for the RN CDE because the PCP was not always readily available, causing delays in the workflow of the RN CDE clinic. Since the PharmD has a scope of practice and is more frequently available to discuss veteran cases, RN CDE/PharmD collaboration was initiated. Based on information gathered during the appointment, medication additions, titrations, and changes are precepted with the PharmD. Veterans can be seen in clinic every 2 to 4 weeks, allowing for continued medication adjustments if warranted until their HbA1c target is achieved. Veterans are discharged to their PCP once their HbA1c is at target.
Within the primary care clinic, this service was compared with usual care by a PCP and was associated with a clinically significant reduction in HbA1c by 2.5% compared with usual care after 1 year (P < .001).13 The same study population was investigated to determine whether there was a difference in glycemic control between veterans with SMI compared with veterans without SMI (non-SMI) to provide insight and better support for veterans with SMI and their DM care.
A retrospective review of the veterans referred to the RN CDE/PharmD clinic from January 1, 2011 to December 31, 2014 was performed with institutional review board approval. Veterans were identified using a pharmacy-generated list searching for clinic note titles from the Computerized Patient Record System (CPRS).
The primary objective of this study was to determine the percentage change in mean HbA1c in veterans with SMI compared with that of veterans without SMI after referral to the RN CDE/PharmD clinic. The following secondary objectives also were investigated: the difference in the percentage of veterans with glycemic relapse after the intervention in veterans with SMI compared with that of veterans without SMI, and the difference in time to glycemic relapse between veterans with SMI compared with that of veterans without SMI. Serious mental illness was defined as schizophrenia, schizoaffective disorder, bipolar disorder, and MDD and identified in CPRS using ICD-9 and ICD-10 codes, medicine progress notes, and psychiatry progress notes. Glycemic relapse was defined as > 1% increase in HbA1c from the lowest HbA1c within 1 year of being followed by the RN CDE/PharmD clinic (nadir).
Veterans were included in the study if they were aged ≥ 18 years, referred to the RN CDE/PharmD clinic from January 1, 2011 to December 31, 2014, had at least 2 clinic visits, an HbA1c > 8% at the date of the first clinic visit, and at least 1 HbA1c test at baseline and 1 HbA1c test at least 2 months after referral to the clinic. Veterans were excluded from the study if they met the following criteria: diagnosed with SMI during the study period, followed by the RN CDE or PharmD in other primary care clinics prior to referral, followed by the PharmD clinic within 365 days after the initial RN CDE/PharmD clinic visit, referred to or followed by endocrinology, or veterans enrolled in a VA DM research trial. Veterans continued to be enrolled until target enrollment was met.
Medical records were reviewed to capture the following information: demographics (age and gender), type of SMI, date diagnosed with SMI, number of mental health-related visits, antidepressant and antipsychotic use, HbA1c prior to referral to the RN CDE/PharmD clinic (initial HbA1c) and date, HbA1c nadir and date, and highest postnadir HbA1c (glycemic relapse) and date, number of clinic visits, time followed by the clinic, and reason for glycemic relapse.
A total sample size of 100 veterans was needed to determine a medium effect size of 0.25 for between-group treatment effect on veterans with SMI compared with that of veterans without SMI, using a 2-group by 2 time-point repeated measures analysis of variance (ANOVA) with a power of 80% and alpha of 0.05. Of the 100 veterans, 50 veterans in each group were necessary to meet power. The percentage change in mean HbA1c from the initial time point to nadir was analyzed using a 2-time point by 2-group repeated measures ANOVA analysis. The secondary objectives were analyzed using descriptive statistics, a repeated measures ANOVA test to determine the percentage change in mean HbA1c from nadir to relapse, and an independent samples Student t test to analyze time to glycemic relapse.
Results
Discussion
Collaborative interventions have improved glycemic control in patients with concurrent SMI and DM. Although there was not a significant difference in mean HbA1c from the initial HbA1c to the nadir HbA1c between study groups, this study provided valuable insight for the RN CDE/PharmD clinic. The mean HbA1c decreased over time in both study groups, demonstrating that the collaborative intervention was effective in improving glycemic control in veterans with SMI and veterans without SMI. The mean HbA1c decrease in the SMI group was slightly higher compared with that of the non-SMI group, but the difference was not significant. The decrease in mean HbA1c also demonstrated that the RN CDE/PharmD interventions were effective in each group. Contrary to this study’s hypothesis that veterans with SMI would have worse glycemic control compared with that of veterans without SMI, this study demonstrated that there was no difference in glycemic control between groups.
Veterans in the SMI group had a significantly greater percentage increase in mean HbA1c postnadir, indicating that their glycemic control worsened postnadir compared with that of the non-SMI group. If veterans with SMI relapsed, they tended to relapse to a greater extent compared with veterans without SMI, as indicated by a larger percentage increase in mean HbA1c. Time to relapse was shorter in veterans with SMI compared with that of veterans without SMI, but the difference was not significant. Using the information gathered, if veterans with SMI relapsed, they tended to relapse sooner and with a greater percentage increase in HbA1c compared with that of veterans without SMI.
Limitations
As a retrospective study, data collection was limited to the information found in the veteran’s EMR: Data collected were dependent on accurate and comprehensive documentation in the veteran’s problem list and progress notes. Additionally, the time between HbA1c tests was not analyzed when determining the differences in mean HbA1c. These data may be helpful in identifying reasons for glycemic relapse. Glycemic relapse depended on the number of HbA1c tests that the veteran completed. Time to glycemic relapse may occur sooner in veterans who completed more frequent HbA1c testing.
Conclusion
There was a significant decrease in mean HbA1c for the entire group over time. In comparing the percentage change in mean HbA1c between groups, there was not a significant difference in the decrease in mean HbA1c from initial to nadir HbA1c in veterans with SMI compared with that of veterans without SMI. However, veterans with SMI had a significantly larger increase in HbA1c postnadir compared with that of veterans without SMI, indicating that support would likely be needed after the veteran achieves his or her HbA1c target. Strategies such as extending the follow-up time in the RN CDE/PharmD clinic, expanding collaborative services with behavioral medicine and psychiatry, additional shared medical appointments or support groups for veterans with DM and SMI, and health literacy assessments may need to be adapted to assist in maintaining glycemic control in veterans with concurrent SMI and DM.
Click here to read the digital edition.
Diabetes mellitus (DM) is considered one of the most psychologically and behaviorally demanding chronic medical conditions. Patients with DM and serious mental illness (SMI), including schizophrenia, schizoaffective disorder, major depressive disorder (MDD), and bipolar disorder, are more likely to have poor adherence to medications as well as poor adherence to diet and lifestyle recommendations, which can lead to poor glycemic control, decreased quality of life, and increased health care expenses.1-4 Up to 27% of patients with DM have a depression diagnosis, and up to 60% of patients with DM experience depressive symptoms.5 Additionally, 1 in 4 patients with schizophrenia have a DM diagnosis.6 Serious mental illness can compromise DM self-management and glycemic control, which increases the risk of DM-related complications.7
These factors combine to make DM self-management essential for optimal glycemic control and prevention of DM-related complications. The American Diabetes Association recommends coordinated management of DM and SMI to achieve DM treatment targets.8 Interventions involving collaborative care teams have assisted in managing patients with concurrent SMI and DM. Collaborative interventions have reduced all-cause mortality, increased the number of patients reaching hemoglobin A1c (HbA1c) targets, increased overall improvement in HbA1c, increased rates of depression remission, and increased medication adherence.9-12
Background
Collaborative interventions have improved glycemic control in patients with concurrent SMI and DM. A study by Desai and colleagues examined the relationship between psychiatric disorders and the quality of DM care in a national sample of veterans.7 Data were collected using chart-abstracted quality data from administrative database records for a sample of veterans with DM who had at least 3 outpatient visits in the previous year (n = 38,020). About 25% of the sample had a diagnosed psychiatric disorder, 91.5% of veterans completed an HbA1c test, and most veterans with a psychiatric disorder completed the 5 quality indicators for DM care (foot inspection, HbA1c determination, pedal pulses examination, foot sensory examination, and retina examination). Veterans with psychiatric disorders did not have a poorer quality of care for secondary prevention of DM compared with that of other veterans.7
In the PROSPECT study (Prevention of Suicide in Primary Care Elderly: Collaborative Trial), a primary care-based depression management program assessed a collaborative intervention to improve care in patients with depression and DM.9 Fifteen depression care managers, including trained social workers, registered nurses (RNs), and psychologists, collaborated with primary care physicians (PCPs) to assist in recognizing depression, offer guideline-based treatment recommendations, and provide algorithm-based care, monitoring, and follow-up. After a median follow-up of 52 months, patients with depression and DM in the intervention group were less likely to die during the 5-year follow-up period compared with those in usual care (adjusted hazard ratio, 0.49 [95% confidence interval (CI), 0.24-0.98]). The study authors concluded that integrated depression care management significantly reduced all-cause mortality in patients with depression and DM.9
A single-blind, randomized, controlled trial conducted by Katon and colleagues examined patients with poorly controlled DM, coronary artery disease (CAD), or both, and concurrent depression in 14 primary care clinics (n = 214).10 The intervention consisted of nurse care managers who were trained RNs with experience in DM education and supervised by PCPs, providing guideline-based, collaborative care over 12 months to improve glycemic control, blood pressure (BP), and lipid control. The nurse care managers followed up with patients every 2 to3 weeks at office visits, and the intervention was compared with usual care by a physician. At 12 months, patients in the intervention group had significant improvement in HbA1c, low-density lipoprotein cholesterol, systolic BP, and depression compared with that of those under usual care. At 12 months, the HbA1c in the patients in the intervention group was significantly improved with an overall percentage change of 0.81 compared with 0.23 in the usual care group (estimated between-group difference, -0.56 [95% CI, -0.85 to -0.27]). The study authors concluded that integrated management and proactive follow-up of medical and psychological illnesses improved both medical outcomes and depression in patients with DM, CAD, or both.10
Another study by Bogner and colleagues investigated an integrated care intervention for patients with depression and DM to improve adherence to antidepressant and antidiabetic medications, glycemic control, and depression remission.11 Two trained research coordinators (a bachelor’s level and a master’s level) administered all intervention activities. The integrated care managers collaborated with physicians, offering education and guideline-based treatment recommendations to patients to monitor medication adherence and clinical status. The intervention supplemented regular primary care follow-up visits and was compared with usual care. At 12 weeks, patients in the integrated care group were more likely to achieve an HbA1c < 7% (60.9% vs 35.7%; P < .001) and remission of depression (58.7% vs 30.7%; P < .001) compared with those in usual care. There also was a significant improvement in adherence to DM and antidepressant medications in the intervention group compared with those in usual care during the study period.11
A systematic review and meta-analysis by Huang and colleagues assessed randomized controlled trials of collaborative care for diabetic patients with depression.12 Trials that reported depression treatment response, depression remission, HbA1c values, and adherence to antidepressant and/or hypoglycemic medications were included. A total of 8 trials randomized 2,238 patients with concurrent depression and DM and compared collaborative care with usual care. Collaborative care was associated with a significant increase in depression treatment response, reduction in HbA1c, and significant improvement in adherence rates for antidepressant and hypoglycemic medications compared with that of usual care. A reduction in HbA1c favored the collaborative care group; however, this reduction was not significant (mean difference, -0.13 [95% CI, -0.46 to 0.19]; P = .08 for heterogeneity; I2 = 51%). The study authors concluded that a collaborative care model significantly improved depression outcomes and adherence to medications in patients with concurrent DM and depression and recommended continued collaborative care for this population.12
Methods
The current study examines a novel service involving the collaboration of a registered nurse-certified DM educator (RN CDE) and clinical pharmacy specialist (PharmD) to improve access to care and maximize DM outcomes. The Louis Stokes Cleveland VAMC defines the PharmD scope of practice. One of the pharmacist’s clinical obligations includes serving as a preceptor for the RN CDE, a collaboration that has not been investigated in previous studies. A primary care provider (PCP) refers veterans to the RN CDE/PharmD clinic, with HbA1c ≥ 8%.
The RN CDE/PharmD clinic tends to receive referrals for the most challenging veterans who may have very elevated HbA1c readings, complex multidrug regimens, or basal/bolus insulin regimens. The RN CDE sees veterans in individual appointments and takes manual BP readings, checks point-of-care glucose/HbA1c readings, downloads home glucometer results into the electronic medical record (EMR), and provides education on DM management specific to the veteran’s individual needs. Because there is no established treatment algorithm for the RN CDE to follow, all medication changes are determined by a preceptor in real time.
When the RN CDE clinic was established, the RN CDE presented veterans to their PCP who determined the veteran’s plan of care. However, this plan was frustrating for the RN CDE because the PCP was not always readily available, causing delays in the workflow of the RN CDE clinic. Since the PharmD has a scope of practice and is more frequently available to discuss veteran cases, RN CDE/PharmD collaboration was initiated. Based on information gathered during the appointment, medication additions, titrations, and changes are precepted with the PharmD. Veterans can be seen in clinic every 2 to 4 weeks, allowing for continued medication adjustments if warranted until their HbA1c target is achieved. Veterans are discharged to their PCP once their HbA1c is at target.
Within the primary care clinic, this service was compared with usual care by a PCP and was associated with a clinically significant reduction in HbA1c by 2.5% compared with usual care after 1 year (P < .001).13 The same study population was investigated to determine whether there was a difference in glycemic control between veterans with SMI compared with veterans without SMI (non-SMI) to provide insight and better support for veterans with SMI and their DM care.
A retrospective review of the veterans referred to the RN CDE/PharmD clinic from January 1, 2011 to December 31, 2014 was performed with institutional review board approval. Veterans were identified using a pharmacy-generated list searching for clinic note titles from the Computerized Patient Record System (CPRS).
The primary objective of this study was to determine the percentage change in mean HbA1c in veterans with SMI compared with that of veterans without SMI after referral to the RN CDE/PharmD clinic. The following secondary objectives also were investigated: the difference in the percentage of veterans with glycemic relapse after the intervention in veterans with SMI compared with that of veterans without SMI, and the difference in time to glycemic relapse between veterans with SMI compared with that of veterans without SMI. Serious mental illness was defined as schizophrenia, schizoaffective disorder, bipolar disorder, and MDD and identified in CPRS using ICD-9 and ICD-10 codes, medicine progress notes, and psychiatry progress notes. Glycemic relapse was defined as > 1% increase in HbA1c from the lowest HbA1c within 1 year of being followed by the RN CDE/PharmD clinic (nadir).
Veterans were included in the study if they were aged ≥ 18 years, referred to the RN CDE/PharmD clinic from January 1, 2011 to December 31, 2014, had at least 2 clinic visits, an HbA1c > 8% at the date of the first clinic visit, and at least 1 HbA1c test at baseline and 1 HbA1c test at least 2 months after referral to the clinic. Veterans were excluded from the study if they met the following criteria: diagnosed with SMI during the study period, followed by the RN CDE or PharmD in other primary care clinics prior to referral, followed by the PharmD clinic within 365 days after the initial RN CDE/PharmD clinic visit, referred to or followed by endocrinology, or veterans enrolled in a VA DM research trial. Veterans continued to be enrolled until target enrollment was met.
Medical records were reviewed to capture the following information: demographics (age and gender), type of SMI, date diagnosed with SMI, number of mental health-related visits, antidepressant and antipsychotic use, HbA1c prior to referral to the RN CDE/PharmD clinic (initial HbA1c) and date, HbA1c nadir and date, and highest postnadir HbA1c (glycemic relapse) and date, number of clinic visits, time followed by the clinic, and reason for glycemic relapse.
A total sample size of 100 veterans was needed to determine a medium effect size of 0.25 for between-group treatment effect on veterans with SMI compared with that of veterans without SMI, using a 2-group by 2 time-point repeated measures analysis of variance (ANOVA) with a power of 80% and alpha of 0.05. Of the 100 veterans, 50 veterans in each group were necessary to meet power. The percentage change in mean HbA1c from the initial time point to nadir was analyzed using a 2-time point by 2-group repeated measures ANOVA analysis. The secondary objectives were analyzed using descriptive statistics, a repeated measures ANOVA test to determine the percentage change in mean HbA1c from nadir to relapse, and an independent samples Student t test to analyze time to glycemic relapse.
Results
Discussion
Collaborative interventions have improved glycemic control in patients with concurrent SMI and DM. Although there was not a significant difference in mean HbA1c from the initial HbA1c to the nadir HbA1c between study groups, this study provided valuable insight for the RN CDE/PharmD clinic. The mean HbA1c decreased over time in both study groups, demonstrating that the collaborative intervention was effective in improving glycemic control in veterans with SMI and veterans without SMI. The mean HbA1c decrease in the SMI group was slightly higher compared with that of the non-SMI group, but the difference was not significant. The decrease in mean HbA1c also demonstrated that the RN CDE/PharmD interventions were effective in each group. Contrary to this study’s hypothesis that veterans with SMI would have worse glycemic control compared with that of veterans without SMI, this study demonstrated that there was no difference in glycemic control between groups.
Veterans in the SMI group had a significantly greater percentage increase in mean HbA1c postnadir, indicating that their glycemic control worsened postnadir compared with that of the non-SMI group. If veterans with SMI relapsed, they tended to relapse to a greater extent compared with veterans without SMI, as indicated by a larger percentage increase in mean HbA1c. Time to relapse was shorter in veterans with SMI compared with that of veterans without SMI, but the difference was not significant. Using the information gathered, if veterans with SMI relapsed, they tended to relapse sooner and with a greater percentage increase in HbA1c compared with that of veterans without SMI.
Limitations
As a retrospective study, data collection was limited to the information found in the veteran’s EMR: Data collected were dependent on accurate and comprehensive documentation in the veteran’s problem list and progress notes. Additionally, the time between HbA1c tests was not analyzed when determining the differences in mean HbA1c. These data may be helpful in identifying reasons for glycemic relapse. Glycemic relapse depended on the number of HbA1c tests that the veteran completed. Time to glycemic relapse may occur sooner in veterans who completed more frequent HbA1c testing.
Conclusion
There was a significant decrease in mean HbA1c for the entire group over time. In comparing the percentage change in mean HbA1c between groups, there was not a significant difference in the decrease in mean HbA1c from initial to nadir HbA1c in veterans with SMI compared with that of veterans without SMI. However, veterans with SMI had a significantly larger increase in HbA1c postnadir compared with that of veterans without SMI, indicating that support would likely be needed after the veteran achieves his or her HbA1c target. Strategies such as extending the follow-up time in the RN CDE/PharmD clinic, expanding collaborative services with behavioral medicine and psychiatry, additional shared medical appointments or support groups for veterans with DM and SMI, and health literacy assessments may need to be adapted to assist in maintaining glycemic control in veterans with concurrent SMI and DM.
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1. Razzano LA, Cook JA, Yost C, et al. Factors associated with co-occurring medical conditions among adults with serious mental disorders. Schizophr Res. 2015;161(2-3):458-464.
2. Lustman PJ, Clouse RE. Depression in diabetic patients: the relationship between mood and glycemic control. J Diabetes Complications. 2005;19(2):113-122.
3. Lustman PJ, Griffith LS, Freeland KE, Clouse RE. The course of major depression in diabetes. Gen Hosp Psychiatry. 1997;19(2):138-143.
4. Cox DJ, Gonder-Fredrick L. Major developments in behavioral diabetes research. J Consult Clin Psychol. 1992;60(4):628-638.
5. Gavard JA, Lustman PJ, Clouse RE. Prevalence of depression in adults with diabetes. An epidemiological evaluation. Diabetes Care. 1993;16(8):1167-1178.
6. Dixon L, Weiden P, Delahanty J, et al. Prevalence and correlates of diabetes in national schizophrenia samples. Schizophr Bull. 2000;26(4):903-912.
7. Desai MM, Rosenheck RA, Druss BG, Perlin JB. Mental disorders and quality of diabetes care in the veterans health administration. Am J Psychiatry. 2002;159(9):1584-1590.
8. American Diabetes Association. Standards of medical care in diabetes—2017. Diabetes Care. 2017;40(suppl 1):S25-S43.
9. Bogner HR, Morales KH, Post EP, Bruce ML. Diabetes, depression, and death: a randomized controlled trial of a depression treatment program for older adults based in primary care (PROSPECT). Diabetes Care. 2007;30(12):3005-3010.
10. Katon WJ, Lin EHB, Von Korff M, et al. Collaborative care for patients with depression and chronic illnesses. N Engl J Med. 2010;363(27):2611-2620.
11. Bogner HR, Morales KH, de Vries HF, Cappola AR. Integrated management of type 2 diabetes mellitus and depression treatment to improve medication adherence: a randomized controlled trial. Ann Fam Med. 2012;10(1):15-22.
12. Huang Y, Wei X, Wu T, Chen R, Guo A. Collaborative care for patients with depression and diabetes mellitus: a systematic review and meta-analysis. BMC Psychiatry. 2013;13(260):1-11.
13. James A, Leciejewski K, Pascuzzi K. Effect on diabetes care by a nurse certified diabetes educator with pharmacist support within a primary care clinic in a Veterans Affairs hospital. Abstract presented at: Ohio College of Clinical Pharmacy Spring Meeting; May 29, 2015; Cleveland, Ohio.
1. Razzano LA, Cook JA, Yost C, et al. Factors associated with co-occurring medical conditions among adults with serious mental disorders. Schizophr Res. 2015;161(2-3):458-464.
2. Lustman PJ, Clouse RE. Depression in diabetic patients: the relationship between mood and glycemic control. J Diabetes Complications. 2005;19(2):113-122.
3. Lustman PJ, Griffith LS, Freeland KE, Clouse RE. The course of major depression in diabetes. Gen Hosp Psychiatry. 1997;19(2):138-143.
4. Cox DJ, Gonder-Fredrick L. Major developments in behavioral diabetes research. J Consult Clin Psychol. 1992;60(4):628-638.
5. Gavard JA, Lustman PJ, Clouse RE. Prevalence of depression in adults with diabetes. An epidemiological evaluation. Diabetes Care. 1993;16(8):1167-1178.
6. Dixon L, Weiden P, Delahanty J, et al. Prevalence and correlates of diabetes in national schizophrenia samples. Schizophr Bull. 2000;26(4):903-912.
7. Desai MM, Rosenheck RA, Druss BG, Perlin JB. Mental disorders and quality of diabetes care in the veterans health administration. Am J Psychiatry. 2002;159(9):1584-1590.
8. American Diabetes Association. Standards of medical care in diabetes—2017. Diabetes Care. 2017;40(suppl 1):S25-S43.
9. Bogner HR, Morales KH, Post EP, Bruce ML. Diabetes, depression, and death: a randomized controlled trial of a depression treatment program for older adults based in primary care (PROSPECT). Diabetes Care. 2007;30(12):3005-3010.
10. Katon WJ, Lin EHB, Von Korff M, et al. Collaborative care for patients with depression and chronic illnesses. N Engl J Med. 2010;363(27):2611-2620.
11. Bogner HR, Morales KH, de Vries HF, Cappola AR. Integrated management of type 2 diabetes mellitus and depression treatment to improve medication adherence: a randomized controlled trial. Ann Fam Med. 2012;10(1):15-22.
12. Huang Y, Wei X, Wu T, Chen R, Guo A. Collaborative care for patients with depression and diabetes mellitus: a systematic review and meta-analysis. BMC Psychiatry. 2013;13(260):1-11.
13. James A, Leciejewski K, Pascuzzi K. Effect on diabetes care by a nurse certified diabetes educator with pharmacist support within a primary care clinic in a Veterans Affairs hospital. Abstract presented at: Ohio College of Clinical Pharmacy Spring Meeting; May 29, 2015; Cleveland, Ohio.
Bolus Insulin Prescribing Recommendations for Patients With Type 2 Diabetes Mellitus (FULL)
Individuals with type 2 diabetes mellitus (T2DM) spend between 5 and 10 years with elevated hemoglobin A1c (HbA1c) before initiation of insulin.1 Once the basal insulin is initiated, the patient can go years with only adjustment of the basal insulin, resulting in over-basalization. In general, the total daily dose (TDD) of insulin should be composed of about 50% basal “background” insulin and 50% bolus “meal” insulin. When the fasting glucose readings are on target but HbA1c is still above the mutually set goal range, postprandial readings need to be evaluated.
This article focuses on initiating and titrating bolus insulin in nonpregnant patients with T2DM. Before initiation of bolus insulin, it is important for the patient to be actively engaged with a diabetes educator for diabetes self-management education and support (DSME/S), including the understanding of the correct use of insulin, carbohydrate counting, and increasing physical activities. Ensuring the correct technique of insulin administration and self-monitoring of blood glucose (SMBG) is critical. A knowledge deficit of carbohydrate information can lead to uncontrolled blood glucose (BG). The authors have encountered numerous times when patients were drinking sugary beverages or consuming large amounts of “healthy” food without realizing the carbohydrate content. Therefore, treatment in concert with a registered dietitian and certified diabetes educator is highly recommended.
Initiation of Bolus Insulin
There are 3 options of postprandial coverage with bolus insulin when a patient is taking basal insulin: basal plus, basal-bolus, or premix insulin. A possible fourth option for postprandial coverage is to add glucagon-like peptide-1 receptor agonist (GLP-1 RA), an injectable noninsulin antihyperglycemic agent, which has shown noninferior efficacy to adding bolus insulin and a favorable effect on weight with less risk of hypoglycemia.2-5 Although it can be expensive, combining GLP-1 RA to basal insulin results in lowering HbA1c of 0.66 up to 1.74% (or lowering mmol/mol of 7 up to 19) from the baseline.6 However, adding bolus insulin may be the only option to avoid glucotoxicity and prevent further diabetes complications when the HbA1c level is well above the goal range. It is usually recommended to discontinue sulfonylurea when bolus insulin is added due to the β-cell exhaustion with advancing natural history of diabetes.7,8
Method 1: Basal Plus
The health care provider (HCP) needs to consider whether the patient is over-basalized when the HbAlc and postprandial BG readings are still not at goal despite careful titration of basal insulin dose to > 0.5 U/kg/d.9 This is the time to discuss with the patient the coverage of mealtime glucose excursions. In the basal plus regimen, the prescribing provider may add 1 bolus insulin injection for the meal with the highest amount of carbohydrates or add 2 bolus injections for the most and second most meals with carbohydrates. Multiple types of bolus insulin are available in the current U.S. market (Table 1).
There are 2 ways to add bolus insulin: fixed and flexible. In the fixed regimen, the patient will take the same amount of bolus insulin regardless of premeal BG readings and carbohydrate content of the food. The authors recommend adding bolus insulin of about 4 to 6 units once or twice a day with meals, depending on the number of meals a day, carbohydrate content of the meal, current and desired degree of diabetes control, and physical activities. Another way to calculate a bolus insulin dose is to start at 0.1 U/kg if adding to the basal insulin.10 Flexible regimen allows various bolus doses based on premeal BG, carbohydrate intake, and activities. Information on this regimen, will be discussed more later.
Patient Cases
Tables 2 and 3 describe 2 patient cases. For example, patient 1 weighs 80 kg. If the prescribing HCP and patient decide to add only 1 bolus to the largest carbohydrate meal at dinnertime, then the patient may take 8 units (80 kg × 0.1 U/kg/meal = 8 units for meal). The patient’s current insulin dose, medical comorbidities, current diabetes control status, living situations, and overall cognition also should be considered.
Imagine patient 1 is taking 14 units daily of a long-acting insulin (LAI). If the patient is taking a fairly low dose of LAI, has multiple comorbidities, recent BG log/HgAlc, and lives alone but demonstrates good cognition to follow instructions, the prescriber may consider adding the bolus insulin of 4 units for dinner; thus, the bolus dose is about one-third of total basal insulin dose. However, if the patient is on 40 units of LAI and has symptoms of hyperglycemia, 6 to 8 units for the dinner is reasonable. An important point to convey to this patient is to make sure there is carbohydrate consistency. The patient’s premeal BG was 137 mg/dL (7.6 mmol/L) on Monday, but it rose significantly to 313 mg/dL (17.4 mmol/L) after dinner. On Wednesday, the patient’s premeal BG prior to dinner was 150 mg/dL (8.3 mmol/L); it rose to 202 mg/dL (11.2 mmol/L) after dinner, which is high but not as high as on Monday.
Multiple factors may affect this variability; for example, on Monday the patient may have consumed more than the usual amount of carbohydrates for dinner, forgot to take oral medication for dinner, or missed his/her usual after-dinner walk. Or simply, the patient may have eaten a lot less than the usual amount of carbohydrates, walked the neighborhood, or vacuumed the entire house after dinner on Wednesday. Thus, it is imperative to carefully assess the patient’s lifestyle and recommend carbohydrate consistency at each meal.
Method 2: Basal-Bolus
When basal plus is insufficient to get the HbA1c and BG readings to goal, taking bolus insulin for all main meals containing carbohydrates must be considered. This is often called basal-bolus, multiple daily injections, or intensive insulin therapy.
In order to understand the concept of basal-bolus, HCPs should consider normal physiology. The pancreas releases a constant amount of insulin, aka background insulin, to cover glucose produced by the liver to the cells between meals. In addition, a burst of insulin, aka bolus insulin, to meet the blood glucose elevation from food to maintain homeostasis. In patients with T2DM, the relative amount produced by the pancreas is insufficient to meet the demand due to pancreatic exhaustion or insulin resistance. This necessitates the need to replace background and bolus insulin.7
The ideal final total bolus insulin amount (the sum of all meal bolus doses) should be about half the basal dosing. Calculation of starting bolus dosing can be done as in the basal plus regimen, either 4 to 6 units per meal or 0.1 U/kg/d.10 Alternatively, if the patient is on 60 units of long-acting analog and BGs are well above goal range, the prescriber could consider about 20 units of bolus dose (60 U divided by 3 meals) if the patient eats 3 routine meals a day with at least 30 g of carbohydrates, and physical activity levels are fairly consistent. If the patient eats the most carbohydrates at lunchtime, consider more bolus at lunch (ie, 18 U of bolus for breakfast and dinner and 24 U of bolus for lunch coverage).
Patients need to separate the time between the bolus doses, usually a minimum of 4 hours apart, to avoid insulin stacking, which is a common reason for hypoglycemia. Insulin stacking occurs when additional quick or rapid insulin is injected when the previous insulin is still in the body or when there is insulin on board.8,11 Typically, bolus analogs stay in the body for about 4 to 6 hours, thus necessitating separation of the doses at least 4 hours apart. Patients sometimes inject more bolus insulin after high postprandial readings, which can result in insulin stacking. In some cases, the patient may misunderstand and take mealtime insulin at a scheduled time instead of at the time of the meal.
Injecting bolus insulin for every snack must be avoided to prevent a vicious cycle: Postprandial hyperglycemia –> extra bolus insulin, resulting in insulin stacking –> hypoglycemia –> overtreatment with food –> hyperglycemia –> extra bolus insulin, resulting in insulin stacking –> and so on. Whenever there are readings in the hypoglycemic and hyperglycemic range, address hypoglycemia first because hyperglycemia often is due to overtreatment of hypoglycemia.
Method 3: Premix or Split-Mix (Patient-Mix) Insulin
Postprandial BG excursions can be minimized by changing basal insulin to premix or split-mix (patient-mix) insulin that has a mixture of mealtime and intermediate action insulin (Table 4).12-16 The use of premixed insulin is a viable option due to its ease of use and for those who have restrictions based on the complexity of the basal-bolus regimen.7 If a patient has routine meals and prefers not to carry around insulin for lunch, the schedule of premix insulin taken at breakfast and dinner is ideal.
Some caveats for safe prescribing should be understood. A recent summary of premixed insulin regimens noted that they seem to have a similar efficacy and safety profile compared with regimens that include basal insulin with or without mealtime insulin; however, cost and patient adherence are improved.17 It is important to monitor insulin-naïve patients for hypoglycemia and reduced efficacy when used twice daily compared with basal plus 3-times daily prandial insulin in patients needing insulin intensification.17
A randomized trial noted that hypoglycemia rates were twice as high with premixed insulin compared with basal-bolus insulin.18 This study also noted that the premixed insulin group experienced the highest dropout rate, partly due to hypoglycemia. A regimen of basal insulin with the option to add a single prandial insulin injection at the main meal was as effective in reducing HbA1c with less hypoglycemia. The premixed insulin is convenient but does not allow a separate correction of either mealtime or intermediate-acting insulin doses. If the premixed dose needs to be adjusted due to fasting hyperglycemia > 180 mg/dL(10.0 mmol/L), the TDD can be increased by 10%.2
In contrast, a split-mix (patient-mix) insulin regimen allows for the ability to vary the amount/ratio of combinations and adjustment of bolus and intermediate insulin doses. The disadvantages of split-mix insulin include the inconvenience of manually mixing of insulin and the potential for dosing errors. The patient needs to be taught additional steps on how to mix both insulins. Ensure the correct mixing order to maintain insulin potency; regular first, then neutral protamine Hagedorn (NPH). An HCP should remember the RN acronym if the patient is combining regular insulin and NPH. If there is doubt about the patient’s insulin injection technique, HCPs should ask the patient to demonstrate how to correctly pull up a dose of normal saline and inject it during a clinic visit. The only basal insulin that can be physically mixed with quick or rapid insulin is NPH. It should never be mixed with long-acting analogs. The patient should not even use the same syringe to draw up bolus analog insulin and inject it and then use the same syringe to draw up long-acting analog insulin.
One caveat to a fixed regimen (same amount of insulin dose) is that providers often expect that the patient will eat a consistent amount of carbohydrates at each meal and premeal glucose readings are fairly stable. Oftentimes, this is not true. If a patient took a bolus dose of 8 units of rapid-acting insulin and ate a 6 oz steak, 3 oz baked potato, steamed broccoli at a dinner; and no bread, the after dinner BG might register 145 mg/dL (8.1 mmol/L). Then, the next day for dinner, if he or she took the same amount of 8 units of rapid-acting insulin and ate 1 cup of spaghetti, ½ cup of spaghetti meat sauce, and 2 slices of garlic bread, the after dinner reading might be 322 mg/dL (17.9 mmol/L). The patient’s BG was higher on the second day because of the higher carbohydrate content of the meal. If the rapid-acting insulin was increased to 12 units of bolus based on the high carbohydrate meal and the patient ate a lower carbohydrate meal, hypoglycemia could ensue. Thus, it is important to work with the patient regarding the consumption of a consistent amount of carbohydrates and refer to a registered dietitian for carbohydrate consistency.
For the flexible regimen, the prescriber may consider using an insulin to carbohydrate (IC) ratio and sensitivity factor (SF), also called sliding scale or correction factor. The IC ratio represents how much insulin is needed to cover consumed carbohydrates. For instance, if the patient uses IC ratio of 1:15, 1 unit of bolus insulin will cover 15 g of carbohydrates. If the patient eats a meal with 60 g of carbohydrates and is using IC ratio of 1:15, the patient will inject 4 units of bolus insulin. Sensitivity factor represents how much BG will be lowered in mg/dL by taking 1 unit of bolus insulin. For example, if the patient uses SF of 1:50, 1 unit of bolus insulin will lower BG by 50 mg/dL (2.8 mmol/L). When the desired (target) BG reading is 100 mg/dL (5.6 mmol/L) and the patient’s current BG is 200 mg/dL (11.1 mmol/L), the patient will divide 100 mg/dL (5.6 mmol/L) by 50 (derived from SF of 1:50). The net result is 2 units of bolus insulin are needed to lower BG by 100 mg/dL (5.6 mmol/L). If the premeal BG is 200 mg/dL (11.1 mmol/L) and 60 g of carbohydrates are eaten, then the patient will need a total of 6 units (4 U for carbohydrate and 2 U for high BG) bolus before the meal. For additional information, readers are encouraged to read the articles by Petznick and by Joslin Diabetes Center for IC and SF.19,20
Bolus Insulin Titration
When the difference in BG readings before and 2 hours after a meal, called the Δ value, is > 50 mg/dL (2.8 mmol/L), the bolus insulin may need to be adjusted after ensuring the patient is ingesting consistent carbohydrates and performs the usual amount of activities around mealtime. For example, if the premeal reading was 130 mg/dL (7.2 mmol/L) but the 2-hour postprandial reading is > 180 mg/dL (10.0 mmol/L), the prescriber can increase the mealtime insulin by 1 unit if the mealtime insulin is < 10 units, by 2 units if < 20 units, or by 10% of the mealtime insulin dose. If the premeal BG is < 80 mg/dL (4.4 mmol/L) and the drop in BG is > Δ value of 50 mg/dL (2.8 mmol/L), the prescriber can decrease the mealtime insulin using the same calculation. Monitoring BG and titration recommendations are shown in Table 5. When adjusting the bolus insulin dose, it is best to make adjustments gradually rather than making several changes at once.
The 15/15 rule needs to be followed in cases involving hypoglycemia.21 When the BG is ≤ 70 mg/dL (3.9 mmol/L) and the patient is conscious and able to eat or drink, it is recommended they eat 15 g (30 g if BG is below 50) of carbohydrates then repeat BG check every 15 minutes until the BG is in the target range.22,23 If the patient is unconscious, providers should administer glucagon (if available), place the patient in a lateral position to avoid aspiration, and call 911. If hyperkalemia is an issue in chronic kidney disease, patients should consume apple juice rather than orange juice due to its lower potassium content. If the patient is taking α glucosidase inhibitors (AGI) like acarbose or miglitol, only pure glucose like glucose tablets needs to be given to treat hypoglycemia instead of regular soda or candy, as the AGI will slow absorption of other types of carbohydrates.24,25 After the severe hypoglycemic episode, it is imperative to assess for the cause and explore ways to prevent subsequent hypoglycemia. Providers also should advise the patient to wear medical emergency identification.
Conclusion
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1. Brown JB, Nichols GA, Perry A. The burden of treatment failure in type 2 diabetes. Diabetes Care. 2004;27(7):1535-1540.
2. American Association of Clinical Endocrinologists. AACE/ACE comprehensive type 2 diabetes management algorithm 2017. https://www.aace.com/publications/algorithm. Accessed August 24, 2017.
3. American Diabetes Association. Standards of medical care in diabetes—2016: summary of revisions. Diabetes Care. 2016;39(suppl 1):S4-S5.
4. Diamant M, Nauck MA, Shaginian R, et al; 4B Study Group. Glucagon-like peptide 1 receptor agonist or bolus insulin with optimized basal insulin in type 2 diabetes. Diabetes Care. 2014;37(10):2763-2773.
5. Rosenstock J, Fonseca VA, Gross JL, et al; Harmony 6 Study Group. Advancing basal insulin replacement in type 2 diabetes inadequately controlled with insulin glargine plus oral agents: a comparison of adding albiglutide, a weekly GLP-1 receptor agonist, versus thrice-daily prandial insulin lispro. Diabetes Care. 2014;37(8):2317-2325.
6. Vora J. Combining incretin-based therapies with insulin: realizing the potential in type 2 diabetes. Diabetes Care. 2013;36(suppl 2):S226-S232.
7. Inzucchi SE, Bergenstal RM, Buse JB, et al. Management of hyperglycemia in type 2 diabetes, 2015: a patient-centered approach: update to a position statement of the American Diabetes Association and the European Association for the study of diabetes. Diabetes Care. 2015;38(1):140-149.
8. McCulloch DK. Insulin therapy in type 2 diabetes mellitus. http://www.uptodate.com/contents/insulin-therapy-in-type-2-diabetes-mellitus?source=machineLearning&search=insulin+on+board&selectedTitle=3%7E150§ionRank=5&anchor=H24#H1331866. Updated July, 21, 2017. Accessed August 24, 2017.
9. Guthrie D, Guthrie R. Management of Diabetes Mellitus: A Guide to the Pattern Approach. 6th ed. New York, NY: Springer Publishing Co; 2008.
10. Presswala L, Shubrook J. What to do after basal insulin: 3 Tx strategies for type 2 diabetes. J Fam Pract. 2015;64(4):214-220.
11. McCulloch DK. Management of blood glucose in adults with type 1 diabetes mellitus. http://www.uptodate.com/contents/management-of-blood-glucose-in-adults -with-type-1-diabetes-mellitus?source=machineLearning&search=insulin+injection+frequency&selectedTitle=7%7E150§ionRank=1&anchor=H17221321#H17221321. Updated July 21, 2017. Accessed August 24, 2017.
12. Humulin 70/30. [package insert]. Indianapolis, IN: Eli Lilly and Co; 2017.
13. Humalog Mix 75/25. [package insert]. Indianapolis, IN: Eli Lilly and Co; 2017.
14. Humalog Mix 50/50. [package insert]. Indianapolis, IN: Eli Lilly and Co; 2017.
15. Novolin 70/30. [package insert]. Plainsboro, NJ: Novo Nordisk Inc; 2016.
16. Novolog Mix 70/30. [package insert]. Plainsboro, NJ: Novo Nordisk Inc; 2017.
17. Tsapas A, Karagiannis T, Bekiari E. Premixed insulin regimens for type 2 diabetes. Endocrine. 2016;51(3):387-389.
18. Riddle MC, Rosenstock J, Vlajnic A, Gao L. Randomized, 1-year comparison of three ways to initiate and advance insulin for type 2 diabetes: twice-daily premixed insulin versus basal insulin with either basal-plus one prandial insulin or basal-bolus up to three prandial injections. Diabetes Obes Metab. 2014;16(5):396-402.
19. Petznick A. Insulin management of type 2 diabetes mellitus. Am Fam Physician. 2011;84(2):183-190.
20. Joslin Diabetes Center. Dosing insulin. http://www.joslin.org/info/dosing-insulin.html. Accessed September 8, 2017.
21. National Institutes of Health. 15-15 rule. https://medlineplus.gov/ency/imagepages/19815.htm. Updated April 15, 2017. Accessed September 3, 2017.
22. University of Michigan Comprehensive Diabetes Center. Diabetes: low blood sugar. http://www.med.umich.edu/1libr/MEND/Diabetes-Hypoglycemia.pdf. Revised July 2017. Accessed September 3, 2017.
23. Diabetes Research Institute, University of Miami. Low blood sugar: hypoglycemia. http://www.diabetesresearch.org/document.doc?id=275. Accessed August 29, 2017.24. Cavanaugh KL. Diabetes management issues for patients with chronic kidney disease. Clin Diabetes. 2007;25(3):90-97.
25. National Institute of Diabetes and Digestive and Kidney Diseases. Low blood glucose (hypoglycemia). http://www.niddk.nih.gov/health-information/health-topics/Diabetes/hypoglycemia/Pages/index.aspx. Accessed August 29, 2017.
26. Deakin TA, McShane CE,Cade JE, Williams R. Group based training for self-management strategies in people with type 2 diabetes mellitus. Cochrane Database Syst Rev. 2005;(2): CD003417.
Individuals with type 2 diabetes mellitus (T2DM) spend between 5 and 10 years with elevated hemoglobin A1c (HbA1c) before initiation of insulin.1 Once the basal insulin is initiated, the patient can go years with only adjustment of the basal insulin, resulting in over-basalization. In general, the total daily dose (TDD) of insulin should be composed of about 50% basal “background” insulin and 50% bolus “meal” insulin. When the fasting glucose readings are on target but HbA1c is still above the mutually set goal range, postprandial readings need to be evaluated.
This article focuses on initiating and titrating bolus insulin in nonpregnant patients with T2DM. Before initiation of bolus insulin, it is important for the patient to be actively engaged with a diabetes educator for diabetes self-management education and support (DSME/S), including the understanding of the correct use of insulin, carbohydrate counting, and increasing physical activities. Ensuring the correct technique of insulin administration and self-monitoring of blood glucose (SMBG) is critical. A knowledge deficit of carbohydrate information can lead to uncontrolled blood glucose (BG). The authors have encountered numerous times when patients were drinking sugary beverages or consuming large amounts of “healthy” food without realizing the carbohydrate content. Therefore, treatment in concert with a registered dietitian and certified diabetes educator is highly recommended.
Initiation of Bolus Insulin
There are 3 options of postprandial coverage with bolus insulin when a patient is taking basal insulin: basal plus, basal-bolus, or premix insulin. A possible fourth option for postprandial coverage is to add glucagon-like peptide-1 receptor agonist (GLP-1 RA), an injectable noninsulin antihyperglycemic agent, which has shown noninferior efficacy to adding bolus insulin and a favorable effect on weight with less risk of hypoglycemia.2-5 Although it can be expensive, combining GLP-1 RA to basal insulin results in lowering HbA1c of 0.66 up to 1.74% (or lowering mmol/mol of 7 up to 19) from the baseline.6 However, adding bolus insulin may be the only option to avoid glucotoxicity and prevent further diabetes complications when the HbA1c level is well above the goal range. It is usually recommended to discontinue sulfonylurea when bolus insulin is added due to the β-cell exhaustion with advancing natural history of diabetes.7,8
Method 1: Basal Plus
The health care provider (HCP) needs to consider whether the patient is over-basalized when the HbAlc and postprandial BG readings are still not at goal despite careful titration of basal insulin dose to > 0.5 U/kg/d.9 This is the time to discuss with the patient the coverage of mealtime glucose excursions. In the basal plus regimen, the prescribing provider may add 1 bolus insulin injection for the meal with the highest amount of carbohydrates or add 2 bolus injections for the most and second most meals with carbohydrates. Multiple types of bolus insulin are available in the current U.S. market (Table 1).
There are 2 ways to add bolus insulin: fixed and flexible. In the fixed regimen, the patient will take the same amount of bolus insulin regardless of premeal BG readings and carbohydrate content of the food. The authors recommend adding bolus insulin of about 4 to 6 units once or twice a day with meals, depending on the number of meals a day, carbohydrate content of the meal, current and desired degree of diabetes control, and physical activities. Another way to calculate a bolus insulin dose is to start at 0.1 U/kg if adding to the basal insulin.10 Flexible regimen allows various bolus doses based on premeal BG, carbohydrate intake, and activities. Information on this regimen, will be discussed more later.
Patient Cases
Tables 2 and 3 describe 2 patient cases. For example, patient 1 weighs 80 kg. If the prescribing HCP and patient decide to add only 1 bolus to the largest carbohydrate meal at dinnertime, then the patient may take 8 units (80 kg × 0.1 U/kg/meal = 8 units for meal). The patient’s current insulin dose, medical comorbidities, current diabetes control status, living situations, and overall cognition also should be considered.
Imagine patient 1 is taking 14 units daily of a long-acting insulin (LAI). If the patient is taking a fairly low dose of LAI, has multiple comorbidities, recent BG log/HgAlc, and lives alone but demonstrates good cognition to follow instructions, the prescriber may consider adding the bolus insulin of 4 units for dinner; thus, the bolus dose is about one-third of total basal insulin dose. However, if the patient is on 40 units of LAI and has symptoms of hyperglycemia, 6 to 8 units for the dinner is reasonable. An important point to convey to this patient is to make sure there is carbohydrate consistency. The patient’s premeal BG was 137 mg/dL (7.6 mmol/L) on Monday, but it rose significantly to 313 mg/dL (17.4 mmol/L) after dinner. On Wednesday, the patient’s premeal BG prior to dinner was 150 mg/dL (8.3 mmol/L); it rose to 202 mg/dL (11.2 mmol/L) after dinner, which is high but not as high as on Monday.
Multiple factors may affect this variability; for example, on Monday the patient may have consumed more than the usual amount of carbohydrates for dinner, forgot to take oral medication for dinner, or missed his/her usual after-dinner walk. Or simply, the patient may have eaten a lot less than the usual amount of carbohydrates, walked the neighborhood, or vacuumed the entire house after dinner on Wednesday. Thus, it is imperative to carefully assess the patient’s lifestyle and recommend carbohydrate consistency at each meal.
Method 2: Basal-Bolus
When basal plus is insufficient to get the HbA1c and BG readings to goal, taking bolus insulin for all main meals containing carbohydrates must be considered. This is often called basal-bolus, multiple daily injections, or intensive insulin therapy.
In order to understand the concept of basal-bolus, HCPs should consider normal physiology. The pancreas releases a constant amount of insulin, aka background insulin, to cover glucose produced by the liver to the cells between meals. In addition, a burst of insulin, aka bolus insulin, to meet the blood glucose elevation from food to maintain homeostasis. In patients with T2DM, the relative amount produced by the pancreas is insufficient to meet the demand due to pancreatic exhaustion or insulin resistance. This necessitates the need to replace background and bolus insulin.7
The ideal final total bolus insulin amount (the sum of all meal bolus doses) should be about half the basal dosing. Calculation of starting bolus dosing can be done as in the basal plus regimen, either 4 to 6 units per meal or 0.1 U/kg/d.10 Alternatively, if the patient is on 60 units of long-acting analog and BGs are well above goal range, the prescriber could consider about 20 units of bolus dose (60 U divided by 3 meals) if the patient eats 3 routine meals a day with at least 30 g of carbohydrates, and physical activity levels are fairly consistent. If the patient eats the most carbohydrates at lunchtime, consider more bolus at lunch (ie, 18 U of bolus for breakfast and dinner and 24 U of bolus for lunch coverage).
Patients need to separate the time between the bolus doses, usually a minimum of 4 hours apart, to avoid insulin stacking, which is a common reason for hypoglycemia. Insulin stacking occurs when additional quick or rapid insulin is injected when the previous insulin is still in the body or when there is insulin on board.8,11 Typically, bolus analogs stay in the body for about 4 to 6 hours, thus necessitating separation of the doses at least 4 hours apart. Patients sometimes inject more bolus insulin after high postprandial readings, which can result in insulin stacking. In some cases, the patient may misunderstand and take mealtime insulin at a scheduled time instead of at the time of the meal.
Injecting bolus insulin for every snack must be avoided to prevent a vicious cycle: Postprandial hyperglycemia –> extra bolus insulin, resulting in insulin stacking –> hypoglycemia –> overtreatment with food –> hyperglycemia –> extra bolus insulin, resulting in insulin stacking –> and so on. Whenever there are readings in the hypoglycemic and hyperglycemic range, address hypoglycemia first because hyperglycemia often is due to overtreatment of hypoglycemia.
Method 3: Premix or Split-Mix (Patient-Mix) Insulin
Postprandial BG excursions can be minimized by changing basal insulin to premix or split-mix (patient-mix) insulin that has a mixture of mealtime and intermediate action insulin (Table 4).12-16 The use of premixed insulin is a viable option due to its ease of use and for those who have restrictions based on the complexity of the basal-bolus regimen.7 If a patient has routine meals and prefers not to carry around insulin for lunch, the schedule of premix insulin taken at breakfast and dinner is ideal.
Some caveats for safe prescribing should be understood. A recent summary of premixed insulin regimens noted that they seem to have a similar efficacy and safety profile compared with regimens that include basal insulin with or without mealtime insulin; however, cost and patient adherence are improved.17 It is important to monitor insulin-naïve patients for hypoglycemia and reduced efficacy when used twice daily compared with basal plus 3-times daily prandial insulin in patients needing insulin intensification.17
A randomized trial noted that hypoglycemia rates were twice as high with premixed insulin compared with basal-bolus insulin.18 This study also noted that the premixed insulin group experienced the highest dropout rate, partly due to hypoglycemia. A regimen of basal insulin with the option to add a single prandial insulin injection at the main meal was as effective in reducing HbA1c with less hypoglycemia. The premixed insulin is convenient but does not allow a separate correction of either mealtime or intermediate-acting insulin doses. If the premixed dose needs to be adjusted due to fasting hyperglycemia > 180 mg/dL(10.0 mmol/L), the TDD can be increased by 10%.2
In contrast, a split-mix (patient-mix) insulin regimen allows for the ability to vary the amount/ratio of combinations and adjustment of bolus and intermediate insulin doses. The disadvantages of split-mix insulin include the inconvenience of manually mixing of insulin and the potential for dosing errors. The patient needs to be taught additional steps on how to mix both insulins. Ensure the correct mixing order to maintain insulin potency; regular first, then neutral protamine Hagedorn (NPH). An HCP should remember the RN acronym if the patient is combining regular insulin and NPH. If there is doubt about the patient’s insulin injection technique, HCPs should ask the patient to demonstrate how to correctly pull up a dose of normal saline and inject it during a clinic visit. The only basal insulin that can be physically mixed with quick or rapid insulin is NPH. It should never be mixed with long-acting analogs. The patient should not even use the same syringe to draw up bolus analog insulin and inject it and then use the same syringe to draw up long-acting analog insulin.
One caveat to a fixed regimen (same amount of insulin dose) is that providers often expect that the patient will eat a consistent amount of carbohydrates at each meal and premeal glucose readings are fairly stable. Oftentimes, this is not true. If a patient took a bolus dose of 8 units of rapid-acting insulin and ate a 6 oz steak, 3 oz baked potato, steamed broccoli at a dinner; and no bread, the after dinner BG might register 145 mg/dL (8.1 mmol/L). Then, the next day for dinner, if he or she took the same amount of 8 units of rapid-acting insulin and ate 1 cup of spaghetti, ½ cup of spaghetti meat sauce, and 2 slices of garlic bread, the after dinner reading might be 322 mg/dL (17.9 mmol/L). The patient’s BG was higher on the second day because of the higher carbohydrate content of the meal. If the rapid-acting insulin was increased to 12 units of bolus based on the high carbohydrate meal and the patient ate a lower carbohydrate meal, hypoglycemia could ensue. Thus, it is important to work with the patient regarding the consumption of a consistent amount of carbohydrates and refer to a registered dietitian for carbohydrate consistency.
For the flexible regimen, the prescriber may consider using an insulin to carbohydrate (IC) ratio and sensitivity factor (SF), also called sliding scale or correction factor. The IC ratio represents how much insulin is needed to cover consumed carbohydrates. For instance, if the patient uses IC ratio of 1:15, 1 unit of bolus insulin will cover 15 g of carbohydrates. If the patient eats a meal with 60 g of carbohydrates and is using IC ratio of 1:15, the patient will inject 4 units of bolus insulin. Sensitivity factor represents how much BG will be lowered in mg/dL by taking 1 unit of bolus insulin. For example, if the patient uses SF of 1:50, 1 unit of bolus insulin will lower BG by 50 mg/dL (2.8 mmol/L). When the desired (target) BG reading is 100 mg/dL (5.6 mmol/L) and the patient’s current BG is 200 mg/dL (11.1 mmol/L), the patient will divide 100 mg/dL (5.6 mmol/L) by 50 (derived from SF of 1:50). The net result is 2 units of bolus insulin are needed to lower BG by 100 mg/dL (5.6 mmol/L). If the premeal BG is 200 mg/dL (11.1 mmol/L) and 60 g of carbohydrates are eaten, then the patient will need a total of 6 units (4 U for carbohydrate and 2 U for high BG) bolus before the meal. For additional information, readers are encouraged to read the articles by Petznick and by Joslin Diabetes Center for IC and SF.19,20
Bolus Insulin Titration
When the difference in BG readings before and 2 hours after a meal, called the Δ value, is > 50 mg/dL (2.8 mmol/L), the bolus insulin may need to be adjusted after ensuring the patient is ingesting consistent carbohydrates and performs the usual amount of activities around mealtime. For example, if the premeal reading was 130 mg/dL (7.2 mmol/L) but the 2-hour postprandial reading is > 180 mg/dL (10.0 mmol/L), the prescriber can increase the mealtime insulin by 1 unit if the mealtime insulin is < 10 units, by 2 units if < 20 units, or by 10% of the mealtime insulin dose. If the premeal BG is < 80 mg/dL (4.4 mmol/L) and the drop in BG is > Δ value of 50 mg/dL (2.8 mmol/L), the prescriber can decrease the mealtime insulin using the same calculation. Monitoring BG and titration recommendations are shown in Table 5. When adjusting the bolus insulin dose, it is best to make adjustments gradually rather than making several changes at once.
The 15/15 rule needs to be followed in cases involving hypoglycemia.21 When the BG is ≤ 70 mg/dL (3.9 mmol/L) and the patient is conscious and able to eat or drink, it is recommended they eat 15 g (30 g if BG is below 50) of carbohydrates then repeat BG check every 15 minutes until the BG is in the target range.22,23 If the patient is unconscious, providers should administer glucagon (if available), place the patient in a lateral position to avoid aspiration, and call 911. If hyperkalemia is an issue in chronic kidney disease, patients should consume apple juice rather than orange juice due to its lower potassium content. If the patient is taking α glucosidase inhibitors (AGI) like acarbose or miglitol, only pure glucose like glucose tablets needs to be given to treat hypoglycemia instead of regular soda or candy, as the AGI will slow absorption of other types of carbohydrates.24,25 After the severe hypoglycemic episode, it is imperative to assess for the cause and explore ways to prevent subsequent hypoglycemia. Providers also should advise the patient to wear medical emergency identification.
Conclusion
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Individuals with type 2 diabetes mellitus (T2DM) spend between 5 and 10 years with elevated hemoglobin A1c (HbA1c) before initiation of insulin.1 Once the basal insulin is initiated, the patient can go years with only adjustment of the basal insulin, resulting in over-basalization. In general, the total daily dose (TDD) of insulin should be composed of about 50% basal “background” insulin and 50% bolus “meal” insulin. When the fasting glucose readings are on target but HbA1c is still above the mutually set goal range, postprandial readings need to be evaluated.
This article focuses on initiating and titrating bolus insulin in nonpregnant patients with T2DM. Before initiation of bolus insulin, it is important for the patient to be actively engaged with a diabetes educator for diabetes self-management education and support (DSME/S), including the understanding of the correct use of insulin, carbohydrate counting, and increasing physical activities. Ensuring the correct technique of insulin administration and self-monitoring of blood glucose (SMBG) is critical. A knowledge deficit of carbohydrate information can lead to uncontrolled blood glucose (BG). The authors have encountered numerous times when patients were drinking sugary beverages or consuming large amounts of “healthy” food without realizing the carbohydrate content. Therefore, treatment in concert with a registered dietitian and certified diabetes educator is highly recommended.
Initiation of Bolus Insulin
There are 3 options of postprandial coverage with bolus insulin when a patient is taking basal insulin: basal plus, basal-bolus, or premix insulin. A possible fourth option for postprandial coverage is to add glucagon-like peptide-1 receptor agonist (GLP-1 RA), an injectable noninsulin antihyperglycemic agent, which has shown noninferior efficacy to adding bolus insulin and a favorable effect on weight with less risk of hypoglycemia.2-5 Although it can be expensive, combining GLP-1 RA to basal insulin results in lowering HbA1c of 0.66 up to 1.74% (or lowering mmol/mol of 7 up to 19) from the baseline.6 However, adding bolus insulin may be the only option to avoid glucotoxicity and prevent further diabetes complications when the HbA1c level is well above the goal range. It is usually recommended to discontinue sulfonylurea when bolus insulin is added due to the β-cell exhaustion with advancing natural history of diabetes.7,8
Method 1: Basal Plus
The health care provider (HCP) needs to consider whether the patient is over-basalized when the HbAlc and postprandial BG readings are still not at goal despite careful titration of basal insulin dose to > 0.5 U/kg/d.9 This is the time to discuss with the patient the coverage of mealtime glucose excursions. In the basal plus regimen, the prescribing provider may add 1 bolus insulin injection for the meal with the highest amount of carbohydrates or add 2 bolus injections for the most and second most meals with carbohydrates. Multiple types of bolus insulin are available in the current U.S. market (Table 1).
There are 2 ways to add bolus insulin: fixed and flexible. In the fixed regimen, the patient will take the same amount of bolus insulin regardless of premeal BG readings and carbohydrate content of the food. The authors recommend adding bolus insulin of about 4 to 6 units once or twice a day with meals, depending on the number of meals a day, carbohydrate content of the meal, current and desired degree of diabetes control, and physical activities. Another way to calculate a bolus insulin dose is to start at 0.1 U/kg if adding to the basal insulin.10 Flexible regimen allows various bolus doses based on premeal BG, carbohydrate intake, and activities. Information on this regimen, will be discussed more later.
Patient Cases
Tables 2 and 3 describe 2 patient cases. For example, patient 1 weighs 80 kg. If the prescribing HCP and patient decide to add only 1 bolus to the largest carbohydrate meal at dinnertime, then the patient may take 8 units (80 kg × 0.1 U/kg/meal = 8 units for meal). The patient’s current insulin dose, medical comorbidities, current diabetes control status, living situations, and overall cognition also should be considered.
Imagine patient 1 is taking 14 units daily of a long-acting insulin (LAI). If the patient is taking a fairly low dose of LAI, has multiple comorbidities, recent BG log/HgAlc, and lives alone but demonstrates good cognition to follow instructions, the prescriber may consider adding the bolus insulin of 4 units for dinner; thus, the bolus dose is about one-third of total basal insulin dose. However, if the patient is on 40 units of LAI and has symptoms of hyperglycemia, 6 to 8 units for the dinner is reasonable. An important point to convey to this patient is to make sure there is carbohydrate consistency. The patient’s premeal BG was 137 mg/dL (7.6 mmol/L) on Monday, but it rose significantly to 313 mg/dL (17.4 mmol/L) after dinner. On Wednesday, the patient’s premeal BG prior to dinner was 150 mg/dL (8.3 mmol/L); it rose to 202 mg/dL (11.2 mmol/L) after dinner, which is high but not as high as on Monday.
Multiple factors may affect this variability; for example, on Monday the patient may have consumed more than the usual amount of carbohydrates for dinner, forgot to take oral medication for dinner, or missed his/her usual after-dinner walk. Or simply, the patient may have eaten a lot less than the usual amount of carbohydrates, walked the neighborhood, or vacuumed the entire house after dinner on Wednesday. Thus, it is imperative to carefully assess the patient’s lifestyle and recommend carbohydrate consistency at each meal.
Method 2: Basal-Bolus
When basal plus is insufficient to get the HbA1c and BG readings to goal, taking bolus insulin for all main meals containing carbohydrates must be considered. This is often called basal-bolus, multiple daily injections, or intensive insulin therapy.
In order to understand the concept of basal-bolus, HCPs should consider normal physiology. The pancreas releases a constant amount of insulin, aka background insulin, to cover glucose produced by the liver to the cells between meals. In addition, a burst of insulin, aka bolus insulin, to meet the blood glucose elevation from food to maintain homeostasis. In patients with T2DM, the relative amount produced by the pancreas is insufficient to meet the demand due to pancreatic exhaustion or insulin resistance. This necessitates the need to replace background and bolus insulin.7
The ideal final total bolus insulin amount (the sum of all meal bolus doses) should be about half the basal dosing. Calculation of starting bolus dosing can be done as in the basal plus regimen, either 4 to 6 units per meal or 0.1 U/kg/d.10 Alternatively, if the patient is on 60 units of long-acting analog and BGs are well above goal range, the prescriber could consider about 20 units of bolus dose (60 U divided by 3 meals) if the patient eats 3 routine meals a day with at least 30 g of carbohydrates, and physical activity levels are fairly consistent. If the patient eats the most carbohydrates at lunchtime, consider more bolus at lunch (ie, 18 U of bolus for breakfast and dinner and 24 U of bolus for lunch coverage).
Patients need to separate the time between the bolus doses, usually a minimum of 4 hours apart, to avoid insulin stacking, which is a common reason for hypoglycemia. Insulin stacking occurs when additional quick or rapid insulin is injected when the previous insulin is still in the body or when there is insulin on board.8,11 Typically, bolus analogs stay in the body for about 4 to 6 hours, thus necessitating separation of the doses at least 4 hours apart. Patients sometimes inject more bolus insulin after high postprandial readings, which can result in insulin stacking. In some cases, the patient may misunderstand and take mealtime insulin at a scheduled time instead of at the time of the meal.
Injecting bolus insulin for every snack must be avoided to prevent a vicious cycle: Postprandial hyperglycemia –> extra bolus insulin, resulting in insulin stacking –> hypoglycemia –> overtreatment with food –> hyperglycemia –> extra bolus insulin, resulting in insulin stacking –> and so on. Whenever there are readings in the hypoglycemic and hyperglycemic range, address hypoglycemia first because hyperglycemia often is due to overtreatment of hypoglycemia.
Method 3: Premix or Split-Mix (Patient-Mix) Insulin
Postprandial BG excursions can be minimized by changing basal insulin to premix or split-mix (patient-mix) insulin that has a mixture of mealtime and intermediate action insulin (Table 4).12-16 The use of premixed insulin is a viable option due to its ease of use and for those who have restrictions based on the complexity of the basal-bolus regimen.7 If a patient has routine meals and prefers not to carry around insulin for lunch, the schedule of premix insulin taken at breakfast and dinner is ideal.
Some caveats for safe prescribing should be understood. A recent summary of premixed insulin regimens noted that they seem to have a similar efficacy and safety profile compared with regimens that include basal insulin with or without mealtime insulin; however, cost and patient adherence are improved.17 It is important to monitor insulin-naïve patients for hypoglycemia and reduced efficacy when used twice daily compared with basal plus 3-times daily prandial insulin in patients needing insulin intensification.17
A randomized trial noted that hypoglycemia rates were twice as high with premixed insulin compared with basal-bolus insulin.18 This study also noted that the premixed insulin group experienced the highest dropout rate, partly due to hypoglycemia. A regimen of basal insulin with the option to add a single prandial insulin injection at the main meal was as effective in reducing HbA1c with less hypoglycemia. The premixed insulin is convenient but does not allow a separate correction of either mealtime or intermediate-acting insulin doses. If the premixed dose needs to be adjusted due to fasting hyperglycemia > 180 mg/dL(10.0 mmol/L), the TDD can be increased by 10%.2
In contrast, a split-mix (patient-mix) insulin regimen allows for the ability to vary the amount/ratio of combinations and adjustment of bolus and intermediate insulin doses. The disadvantages of split-mix insulin include the inconvenience of manually mixing of insulin and the potential for dosing errors. The patient needs to be taught additional steps on how to mix both insulins. Ensure the correct mixing order to maintain insulin potency; regular first, then neutral protamine Hagedorn (NPH). An HCP should remember the RN acronym if the patient is combining regular insulin and NPH. If there is doubt about the patient’s insulin injection technique, HCPs should ask the patient to demonstrate how to correctly pull up a dose of normal saline and inject it during a clinic visit. The only basal insulin that can be physically mixed with quick or rapid insulin is NPH. It should never be mixed with long-acting analogs. The patient should not even use the same syringe to draw up bolus analog insulin and inject it and then use the same syringe to draw up long-acting analog insulin.
One caveat to a fixed regimen (same amount of insulin dose) is that providers often expect that the patient will eat a consistent amount of carbohydrates at each meal and premeal glucose readings are fairly stable. Oftentimes, this is not true. If a patient took a bolus dose of 8 units of rapid-acting insulin and ate a 6 oz steak, 3 oz baked potato, steamed broccoli at a dinner; and no bread, the after dinner BG might register 145 mg/dL (8.1 mmol/L). Then, the next day for dinner, if he or she took the same amount of 8 units of rapid-acting insulin and ate 1 cup of spaghetti, ½ cup of spaghetti meat sauce, and 2 slices of garlic bread, the after dinner reading might be 322 mg/dL (17.9 mmol/L). The patient’s BG was higher on the second day because of the higher carbohydrate content of the meal. If the rapid-acting insulin was increased to 12 units of bolus based on the high carbohydrate meal and the patient ate a lower carbohydrate meal, hypoglycemia could ensue. Thus, it is important to work with the patient regarding the consumption of a consistent amount of carbohydrates and refer to a registered dietitian for carbohydrate consistency.
For the flexible regimen, the prescriber may consider using an insulin to carbohydrate (IC) ratio and sensitivity factor (SF), also called sliding scale or correction factor. The IC ratio represents how much insulin is needed to cover consumed carbohydrates. For instance, if the patient uses IC ratio of 1:15, 1 unit of bolus insulin will cover 15 g of carbohydrates. If the patient eats a meal with 60 g of carbohydrates and is using IC ratio of 1:15, the patient will inject 4 units of bolus insulin. Sensitivity factor represents how much BG will be lowered in mg/dL by taking 1 unit of bolus insulin. For example, if the patient uses SF of 1:50, 1 unit of bolus insulin will lower BG by 50 mg/dL (2.8 mmol/L). When the desired (target) BG reading is 100 mg/dL (5.6 mmol/L) and the patient’s current BG is 200 mg/dL (11.1 mmol/L), the patient will divide 100 mg/dL (5.6 mmol/L) by 50 (derived from SF of 1:50). The net result is 2 units of bolus insulin are needed to lower BG by 100 mg/dL (5.6 mmol/L). If the premeal BG is 200 mg/dL (11.1 mmol/L) and 60 g of carbohydrates are eaten, then the patient will need a total of 6 units (4 U for carbohydrate and 2 U for high BG) bolus before the meal. For additional information, readers are encouraged to read the articles by Petznick and by Joslin Diabetes Center for IC and SF.19,20
Bolus Insulin Titration
When the difference in BG readings before and 2 hours after a meal, called the Δ value, is > 50 mg/dL (2.8 mmol/L), the bolus insulin may need to be adjusted after ensuring the patient is ingesting consistent carbohydrates and performs the usual amount of activities around mealtime. For example, if the premeal reading was 130 mg/dL (7.2 mmol/L) but the 2-hour postprandial reading is > 180 mg/dL (10.0 mmol/L), the prescriber can increase the mealtime insulin by 1 unit if the mealtime insulin is < 10 units, by 2 units if < 20 units, or by 10% of the mealtime insulin dose. If the premeal BG is < 80 mg/dL (4.4 mmol/L) and the drop in BG is > Δ value of 50 mg/dL (2.8 mmol/L), the prescriber can decrease the mealtime insulin using the same calculation. Monitoring BG and titration recommendations are shown in Table 5. When adjusting the bolus insulin dose, it is best to make adjustments gradually rather than making several changes at once.
The 15/15 rule needs to be followed in cases involving hypoglycemia.21 When the BG is ≤ 70 mg/dL (3.9 mmol/L) and the patient is conscious and able to eat or drink, it is recommended they eat 15 g (30 g if BG is below 50) of carbohydrates then repeat BG check every 15 minutes until the BG is in the target range.22,23 If the patient is unconscious, providers should administer glucagon (if available), place the patient in a lateral position to avoid aspiration, and call 911. If hyperkalemia is an issue in chronic kidney disease, patients should consume apple juice rather than orange juice due to its lower potassium content. If the patient is taking α glucosidase inhibitors (AGI) like acarbose or miglitol, only pure glucose like glucose tablets needs to be given to treat hypoglycemia instead of regular soda or candy, as the AGI will slow absorption of other types of carbohydrates.24,25 After the severe hypoglycemic episode, it is imperative to assess for the cause and explore ways to prevent subsequent hypoglycemia. Providers also should advise the patient to wear medical emergency identification.
Conclusion
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1. Brown JB, Nichols GA, Perry A. The burden of treatment failure in type 2 diabetes. Diabetes Care. 2004;27(7):1535-1540.
2. American Association of Clinical Endocrinologists. AACE/ACE comprehensive type 2 diabetes management algorithm 2017. https://www.aace.com/publications/algorithm. Accessed August 24, 2017.
3. American Diabetes Association. Standards of medical care in diabetes—2016: summary of revisions. Diabetes Care. 2016;39(suppl 1):S4-S5.
4. Diamant M, Nauck MA, Shaginian R, et al; 4B Study Group. Glucagon-like peptide 1 receptor agonist or bolus insulin with optimized basal insulin in type 2 diabetes. Diabetes Care. 2014;37(10):2763-2773.
5. Rosenstock J, Fonseca VA, Gross JL, et al; Harmony 6 Study Group. Advancing basal insulin replacement in type 2 diabetes inadequately controlled with insulin glargine plus oral agents: a comparison of adding albiglutide, a weekly GLP-1 receptor agonist, versus thrice-daily prandial insulin lispro. Diabetes Care. 2014;37(8):2317-2325.
6. Vora J. Combining incretin-based therapies with insulin: realizing the potential in type 2 diabetes. Diabetes Care. 2013;36(suppl 2):S226-S232.
7. Inzucchi SE, Bergenstal RM, Buse JB, et al. Management of hyperglycemia in type 2 diabetes, 2015: a patient-centered approach: update to a position statement of the American Diabetes Association and the European Association for the study of diabetes. Diabetes Care. 2015;38(1):140-149.
8. McCulloch DK. Insulin therapy in type 2 diabetes mellitus. http://www.uptodate.com/contents/insulin-therapy-in-type-2-diabetes-mellitus?source=machineLearning&search=insulin+on+board&selectedTitle=3%7E150§ionRank=5&anchor=H24#H1331866. Updated July, 21, 2017. Accessed August 24, 2017.
9. Guthrie D, Guthrie R. Management of Diabetes Mellitus: A Guide to the Pattern Approach. 6th ed. New York, NY: Springer Publishing Co; 2008.
10. Presswala L, Shubrook J. What to do after basal insulin: 3 Tx strategies for type 2 diabetes. J Fam Pract. 2015;64(4):214-220.
11. McCulloch DK. Management of blood glucose in adults with type 1 diabetes mellitus. http://www.uptodate.com/contents/management-of-blood-glucose-in-adults -with-type-1-diabetes-mellitus?source=machineLearning&search=insulin+injection+frequency&selectedTitle=7%7E150§ionRank=1&anchor=H17221321#H17221321. Updated July 21, 2017. Accessed August 24, 2017.
12. Humulin 70/30. [package insert]. Indianapolis, IN: Eli Lilly and Co; 2017.
13. Humalog Mix 75/25. [package insert]. Indianapolis, IN: Eli Lilly and Co; 2017.
14. Humalog Mix 50/50. [package insert]. Indianapolis, IN: Eli Lilly and Co; 2017.
15. Novolin 70/30. [package insert]. Plainsboro, NJ: Novo Nordisk Inc; 2016.
16. Novolog Mix 70/30. [package insert]. Plainsboro, NJ: Novo Nordisk Inc; 2017.
17. Tsapas A, Karagiannis T, Bekiari E. Premixed insulin regimens for type 2 diabetes. Endocrine. 2016;51(3):387-389.
18. Riddle MC, Rosenstock J, Vlajnic A, Gao L. Randomized, 1-year comparison of three ways to initiate and advance insulin for type 2 diabetes: twice-daily premixed insulin versus basal insulin with either basal-plus one prandial insulin or basal-bolus up to three prandial injections. Diabetes Obes Metab. 2014;16(5):396-402.
19. Petznick A. Insulin management of type 2 diabetes mellitus. Am Fam Physician. 2011;84(2):183-190.
20. Joslin Diabetes Center. Dosing insulin. http://www.joslin.org/info/dosing-insulin.html. Accessed September 8, 2017.
21. National Institutes of Health. 15-15 rule. https://medlineplus.gov/ency/imagepages/19815.htm. Updated April 15, 2017. Accessed September 3, 2017.
22. University of Michigan Comprehensive Diabetes Center. Diabetes: low blood sugar. http://www.med.umich.edu/1libr/MEND/Diabetes-Hypoglycemia.pdf. Revised July 2017. Accessed September 3, 2017.
23. Diabetes Research Institute, University of Miami. Low blood sugar: hypoglycemia. http://www.diabetesresearch.org/document.doc?id=275. Accessed August 29, 2017.24. Cavanaugh KL. Diabetes management issues for patients with chronic kidney disease. Clin Diabetes. 2007;25(3):90-97.
25. National Institute of Diabetes and Digestive and Kidney Diseases. Low blood glucose (hypoglycemia). http://www.niddk.nih.gov/health-information/health-topics/Diabetes/hypoglycemia/Pages/index.aspx. Accessed August 29, 2017.
26. Deakin TA, McShane CE,Cade JE, Williams R. Group based training for self-management strategies in people with type 2 diabetes mellitus. Cochrane Database Syst Rev. 2005;(2): CD003417.
1. Brown JB, Nichols GA, Perry A. The burden of treatment failure in type 2 diabetes. Diabetes Care. 2004;27(7):1535-1540.
2. American Association of Clinical Endocrinologists. AACE/ACE comprehensive type 2 diabetes management algorithm 2017. https://www.aace.com/publications/algorithm. Accessed August 24, 2017.
3. American Diabetes Association. Standards of medical care in diabetes—2016: summary of revisions. Diabetes Care. 2016;39(suppl 1):S4-S5.
4. Diamant M, Nauck MA, Shaginian R, et al; 4B Study Group. Glucagon-like peptide 1 receptor agonist or bolus insulin with optimized basal insulin in type 2 diabetes. Diabetes Care. 2014;37(10):2763-2773.
5. Rosenstock J, Fonseca VA, Gross JL, et al; Harmony 6 Study Group. Advancing basal insulin replacement in type 2 diabetes inadequately controlled with insulin glargine plus oral agents: a comparison of adding albiglutide, a weekly GLP-1 receptor agonist, versus thrice-daily prandial insulin lispro. Diabetes Care. 2014;37(8):2317-2325.
6. Vora J. Combining incretin-based therapies with insulin: realizing the potential in type 2 diabetes. Diabetes Care. 2013;36(suppl 2):S226-S232.
7. Inzucchi SE, Bergenstal RM, Buse JB, et al. Management of hyperglycemia in type 2 diabetes, 2015: a patient-centered approach: update to a position statement of the American Diabetes Association and the European Association for the study of diabetes. Diabetes Care. 2015;38(1):140-149.
8. McCulloch DK. Insulin therapy in type 2 diabetes mellitus. http://www.uptodate.com/contents/insulin-therapy-in-type-2-diabetes-mellitus?source=machineLearning&search=insulin+on+board&selectedTitle=3%7E150§ionRank=5&anchor=H24#H1331866. Updated July, 21, 2017. Accessed August 24, 2017.
9. Guthrie D, Guthrie R. Management of Diabetes Mellitus: A Guide to the Pattern Approach. 6th ed. New York, NY: Springer Publishing Co; 2008.
10. Presswala L, Shubrook J. What to do after basal insulin: 3 Tx strategies for type 2 diabetes. J Fam Pract. 2015;64(4):214-220.
11. McCulloch DK. Management of blood glucose in adults with type 1 diabetes mellitus. http://www.uptodate.com/contents/management-of-blood-glucose-in-adults -with-type-1-diabetes-mellitus?source=machineLearning&search=insulin+injection+frequency&selectedTitle=7%7E150§ionRank=1&anchor=H17221321#H17221321. Updated July 21, 2017. Accessed August 24, 2017.
12. Humulin 70/30. [package insert]. Indianapolis, IN: Eli Lilly and Co; 2017.
13. Humalog Mix 75/25. [package insert]. Indianapolis, IN: Eli Lilly and Co; 2017.
14. Humalog Mix 50/50. [package insert]. Indianapolis, IN: Eli Lilly and Co; 2017.
15. Novolin 70/30. [package insert]. Plainsboro, NJ: Novo Nordisk Inc; 2016.
16. Novolog Mix 70/30. [package insert]. Plainsboro, NJ: Novo Nordisk Inc; 2017.
17. Tsapas A, Karagiannis T, Bekiari E. Premixed insulin regimens for type 2 diabetes. Endocrine. 2016;51(3):387-389.
18. Riddle MC, Rosenstock J, Vlajnic A, Gao L. Randomized, 1-year comparison of three ways to initiate and advance insulin for type 2 diabetes: twice-daily premixed insulin versus basal insulin with either basal-plus one prandial insulin or basal-bolus up to three prandial injections. Diabetes Obes Metab. 2014;16(5):396-402.
19. Petznick A. Insulin management of type 2 diabetes mellitus. Am Fam Physician. 2011;84(2):183-190.
20. Joslin Diabetes Center. Dosing insulin. http://www.joslin.org/info/dosing-insulin.html. Accessed September 8, 2017.
21. National Institutes of Health. 15-15 rule. https://medlineplus.gov/ency/imagepages/19815.htm. Updated April 15, 2017. Accessed September 3, 2017.
22. University of Michigan Comprehensive Diabetes Center. Diabetes: low blood sugar. http://www.med.umich.edu/1libr/MEND/Diabetes-Hypoglycemia.pdf. Revised July 2017. Accessed September 3, 2017.
23. Diabetes Research Institute, University of Miami. Low blood sugar: hypoglycemia. http://www.diabetesresearch.org/document.doc?id=275. Accessed August 29, 2017.24. Cavanaugh KL. Diabetes management issues for patients with chronic kidney disease. Clin Diabetes. 2007;25(3):90-97.
25. National Institute of Diabetes and Digestive and Kidney Diseases. Low blood glucose (hypoglycemia). http://www.niddk.nih.gov/health-information/health-topics/Diabetes/hypoglycemia/Pages/index.aspx. Accessed August 29, 2017.
26. Deakin TA, McShane CE,Cade JE, Williams R. Group based training for self-management strategies in people with type 2 diabetes mellitus. Cochrane Database Syst Rev. 2005;(2): CD003417.