Hospital Medicine

Each year, 40,000 to 98,000 deaths occur due to medical errors.¹ The Harvard Medical Practice Study (HMPS), published in 1991, found that 3.7% of hospitalized patients were harmed by adverse events and 1% were harmed by adverse events due to negligence.² The latest HMPS showed that, despite significant improvements in patient safety over the past 3 decades, patient safety challenges persist. This study found that inpatient care leads to harm in nearly a quarter of patients, and that 1 in 4 of these adverse events are preventable.³

Since the first HMPS study was published, efforts to improve patient safety have focused on identifying causes of medical error and the design and implementation of interventions to mitigate errors. Factors contributing to medical errors have been well documented: the complexity of care delivery from inpatient to outpatient settings, with transitions of care and extensive use of medications; multiple comorbidities; and the fragmentation of care across multiple systems and specialties. Although most errors are related to process or system failure, accountability of each practitioner and clinician is essential to promoting a culture of safety. Many medical errors are preventable through multifaceted approaches employed throughout the phases of the care,⁴ with medication errors, both prescribing and administration, and diagnostic and treatment errors encompassing most risk prevention areas. Broadly, safety efforts should emphasize building a culture of safety where all safety events are reported, including near-miss events.

Two articles in this issue of JCOM address key elements of patient safety: building a safety culture and diagnostic error. Merchant et al⁵ report on an initiative designed to promote a safety culture by recognizing and rewarding staff who identify and report near misses. The tiered awards program they designed led to significantly increased staff participation in the safety awards nomination process and was associated with increased reporting of actual and close-call events and greater attendance at monthly safety forums. Goyal et al,⁶ noting that diagnostic error rates in hospitalized patients remain unacceptably high, provide a concise update on diagnostic error among inpatients, focusing on issues related to defining and measuring diagnostic errors and current strategies to improve diagnostic safety in hospitalized patients. In a third article, Sathi et al report on efforts to teach quality improvement (QI) methods to internal medicine trainees; their project increased residents’ knowledge of their patient panels and comfort with QI approaches and led to improved patient outcomes.

Major progress has been made to improve health care safety since the first HMPS was published. However, the latest HMPS shows that patient safety efforts must continue, given the persistent risk for patient harm in the current health care delivery system. Safety, along with clear accountability for identifying, reporting, and addressing errors, should be a top priority for health care systems throughout the preventive, diagnostic, and therapeutic phases of care.

Corresponding author: Ebrahim Barkoudah, MD, MPH; [email protected]

Each year, 40,000 to 98,000 deaths occur due to medical errors.¹ The Harvard Medical Practice Study (HMPS), published in 1991, found that 3.7% of hospitalized patients were harmed by adverse events and 1% were harmed by adverse events due to negligence.² The latest HMPS showed that, despite significant improvements in patient safety over the past 3 decades, patient safety challenges persist. This study found that inpatient care leads to harm in nearly a quarter of patients, and that 1 in 4 of these adverse events are preventable.³

Since the first HMPS study was published, efforts to improve patient safety have focused on identifying causes of medical error and the design and implementation of interventions to mitigate errors. Factors contributing to medical errors have been well documented: the complexity of care delivery from inpatient to outpatient settings, with transitions of care and extensive use of medications; multiple comorbidities; and the fragmentation of care across multiple systems and specialties. Although most errors are related to process or system failure, accountability of each practitioner and clinician is essential to promoting a culture of safety. Many medical errors are preventable through multifaceted approaches employed throughout the phases of the care,⁴ with medication errors, both prescribing and administration, and diagnostic and treatment errors encompassing most risk prevention areas. Broadly, safety efforts should emphasize building a culture of safety where all safety events are reported, including near-miss events.

Two articles in this issue of JCOM address key elements of patient safety: building a safety culture and diagnostic error. Merchant et al⁵ report on an initiative designed to promote a safety culture by recognizing and rewarding staff who identify and report near misses. The tiered awards program they designed led to significantly increased staff participation in the safety awards nomination process and was associated with increased reporting of actual and close-call events and greater attendance at monthly safety forums. Goyal et al,⁶ noting that diagnostic error rates in hospitalized patients remain unacceptably high, provide a concise update on diagnostic error among inpatients, focusing on issues related to defining and measuring diagnostic errors and current strategies to improve diagnostic safety in hospitalized patients. In a third article, Sathi et al report on efforts to teach quality improvement (QI) methods to internal medicine trainees; their project increased residents’ knowledge of their patient panels and comfort with QI approaches and led to improved patient outcomes.

Major progress has been made to improve health care safety since the first HMPS was published. However, the latest HMPS shows that patient safety efforts must continue, given the persistent risk for patient harm in the current health care delivery system. Safety, along with clear accountability for identifying, reporting, and addressing errors, should be a top priority for health care systems throughout the preventive, diagnostic, and therapeutic phases of care.

Corresponding author: Ebrahim Barkoudah, MD, MPH; [email protected]

Each year, 40,000 to 98,000 deaths occur due to medical errors.¹ The Harvard Medical Practice Study (HMPS), published in 1991, found that 3.7% of hospitalized patients were harmed by adverse events and 1% were harmed by adverse events due to negligence.² The latest HMPS showed that, despite significant improvements in patient safety over the past 3 decades, patient safety challenges persist. This study found that inpatient care leads to harm in nearly a quarter of patients, and that 1 in 4 of these adverse events are preventable.³

Since the first HMPS study was published, efforts to improve patient safety have focused on identifying causes of medical error and the design and implementation of interventions to mitigate errors. Factors contributing to medical errors have been well documented: the complexity of care delivery from inpatient to outpatient settings, with transitions of care and extensive use of medications; multiple comorbidities; and the fragmentation of care across multiple systems and specialties. Although most errors are related to process or system failure, accountability of each practitioner and clinician is essential to promoting a culture of safety. Many medical errors are preventable through multifaceted approaches employed throughout the phases of the care,⁴ with medication errors, both prescribing and administration, and diagnostic and treatment errors encompassing most risk prevention areas. Broadly, safety efforts should emphasize building a culture of safety where all safety events are reported, including near-miss events.

Two articles in this issue of JCOM address key elements of patient safety: building a safety culture and diagnostic error. Merchant et al⁵ report on an initiative designed to promote a safety culture by recognizing and rewarding staff who identify and report near misses. The tiered awards program they designed led to significantly increased staff participation in the safety awards nomination process and was associated with increased reporting of actual and close-call events and greater attendance at monthly safety forums. Goyal et al,⁶ noting that diagnostic error rates in hospitalized patients remain unacceptably high, provide a concise update on diagnostic error among inpatients, focusing on issues related to defining and measuring diagnostic errors and current strategies to improve diagnostic safety in hospitalized patients. In a third article, Sathi et al report on efforts to teach quality improvement (QI) methods to internal medicine trainees; their project increased residents’ knowledge of their patient panels and comfort with QI approaches and led to improved patient outcomes.

Major progress has been made to improve health care safety since the first HMPS was published. However, the latest HMPS shows that patient safety efforts must continue, given the persistent risk for patient harm in the current health care delivery system. Safety, along with clear accountability for identifying, reporting, and addressing errors, should be a top priority for health care systems throughout the preventive, diagnostic, and therapeutic phases of care.

Corresponding author: Ebrahim Barkoudah, MD, MPH; [email protected]

Study 1 Overview (Chang et al)

Objective: To assess the incidence of postoperative delirium (POD) following propofol- vs sevoflurane-based anesthesia in geriatric spine surgery patients.

Design: Retrospective, single-blinded observational study of propofol- and sevoflurane-based anesthesia cohorts.

Setting and participants: Patients eligible for this study were aged 65 years or older admitted to the SMG-SNU Boramae Medical Center (Seoul, South Korea). All patients underwent general anesthesia either via intravenous propofol or inhalational sevoflurane for spine surgery between January 2015 and December 2019. Patients were retrospectively identified via electronic medical records. Patient exclusion criteria included preoperative delirium, history of dementia, psychiatric disease, alcoholism, hepatic or renal dysfunction, postoperative mechanical ventilation dependence, other surgery within the recent 6 months, maintenance of intraoperative anesthesia with combined anesthetics, or incomplete medical record.

Main outcome measures: The primary outcome was the incidence of POD after administration of propofol- and sevoflurane-based anesthesia during hospitalization. Patients were screened for POD regularly by attending nurses using the Nursing Delirium Screening Scale (disorientation, inappropriate behavior, inappropriate communication, hallucination, and psychomotor retardation) during the entirety of the patient’s hospital stay; if 1 or more screening criteria were met, a psychiatrist was consulted for the proper diagnosis and management of delirium. A psychiatric diagnosis was required for a case to be counted toward the incidence of POD in this study. Secondary outcomes included postoperative 30-day complications (angina, myocardial infarction, transient ischemic attack/stroke, pneumonia, deep vein thrombosis, pulmonary embolism, acute kidney injury, or infection) and length of postoperative hospital stay.

Main results: POD occurred in 29 patients (10.3%) out of the total cohort of 281. POD was more common in the sevoflurane group than in the propofol group (15.7% vs 5.0%; P = .003). Using multivariable logistic regression, inhalational sevoflurane was associated with an increased risk of POD as compared to propofol-based anesthesia (odds ratio [OR], 4.120; 95% CI, 1.549-10.954; P = .005). There was no association between choice of anesthetic and postoperative 30-day complications or the length of postoperative hospital stay. Both older age (OR, 1.242; 95% CI, 1.130-1.366; P < .001) and higher pain score at postoperative day 1 (OR, 1.338; 95% CI, 1.056-1.696; P = .016) were associated with increased risk of POD.

Conclusion: Propofol-based anesthesia was associated with a lower incidence of and risk for POD than sevoflurane-based anesthesia in older patients undergoing spine surgery.

Study 2 Overview (Mei et al)

Objective: To determine the incidence and duration of POD in older patients after total knee/hip replacement (TKR/THR) under intravenous propofol or inhalational sevoflurane general anesthesia.

Design: Randomized clinical trial of propofol and sevoflurane groups.

Setting and participants: This study was conducted at the Shanghai Tenth People’s Hospital and involved 209 participants enrolled between June 2016 and November 2019. All participants were 60 years of age or older, scheduled for TKR/THR surgery under general anesthesia, American Society of Anesthesiologists (ASA) class I to III, and assessed to be of normal cognitive function preoperatively via a Mini-Mental State Examination. Participant exclusion criteria included preexisting delirium as assessed by the Confusion Assessment Method (CAM), prior diagnosed neurological diseases (eg, Parkinson’s disease), prior diagnosed mental disorders (eg, schizophrenia), or impaired vision or hearing that would influence cognitive assessments. All participants were randomly assigned to either sevoflurane or propofol anesthesia for their surgery via a computer-generated list. Of these, 103 received inhalational sevoflurane and 106 received intravenous propofol. All participants received standardized postoperative care.

Main outcome measures: All participants were interviewed by investigators, who were blinded to the anesthesia regimen, twice daily on postoperative days 1, 2, and 3 using CAM and a CAM-based scoring system (CAM-S) to assess delirium severity. The CAM encapsulated 4 criteria: acute onset and fluctuating course, agitation, disorganized thinking, and altered level of consciousness. To diagnose delirium, both the first and second criteria must be met, in addition to either the third or fourth criterion. The averages of the scores across the 3 postoperative days indicated delirium severity, while the incidence and duration of delirium was assessed by the presence of delirium as determined by CAM on any postoperative day.

Main results: All eligible participants (N = 209; mean [SD] age 71.2 [6.7] years; 29.2% male) were included in the final analysis. The incidence of POD was not statistically different between the propofol and sevoflurane groups (33.0% vs 23.3%; P = .119, Chi-square test). It was estimated that 316 participants in each arm of the study were needed to detect statistical differences. The number of days of POD per person were higher with propofol anesthesia as compared to sevoflurane (0.5 [0.8] vs 0.3 [0.5]; P =  .049, Student’s t-test).

Conclusion: This underpowered study showed a 9.7% difference in the incidence of POD between older adults who received propofol (33.0%) and sevoflurane (23.3%) after THR/TKR. Further studies with a larger sample size are needed to compare general anesthetics and their role in POD.

Commentary

Delirium is characterized by an acute state of confusion with fluctuating mental status, inattention, disorganized thinking, and altered level of consciousness. It is often caused by medications and/or their related adverse effects, infections, electrolyte imbalances, and other clinical etiologies. Delirium often manifests in post-surgical settings, disproportionately affecting older patients and leading to increased risk of morbidity, mortality, hospital length of stay, and health care costs.¹ Intraoperative risk factors for POD are determined by the degree of operative stress (eg, lower-risk surgeries put the patient at reduced risk for POD as compared to higher-risk surgeries) and are additive to preexisting patient-specific risk factors, such as older age and functional impairment.¹ Because operative stress is associated with risk for POD, limiting operative stress in controlled ways, such as through the choice of anesthetic agent administered, may be a pragmatic way to manage operative risks and optimize outcomes, especially when serving a surgically vulnerable population.

In Study 1, Chang et al sought to assess whether 2 commonly utilized general anesthetics, propofol and sevoflurane, in older patients undergoing spine surgery differentially affected the incidence of POD. In this retrospective, single-blinded observational study of 281 geriatric patients, the researchers found that sevoflurane was associated with a higher risk of POD as compared to propofol. However, these anesthetics were not associated with surgical outcomes such as postoperative 30-day complications or the length of postoperative hospital stay. While these findings added new knowledge to this field of research, several limitations should be kept in mind when interpreting this study’s results. For instance, the sample size was relatively small, with all cases selected from a single center utilizing a retrospective analysis. In addition, although a standardized nursing screening tool was used as a method for delirium detection, hypoactive delirium or less symptomatic delirium may have been missed, which in turn would lead to an underestimation of POD incidence. The latter is a common limitation in delirium research.

In Study 2, Mei et al similarly explored the effects of general anesthetics on POD in older surgical patients. Specifically, using a randomized clinical trial design, the investigators compared propofol with sevoflurane in older patients who underwent TKR/THR, and their roles in POD severity and duration. Although the incidence of POD was higher in those who received propofol compared to sevoflurane, this trial was underpowered and the results did not reach statistical significance. In addition, while the duration of POD was slightly longer in the propofol group compared to the sevoflurane group (0.5 vs 0.3 days), it was unclear if this finding was clinically significant. Similar to many research studies in POD, limitations of Study 2 included a small sample size of 209 patients, with all participants enrolled from a single center. On the other hand, this study illustrated the feasibility of a method that allowed reproducible prospective assessment of POD time course using CAM and CAM-S.

Applications for Clinical Practice and System Implementation

The delineation of risk factors that contribute to delirium after surgery in older patients is key to mitigating risks for POD and improving clinical outcomes. An important step towards a better understanding of these modifiable risk factors is to clearly quantify intraoperative risk of POD attributable to specific anesthetics. While preclinical studies have shown differential neurotoxicity effects of propofol and sevoflurane, their impact on clinically important neurologic outcomes such as delirium and cognitive decline remains poorly understood. Although Studies 1 and 2 both provided head-to-head comparisons of propofol and sevoflurane as risk factors for POD in high-operative-stress surgeries in older patients, the results were inconsistent. That being said, this small incremental increase in knowledge was not unexpected in the course of discovery around a clinically complex research question. Importantly, these studies provided evidence regarding the methodological approaches that could be taken to further this line of research.

The mediating factors of the differences on neurologic outcomes between anesthetic agents are likely pharmacological, biological, and methodological. Pharmacologically, the differences between target receptors, such as GABAA (propofol, etomidate) or NMDA (ketamine), could be a defining feature in the difference in incidence of POD. Additionally, secondary actions of anesthetic agents on glycine, nicotinic, and acetylcholine receptors could play a role as well. Biologically, genes such as CYP2E1, CYP2B6, CYP2C9, GSTP1, UGT1A9, SULT1A1, and NQO1 have all been identified as genetic factors in the metabolism of anesthetics, and variations in such genes could result in different responses to anesthetics.² Methodologically, routes of anesthetic administration (eg, inhalation vs intravenous), preexisting anatomical structures, or confounding medical conditions (eg, lower respiratory volume due to older age) may influence POD incidence, duration, or severity. Moreover, methodological differences between Studies 1 and 2, such as surgeries performed (spinal vs TKR/THR), patient populations (South Korean vs Chinese), and the diagnosis and monitoring of delirium (retrospective screening and diagnosis vs prospective CAM/CAM-S) may impact delirium outcomes. Thus, these factors should be considered in the design of future clinical trials undertaken to investigate the effects of anesthetics on POD.

Given the high prevalence of delirium and its associated adverse outcomes in the immediate postoperative period in older patients, further research is warranted to determine how anesthetics affect POD in order to optimize perioperative care and mitigate risks in this vulnerable population. Moreover, parallel investigations into how anesthetics differentially impact the development of transient or longer-term cognitive impairment after a surgical procedure (ie, postoperative cognitive dysfunction) in older adults are urgently needed in order to improve their cognitive health.

Practice Points

• Intravenous propofol and inhalational sevoflurane may be differentially associated with incidence, duration, and severity of POD in geriatric surgical patients.
• Further larger-scale studies are warranted to clarify the role of anesthetic choice in POD in order to optimize surgical outcomes in older patients.

–Jared Doan, BS, and Fred Ko, MD
Icahn School of Medicine at Mount Sinai

Study 1 Overview (Chang et al)

Objective: To assess the incidence of postoperative delirium (POD) following propofol- vs sevoflurane-based anesthesia in geriatric spine surgery patients.

Design: Retrospective, single-blinded observational study of propofol- and sevoflurane-based anesthesia cohorts.

Setting and participants: Patients eligible for this study were aged 65 years or older admitted to the SMG-SNU Boramae Medical Center (Seoul, South Korea). All patients underwent general anesthesia either via intravenous propofol or inhalational sevoflurane for spine surgery between January 2015 and December 2019. Patients were retrospectively identified via electronic medical records. Patient exclusion criteria included preoperative delirium, history of dementia, psychiatric disease, alcoholism, hepatic or renal dysfunction, postoperative mechanical ventilation dependence, other surgery within the recent 6 months, maintenance of intraoperative anesthesia with combined anesthetics, or incomplete medical record.

Main outcome measures: The primary outcome was the incidence of POD after administration of propofol- and sevoflurane-based anesthesia during hospitalization. Patients were screened for POD regularly by attending nurses using the Nursing Delirium Screening Scale (disorientation, inappropriate behavior, inappropriate communication, hallucination, and psychomotor retardation) during the entirety of the patient’s hospital stay; if 1 or more screening criteria were met, a psychiatrist was consulted for the proper diagnosis and management of delirium. A psychiatric diagnosis was required for a case to be counted toward the incidence of POD in this study. Secondary outcomes included postoperative 30-day complications (angina, myocardial infarction, transient ischemic attack/stroke, pneumonia, deep vein thrombosis, pulmonary embolism, acute kidney injury, or infection) and length of postoperative hospital stay.

Main results: POD occurred in 29 patients (10.3%) out of the total cohort of 281. POD was more common in the sevoflurane group than in the propofol group (15.7% vs 5.0%; P = .003). Using multivariable logistic regression, inhalational sevoflurane was associated with an increased risk of POD as compared to propofol-based anesthesia (odds ratio [OR], 4.120; 95% CI, 1.549-10.954; P = .005). There was no association between choice of anesthetic and postoperative 30-day complications or the length of postoperative hospital stay. Both older age (OR, 1.242; 95% CI, 1.130-1.366; P < .001) and higher pain score at postoperative day 1 (OR, 1.338; 95% CI, 1.056-1.696; P = .016) were associated with increased risk of POD.

Conclusion: Propofol-based anesthesia was associated with a lower incidence of and risk for POD than sevoflurane-based anesthesia in older patients undergoing spine surgery.

Study 2 Overview (Mei et al)

Objective: To determine the incidence and duration of POD in older patients after total knee/hip replacement (TKR/THR) under intravenous propofol or inhalational sevoflurane general anesthesia.

Design: Randomized clinical trial of propofol and sevoflurane groups.

Setting and participants: This study was conducted at the Shanghai Tenth People’s Hospital and involved 209 participants enrolled between June 2016 and November 2019. All participants were 60 years of age or older, scheduled for TKR/THR surgery under general anesthesia, American Society of Anesthesiologists (ASA) class I to III, and assessed to be of normal cognitive function preoperatively via a Mini-Mental State Examination. Participant exclusion criteria included preexisting delirium as assessed by the Confusion Assessment Method (CAM), prior diagnosed neurological diseases (eg, Parkinson’s disease), prior diagnosed mental disorders (eg, schizophrenia), or impaired vision or hearing that would influence cognitive assessments. All participants were randomly assigned to either sevoflurane or propofol anesthesia for their surgery via a computer-generated list. Of these, 103 received inhalational sevoflurane and 106 received intravenous propofol. All participants received standardized postoperative care.

Main outcome measures: All participants were interviewed by investigators, who were blinded to the anesthesia regimen, twice daily on postoperative days 1, 2, and 3 using CAM and a CAM-based scoring system (CAM-S) to assess delirium severity. The CAM encapsulated 4 criteria: acute onset and fluctuating course, agitation, disorganized thinking, and altered level of consciousness. To diagnose delirium, both the first and second criteria must be met, in addition to either the third or fourth criterion. The averages of the scores across the 3 postoperative days indicated delirium severity, while the incidence and duration of delirium was assessed by the presence of delirium as determined by CAM on any postoperative day.

Main results: All eligible participants (N = 209; mean [SD] age 71.2 [6.7] years; 29.2% male) were included in the final analysis. The incidence of POD was not statistically different between the propofol and sevoflurane groups (33.0% vs 23.3%; P = .119, Chi-square test). It was estimated that 316 participants in each arm of the study were needed to detect statistical differences. The number of days of POD per person were higher with propofol anesthesia as compared to sevoflurane (0.5 [0.8] vs 0.3 [0.5]; P =  .049, Student’s t-test).

Conclusion: This underpowered study showed a 9.7% difference in the incidence of POD between older adults who received propofol (33.0%) and sevoflurane (23.3%) after THR/TKR. Further studies with a larger sample size are needed to compare general anesthetics and their role in POD.

Commentary

Delirium is characterized by an acute state of confusion with fluctuating mental status, inattention, disorganized thinking, and altered level of consciousness. It is often caused by medications and/or their related adverse effects, infections, electrolyte imbalances, and other clinical etiologies. Delirium often manifests in post-surgical settings, disproportionately affecting older patients and leading to increased risk of morbidity, mortality, hospital length of stay, and health care costs.¹ Intraoperative risk factors for POD are determined by the degree of operative stress (eg, lower-risk surgeries put the patient at reduced risk for POD as compared to higher-risk surgeries) and are additive to preexisting patient-specific risk factors, such as older age and functional impairment.¹ Because operative stress is associated with risk for POD, limiting operative stress in controlled ways, such as through the choice of anesthetic agent administered, may be a pragmatic way to manage operative risks and optimize outcomes, especially when serving a surgically vulnerable population.

In Study 1, Chang et al sought to assess whether 2 commonly utilized general anesthetics, propofol and sevoflurane, in older patients undergoing spine surgery differentially affected the incidence of POD. In this retrospective, single-blinded observational study of 281 geriatric patients, the researchers found that sevoflurane was associated with a higher risk of POD as compared to propofol. However, these anesthetics were not associated with surgical outcomes such as postoperative 30-day complications or the length of postoperative hospital stay. While these findings added new knowledge to this field of research, several limitations should be kept in mind when interpreting this study’s results. For instance, the sample size was relatively small, with all cases selected from a single center utilizing a retrospective analysis. In addition, although a standardized nursing screening tool was used as a method for delirium detection, hypoactive delirium or less symptomatic delirium may have been missed, which in turn would lead to an underestimation of POD incidence. The latter is a common limitation in delirium research.

In Study 2, Mei et al similarly explored the effects of general anesthetics on POD in older surgical patients. Specifically, using a randomized clinical trial design, the investigators compared propofol with sevoflurane in older patients who underwent TKR/THR, and their roles in POD severity and duration. Although the incidence of POD was higher in those who received propofol compared to sevoflurane, this trial was underpowered and the results did not reach statistical significance. In addition, while the duration of POD was slightly longer in the propofol group compared to the sevoflurane group (0.5 vs 0.3 days), it was unclear if this finding was clinically significant. Similar to many research studies in POD, limitations of Study 2 included a small sample size of 209 patients, with all participants enrolled from a single center. On the other hand, this study illustrated the feasibility of a method that allowed reproducible prospective assessment of POD time course using CAM and CAM-S.

Applications for Clinical Practice and System Implementation

The delineation of risk factors that contribute to delirium after surgery in older patients is key to mitigating risks for POD and improving clinical outcomes. An important step towards a better understanding of these modifiable risk factors is to clearly quantify intraoperative risk of POD attributable to specific anesthetics. While preclinical studies have shown differential neurotoxicity effects of propofol and sevoflurane, their impact on clinically important neurologic outcomes such as delirium and cognitive decline remains poorly understood. Although Studies 1 and 2 both provided head-to-head comparisons of propofol and sevoflurane as risk factors for POD in high-operative-stress surgeries in older patients, the results were inconsistent. That being said, this small incremental increase in knowledge was not unexpected in the course of discovery around a clinically complex research question. Importantly, these studies provided evidence regarding the methodological approaches that could be taken to further this line of research.

The mediating factors of the differences on neurologic outcomes between anesthetic agents are likely pharmacological, biological, and methodological. Pharmacologically, the differences between target receptors, such as GABAA (propofol, etomidate) or NMDA (ketamine), could be a defining feature in the difference in incidence of POD. Additionally, secondary actions of anesthetic agents on glycine, nicotinic, and acetylcholine receptors could play a role as well. Biologically, genes such as CYP2E1, CYP2B6, CYP2C9, GSTP1, UGT1A9, SULT1A1, and NQO1 have all been identified as genetic factors in the metabolism of anesthetics, and variations in such genes could result in different responses to anesthetics.² Methodologically, routes of anesthetic administration (eg, inhalation vs intravenous), preexisting anatomical structures, or confounding medical conditions (eg, lower respiratory volume due to older age) may influence POD incidence, duration, or severity. Moreover, methodological differences between Studies 1 and 2, such as surgeries performed (spinal vs TKR/THR), patient populations (South Korean vs Chinese), and the diagnosis and monitoring of delirium (retrospective screening and diagnosis vs prospective CAM/CAM-S) may impact delirium outcomes. Thus, these factors should be considered in the design of future clinical trials undertaken to investigate the effects of anesthetics on POD.

Given the high prevalence of delirium and its associated adverse outcomes in the immediate postoperative period in older patients, further research is warranted to determine how anesthetics affect POD in order to optimize perioperative care and mitigate risks in this vulnerable population. Moreover, parallel investigations into how anesthetics differentially impact the development of transient or longer-term cognitive impairment after a surgical procedure (ie, postoperative cognitive dysfunction) in older adults are urgently needed in order to improve their cognitive health.

Practice Points

• Intravenous propofol and inhalational sevoflurane may be differentially associated with incidence, duration, and severity of POD in geriatric surgical patients.
• Further larger-scale studies are warranted to clarify the role of anesthetic choice in POD in order to optimize surgical outcomes in older patients.

–Jared Doan, BS, and Fred Ko, MD
Icahn School of Medicine at Mount Sinai

Study 1 Overview (Chang et al)

Objective: To assess the incidence of postoperative delirium (POD) following propofol- vs sevoflurane-based anesthesia in geriatric spine surgery patients.

Design: Retrospective, single-blinded observational study of propofol- and sevoflurane-based anesthesia cohorts.

Setting and participants: Patients eligible for this study were aged 65 years or older admitted to the SMG-SNU Boramae Medical Center (Seoul, South Korea). All patients underwent general anesthesia either via intravenous propofol or inhalational sevoflurane for spine surgery between January 2015 and December 2019. Patients were retrospectively identified via electronic medical records. Patient exclusion criteria included preoperative delirium, history of dementia, psychiatric disease, alcoholism, hepatic or renal dysfunction, postoperative mechanical ventilation dependence, other surgery within the recent 6 months, maintenance of intraoperative anesthesia with combined anesthetics, or incomplete medical record.

Main outcome measures: The primary outcome was the incidence of POD after administration of propofol- and sevoflurane-based anesthesia during hospitalization. Patients were screened for POD regularly by attending nurses using the Nursing Delirium Screening Scale (disorientation, inappropriate behavior, inappropriate communication, hallucination, and psychomotor retardation) during the entirety of the patient’s hospital stay; if 1 or more screening criteria were met, a psychiatrist was consulted for the proper diagnosis and management of delirium. A psychiatric diagnosis was required for a case to be counted toward the incidence of POD in this study. Secondary outcomes included postoperative 30-day complications (angina, myocardial infarction, transient ischemic attack/stroke, pneumonia, deep vein thrombosis, pulmonary embolism, acute kidney injury, or infection) and length of postoperative hospital stay.

Main results: POD occurred in 29 patients (10.3%) out of the total cohort of 281. POD was more common in the sevoflurane group than in the propofol group (15.7% vs 5.0%; P = .003). Using multivariable logistic regression, inhalational sevoflurane was associated with an increased risk of POD as compared to propofol-based anesthesia (odds ratio [OR], 4.120; 95% CI, 1.549-10.954; P = .005). There was no association between choice of anesthetic and postoperative 30-day complications or the length of postoperative hospital stay. Both older age (OR, 1.242; 95% CI, 1.130-1.366; P < .001) and higher pain score at postoperative day 1 (OR, 1.338; 95% CI, 1.056-1.696; P = .016) were associated with increased risk of POD.

Conclusion: Propofol-based anesthesia was associated with a lower incidence of and risk for POD than sevoflurane-based anesthesia in older patients undergoing spine surgery.

Study 2 Overview (Mei et al)

Objective: To determine the incidence and duration of POD in older patients after total knee/hip replacement (TKR/THR) under intravenous propofol or inhalational sevoflurane general anesthesia.

Design: Randomized clinical trial of propofol and sevoflurane groups.

Setting and participants: This study was conducted at the Shanghai Tenth People’s Hospital and involved 209 participants enrolled between June 2016 and November 2019. All participants were 60 years of age or older, scheduled for TKR/THR surgery under general anesthesia, American Society of Anesthesiologists (ASA) class I to III, and assessed to be of normal cognitive function preoperatively via a Mini-Mental State Examination. Participant exclusion criteria included preexisting delirium as assessed by the Confusion Assessment Method (CAM), prior diagnosed neurological diseases (eg, Parkinson’s disease), prior diagnosed mental disorders (eg, schizophrenia), or impaired vision or hearing that would influence cognitive assessments. All participants were randomly assigned to either sevoflurane or propofol anesthesia for their surgery via a computer-generated list. Of these, 103 received inhalational sevoflurane and 106 received intravenous propofol. All participants received standardized postoperative care.

Main outcome measures: All participants were interviewed by investigators, who were blinded to the anesthesia regimen, twice daily on postoperative days 1, 2, and 3 using CAM and a CAM-based scoring system (CAM-S) to assess delirium severity. The CAM encapsulated 4 criteria: acute onset and fluctuating course, agitation, disorganized thinking, and altered level of consciousness. To diagnose delirium, both the first and second criteria must be met, in addition to either the third or fourth criterion. The averages of the scores across the 3 postoperative days indicated delirium severity, while the incidence and duration of delirium was assessed by the presence of delirium as determined by CAM on any postoperative day.

Main results: All eligible participants (N = 209; mean [SD] age 71.2 [6.7] years; 29.2% male) were included in the final analysis. The incidence of POD was not statistically different between the propofol and sevoflurane groups (33.0% vs 23.3%; P = .119, Chi-square test). It was estimated that 316 participants in each arm of the study were needed to detect statistical differences. The number of days of POD per person were higher with propofol anesthesia as compared to sevoflurane (0.5 [0.8] vs 0.3 [0.5]; P =  .049, Student’s t-test).

Conclusion: This underpowered study showed a 9.7% difference in the incidence of POD between older adults who received propofol (33.0%) and sevoflurane (23.3%) after THR/TKR. Further studies with a larger sample size are needed to compare general anesthetics and their role in POD.

Commentary

Delirium is characterized by an acute state of confusion with fluctuating mental status, inattention, disorganized thinking, and altered level of consciousness. It is often caused by medications and/or their related adverse effects, infections, electrolyte imbalances, and other clinical etiologies. Delirium often manifests in post-surgical settings, disproportionately affecting older patients and leading to increased risk of morbidity, mortality, hospital length of stay, and health care costs.¹ Intraoperative risk factors for POD are determined by the degree of operative stress (eg, lower-risk surgeries put the patient at reduced risk for POD as compared to higher-risk surgeries) and are additive to preexisting patient-specific risk factors, such as older age and functional impairment.¹ Because operative stress is associated with risk for POD, limiting operative stress in controlled ways, such as through the choice of anesthetic agent administered, may be a pragmatic way to manage operative risks and optimize outcomes, especially when serving a surgically vulnerable population.

In Study 1, Chang et al sought to assess whether 2 commonly utilized general anesthetics, propofol and sevoflurane, in older patients undergoing spine surgery differentially affected the incidence of POD. In this retrospective, single-blinded observational study of 281 geriatric patients, the researchers found that sevoflurane was associated with a higher risk of POD as compared to propofol. However, these anesthetics were not associated with surgical outcomes such as postoperative 30-day complications or the length of postoperative hospital stay. While these findings added new knowledge to this field of research, several limitations should be kept in mind when interpreting this study’s results. For instance, the sample size was relatively small, with all cases selected from a single center utilizing a retrospective analysis. In addition, although a standardized nursing screening tool was used as a method for delirium detection, hypoactive delirium or less symptomatic delirium may have been missed, which in turn would lead to an underestimation of POD incidence. The latter is a common limitation in delirium research.

In Study 2, Mei et al similarly explored the effects of general anesthetics on POD in older surgical patients. Specifically, using a randomized clinical trial design, the investigators compared propofol with sevoflurane in older patients who underwent TKR/THR, and their roles in POD severity and duration. Although the incidence of POD was higher in those who received propofol compared to sevoflurane, this trial was underpowered and the results did not reach statistical significance. In addition, while the duration of POD was slightly longer in the propofol group compared to the sevoflurane group (0.5 vs 0.3 days), it was unclear if this finding was clinically significant. Similar to many research studies in POD, limitations of Study 2 included a small sample size of 209 patients, with all participants enrolled from a single center. On the other hand, this study illustrated the feasibility of a method that allowed reproducible prospective assessment of POD time course using CAM and CAM-S.

Applications for Clinical Practice and System Implementation

The delineation of risk factors that contribute to delirium after surgery in older patients is key to mitigating risks for POD and improving clinical outcomes. An important step towards a better understanding of these modifiable risk factors is to clearly quantify intraoperative risk of POD attributable to specific anesthetics. While preclinical studies have shown differential neurotoxicity effects of propofol and sevoflurane, their impact on clinically important neurologic outcomes such as delirium and cognitive decline remains poorly understood. Although Studies 1 and 2 both provided head-to-head comparisons of propofol and sevoflurane as risk factors for POD in high-operative-stress surgeries in older patients, the results were inconsistent. That being said, this small incremental increase in knowledge was not unexpected in the course of discovery around a clinically complex research question. Importantly, these studies provided evidence regarding the methodological approaches that could be taken to further this line of research.

The mediating factors of the differences on neurologic outcomes between anesthetic agents are likely pharmacological, biological, and methodological. Pharmacologically, the differences between target receptors, such as GABAA (propofol, etomidate) or NMDA (ketamine), could be a defining feature in the difference in incidence of POD. Additionally, secondary actions of anesthetic agents on glycine, nicotinic, and acetylcholine receptors could play a role as well. Biologically, genes such as CYP2E1, CYP2B6, CYP2C9, GSTP1, UGT1A9, SULT1A1, and NQO1 have all been identified as genetic factors in the metabolism of anesthetics, and variations in such genes could result in different responses to anesthetics.² Methodologically, routes of anesthetic administration (eg, inhalation vs intravenous), preexisting anatomical structures, or confounding medical conditions (eg, lower respiratory volume due to older age) may influence POD incidence, duration, or severity. Moreover, methodological differences between Studies 1 and 2, such as surgeries performed (spinal vs TKR/THR), patient populations (South Korean vs Chinese), and the diagnosis and monitoring of delirium (retrospective screening and diagnosis vs prospective CAM/CAM-S) may impact delirium outcomes. Thus, these factors should be considered in the design of future clinical trials undertaken to investigate the effects of anesthetics on POD.

Given the high prevalence of delirium and its associated adverse outcomes in the immediate postoperative period in older patients, further research is warranted to determine how anesthetics affect POD in order to optimize perioperative care and mitigate risks in this vulnerable population. Moreover, parallel investigations into how anesthetics differentially impact the development of transient or longer-term cognitive impairment after a surgical procedure (ie, postoperative cognitive dysfunction) in older adults are urgently needed in order to improve their cognitive health.

Practice Points

• Intravenous propofol and inhalational sevoflurane may be differentially associated with incidence, duration, and severity of POD in geriatric surgical patients.
• Further larger-scale studies are warranted to clarify the role of anesthetic choice in POD in order to optimize surgical outcomes in older patients.

–Jared Doan, BS, and Fred Ko, MD
Icahn School of Medicine at Mount Sinai

From the Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC.

Abstract

Objective: Inpatient vaccination initiatives are well described in the literature. During the COVID-19 pandemic, hospitals began administering COVID-19 vaccines to hospitalized patients. Although vaccination rates increased, there remained many unvaccinated patients despite community efforts. This quality improvement project aimed to increase the COVID-19 vaccination rates of hospitalized patients on the medicine service at the George Washington University Hospital (GWUH).

Methods: From November 2021 through February 2022, we conducted a Plan-Do-Study-Act (PDSA) cycle with 3 phases. Initial steps included gathering baseline data from the electronic health record and consulting stakeholders. The first 2 phases focused on educating housestaff on the availability, ordering process, and administration of the Pfizer vaccine. The third phase consisted of developing educational pamphlets for patients to be included in their admission packets.

Results: The baseline mean COVID-19 vaccination rate (August to October 2021) of eligible patients on the medicine service was 10.7%. In the months after we implemented the PDSA cycle (November 2021 to February 2022), the mean vaccination rate increased to 15.4%.

Conclusion: This quality improvement project implemented measures to increase administration of the Pfizer vaccine to eligible patients admitted to the medicine service at GWUH. The mean vaccination rate increased from 10.7% in the 3 months prior to implementation to 15.4% during the 4 months post implementation. Other measures to consider in the future include increasing the availability of other COVID-19 vaccines at our hospital and incorporating the vaccine into the admission order set to help facilitate vaccination early in the hospital course.

Keywords: housestaff, quality improvement, PDSA, COVID-19, BNT162b2 vaccine, patient education

Throughout the COVID-19 pandemic, case rates in the United States have fluctuated considerably, corresponding to epidemic waves. In 2021, US daily cases of COVID-19 peaked at nearly 300,000 in early January and reached a nadir of 8000 cases in mid-June.¹ In September 2021, new cases had increased to 200,000 per day due to the prevalence of the Delta variant.¹ Particularly with the emergence of new variants of SARS-CoV-2, vaccination efforts to limit the spread of infection and severity of illness are critical. Data have shown that 2 doses of the BNT162b2 vaccine (Pfizer-BioNTech) were largely protective against severe infection for approximately 6 months.^2,3 When we began this quality improvement (QI) project in September 2021, only 179 million Americans had been fully vaccinated, according to data from the Centers for Disease Control and Prevention, which is just over half of the US population.⁴ An electronic survey conducted in the United States with more than 5 million responses found that, of those who were hesitant about receiving the vaccine, 49% reported a fear of adverse effects and 48% reported a lack of trust in the vaccine.⁵

This QI project sought to target unvaccinated individuals admitted to the internal medicine inpatient service. Vaccinating hospitalized patients is especially important since they are sicker than the general population and at higher risk of having poor outcomes from COVID-19. Inpatient vaccine initiatives, such as administering influenza vaccine prior to discharge, have been successfully implemented in the past.⁶ One large COVID-19 vaccination program featured an admission order set to increase the rates of vaccination among hospitalized patients.⁷ Our QI project piloted a multidisciplinary approach involving the nursing staff, pharmacy, information technology (IT) department, and internal medicine housestaff to increase COVID-19 vaccination rates among hospitalized patients on the medical service. This project aimed to increase inpatient vaccination rates through interventions targeting both primary providers as well as the patients themselves.

Methods

Setting and Interventions

This project was conducted at the George Washington University Hospital (GWUH) in Washington, DC. The clinicians involved in the study were the internal medicine housestaff, and the patients included were adults admitted to the resident medicine ward teams. The project was exempt by the institutional review board and did not require informed consent.

The quality improvement initiative had 3 phases, each featuring a different intervention (Table 1). The first phase involved sending a weekly announcement (via email and a secure health care messaging app) to current residents rotating on the inpatient medicine service. The announcement contained information regarding COVID-19 vaccine availability at the hospital, instructions on ordering the vaccine, and the process of coordinating with pharmacy to facilitate vaccine administration. Thereafter, residents were educated on the process of giving a COVID-19 vaccine to a patient from start to finish. Due to the nature of the residency schedule, different housestaff members rotated in and out of the medicine wards during the intervention periods. The weekly email was sent to the entire internal medicine housestaff, informing all residents about the QI project, while the weekly secure messages served as reminders and were only sent to residents currently on the medicine wards.

In the second phase, we posted paper flyers throughout the hospital to remind housestaff to give the vaccine and again educate them on the process of ordering the vaccine. For the third intervention, a COVID-19 vaccine educational pamphlet was developed for distribution to inpatients at GWUH. The pamphlet included information on vaccine efficacy, safety, side effects, and eligibility. The pamphlet was incorporated in the admission packet that every patient receives upon admission to the hospital. The patients reviewed the pamphlets with nursing staff, who would answer any questions, with residents available to discuss any outstanding concerns.

Measures and Data Gathering

The primary endpoint of the study was inpatient vaccination rate, defined as the number of COVID-19 vaccines administered divided by the number of patients eligible to receive a vaccine (not fully vaccinated). During initial triage, nursing staff documented vaccination status in the electronic health record (EHR), checking a box in a data entry form if a patient had received 0, 1, or 2 doses of the COVID-19 vaccine. The GWUH IT department generated data from this form to determine the number of patients eligible to receive a COVID-19 vaccine. Data were extracted from the medication administration record in the EHR to determine the number of vaccines that were administered to patients during their hospitalization on the inpatient medical service. Each month, the IT department extracted data for the number of eligible patients and the number of vaccines administered. This yielded the monthly vaccination rates. The monthly vaccination rates in the period prior to starting the QI initiative were compared to the rates in the period after the interventions were implemented.

Of note, during the course of this project, patients became eligible for a third COVID-19 vaccine (booster). We decided to continue with the original aim of vaccinating adults who had only received 0 or 1 dose of the vaccine. Therefore, the eligibility criteria remained the same throughout the study. We obtained retrospective data to ensure that the vaccines being counted toward the vaccination rate were vaccines given to patients not yet fully vaccinated and not vaccines given as boosters.

Results

From August to October 2021, the baseline average monthly vaccination rate of patients on the medicine service who were eligible to receive a COVID-19 vaccine was 10.7%. After the first intervention, the vaccination rate increased to 19.7% in November 2021 (Table 2). The second intervention yielded vaccination rates of 11.4% and 11.8% in December 2021 and January 2022, respectively. During the final phase in February 2022, the vaccination rate was 19.0%. At the conclusion of the study, the mean vaccination rate for the intervention months was 15.4% (Figure 1). Process stability and variation are demonstrated with a statistical process control chart (Figure 2).

 

 

Discussion

For this housestaff-driven QI project, we implemented an inpatient COVID-19 vaccination campaign consisting of 3 phases that targeted both providers and patients. During the intervention period, we observed an increased vaccination rate compared to the period just prior to implementation of the QI project. While our interventions may certainly have boosted vaccination rates, we understand other variables could have contributed to increased rates as well. The emergence of variants in the United States, such as omicron in December 2021,⁸ could have precipitated a demand for vaccinations among patients. Holidays in November and December may also have increased patients’ desire to get vaccinated before travel.

We encountered a number of roadblocks that challenged our project, including difficulty identifying patients who were eligible for the vaccine, logistical vaccine administration challenges, and hesitancy among the inpatient population. Accurately identifying patients who were eligible for a vaccine in the EHR was especially challenging in the setting of rapidly changing guidelines regarding COVID-19 vaccination. In September 2021, the US Food and Drug Administration authorized the Pfizer booster for certain populations and later, in November 2021, for all adults. This meant that some fully vaccinated hospitalized patients (those with 2 doses) then qualified for an additional dose of the vaccine and received a dose during hospitalization. To determine the true vaccination rate, we obtained retrospective data that allowed us to track each vaccine administered. If a patient had already received 2 doses of the COVID-19 vaccine, the vaccine administered was counted as a booster and excluded from the calculation of the vaccination rate. Future PDSA cycles could include updating the EHR to capture the whole range of COVID-19 vaccination status (unvaccinated, partially vaccinated, fully vaccinated, fully vaccinated with 1 booster, fully vaccinated with 2 boosters).

We also encountered logistical challenges with the administration of the COVID-19 vaccine to hospitalized patients. During the intervention period, our pharmacy department required 5 COVID-19 vaccination orders before opening a vial and administering the vaccine doses in order to reduce waste. This policy may have limited our ability to vaccinate eligible inpatients because we were not always able to identify 5 patients simultaneously on the service who were eligible and consented to the vaccine.

The majority of patients who were interested in receiving COVID-19 vaccination had already been vaccinated in the outpatient setting. This fact made the inpatient internal medicine subset of patients a particularly challenging population to target, given their possible hesitancy regarding vaccination. By utilizing a multidisciplinary team and increasing communication of providers and nursing staff, we helped to increase the COVID-19 vaccination rates at our hospital from 10.7% to 15.4%.

Future Directions

Future interventions to consider include increasing the availability of other approved COVID-19 vaccines at our hospital besides the Pfizer-BioNTech vaccine. Furthermore, incorporating the vaccine into the admission order set would help initiate the vaccination process early in the hospital course. We encourage other institutions to utilize similar approaches to not only remind providers about inpatient vaccination, but also educate and encourage patients to receive the vaccine. These measures will help institutions increase inpatient COVID-19 vaccination rates in a high-risk population.

Corresponding author: Anna Rubin, MD, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC; [email protected]

Disclosures: None reported.

From the Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC.

Abstract

Objective: Inpatient vaccination initiatives are well described in the literature. During the COVID-19 pandemic, hospitals began administering COVID-19 vaccines to hospitalized patients. Although vaccination rates increased, there remained many unvaccinated patients despite community efforts. This quality improvement project aimed to increase the COVID-19 vaccination rates of hospitalized patients on the medicine service at the George Washington University Hospital (GWUH).

Methods: From November 2021 through February 2022, we conducted a Plan-Do-Study-Act (PDSA) cycle with 3 phases. Initial steps included gathering baseline data from the electronic health record and consulting stakeholders. The first 2 phases focused on educating housestaff on the availability, ordering process, and administration of the Pfizer vaccine. The third phase consisted of developing educational pamphlets for patients to be included in their admission packets.

Results: The baseline mean COVID-19 vaccination rate (August to October 2021) of eligible patients on the medicine service was 10.7%. In the months after we implemented the PDSA cycle (November 2021 to February 2022), the mean vaccination rate increased to 15.4%.

Conclusion: This quality improvement project implemented measures to increase administration of the Pfizer vaccine to eligible patients admitted to the medicine service at GWUH. The mean vaccination rate increased from 10.7% in the 3 months prior to implementation to 15.4% during the 4 months post implementation. Other measures to consider in the future include increasing the availability of other COVID-19 vaccines at our hospital and incorporating the vaccine into the admission order set to help facilitate vaccination early in the hospital course.

Keywords: housestaff, quality improvement, PDSA, COVID-19, BNT162b2 vaccine, patient education

Throughout the COVID-19 pandemic, case rates in the United States have fluctuated considerably, corresponding to epidemic waves. In 2021, US daily cases of COVID-19 peaked at nearly 300,000 in early January and reached a nadir of 8000 cases in mid-June.¹ In September 2021, new cases had increased to 200,000 per day due to the prevalence of the Delta variant.¹ Particularly with the emergence of new variants of SARS-CoV-2, vaccination efforts to limit the spread of infection and severity of illness are critical. Data have shown that 2 doses of the BNT162b2 vaccine (Pfizer-BioNTech) were largely protective against severe infection for approximately 6 months.^2,3 When we began this quality improvement (QI) project in September 2021, only 179 million Americans had been fully vaccinated, according to data from the Centers for Disease Control and Prevention, which is just over half of the US population.⁴ An electronic survey conducted in the United States with more than 5 million responses found that, of those who were hesitant about receiving the vaccine, 49% reported a fear of adverse effects and 48% reported a lack of trust in the vaccine.⁵

This QI project sought to target unvaccinated individuals admitted to the internal medicine inpatient service. Vaccinating hospitalized patients is especially important since they are sicker than the general population and at higher risk of having poor outcomes from COVID-19. Inpatient vaccine initiatives, such as administering influenza vaccine prior to discharge, have been successfully implemented in the past.⁶ One large COVID-19 vaccination program featured an admission order set to increase the rates of vaccination among hospitalized patients.⁷ Our QI project piloted a multidisciplinary approach involving the nursing staff, pharmacy, information technology (IT) department, and internal medicine housestaff to increase COVID-19 vaccination rates among hospitalized patients on the medical service. This project aimed to increase inpatient vaccination rates through interventions targeting both primary providers as well as the patients themselves.

Methods

Setting and Interventions

This project was conducted at the George Washington University Hospital (GWUH) in Washington, DC. The clinicians involved in the study were the internal medicine housestaff, and the patients included were adults admitted to the resident medicine ward teams. The project was exempt by the institutional review board and did not require informed consent.

The quality improvement initiative had 3 phases, each featuring a different intervention (Table 1). The first phase involved sending a weekly announcement (via email and a secure health care messaging app) to current residents rotating on the inpatient medicine service. The announcement contained information regarding COVID-19 vaccine availability at the hospital, instructions on ordering the vaccine, and the process of coordinating with pharmacy to facilitate vaccine administration. Thereafter, residents were educated on the process of giving a COVID-19 vaccine to a patient from start to finish. Due to the nature of the residency schedule, different housestaff members rotated in and out of the medicine wards during the intervention periods. The weekly email was sent to the entire internal medicine housestaff, informing all residents about the QI project, while the weekly secure messages served as reminders and were only sent to residents currently on the medicine wards.

In the second phase, we posted paper flyers throughout the hospital to remind housestaff to give the vaccine and again educate them on the process of ordering the vaccine. For the third intervention, a COVID-19 vaccine educational pamphlet was developed for distribution to inpatients at GWUH. The pamphlet included information on vaccine efficacy, safety, side effects, and eligibility. The pamphlet was incorporated in the admission packet that every patient receives upon admission to the hospital. The patients reviewed the pamphlets with nursing staff, who would answer any questions, with residents available to discuss any outstanding concerns.

Measures and Data Gathering

The primary endpoint of the study was inpatient vaccination rate, defined as the number of COVID-19 vaccines administered divided by the number of patients eligible to receive a vaccine (not fully vaccinated). During initial triage, nursing staff documented vaccination status in the electronic health record (EHR), checking a box in a data entry form if a patient had received 0, 1, or 2 doses of the COVID-19 vaccine. The GWUH IT department generated data from this form to determine the number of patients eligible to receive a COVID-19 vaccine. Data were extracted from the medication administration record in the EHR to determine the number of vaccines that were administered to patients during their hospitalization on the inpatient medical service. Each month, the IT department extracted data for the number of eligible patients and the number of vaccines administered. This yielded the monthly vaccination rates. The monthly vaccination rates in the period prior to starting the QI initiative were compared to the rates in the period after the interventions were implemented.

Of note, during the course of this project, patients became eligible for a third COVID-19 vaccine (booster). We decided to continue with the original aim of vaccinating adults who had only received 0 or 1 dose of the vaccine. Therefore, the eligibility criteria remained the same throughout the study. We obtained retrospective data to ensure that the vaccines being counted toward the vaccination rate were vaccines given to patients not yet fully vaccinated and not vaccines given as boosters.

Results

From August to October 2021, the baseline average monthly vaccination rate of patients on the medicine service who were eligible to receive a COVID-19 vaccine was 10.7%. After the first intervention, the vaccination rate increased to 19.7% in November 2021 (Table 2). The second intervention yielded vaccination rates of 11.4% and 11.8% in December 2021 and January 2022, respectively. During the final phase in February 2022, the vaccination rate was 19.0%. At the conclusion of the study, the mean vaccination rate for the intervention months was 15.4% (Figure 1). Process stability and variation are demonstrated with a statistical process control chart (Figure 2).

 

 

Discussion

For this housestaff-driven QI project, we implemented an inpatient COVID-19 vaccination campaign consisting of 3 phases that targeted both providers and patients. During the intervention period, we observed an increased vaccination rate compared to the period just prior to implementation of the QI project. While our interventions may certainly have boosted vaccination rates, we understand other variables could have contributed to increased rates as well. The emergence of variants in the United States, such as omicron in December 2021,⁸ could have precipitated a demand for vaccinations among patients. Holidays in November and December may also have increased patients’ desire to get vaccinated before travel.

We encountered a number of roadblocks that challenged our project, including difficulty identifying patients who were eligible for the vaccine, logistical vaccine administration challenges, and hesitancy among the inpatient population. Accurately identifying patients who were eligible for a vaccine in the EHR was especially challenging in the setting of rapidly changing guidelines regarding COVID-19 vaccination. In September 2021, the US Food and Drug Administration authorized the Pfizer booster for certain populations and later, in November 2021, for all adults. This meant that some fully vaccinated hospitalized patients (those with 2 doses) then qualified for an additional dose of the vaccine and received a dose during hospitalization. To determine the true vaccination rate, we obtained retrospective data that allowed us to track each vaccine administered. If a patient had already received 2 doses of the COVID-19 vaccine, the vaccine administered was counted as a booster and excluded from the calculation of the vaccination rate. Future PDSA cycles could include updating the EHR to capture the whole range of COVID-19 vaccination status (unvaccinated, partially vaccinated, fully vaccinated, fully vaccinated with 1 booster, fully vaccinated with 2 boosters).

We also encountered logistical challenges with the administration of the COVID-19 vaccine to hospitalized patients. During the intervention period, our pharmacy department required 5 COVID-19 vaccination orders before opening a vial and administering the vaccine doses in order to reduce waste. This policy may have limited our ability to vaccinate eligible inpatients because we were not always able to identify 5 patients simultaneously on the service who were eligible and consented to the vaccine.

The majority of patients who were interested in receiving COVID-19 vaccination had already been vaccinated in the outpatient setting. This fact made the inpatient internal medicine subset of patients a particularly challenging population to target, given their possible hesitancy regarding vaccination. By utilizing a multidisciplinary team and increasing communication of providers and nursing staff, we helped to increase the COVID-19 vaccination rates at our hospital from 10.7% to 15.4%.

Future Directions

Future interventions to consider include increasing the availability of other approved COVID-19 vaccines at our hospital besides the Pfizer-BioNTech vaccine. Furthermore, incorporating the vaccine into the admission order set would help initiate the vaccination process early in the hospital course. We encourage other institutions to utilize similar approaches to not only remind providers about inpatient vaccination, but also educate and encourage patients to receive the vaccine. These measures will help institutions increase inpatient COVID-19 vaccination rates in a high-risk population.

Corresponding author: Anna Rubin, MD, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC; [email protected]

Disclosures: None reported.

From the Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC.

Abstract

Objective: Inpatient vaccination initiatives are well described in the literature. During the COVID-19 pandemic, hospitals began administering COVID-19 vaccines to hospitalized patients. Although vaccination rates increased, there remained many unvaccinated patients despite community efforts. This quality improvement project aimed to increase the COVID-19 vaccination rates of hospitalized patients on the medicine service at the George Washington University Hospital (GWUH).

Methods: From November 2021 through February 2022, we conducted a Plan-Do-Study-Act (PDSA) cycle with 3 phases. Initial steps included gathering baseline data from the electronic health record and consulting stakeholders. The first 2 phases focused on educating housestaff on the availability, ordering process, and administration of the Pfizer vaccine. The third phase consisted of developing educational pamphlets for patients to be included in their admission packets.

Results: The baseline mean COVID-19 vaccination rate (August to October 2021) of eligible patients on the medicine service was 10.7%. In the months after we implemented the PDSA cycle (November 2021 to February 2022), the mean vaccination rate increased to 15.4%.

Conclusion: This quality improvement project implemented measures to increase administration of the Pfizer vaccine to eligible patients admitted to the medicine service at GWUH. The mean vaccination rate increased from 10.7% in the 3 months prior to implementation to 15.4% during the 4 months post implementation. Other measures to consider in the future include increasing the availability of other COVID-19 vaccines at our hospital and incorporating the vaccine into the admission order set to help facilitate vaccination early in the hospital course.

Keywords: housestaff, quality improvement, PDSA, COVID-19, BNT162b2 vaccine, patient education

Throughout the COVID-19 pandemic, case rates in the United States have fluctuated considerably, corresponding to epidemic waves. In 2021, US daily cases of COVID-19 peaked at nearly 300,000 in early January and reached a nadir of 8000 cases in mid-June.¹ In September 2021, new cases had increased to 200,000 per day due to the prevalence of the Delta variant.¹ Particularly with the emergence of new variants of SARS-CoV-2, vaccination efforts to limit the spread of infection and severity of illness are critical. Data have shown that 2 doses of the BNT162b2 vaccine (Pfizer-BioNTech) were largely protective against severe infection for approximately 6 months.^2,3 When we began this quality improvement (QI) project in September 2021, only 179 million Americans had been fully vaccinated, according to data from the Centers for Disease Control and Prevention, which is just over half of the US population.⁴ An electronic survey conducted in the United States with more than 5 million responses found that, of those who were hesitant about receiving the vaccine, 49% reported a fear of adverse effects and 48% reported a lack of trust in the vaccine.⁵

This QI project sought to target unvaccinated individuals admitted to the internal medicine inpatient service. Vaccinating hospitalized patients is especially important since they are sicker than the general population and at higher risk of having poor outcomes from COVID-19. Inpatient vaccine initiatives, such as administering influenza vaccine prior to discharge, have been successfully implemented in the past.⁶ One large COVID-19 vaccination program featured an admission order set to increase the rates of vaccination among hospitalized patients.⁷ Our QI project piloted a multidisciplinary approach involving the nursing staff, pharmacy, information technology (IT) department, and internal medicine housestaff to increase COVID-19 vaccination rates among hospitalized patients on the medical service. This project aimed to increase inpatient vaccination rates through interventions targeting both primary providers as well as the patients themselves.

Methods

Setting and Interventions

This project was conducted at the George Washington University Hospital (GWUH) in Washington, DC. The clinicians involved in the study were the internal medicine housestaff, and the patients included were adults admitted to the resident medicine ward teams. The project was exempt by the institutional review board and did not require informed consent.

The quality improvement initiative had 3 phases, each featuring a different intervention (Table 1). The first phase involved sending a weekly announcement (via email and a secure health care messaging app) to current residents rotating on the inpatient medicine service. The announcement contained information regarding COVID-19 vaccine availability at the hospital, instructions on ordering the vaccine, and the process of coordinating with pharmacy to facilitate vaccine administration. Thereafter, residents were educated on the process of giving a COVID-19 vaccine to a patient from start to finish. Due to the nature of the residency schedule, different housestaff members rotated in and out of the medicine wards during the intervention periods. The weekly email was sent to the entire internal medicine housestaff, informing all residents about the QI project, while the weekly secure messages served as reminders and were only sent to residents currently on the medicine wards.

In the second phase, we posted paper flyers throughout the hospital to remind housestaff to give the vaccine and again educate them on the process of ordering the vaccine. For the third intervention, a COVID-19 vaccine educational pamphlet was developed for distribution to inpatients at GWUH. The pamphlet included information on vaccine efficacy, safety, side effects, and eligibility. The pamphlet was incorporated in the admission packet that every patient receives upon admission to the hospital. The patients reviewed the pamphlets with nursing staff, who would answer any questions, with residents available to discuss any outstanding concerns.

Measures and Data Gathering

The primary endpoint of the study was inpatient vaccination rate, defined as the number of COVID-19 vaccines administered divided by the number of patients eligible to receive a vaccine (not fully vaccinated). During initial triage, nursing staff documented vaccination status in the electronic health record (EHR), checking a box in a data entry form if a patient had received 0, 1, or 2 doses of the COVID-19 vaccine. The GWUH IT department generated data from this form to determine the number of patients eligible to receive a COVID-19 vaccine. Data were extracted from the medication administration record in the EHR to determine the number of vaccines that were administered to patients during their hospitalization on the inpatient medical service. Each month, the IT department extracted data for the number of eligible patients and the number of vaccines administered. This yielded the monthly vaccination rates. The monthly vaccination rates in the period prior to starting the QI initiative were compared to the rates in the period after the interventions were implemented.

Of note, during the course of this project, patients became eligible for a third COVID-19 vaccine (booster). We decided to continue with the original aim of vaccinating adults who had only received 0 or 1 dose of the vaccine. Therefore, the eligibility criteria remained the same throughout the study. We obtained retrospective data to ensure that the vaccines being counted toward the vaccination rate were vaccines given to patients not yet fully vaccinated and not vaccines given as boosters.

Results

From August to October 2021, the baseline average monthly vaccination rate of patients on the medicine service who were eligible to receive a COVID-19 vaccine was 10.7%. After the first intervention, the vaccination rate increased to 19.7% in November 2021 (Table 2). The second intervention yielded vaccination rates of 11.4% and 11.8% in December 2021 and January 2022, respectively. During the final phase in February 2022, the vaccination rate was 19.0%. At the conclusion of the study, the mean vaccination rate for the intervention months was 15.4% (Figure 1). Process stability and variation are demonstrated with a statistical process control chart (Figure 2).

 

 

Discussion

For this housestaff-driven QI project, we implemented an inpatient COVID-19 vaccination campaign consisting of 3 phases that targeted both providers and patients. During the intervention period, we observed an increased vaccination rate compared to the period just prior to implementation of the QI project. While our interventions may certainly have boosted vaccination rates, we understand other variables could have contributed to increased rates as well. The emergence of variants in the United States, such as omicron in December 2021,⁸ could have precipitated a demand for vaccinations among patients. Holidays in November and December may also have increased patients’ desire to get vaccinated before travel.

We encountered a number of roadblocks that challenged our project, including difficulty identifying patients who were eligible for the vaccine, logistical vaccine administration challenges, and hesitancy among the inpatient population. Accurately identifying patients who were eligible for a vaccine in the EHR was especially challenging in the setting of rapidly changing guidelines regarding COVID-19 vaccination. In September 2021, the US Food and Drug Administration authorized the Pfizer booster for certain populations and later, in November 2021, for all adults. This meant that some fully vaccinated hospitalized patients (those with 2 doses) then qualified for an additional dose of the vaccine and received a dose during hospitalization. To determine the true vaccination rate, we obtained retrospective data that allowed us to track each vaccine administered. If a patient had already received 2 doses of the COVID-19 vaccine, the vaccine administered was counted as a booster and excluded from the calculation of the vaccination rate. Future PDSA cycles could include updating the EHR to capture the whole range of COVID-19 vaccination status (unvaccinated, partially vaccinated, fully vaccinated, fully vaccinated with 1 booster, fully vaccinated with 2 boosters).

We also encountered logistical challenges with the administration of the COVID-19 vaccine to hospitalized patients. During the intervention period, our pharmacy department required 5 COVID-19 vaccination orders before opening a vial and administering the vaccine doses in order to reduce waste. This policy may have limited our ability to vaccinate eligible inpatients because we were not always able to identify 5 patients simultaneously on the service who were eligible and consented to the vaccine.

The majority of patients who were interested in receiving COVID-19 vaccination had already been vaccinated in the outpatient setting. This fact made the inpatient internal medicine subset of patients a particularly challenging population to target, given their possible hesitancy regarding vaccination. By utilizing a multidisciplinary team and increasing communication of providers and nursing staff, we helped to increase the COVID-19 vaccination rates at our hospital from 10.7% to 15.4%.

Future Directions

Future interventions to consider include increasing the availability of other approved COVID-19 vaccines at our hospital besides the Pfizer-BioNTech vaccine. Furthermore, incorporating the vaccine into the admission order set would help initiate the vaccination process early in the hospital course. We encourage other institutions to utilize similar approaches to not only remind providers about inpatient vaccination, but also educate and encourage patients to receive the vaccine. These measures will help institutions increase inpatient COVID-19 vaccination rates in a high-risk population.

Corresponding author: Anna Rubin, MD, Department of Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC; [email protected]

Disclosures: None reported.

From the T1D Exchange, Boston, MA (Ann Mungmode, Nicole Rioles, Jesse Cases, Dr. Ebekozien); The Leona M. and Harry B. Hemsley Charitable Trust, New York, NY (Laurel Koester); and the University of Mississippi School of Population Health, Jackson, MS (Dr. Ebekozien).

Abstract

There have been remarkable innovations in diabetes management since the start of the COVID-19 pandemic, but these groundbreaking innovations are drawing limited focus as the field focuses on the adverse impact of the pandemic on patients with diabetes. This article reviews select population health innovations in diabetes management that have become available over the past 2 years of the COVID-19 pandemic from the perspective of the T1D Exchange Quality Improvement Collaborative, a learning health network that focuses on improving care and outcomes for individuals with type 1 diabetes (T1D). Such innovations include expanded telemedicine access, collection of real-world data, machine learning and artificial intelligence, and new diabetes medications and devices. In addition, multiple innovative studies have been undertaken to explore contributors to health inequities in diabetes, and advocacy efforts for specific populations have been successful. Looking to the future, work is required to explore additional health equity successes that do not further exacerbate inequities and to look for additional innovative ways to engage people with T1D in their health care through conversations on social determinants of health and societal structures.

Keywords: type 1 diabetes, learning health network, continuous glucose monitoring, health equity

One in 10 people in the United States has diabetes.¹ Diabetes is the nation’s second leading cause of death, costing the US health system more than $300 billion annually.² The COVID-19 pandemic presented additional health burdens for people living with diabetes. For example, preexisting diabetes was identified as a risk factor for COVID-19–associated morbidity and mortality.^3,4 Over the past 2 years, there have been remarkable innovations in diabetes management, including stem cell therapy and new medication options. Additionally, improved technology solutions have aided in diabetes management through continuous glucose monitors (CGM), smart insulin pens, advanced hybrid closed-loop systems, and continuous subcutaneous insulin injections.^5,6 Unfortunately, these groundbreaking innovations are drawing limited focus, as the field is rightfully focused on the adverse impact of the pandemic on patients with diabetes.

Learning health networks like the T1D Exchange Quality Improvement Collaborative (T1DX-QI) have implemented some of these innovative solutions to improve care for people with diabetes.⁷ T1DX-QI has more than 50 data-sharing endocrinology centers that care for over 75,000 people with diabetes across the United States (Figure 1). Centers participating in the T1DX-QI use quality improvement (QI) and implementation science methods to quickly translate research into evidence-based clinical practice. T1DX-QI leads diabetes population health and health system research and supports widespread transferability across health care organizations through regular collaborative calls, conferences, and case study documentation.⁸

In this review, we summarize impactful population health innovations in diabetes management that have become available over the past 2 years of the COVID-19 pandemic from the perspective of T1DX-QI (see Figure 2 for relevant definitions). This review is limited in scope and is not meant to be an exhaustive list of innovations. The review also reflects significant changes from the perspective of academic diabetes centers, which may not apply to rural or primary care diabetes practices.

 

 

Methods

The first (A.M.), second (H.H.), and senior (O.E.) authors conducted a scoping review of published literature using terms related to diabetes, population health, and innovation on PubMed Central and Google Scholar for the period March 2020 to June 2022. To complement the review, A.M. and O.E. also reviewed abstracts from presentations at major international diabetes conferences, including the American Diabetes Association (ADA), the International Society for Pediatric and Adolescent Diabetes (ISPAD), the T1DX-QI Learning Session Conference, and the Advanced Technologies & Treatments for Diabetes (ATTD) 2020 to 2022 conferences.^9-14 The authors also searched FDA.gov and ClinicalTrials.gov for relevant insights. A.M. and O.E. sorted the reviewed literature into major themes (Figure 3) from the population health improvement perspective of the T1DX-QI.

Population Health Innovations in Diabetes Management

Expansion of Telemedicine Access

Telemedicine is cost-effective for patients with diabetes,¹⁵ including those with complex cases.¹⁶ Before the COVID-19 pandemic, telemedicine and virtual care were rare in diabetes management. However, the pandemic offered a new opportunity to expand the practice of telemedicine in diabetes management. A study from the T1DX-QI showed that telemedicine visits grew from comprising <1% of visits pre-pandemic (December 2019) to 95.2% during the pandemic (August 2020).¹⁷ Additional studies, like those conducted by Phillip et al,¹⁸ confirmed the noninferiority of telemedicine practice for patients with diabetes.Telemedicine was also found to be an effective strategy to educate patients on the use of diabetes technologies.¹⁹

Real-World Data and Disease Surveillance

As the COVID-19 pandemic exacerbated outcomes for people with type 1 diabetes (T1D), a need arose to understand the immediate effects of the pandemic on people with T1D through real-world data and disease surveillance. In April 2020, the T1DX-QI initiated a multicenter surveillance study to collect data and analyze the impact of COVID-19 on people with T1D. The existing health collaborative served as a springboard for robust surveillance study, documenting numerous works on the effects of COVID-19.^3,4,20-28 Other investigators also embraced the power of real-world surveillance and real-world data.^29,30

Big Data, Machine Learning, and Artificial Intelligence

The past 2 years have seen a shift toward embracing the incredible opportunity to tap the large volume of data generated from routine care for practical insights.³¹ In particular, researchers have demonstrated the widespread application of machine learning and artificial intelligence to improve diabetes management.³² The T1DX-QI also harnessed the growing power of big data by expanding the functionality of innovative benchmarking software. The T1DX QI Portal uses electronic medical record data of diabetes patients for clinic-to-clinic benchmarking and data analysis, using business intelligence solutions.³³

Health Equity

While inequities across various health outcomes have been well documented for years,³⁴ the COVID-19 pandemic further exaggerated racial/ethnic health inequities in T1D.^23,35 In response, several organizations have outlined specific strategies to address these health inequities. Emboldened by the pandemic, the T1DX-QI announced a multipronged approach to address health inequities among patients with T1D through the Health Equity Advancement Lab (HEAL).³⁶ One of HEAL’s main components is using real-world data to champion population-level insights and demonstrate progress in QI efforts.

Multiple innovative studies have been undertaken to explore contributors to health inequities in diabetes, and these studies are expanding our understanding of the chasm.³⁷ There have also been innovative solutions to addressing these inequities, with multiple studies published over the past 2 years.³⁸ A source of inequity among patients with T1D is the lack of representation of racial/ethnic minorities with T1D in clinical trials.³⁹ The T1DX-QI suggests that the equity-adapted framework for QI can be applied by research leaders to support trial diversity and representation, ensuring future device innovations are meaningful for all people with T1D.⁴⁰

Diabetes Devices

Glucose monitoring and insulin therapy are vital tools to support individuals living with T1D, and devices such as CGM and insulin pumps have become the standard of care for diabetes management (Table).⁴¹ Innovations in diabetes technology and device access are imperative for a chronic disease with no cure.

The COVID-19 pandemic created an opportunity to increase access to diabetes devices in inpatient settings. In 2020, the US Food and Drug Administration expanded the use of CGM to support remote monitoring of patients in inpatient hospital settings, simultaneously supporting the glucose monitoring needs of patients with T1D and reducing COVID-19 transmission through reduced patient-clinician contact.⁴² This effort has been expanded and will continue in 2022 and beyond,⁴³ and aligns with the growing consensus that supports patients wearing both CGMs and insulin pumps in ambulatory settings to improve patient health outcomes.⁴⁴

Since 2020, innovations in diabetes technology have improved and increased the variety of options available to people with T1D and made them easier to use (Table). New, advanced hybrid closed-loop systems have progressed to offer Bluetooth features, including automatic software upgrades, tubeless systems, and the ability to allow parents to use their smartphones to bolus for children.^45-47 The next big step in insulin delivery innovation is the release of functioning, fully closed loop systems, of which several are currently in clinical trials.⁴⁸ These systems support reduced hypoglycemia and improved time in range.⁴⁹

Additional innovations in insulin delivery have improved the user experience and expanded therapeutic options, including a variety of smart insulin pens complete with dosing logs^50,51 and even a patch to deliver insulin without the burden of injections.⁵² As barriers to diabetes technology persist,⁵³ innovations in alternate insulin delivery provide people with T1D more options to align with their personal access and technology preferences.

Innovations in CGM address cited barriers to their use, including size or overall wear.^53-55 CGMs released in the past few years are smaller in physical size, have longer durations of time between changings, are more accurate, and do not require calibrations for accuracy.

New Diabetes Medications

Many new medications and therapeutic advances have become available in the past 2 years.⁵⁶ Additionally, more medications are being tested as adjunct therapies to support glycemic management in patients with T1D, including metformin, sodium-glucose cotransporter 1 and 2 inhibitors, pramlintide, glucagon-like polypeptide-1 analogs, and glucagon receptor agonists.⁵⁷ Other recent advances include stem cell replacement therapy for patients with T1D.⁵⁸ The ultra-long-acting biosimilar insulins are one medical innovation that has been stalled, rather than propelled, during the COVID-19 pandemic.⁵⁹

Diabetes Policy Advocacy

People with T1D require insulin to survive. The cost of insulin has increased in recent years, with some studies citing a 64% to 100% increase in the past decade.^60,61 In fact, 1 in 4 insulin users report that cost has impacted their insulin use, including rationing their insulin.⁶² Lockdowns during the COVID-19 pandemic stressed US families financially, increasing the urgency for insulin cost caps.

Although the COVID-19 pandemic halted national conversations on drug financing,⁶³ advocacy efforts have succeeded for specific populations. The new Medicare Part D Senior Savings Model will cap the cost of insulin at $35 for a 30-day supply,⁶⁴ and 20 states passed legislation capping insulin pricing.⁶² Efforts to codify national cost caps are under debate, including the passage of the Affordable Insulin Now Act, which passed the House in March 2022 and is currently under review in the Senate.⁶⁵

 

 

Perspective: The Role of Private Philanthropy in Supporting Population Health Innovations

Funders and industry partners play a crucial role in leading and supporting innovations that improve the lives of people with T1D and reduce society’s costs of living with the disease. Data infrastructure is critical to supporting population health. While building the data infrastructure to support population health is both time- and resource-intensive, private foundations such as Helmsley are uniquely positioned—and have a responsibility—to take large, informed risks to help reach all communities with T1D.

The T1DX-QI is the largest source of population health data on T1D in the United States and is becoming the premiere data authority on its incidence, prevalence, and outcomes. The T1DX-QI enables a robust understanding of T1D-related health trends at the population level, as well as trends among clinics and providers. Pilot centers in the T1DX-QI have reported reductions in patients’ A1c and acute diabetes-related events, as well as improvements in device usage and depression screening. The ability to capture changes speaks to the promise and power of these data to demonstrate the clinical impact of QI interventions and to support the spread of best practices and learnings across health systems.

Additional philanthropic efforts have supported innovation in the last 2 years. For example, the JDRF, a nonprofit philanthropic equity firm, has supported efforts in developing artificial pancreas systems and cell therapies currently in clinical trials like teplizumab, a drug that has demonstrated delayed onset of T1D through JDRF’s T1D Fund.⁶⁶ Industry partners also have an opportunity for significant influence in this area, as they continue to fund meaningful projects to advance care for people with T1D.⁶⁷

Conclusion

We are optimistic that the innovations summarized here describe a shift in the tide of equitable T1D outcomes; however, future work is required to explore additional health equity successes that do not further exacerbate inequities. We also see further opportunities for innovative ways to engage people with T1D in their health care through conversations on social determinants of health and societal structures.

Corresponding author: Ann Mungmode, MPH, T1D Exchange, 11 Avenue de Lafayette, Boston, MA 02111; Email: [email protected]

Disclosures: Dr. Ebekozien serve(d) as a director, officer, partner, employee, advisor, consultant, or trustee for the Medtronic Advisory Board and received research grants from Medtronic Diabetes, Eli Lilly, and Dexcom.

Funding: The T1DX-QI is funded by The Leona M. and Harry B. Hemsley Charitable Trust.

From the T1D Exchange, Boston, MA (Ann Mungmode, Nicole Rioles, Jesse Cases, Dr. Ebekozien); The Leona M. and Harry B. Hemsley Charitable Trust, New York, NY (Laurel Koester); and the University of Mississippi School of Population Health, Jackson, MS (Dr. Ebekozien).

Abstract

There have been remarkable innovations in diabetes management since the start of the COVID-19 pandemic, but these groundbreaking innovations are drawing limited focus as the field focuses on the adverse impact of the pandemic on patients with diabetes. This article reviews select population health innovations in diabetes management that have become available over the past 2 years of the COVID-19 pandemic from the perspective of the T1D Exchange Quality Improvement Collaborative, a learning health network that focuses on improving care and outcomes for individuals with type 1 diabetes (T1D). Such innovations include expanded telemedicine access, collection of real-world data, machine learning and artificial intelligence, and new diabetes medications and devices. In addition, multiple innovative studies have been undertaken to explore contributors to health inequities in diabetes, and advocacy efforts for specific populations have been successful. Looking to the future, work is required to explore additional health equity successes that do not further exacerbate inequities and to look for additional innovative ways to engage people with T1D in their health care through conversations on social determinants of health and societal structures.

Keywords: type 1 diabetes, learning health network, continuous glucose monitoring, health equity

One in 10 people in the United States has diabetes.¹ Diabetes is the nation’s second leading cause of death, costing the US health system more than $300 billion annually.² The COVID-19 pandemic presented additional health burdens for people living with diabetes. For example, preexisting diabetes was identified as a risk factor for COVID-19–associated morbidity and mortality.^3,4 Over the past 2 years, there have been remarkable innovations in diabetes management, including stem cell therapy and new medication options. Additionally, improved technology solutions have aided in diabetes management through continuous glucose monitors (CGM), smart insulin pens, advanced hybrid closed-loop systems, and continuous subcutaneous insulin injections.^5,6 Unfortunately, these groundbreaking innovations are drawing limited focus, as the field is rightfully focused on the adverse impact of the pandemic on patients with diabetes.

Learning health networks like the T1D Exchange Quality Improvement Collaborative (T1DX-QI) have implemented some of these innovative solutions to improve care for people with diabetes.⁷ T1DX-QI has more than 50 data-sharing endocrinology centers that care for over 75,000 people with diabetes across the United States (Figure 1). Centers participating in the T1DX-QI use quality improvement (QI) and implementation science methods to quickly translate research into evidence-based clinical practice. T1DX-QI leads diabetes population health and health system research and supports widespread transferability across health care organizations through regular collaborative calls, conferences, and case study documentation.⁸

In this review, we summarize impactful population health innovations in diabetes management that have become available over the past 2 years of the COVID-19 pandemic from the perspective of T1DX-QI (see Figure 2 for relevant definitions). This review is limited in scope and is not meant to be an exhaustive list of innovations. The review also reflects significant changes from the perspective of academic diabetes centers, which may not apply to rural or primary care diabetes practices.

 

 

Methods

The first (A.M.), second (H.H.), and senior (O.E.) authors conducted a scoping review of published literature using terms related to diabetes, population health, and innovation on PubMed Central and Google Scholar for the period March 2020 to June 2022. To complement the review, A.M. and O.E. also reviewed abstracts from presentations at major international diabetes conferences, including the American Diabetes Association (ADA), the International Society for Pediatric and Adolescent Diabetes (ISPAD), the T1DX-QI Learning Session Conference, and the Advanced Technologies & Treatments for Diabetes (ATTD) 2020 to 2022 conferences.^9-14 The authors also searched FDA.gov and ClinicalTrials.gov for relevant insights. A.M. and O.E. sorted the reviewed literature into major themes (Figure 3) from the population health improvement perspective of the T1DX-QI.

Population Health Innovations in Diabetes Management

Expansion of Telemedicine Access

Telemedicine is cost-effective for patients with diabetes,¹⁵ including those with complex cases.¹⁶ Before the COVID-19 pandemic, telemedicine and virtual care were rare in diabetes management. However, the pandemic offered a new opportunity to expand the practice of telemedicine in diabetes management. A study from the T1DX-QI showed that telemedicine visits grew from comprising <1% of visits pre-pandemic (December 2019) to 95.2% during the pandemic (August 2020).¹⁷ Additional studies, like those conducted by Phillip et al,¹⁸ confirmed the noninferiority of telemedicine practice for patients with diabetes.Telemedicine was also found to be an effective strategy to educate patients on the use of diabetes technologies.¹⁹

Real-World Data and Disease Surveillance

As the COVID-19 pandemic exacerbated outcomes for people with type 1 diabetes (T1D), a need arose to understand the immediate effects of the pandemic on people with T1D through real-world data and disease surveillance. In April 2020, the T1DX-QI initiated a multicenter surveillance study to collect data and analyze the impact of COVID-19 on people with T1D. The existing health collaborative served as a springboard for robust surveillance study, documenting numerous works on the effects of COVID-19.^3,4,20-28 Other investigators also embraced the power of real-world surveillance and real-world data.^29,30

Big Data, Machine Learning, and Artificial Intelligence

The past 2 years have seen a shift toward embracing the incredible opportunity to tap the large volume of data generated from routine care for practical insights.³¹ In particular, researchers have demonstrated the widespread application of machine learning and artificial intelligence to improve diabetes management.³² The T1DX-QI also harnessed the growing power of big data by expanding the functionality of innovative benchmarking software. The T1DX QI Portal uses electronic medical record data of diabetes patients for clinic-to-clinic benchmarking and data analysis, using business intelligence solutions.³³

Health Equity

While inequities across various health outcomes have been well documented for years,³⁴ the COVID-19 pandemic further exaggerated racial/ethnic health inequities in T1D.^23,35 In response, several organizations have outlined specific strategies to address these health inequities. Emboldened by the pandemic, the T1DX-QI announced a multipronged approach to address health inequities among patients with T1D through the Health Equity Advancement Lab (HEAL).³⁶ One of HEAL’s main components is using real-world data to champion population-level insights and demonstrate progress in QI efforts.

Multiple innovative studies have been undertaken to explore contributors to health inequities in diabetes, and these studies are expanding our understanding of the chasm.³⁷ There have also been innovative solutions to addressing these inequities, with multiple studies published over the past 2 years.³⁸ A source of inequity among patients with T1D is the lack of representation of racial/ethnic minorities with T1D in clinical trials.³⁹ The T1DX-QI suggests that the equity-adapted framework for QI can be applied by research leaders to support trial diversity and representation, ensuring future device innovations are meaningful for all people with T1D.⁴⁰

Diabetes Devices

Glucose monitoring and insulin therapy are vital tools to support individuals living with T1D, and devices such as CGM and insulin pumps have become the standard of care for diabetes management (Table).⁴¹ Innovations in diabetes technology and device access are imperative for a chronic disease with no cure.

The COVID-19 pandemic created an opportunity to increase access to diabetes devices in inpatient settings. In 2020, the US Food and Drug Administration expanded the use of CGM to support remote monitoring of patients in inpatient hospital settings, simultaneously supporting the glucose monitoring needs of patients with T1D and reducing COVID-19 transmission through reduced patient-clinician contact.⁴² This effort has been expanded and will continue in 2022 and beyond,⁴³ and aligns with the growing consensus that supports patients wearing both CGMs and insulin pumps in ambulatory settings to improve patient health outcomes.⁴⁴

Since 2020, innovations in diabetes technology have improved and increased the variety of options available to people with T1D and made them easier to use (Table). New, advanced hybrid closed-loop systems have progressed to offer Bluetooth features, including automatic software upgrades, tubeless systems, and the ability to allow parents to use their smartphones to bolus for children.^45-47 The next big step in insulin delivery innovation is the release of functioning, fully closed loop systems, of which several are currently in clinical trials.⁴⁸ These systems support reduced hypoglycemia and improved time in range.⁴⁹

Additional innovations in insulin delivery have improved the user experience and expanded therapeutic options, including a variety of smart insulin pens complete with dosing logs^50,51 and even a patch to deliver insulin without the burden of injections.⁵² As barriers to diabetes technology persist,⁵³ innovations in alternate insulin delivery provide people with T1D more options to align with their personal access and technology preferences.

Innovations in CGM address cited barriers to their use, including size or overall wear.^53-55 CGMs released in the past few years are smaller in physical size, have longer durations of time between changings, are more accurate, and do not require calibrations for accuracy.

New Diabetes Medications

Many new medications and therapeutic advances have become available in the past 2 years.⁵⁶ Additionally, more medications are being tested as adjunct therapies to support glycemic management in patients with T1D, including metformin, sodium-glucose cotransporter 1 and 2 inhibitors, pramlintide, glucagon-like polypeptide-1 analogs, and glucagon receptor agonists.⁵⁷ Other recent advances include stem cell replacement therapy for patients with T1D.⁵⁸ The ultra-long-acting biosimilar insulins are one medical innovation that has been stalled, rather than propelled, during the COVID-19 pandemic.⁵⁹

Diabetes Policy Advocacy

People with T1D require insulin to survive. The cost of insulin has increased in recent years, with some studies citing a 64% to 100% increase in the past decade.^60,61 In fact, 1 in 4 insulin users report that cost has impacted their insulin use, including rationing their insulin.⁶² Lockdowns during the COVID-19 pandemic stressed US families financially, increasing the urgency for insulin cost caps.

Although the COVID-19 pandemic halted national conversations on drug financing,⁶³ advocacy efforts have succeeded for specific populations. The new Medicare Part D Senior Savings Model will cap the cost of insulin at $35 for a 30-day supply,⁶⁴ and 20 states passed legislation capping insulin pricing.⁶² Efforts to codify national cost caps are under debate, including the passage of the Affordable Insulin Now Act, which passed the House in March 2022 and is currently under review in the Senate.⁶⁵

 

 

Perspective: The Role of Private Philanthropy in Supporting Population Health Innovations

Funders and industry partners play a crucial role in leading and supporting innovations that improve the lives of people with T1D and reduce society’s costs of living with the disease. Data infrastructure is critical to supporting population health. While building the data infrastructure to support population health is both time- and resource-intensive, private foundations such as Helmsley are uniquely positioned—and have a responsibility—to take large, informed risks to help reach all communities with T1D.

The T1DX-QI is the largest source of population health data on T1D in the United States and is becoming the premiere data authority on its incidence, prevalence, and outcomes. The T1DX-QI enables a robust understanding of T1D-related health trends at the population level, as well as trends among clinics and providers. Pilot centers in the T1DX-QI have reported reductions in patients’ A1c and acute diabetes-related events, as well as improvements in device usage and depression screening. The ability to capture changes speaks to the promise and power of these data to demonstrate the clinical impact of QI interventions and to support the spread of best practices and learnings across health systems.

Additional philanthropic efforts have supported innovation in the last 2 years. For example, the JDRF, a nonprofit philanthropic equity firm, has supported efforts in developing artificial pancreas systems and cell therapies currently in clinical trials like teplizumab, a drug that has demonstrated delayed onset of T1D through JDRF’s T1D Fund.⁶⁶ Industry partners also have an opportunity for significant influence in this area, as they continue to fund meaningful projects to advance care for people with T1D.⁶⁷

Conclusion

We are optimistic that the innovations summarized here describe a shift in the tide of equitable T1D outcomes; however, future work is required to explore additional health equity successes that do not further exacerbate inequities. We also see further opportunities for innovative ways to engage people with T1D in their health care through conversations on social determinants of health and societal structures.

Corresponding author: Ann Mungmode, MPH, T1D Exchange, 11 Avenue de Lafayette, Boston, MA 02111; Email: [email protected]

Disclosures: Dr. Ebekozien serve(d) as a director, officer, partner, employee, advisor, consultant, or trustee for the Medtronic Advisory Board and received research grants from Medtronic Diabetes, Eli Lilly, and Dexcom.

Funding: The T1DX-QI is funded by The Leona M. and Harry B. Hemsley Charitable Trust.

From the T1D Exchange, Boston, MA (Ann Mungmode, Nicole Rioles, Jesse Cases, Dr. Ebekozien); The Leona M. and Harry B. Hemsley Charitable Trust, New York, NY (Laurel Koester); and the University of Mississippi School of Population Health, Jackson, MS (Dr. Ebekozien).

Abstract

There have been remarkable innovations in diabetes management since the start of the COVID-19 pandemic, but these groundbreaking innovations are drawing limited focus as the field focuses on the adverse impact of the pandemic on patients with diabetes. This article reviews select population health innovations in diabetes management that have become available over the past 2 years of the COVID-19 pandemic from the perspective of the T1D Exchange Quality Improvement Collaborative, a learning health network that focuses on improving care and outcomes for individuals with type 1 diabetes (T1D). Such innovations include expanded telemedicine access, collection of real-world data, machine learning and artificial intelligence, and new diabetes medications and devices. In addition, multiple innovative studies have been undertaken to explore contributors to health inequities in diabetes, and advocacy efforts for specific populations have been successful. Looking to the future, work is required to explore additional health equity successes that do not further exacerbate inequities and to look for additional innovative ways to engage people with T1D in their health care through conversations on social determinants of health and societal structures.

Keywords: type 1 diabetes, learning health network, continuous glucose monitoring, health equity

One in 10 people in the United States has diabetes.¹ Diabetes is the nation’s second leading cause of death, costing the US health system more than $300 billion annually.² The COVID-19 pandemic presented additional health burdens for people living with diabetes. For example, preexisting diabetes was identified as a risk factor for COVID-19–associated morbidity and mortality.^3,4 Over the past 2 years, there have been remarkable innovations in diabetes management, including stem cell therapy and new medication options. Additionally, improved technology solutions have aided in diabetes management through continuous glucose monitors (CGM), smart insulin pens, advanced hybrid closed-loop systems, and continuous subcutaneous insulin injections.^5,6 Unfortunately, these groundbreaking innovations are drawing limited focus, as the field is rightfully focused on the adverse impact of the pandemic on patients with diabetes.

Learning health networks like the T1D Exchange Quality Improvement Collaborative (T1DX-QI) have implemented some of these innovative solutions to improve care for people with diabetes.⁷ T1DX-QI has more than 50 data-sharing endocrinology centers that care for over 75,000 people with diabetes across the United States (Figure 1). Centers participating in the T1DX-QI use quality improvement (QI) and implementation science methods to quickly translate research into evidence-based clinical practice. T1DX-QI leads diabetes population health and health system research and supports widespread transferability across health care organizations through regular collaborative calls, conferences, and case study documentation.⁸

In this review, we summarize impactful population health innovations in diabetes management that have become available over the past 2 years of the COVID-19 pandemic from the perspective of T1DX-QI (see Figure 2 for relevant definitions). This review is limited in scope and is not meant to be an exhaustive list of innovations. The review also reflects significant changes from the perspective of academic diabetes centers, which may not apply to rural or primary care diabetes practices.

 

 

Methods

The first (A.M.), second (H.H.), and senior (O.E.) authors conducted a scoping review of published literature using terms related to diabetes, population health, and innovation on PubMed Central and Google Scholar for the period March 2020 to June 2022. To complement the review, A.M. and O.E. also reviewed abstracts from presentations at major international diabetes conferences, including the American Diabetes Association (ADA), the International Society for Pediatric and Adolescent Diabetes (ISPAD), the T1DX-QI Learning Session Conference, and the Advanced Technologies & Treatments for Diabetes (ATTD) 2020 to 2022 conferences.^9-14 The authors also searched FDA.gov and ClinicalTrials.gov for relevant insights. A.M. and O.E. sorted the reviewed literature into major themes (Figure 3) from the population health improvement perspective of the T1DX-QI.

Population Health Innovations in Diabetes Management

Expansion of Telemedicine Access

Telemedicine is cost-effective for patients with diabetes,¹⁵ including those with complex cases.¹⁶ Before the COVID-19 pandemic, telemedicine and virtual care were rare in diabetes management. However, the pandemic offered a new opportunity to expand the practice of telemedicine in diabetes management. A study from the T1DX-QI showed that telemedicine visits grew from comprising <1% of visits pre-pandemic (December 2019) to 95.2% during the pandemic (August 2020).¹⁷ Additional studies, like those conducted by Phillip et al,¹⁸ confirmed the noninferiority of telemedicine practice for patients with diabetes.Telemedicine was also found to be an effective strategy to educate patients on the use of diabetes technologies.¹⁹

Real-World Data and Disease Surveillance

As the COVID-19 pandemic exacerbated outcomes for people with type 1 diabetes (T1D), a need arose to understand the immediate effects of the pandemic on people with T1D through real-world data and disease surveillance. In April 2020, the T1DX-QI initiated a multicenter surveillance study to collect data and analyze the impact of COVID-19 on people with T1D. The existing health collaborative served as a springboard for robust surveillance study, documenting numerous works on the effects of COVID-19.^3,4,20-28 Other investigators also embraced the power of real-world surveillance and real-world data.^29,30

Big Data, Machine Learning, and Artificial Intelligence

The past 2 years have seen a shift toward embracing the incredible opportunity to tap the large volume of data generated from routine care for practical insights.³¹ In particular, researchers have demonstrated the widespread application of machine learning and artificial intelligence to improve diabetes management.³² The T1DX-QI also harnessed the growing power of big data by expanding the functionality of innovative benchmarking software. The T1DX QI Portal uses electronic medical record data of diabetes patients for clinic-to-clinic benchmarking and data analysis, using business intelligence solutions.³³

Health Equity

While inequities across various health outcomes have been well documented for years,³⁴ the COVID-19 pandemic further exaggerated racial/ethnic health inequities in T1D.^23,35 In response, several organizations have outlined specific strategies to address these health inequities. Emboldened by the pandemic, the T1DX-QI announced a multipronged approach to address health inequities among patients with T1D through the Health Equity Advancement Lab (HEAL).³⁶ One of HEAL’s main components is using real-world data to champion population-level insights and demonstrate progress in QI efforts.

Multiple innovative studies have been undertaken to explore contributors to health inequities in diabetes, and these studies are expanding our understanding of the chasm.³⁷ There have also been innovative solutions to addressing these inequities, with multiple studies published over the past 2 years.³⁸ A source of inequity among patients with T1D is the lack of representation of racial/ethnic minorities with T1D in clinical trials.³⁹ The T1DX-QI suggests that the equity-adapted framework for QI can be applied by research leaders to support trial diversity and representation, ensuring future device innovations are meaningful for all people with T1D.⁴⁰

Diabetes Devices

Glucose monitoring and insulin therapy are vital tools to support individuals living with T1D, and devices such as CGM and insulin pumps have become the standard of care for diabetes management (Table).⁴¹ Innovations in diabetes technology and device access are imperative for a chronic disease with no cure.

The COVID-19 pandemic created an opportunity to increase access to diabetes devices in inpatient settings. In 2020, the US Food and Drug Administration expanded the use of CGM to support remote monitoring of patients in inpatient hospital settings, simultaneously supporting the glucose monitoring needs of patients with T1D and reducing COVID-19 transmission through reduced patient-clinician contact.⁴² This effort has been expanded and will continue in 2022 and beyond,⁴³ and aligns with the growing consensus that supports patients wearing both CGMs and insulin pumps in ambulatory settings to improve patient health outcomes.⁴⁴

Since 2020, innovations in diabetes technology have improved and increased the variety of options available to people with T1D and made them easier to use (Table). New, advanced hybrid closed-loop systems have progressed to offer Bluetooth features, including automatic software upgrades, tubeless systems, and the ability to allow parents to use their smartphones to bolus for children.^45-47 The next big step in insulin delivery innovation is the release of functioning, fully closed loop systems, of which several are currently in clinical trials.⁴⁸ These systems support reduced hypoglycemia and improved time in range.⁴⁹

Additional innovations in insulin delivery have improved the user experience and expanded therapeutic options, including a variety of smart insulin pens complete with dosing logs^50,51 and even a patch to deliver insulin without the burden of injections.⁵² As barriers to diabetes technology persist,⁵³ innovations in alternate insulin delivery provide people with T1D more options to align with their personal access and technology preferences.

Innovations in CGM address cited barriers to their use, including size or overall wear.^53-55 CGMs released in the past few years are smaller in physical size, have longer durations of time between changings, are more accurate, and do not require calibrations for accuracy.

New Diabetes Medications

Many new medications and therapeutic advances have become available in the past 2 years.⁵⁶ Additionally, more medications are being tested as adjunct therapies to support glycemic management in patients with T1D, including metformin, sodium-glucose cotransporter 1 and 2 inhibitors, pramlintide, glucagon-like polypeptide-1 analogs, and glucagon receptor agonists.⁵⁷ Other recent advances include stem cell replacement therapy for patients with T1D.⁵⁸ The ultra-long-acting biosimilar insulins are one medical innovation that has been stalled, rather than propelled, during the COVID-19 pandemic.⁵⁹

Diabetes Policy Advocacy

People with T1D require insulin to survive. The cost of insulin has increased in recent years, with some studies citing a 64% to 100% increase in the past decade.^60,61 In fact, 1 in 4 insulin users report that cost has impacted their insulin use, including rationing their insulin.⁶² Lockdowns during the COVID-19 pandemic stressed US families financially, increasing the urgency for insulin cost caps.

Although the COVID-19 pandemic halted national conversations on drug financing,⁶³ advocacy efforts have succeeded for specific populations. The new Medicare Part D Senior Savings Model will cap the cost of insulin at $35 for a 30-day supply,⁶⁴ and 20 states passed legislation capping insulin pricing.⁶² Efforts to codify national cost caps are under debate, including the passage of the Affordable Insulin Now Act, which passed the House in March 2022 and is currently under review in the Senate.⁶⁵

 

 

Perspective: The Role of Private Philanthropy in Supporting Population Health Innovations

Funders and industry partners play a crucial role in leading and supporting innovations that improve the lives of people with T1D and reduce society’s costs of living with the disease. Data infrastructure is critical to supporting population health. While building the data infrastructure to support population health is both time- and resource-intensive, private foundations such as Helmsley are uniquely positioned—and have a responsibility—to take large, informed risks to help reach all communities with T1D.

The T1DX-QI is the largest source of population health data on T1D in the United States and is becoming the premiere data authority on its incidence, prevalence, and outcomes. The T1DX-QI enables a robust understanding of T1D-related health trends at the population level, as well as trends among clinics and providers. Pilot centers in the T1DX-QI have reported reductions in patients’ A1c and acute diabetes-related events, as well as improvements in device usage and depression screening. The ability to capture changes speaks to the promise and power of these data to demonstrate the clinical impact of QI interventions and to support the spread of best practices and learnings across health systems.

Additional philanthropic efforts have supported innovation in the last 2 years. For example, the JDRF, a nonprofit philanthropic equity firm, has supported efforts in developing artificial pancreas systems and cell therapies currently in clinical trials like teplizumab, a drug that has demonstrated delayed onset of T1D through JDRF’s T1D Fund.⁶⁶ Industry partners also have an opportunity for significant influence in this area, as they continue to fund meaningful projects to advance care for people with T1D.⁶⁷

Conclusion

We are optimistic that the innovations summarized here describe a shift in the tide of equitable T1D outcomes; however, future work is required to explore additional health equity successes that do not further exacerbate inequities. We also see further opportunities for innovative ways to engage people with T1D in their health care through conversations on social determinants of health and societal structures.

Corresponding author: Ann Mungmode, MPH, T1D Exchange, 11 Avenue de Lafayette, Boston, MA 02111; Email: [email protected]

Disclosures: Dr. Ebekozien serve(d) as a director, officer, partner, employee, advisor, consultant, or trustee for the Medtronic Advisory Board and received research grants from Medtronic Diabetes, Eli Lilly, and Dexcom.

Funding: The T1DX-QI is funded by The Leona M. and Harry B. Hemsley Charitable Trust.

Study 1 Overview (Bayliss et al)

Objective: To examine the effect of a deprescribing educational intervention on medication use in older adults with cognitive impairment.

Design: This was a pragmatic, cluster randomized trial conducted in 8 primary care clinics that are part of a nonprofit health care system.

Setting and participants: The primary care clinic populations ranged from 170 to 1125 patients per clinic. The primary care clinics were randomly assigned to intervention or control using a uniform distribution in blocks by clinic size. Eligibility criteria for participants at those practices included age 65 years or older; health plan enrollment at least 1 year prior to intervention; diagnosis of Alzheimer disease and related dementia (ADRD) or mild cognitive impairment (MCI) by International Statistical Classification of Diseases and Related Health Problems, Tenth Revision code or from problem list; 1 or more chronic conditions from those in the Chronic Conditions Warehouse; and 5 or more long-term medications. Those who scheduled a visit at their primary care clinic in advance were eligible for the intervention. Primary care clinicians in intervention clinics were eligible to receive the clinician portion of the intervention. A total of 1433 participants were enrolled in the intervention group, and 1579 participants were enrolled in the control group.

Intervention: The intervention included 2 components: a patient and family component with materials mailed in advance of their primary care visits and a clinician component comprising monthly educational materials on deprescribing and notification in the electronic health record about visits with patient participants. The patient and family component consisted of a brochure titled “Managing Medication” and a questionnaire on attitudes toward deprescribing intended to educate patients and family about deprescribing. Clinicians at intervention clinics received an educational presentation at a monthly clinician meeting as well as tip sheets and a poster on deprescribing topics, and they also were notified of upcoming appointments with patients who received the patient component of the intervention. For the control group, patients and family did not receive any materials, and clinicians did not receive intervention materials or notification of participants enrolled in the trial. Usual care in both intervention and control groups included medication reconciliation and electronic health record alerts for potentially high-risk medications.

Main outcome measures: The primary outcomes of the study were the number of long-term medications per individual and the proportion of patients prescribed 1 or more potentially inappropriate medications. Outcome measurements were extracted from the electronic clinical data, and outcomes were assessed at 6 months, which involved comparing counts of medications at baseline with medications at 6 months. Long-term medications were defined as medications that are prescribed for 28 days or more based on pharmacy dispensing data. Potentially inappropriate medications (PIMs) were defined using the Beers list of medications to avoid in those with cognitive impairment and opioid medications. Analyses were conducted as intention to treat.

Main results: In the intervention group and control group, 56.2% and 54.4% of participants were women, and the mean age was 80.1 years (SD, 7.2) and 79.9 years (SD, 7.5), respectively. At baseline, the mean number of long-term medications was 7.0 (SD, 2.1) in the intervention group and 7.0 (SD, 2.2) in the control group. The proportion of patients taking any PIMs was 30.5% in the intervention group and 29.6% in the control group. At 6 months, the mean number of long-term medications was 6.4 in the intervention group and 6.5 in the control group, with an adjusted difference of –0.1 (95% CI, –0.2 to 0.04; P = .14); the proportion of patients with any PIMs was 17.8% in the intervention group and 20.9% in the control group, with an adjusted difference of –3.2% (95% CI, –6.2 to 0.4; P = .08). Preplanned analyses to examine subgroup differences for those with a higher number of medications (7+ vs 5 or 6 medications) did not find different effects of the intervention.

Conclusion: This educational intervention on deprescribing did not result in reductions in the number of medications or the use of PIMs in patients with cognitive impairment.

Study 2 Overview (Gedde et al)

Objective: To examine the effect of a deprescribing intervention (COSMOS) on medication use for nursing home residents.

Design: This was a randomized clinical trial.

Setting and participants: This trial was conducted in 67 units in 33 nursing homes in Norway. Participants were nursing home residents recruited from August 2014 to March 2015. Inclusion criteria included adults aged 65 years and older with at least 2 years of residency in nursing homes. Exclusion criteria included diagnosis of schizophrenia and a life expectancy of 6 months or less. Participants were followed for 4 months; participants were considered lost to follow-up if they died or moved from the nursing home unit. The analyses were per protocol and did not include those lost to follow-up or those who did not undergo a medication review in the intervention group. A total of 217 and 211 residents were included in the intervention and control groups, respectively.

Intervention: The intervention contained 5 components: communication and advance care planning, systematic pain management, medication reviews with collegial mentoring, organization of activities adjusted to needs and preferences, and safety. For medication review, the nursing home physician reviewed medications together with a nurse and study physicians who provided mentoring. The medication review involved a structured process that used assessment tools for behavioral and psychological symptoms of dementia (BPSD), activities of daily living (ADL), pain, cognitive status, well-being and quality of life, and clinical metrics of blood pressure, pulse, and body mass index. The study utilized the START/STOPP criteria¹ for medication use in addition to a list of medications with anticholinergic properties for the medication review. In addition, drug interactions were documented through a drug interaction database; the team also incorporated patient wishes and concerns in the medication reviews. The nursing home physician made final decisions on medications. For the control group, nursing home residents received usual care without this intervention.

Main outcome measures: The primary outcome of the study was the mean change in the number of prescribed psychotropic medications, both regularly scheduled and total medications (which also included on-demand drugs) received at 4 months when compared to baseline. Psychotropic medications included antipsychotics, anxiolytics, hypnotics or sedatives, antidepressants, and antidementia drugs. Secondary outcomes included mean changes in BPSD using the Neuropsychiatric Inventory-Nursing home version (NPI-NH) and the Cornell Scale for Depression for Dementia (CSDD) and ADL using the Physical Self Maintenance Scale (PSMS).

Main results: In both the intervention and control groups, 76% of participants were women, and mean age was 86.3 years (SD, 7.95) in the intervention group and 86.6 years (SD, 7.21) in the control group. At baseline, the mean number of total medications was 10.9 (SD, 4.6) in the intervention group and 10.9 (SD, 4.7) in the control group, and the mean number of psychotropic medications was 2.2 (SD, 1.6) and 2.2 (SD, 1.7) in the intervention and control groups, respectively. At 4 months, the mean change from baseline of total psychotropic medications was –0.34 in the intervention group and 0.01 in the control group (P < .001), and the mean change of regularly scheduled psychotropic medications was –0.21 in the intervention group and 0.02 in the control group (P < .001). Measures of BPSD and depression did not differ between intervention and control groups, and ADL showed a small improvement in the intervention group.

Conclusion: This intervention reduced the use of psychotropic medications in nursing home residents without worsening BPSD or depression and may have yielded improvements in ADL.

Commentary

Polypharmacy is common among older adults, as many of them have multiple chronic conditions and often take multiple medications for managing them. Polypharmacy increases the risk of drug interactions and adverse effects from medications; older adults who are frail and/or who have cognitive impairment are especially at risk. Reducing medication use, especially medications likely to cause adverse effects such as those with anticholinergic properties, has the potential to yield beneficial effects while reducing the burden of taking medications. A large randomized trial found that a pharmacist-led education intervention can be effective in reducing PIM use in community-dwelling older adults,² and that targeting patient motivation and capacity to deprescribe could be effective.³ This study by Bayliss and colleagues (Study 1), however, fell short of the effects seen in the earlier D-PRESCRIBE trial. One of the reasons for these findings may be that the clinician portion of the intervention was less intensive than that used in the earlier trial; specifically, in the present study, clinicians were not provided with or expected to utilize tools for structured medication review or deprescribing. Although the intervention primes the patient and family for discussions around deprescribing through the use of a brochure and questionnaire, the clinician portion of the intervention was less structured. Another example of an effective intervention that provided a more structured deprescribing intervention beyond education of clinicians utilized electronic decision-support to assist with deprescribing.⁴

The findings from the Gedde et al study (Study 2) are comparable to those of prior studies in the nursing home population,⁵ where participants are likely to take a large number of medications, including psychotropic medications, and are more likely to be frail. However, Gedde and colleagues employed a bundled intervention⁶ that included other components besides medication review, and thus it is unclear whether the effect on ADL can be attributed to the deprescribing of medications alone. Gedde et al’s finding that deprescribing can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression is an important contribution to our knowledge about polypharmacy and deprescribing in older patients. Thus, nursing home residents, their families, and clinicians could expect that the deprescribing of psychotropic medications does not lead to worsening symptoms. Of note, the clinician portion of the intervention in the Gedde et al study was quite structured, and this structure may have contributed to the observed effects.

Applications for Clinical Practice and System Implementation

Both studies add to the literature on deprescribing and may offer options for researchers and clinicians who are considering potential components of an effective deprescribing intervention. Patient activation for deprescribing via the methods used in these 2 studies may help to prime patients for conversations about deprescribing; however, as shown by the Bayliss et al study, a more structured approach to clinical encounters may be needed when deprescribing, such as the use of tools in the electronic health record, in order to reduce the use of medication deemed unnecessary or potentially harmful. Further studies should examine the effect of deprescribing on medication use, but perhaps even more importantly, how deprescribing impacts patient outcomes both in terms of risks and benefits.

Practice Points

• A more structured approach to clinical encounters (eg, the use of tools in the electronic health record) may be needed when deprescribing unnecessary or potentially harmful medications in older patients in community settings.
• In the nursing home setting, structured deprescribing intervention can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression.

–William W. Hung, MD, MPH

Study 1 Overview (Bayliss et al)

Objective: To examine the effect of a deprescribing educational intervention on medication use in older adults with cognitive impairment.

Design: This was a pragmatic, cluster randomized trial conducted in 8 primary care clinics that are part of a nonprofit health care system.

Setting and participants: The primary care clinic populations ranged from 170 to 1125 patients per clinic. The primary care clinics were randomly assigned to intervention or control using a uniform distribution in blocks by clinic size. Eligibility criteria for participants at those practices included age 65 years or older; health plan enrollment at least 1 year prior to intervention; diagnosis of Alzheimer disease and related dementia (ADRD) or mild cognitive impairment (MCI) by International Statistical Classification of Diseases and Related Health Problems, Tenth Revision code or from problem list; 1 or more chronic conditions from those in the Chronic Conditions Warehouse; and 5 or more long-term medications. Those who scheduled a visit at their primary care clinic in advance were eligible for the intervention. Primary care clinicians in intervention clinics were eligible to receive the clinician portion of the intervention. A total of 1433 participants were enrolled in the intervention group, and 1579 participants were enrolled in the control group.

Intervention: The intervention included 2 components: a patient and family component with materials mailed in advance of their primary care visits and a clinician component comprising monthly educational materials on deprescribing and notification in the electronic health record about visits with patient participants. The patient and family component consisted of a brochure titled “Managing Medication” and a questionnaire on attitudes toward deprescribing intended to educate patients and family about deprescribing. Clinicians at intervention clinics received an educational presentation at a monthly clinician meeting as well as tip sheets and a poster on deprescribing topics, and they also were notified of upcoming appointments with patients who received the patient component of the intervention. For the control group, patients and family did not receive any materials, and clinicians did not receive intervention materials or notification of participants enrolled in the trial. Usual care in both intervention and control groups included medication reconciliation and electronic health record alerts for potentially high-risk medications.

Main outcome measures: The primary outcomes of the study were the number of long-term medications per individual and the proportion of patients prescribed 1 or more potentially inappropriate medications. Outcome measurements were extracted from the electronic clinical data, and outcomes were assessed at 6 months, which involved comparing counts of medications at baseline with medications at 6 months. Long-term medications were defined as medications that are prescribed for 28 days or more based on pharmacy dispensing data. Potentially inappropriate medications (PIMs) were defined using the Beers list of medications to avoid in those with cognitive impairment and opioid medications. Analyses were conducted as intention to treat.

Main results: In the intervention group and control group, 56.2% and 54.4% of participants were women, and the mean age was 80.1 years (SD, 7.2) and 79.9 years (SD, 7.5), respectively. At baseline, the mean number of long-term medications was 7.0 (SD, 2.1) in the intervention group and 7.0 (SD, 2.2) in the control group. The proportion of patients taking any PIMs was 30.5% in the intervention group and 29.6% in the control group. At 6 months, the mean number of long-term medications was 6.4 in the intervention group and 6.5 in the control group, with an adjusted difference of –0.1 (95% CI, –0.2 to 0.04; P = .14); the proportion of patients with any PIMs was 17.8% in the intervention group and 20.9% in the control group, with an adjusted difference of –3.2% (95% CI, –6.2 to 0.4; P = .08). Preplanned analyses to examine subgroup differences for those with a higher number of medications (7+ vs 5 or 6 medications) did not find different effects of the intervention.

Conclusion: This educational intervention on deprescribing did not result in reductions in the number of medications or the use of PIMs in patients with cognitive impairment.

Study 2 Overview (Gedde et al)

Objective: To examine the effect of a deprescribing intervention (COSMOS) on medication use for nursing home residents.

Design: This was a randomized clinical trial.

Setting and participants: This trial was conducted in 67 units in 33 nursing homes in Norway. Participants were nursing home residents recruited from August 2014 to March 2015. Inclusion criteria included adults aged 65 years and older with at least 2 years of residency in nursing homes. Exclusion criteria included diagnosis of schizophrenia and a life expectancy of 6 months or less. Participants were followed for 4 months; participants were considered lost to follow-up if they died or moved from the nursing home unit. The analyses were per protocol and did not include those lost to follow-up or those who did not undergo a medication review in the intervention group. A total of 217 and 211 residents were included in the intervention and control groups, respectively.

Intervention: The intervention contained 5 components: communication and advance care planning, systematic pain management, medication reviews with collegial mentoring, organization of activities adjusted to needs and preferences, and safety. For medication review, the nursing home physician reviewed medications together with a nurse and study physicians who provided mentoring. The medication review involved a structured process that used assessment tools for behavioral and psychological symptoms of dementia (BPSD), activities of daily living (ADL), pain, cognitive status, well-being and quality of life, and clinical metrics of blood pressure, pulse, and body mass index. The study utilized the START/STOPP criteria¹ for medication use in addition to a list of medications with anticholinergic properties for the medication review. In addition, drug interactions were documented through a drug interaction database; the team also incorporated patient wishes and concerns in the medication reviews. The nursing home physician made final decisions on medications. For the control group, nursing home residents received usual care without this intervention.

Main outcome measures: The primary outcome of the study was the mean change in the number of prescribed psychotropic medications, both regularly scheduled and total medications (which also included on-demand drugs) received at 4 months when compared to baseline. Psychotropic medications included antipsychotics, anxiolytics, hypnotics or sedatives, antidepressants, and antidementia drugs. Secondary outcomes included mean changes in BPSD using the Neuropsychiatric Inventory-Nursing home version (NPI-NH) and the Cornell Scale for Depression for Dementia (CSDD) and ADL using the Physical Self Maintenance Scale (PSMS).

Main results: In both the intervention and control groups, 76% of participants were women, and mean age was 86.3 years (SD, 7.95) in the intervention group and 86.6 years (SD, 7.21) in the control group. At baseline, the mean number of total medications was 10.9 (SD, 4.6) in the intervention group and 10.9 (SD, 4.7) in the control group, and the mean number of psychotropic medications was 2.2 (SD, 1.6) and 2.2 (SD, 1.7) in the intervention and control groups, respectively. At 4 months, the mean change from baseline of total psychotropic medications was –0.34 in the intervention group and 0.01 in the control group (P < .001), and the mean change of regularly scheduled psychotropic medications was –0.21 in the intervention group and 0.02 in the control group (P < .001). Measures of BPSD and depression did not differ between intervention and control groups, and ADL showed a small improvement in the intervention group.

Conclusion: This intervention reduced the use of psychotropic medications in nursing home residents without worsening BPSD or depression and may have yielded improvements in ADL.

Commentary

Polypharmacy is common among older adults, as many of them have multiple chronic conditions and often take multiple medications for managing them. Polypharmacy increases the risk of drug interactions and adverse effects from medications; older adults who are frail and/or who have cognitive impairment are especially at risk. Reducing medication use, especially medications likely to cause adverse effects such as those with anticholinergic properties, has the potential to yield beneficial effects while reducing the burden of taking medications. A large randomized trial found that a pharmacist-led education intervention can be effective in reducing PIM use in community-dwelling older adults,² and that targeting patient motivation and capacity to deprescribe could be effective.³ This study by Bayliss and colleagues (Study 1), however, fell short of the effects seen in the earlier D-PRESCRIBE trial. One of the reasons for these findings may be that the clinician portion of the intervention was less intensive than that used in the earlier trial; specifically, in the present study, clinicians were not provided with or expected to utilize tools for structured medication review or deprescribing. Although the intervention primes the patient and family for discussions around deprescribing through the use of a brochure and questionnaire, the clinician portion of the intervention was less structured. Another example of an effective intervention that provided a more structured deprescribing intervention beyond education of clinicians utilized electronic decision-support to assist with deprescribing.⁴

The findings from the Gedde et al study (Study 2) are comparable to those of prior studies in the nursing home population,⁵ where participants are likely to take a large number of medications, including psychotropic medications, and are more likely to be frail. However, Gedde and colleagues employed a bundled intervention⁶ that included other components besides medication review, and thus it is unclear whether the effect on ADL can be attributed to the deprescribing of medications alone. Gedde et al’s finding that deprescribing can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression is an important contribution to our knowledge about polypharmacy and deprescribing in older patients. Thus, nursing home residents, their families, and clinicians could expect that the deprescribing of psychotropic medications does not lead to worsening symptoms. Of note, the clinician portion of the intervention in the Gedde et al study was quite structured, and this structure may have contributed to the observed effects.

Applications for Clinical Practice and System Implementation

Both studies add to the literature on deprescribing and may offer options for researchers and clinicians who are considering potential components of an effective deprescribing intervention. Patient activation for deprescribing via the methods used in these 2 studies may help to prime patients for conversations about deprescribing; however, as shown by the Bayliss et al study, a more structured approach to clinical encounters may be needed when deprescribing, such as the use of tools in the electronic health record, in order to reduce the use of medication deemed unnecessary or potentially harmful. Further studies should examine the effect of deprescribing on medication use, but perhaps even more importantly, how deprescribing impacts patient outcomes both in terms of risks and benefits.

Practice Points

• A more structured approach to clinical encounters (eg, the use of tools in the electronic health record) may be needed when deprescribing unnecessary or potentially harmful medications in older patients in community settings.
• In the nursing home setting, structured deprescribing intervention can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression.

–William W. Hung, MD, MPH

Study 1 Overview (Bayliss et al)

Objective: To examine the effect of a deprescribing educational intervention on medication use in older adults with cognitive impairment.

Design: This was a pragmatic, cluster randomized trial conducted in 8 primary care clinics that are part of a nonprofit health care system.

Setting and participants: The primary care clinic populations ranged from 170 to 1125 patients per clinic. The primary care clinics were randomly assigned to intervention or control using a uniform distribution in blocks by clinic size. Eligibility criteria for participants at those practices included age 65 years or older; health plan enrollment at least 1 year prior to intervention; diagnosis of Alzheimer disease and related dementia (ADRD) or mild cognitive impairment (MCI) by International Statistical Classification of Diseases and Related Health Problems, Tenth Revision code or from problem list; 1 or more chronic conditions from those in the Chronic Conditions Warehouse; and 5 or more long-term medications. Those who scheduled a visit at their primary care clinic in advance were eligible for the intervention. Primary care clinicians in intervention clinics were eligible to receive the clinician portion of the intervention. A total of 1433 participants were enrolled in the intervention group, and 1579 participants were enrolled in the control group.

Intervention: The intervention included 2 components: a patient and family component with materials mailed in advance of their primary care visits and a clinician component comprising monthly educational materials on deprescribing and notification in the electronic health record about visits with patient participants. The patient and family component consisted of a brochure titled “Managing Medication” and a questionnaire on attitudes toward deprescribing intended to educate patients and family about deprescribing. Clinicians at intervention clinics received an educational presentation at a monthly clinician meeting as well as tip sheets and a poster on deprescribing topics, and they also were notified of upcoming appointments with patients who received the patient component of the intervention. For the control group, patients and family did not receive any materials, and clinicians did not receive intervention materials or notification of participants enrolled in the trial. Usual care in both intervention and control groups included medication reconciliation and electronic health record alerts for potentially high-risk medications.

Main outcome measures: The primary outcomes of the study were the number of long-term medications per individual and the proportion of patients prescribed 1 or more potentially inappropriate medications. Outcome measurements were extracted from the electronic clinical data, and outcomes were assessed at 6 months, which involved comparing counts of medications at baseline with medications at 6 months. Long-term medications were defined as medications that are prescribed for 28 days or more based on pharmacy dispensing data. Potentially inappropriate medications (PIMs) were defined using the Beers list of medications to avoid in those with cognitive impairment and opioid medications. Analyses were conducted as intention to treat.

Main results: In the intervention group and control group, 56.2% and 54.4% of participants were women, and the mean age was 80.1 years (SD, 7.2) and 79.9 years (SD, 7.5), respectively. At baseline, the mean number of long-term medications was 7.0 (SD, 2.1) in the intervention group and 7.0 (SD, 2.2) in the control group. The proportion of patients taking any PIMs was 30.5% in the intervention group and 29.6% in the control group. At 6 months, the mean number of long-term medications was 6.4 in the intervention group and 6.5 in the control group, with an adjusted difference of –0.1 (95% CI, –0.2 to 0.04; P = .14); the proportion of patients with any PIMs was 17.8% in the intervention group and 20.9% in the control group, with an adjusted difference of –3.2% (95% CI, –6.2 to 0.4; P = .08). Preplanned analyses to examine subgroup differences for those with a higher number of medications (7+ vs 5 or 6 medications) did not find different effects of the intervention.

Conclusion: This educational intervention on deprescribing did not result in reductions in the number of medications or the use of PIMs in patients with cognitive impairment.

Study 2 Overview (Gedde et al)

Objective: To examine the effect of a deprescribing intervention (COSMOS) on medication use for nursing home residents.

Design: This was a randomized clinical trial.

Setting and participants: This trial was conducted in 67 units in 33 nursing homes in Norway. Participants were nursing home residents recruited from August 2014 to March 2015. Inclusion criteria included adults aged 65 years and older with at least 2 years of residency in nursing homes. Exclusion criteria included diagnosis of schizophrenia and a life expectancy of 6 months or less. Participants were followed for 4 months; participants were considered lost to follow-up if they died or moved from the nursing home unit. The analyses were per protocol and did not include those lost to follow-up or those who did not undergo a medication review in the intervention group. A total of 217 and 211 residents were included in the intervention and control groups, respectively.

Intervention: The intervention contained 5 components: communication and advance care planning, systematic pain management, medication reviews with collegial mentoring, organization of activities adjusted to needs and preferences, and safety. For medication review, the nursing home physician reviewed medications together with a nurse and study physicians who provided mentoring. The medication review involved a structured process that used assessment tools for behavioral and psychological symptoms of dementia (BPSD), activities of daily living (ADL), pain, cognitive status, well-being and quality of life, and clinical metrics of blood pressure, pulse, and body mass index. The study utilized the START/STOPP criteria¹ for medication use in addition to a list of medications with anticholinergic properties for the medication review. In addition, drug interactions were documented through a drug interaction database; the team also incorporated patient wishes and concerns in the medication reviews. The nursing home physician made final decisions on medications. For the control group, nursing home residents received usual care without this intervention.

Main outcome measures: The primary outcome of the study was the mean change in the number of prescribed psychotropic medications, both regularly scheduled and total medications (which also included on-demand drugs) received at 4 months when compared to baseline. Psychotropic medications included antipsychotics, anxiolytics, hypnotics or sedatives, antidepressants, and antidementia drugs. Secondary outcomes included mean changes in BPSD using the Neuropsychiatric Inventory-Nursing home version (NPI-NH) and the Cornell Scale for Depression for Dementia (CSDD) and ADL using the Physical Self Maintenance Scale (PSMS).

Main results: In both the intervention and control groups, 76% of participants were women, and mean age was 86.3 years (SD, 7.95) in the intervention group and 86.6 years (SD, 7.21) in the control group. At baseline, the mean number of total medications was 10.9 (SD, 4.6) in the intervention group and 10.9 (SD, 4.7) in the control group, and the mean number of psychotropic medications was 2.2 (SD, 1.6) and 2.2 (SD, 1.7) in the intervention and control groups, respectively. At 4 months, the mean change from baseline of total psychotropic medications was –0.34 in the intervention group and 0.01 in the control group (P < .001), and the mean change of regularly scheduled psychotropic medications was –0.21 in the intervention group and 0.02 in the control group (P < .001). Measures of BPSD and depression did not differ between intervention and control groups, and ADL showed a small improvement in the intervention group.

Conclusion: This intervention reduced the use of psychotropic medications in nursing home residents without worsening BPSD or depression and may have yielded improvements in ADL.

Commentary

Polypharmacy is common among older adults, as many of them have multiple chronic conditions and often take multiple medications for managing them. Polypharmacy increases the risk of drug interactions and adverse effects from medications; older adults who are frail and/or who have cognitive impairment are especially at risk. Reducing medication use, especially medications likely to cause adverse effects such as those with anticholinergic properties, has the potential to yield beneficial effects while reducing the burden of taking medications. A large randomized trial found that a pharmacist-led education intervention can be effective in reducing PIM use in community-dwelling older adults,² and that targeting patient motivation and capacity to deprescribe could be effective.³ This study by Bayliss and colleagues (Study 1), however, fell short of the effects seen in the earlier D-PRESCRIBE trial. One of the reasons for these findings may be that the clinician portion of the intervention was less intensive than that used in the earlier trial; specifically, in the present study, clinicians were not provided with or expected to utilize tools for structured medication review or deprescribing. Although the intervention primes the patient and family for discussions around deprescribing through the use of a brochure and questionnaire, the clinician portion of the intervention was less structured. Another example of an effective intervention that provided a more structured deprescribing intervention beyond education of clinicians utilized electronic decision-support to assist with deprescribing.⁴

The findings from the Gedde et al study (Study 2) are comparable to those of prior studies in the nursing home population,⁵ where participants are likely to take a large number of medications, including psychotropic medications, and are more likely to be frail. However, Gedde and colleagues employed a bundled intervention⁶ that included other components besides medication review, and thus it is unclear whether the effect on ADL can be attributed to the deprescribing of medications alone. Gedde et al’s finding that deprescribing can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression is an important contribution to our knowledge about polypharmacy and deprescribing in older patients. Thus, nursing home residents, their families, and clinicians could expect that the deprescribing of psychotropic medications does not lead to worsening symptoms. Of note, the clinician portion of the intervention in the Gedde et al study was quite structured, and this structure may have contributed to the observed effects.

Applications for Clinical Practice and System Implementation

Both studies add to the literature on deprescribing and may offer options for researchers and clinicians who are considering potential components of an effective deprescribing intervention. Patient activation for deprescribing via the methods used in these 2 studies may help to prime patients for conversations about deprescribing; however, as shown by the Bayliss et al study, a more structured approach to clinical encounters may be needed when deprescribing, such as the use of tools in the electronic health record, in order to reduce the use of medication deemed unnecessary or potentially harmful. Further studies should examine the effect of deprescribing on medication use, but perhaps even more importantly, how deprescribing impacts patient outcomes both in terms of risks and benefits.

Practice Points

• A more structured approach to clinical encounters (eg, the use of tools in the electronic health record) may be needed when deprescribing unnecessary or potentially harmful medications in older patients in community settings.
• In the nursing home setting, structured deprescribing intervention can reduce the use of psychotropic medications while not leading to differences in behavioral and psychologic symptoms or depression.

–William W. Hung, MD, MPH

Study 1 Overview (Oberhaus et al)

Objective: To compare the 3-Minute Diagnostic Confusion Assessment Method (3D-CAM) to the long-form Confusion Assessment Method (CAM) in detecting postoperative delirium.

Design: Prospective concurrent comparison of 3D-CAM and CAM evaluations in a cohort of postoperative geriatric patients.

Setting and participants: Eligible participants were patients aged 60 years or older undergoing major elective surgery at Barnes Jewish Hospital (St. Louis, Missouri) who were enrolled in ongoing clinical trials (PODCAST, ENGAGES, SATISFY-SOS) between 2015 and 2018. Surgeries were at least 2 hours in length and required general anesthesia, planned extubation, and a minimum 2-day hospital stay. Investigators were extensively trained in administering 3D-CAM and CAM instruments. Participants were evaluated 2 hours after the end of anesthesia care on the day of surgery, then daily until follow-up was completed per clinical trial protocol or until the participant was determined by CAM to be nondelirious for 3 consecutive days. For each evaluation, both 3D-CAM and CAM assessors approached the participant together, but the evaluation was conducted such that the 3D-CAM assessor was masked to the additional questions ascertained by the long-form CAM assessment. The 3D-CAM or CAM assessor independently scored their respective assessments blinded to the results of the other assessor.

Main outcome measures: Participants were concurrently evaluated for postoperative delirium by both 3D-CAM and long-form CAM assessments. Comparisons between 3D-CAM and CAM scores were made using Cohen κ with repeated measures, generalized linear mixed-effects model, and Bland-Altman analysis.

Main results: Sixteen raters performed 471 concurrent 3D-CAM and CAM assessments in 299 participants (mean [SD] age, 69 [6.5] years). Of these participants, 152 (50.8%) were men, 263 (88.0%) were White, and 211 (70.6%) underwent noncardiac surgery. Both instruments showed good intraclass correlation (0.98 for 3D-CAM, 0.84 for CAM) with good overall agreement (Cohen κ = 0.71; 95% CI, 0.58-0.83). The mixed-effects model indicated a significant disagreement between the 3D-CAM and CAM assessments (estimated difference in fixed effect, –0.68; 95% CI, –1.32 to –0.05; P = .04). The Bland-Altman analysis showed that the probability of a delirium diagnosis with the 3D-CAM was more than twice that with the CAM (probability ratio, 2.78; 95% CI, 2.44-3.23).

Conclusion: The high degree of agreement between 3D-CAM and long-form CAM assessments suggests that the former may be a pragmatic and easy-to-administer clinical tool to screen for postoperative delirium in vulnerable older surgical patients.

Study 2 Overview (Shenkin et al)

Objective: To assess the accuracy of the 4 ‘A’s Test (4AT) for delirium detection in the medical inpatient setting and to compare the 4AT to the CAM.

Design: Prospective randomized diagnostic test accuracy study.

Setting and participants: This study was conducted in emergency departments and acute medical wards at 3 UK sites (Edinburgh, Bradford, and Sheffield) and enrolled acute medical patients aged 70 years or older without acute life-threatening illnesses and/or coma. Assessors administering the delirium evaluation were nurses or graduate clinical research associates who underwent systematic training in delirium and delirium assessment. Additional training was provided to those administering the CAM but not to those administering the 4AT as the latter is designed to be administered without special training. First, all participants underwent a reference standard delirium assessment using Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition) (DSM-IV) criteria to derive a final definitive diagnosis of delirium via expert consensus (1 psychiatrist and 2 geriatricians). Then, the participants were randomized to either the 4AT or the comparator CAM group using computer-generated pseudo-random numbers, stratified by study site, with block allocation. All assessments were performed by pairs of independent assessors blinded to the results of the other assessment.

Main outcome measures: All participants were evaluated by the reference standard (DSM-IV criteria for delirium) and by either 4AT or CAM instruments for delirium. The accuracy of the 4AT instrument was evaluated by comparing its positive and negative predictive values, sensitivity, and specificity to the reference standard and analyzed via the area under the receiver operating characteristic curve. The diagnostic accuracy of 4AT, compared to the CAM, was evaluated by comparing positive and negative predictive values, sensitivity, and specificity using Fisher’s exact test. The overall performance of 4AT and CAM was summarized using Youden’s Index and the diagnostic odds ratio of sensitivity to specificity.

Results: All 843 individuals enrolled in the study were randomized and 785 were included in the analysis (23 withdrew, 3 lost contact, 32 indeterminate diagnosis, 2 missing outcome). Of the participants analyzed, the mean age was 81.4 [6.4] years, and 12.1% (95/785) had delirium by reference standard assessment, 14.3% (56/392) by 4AT, and 4.7% (18/384) by CAM. The 4AT group had an area under the receiver operating characteristic curve of 0.90 (95% CI, 0.84-0.96), a sensitivity of 76% (95% CI, 61%-87%), and a specificity of 94% (95% CI, 92%-97%). In comparison, the CAM group had a sensitivity of 40% (95% CI, 26%-57%) and a specificity of 100% (95% CI, 98%-100%).

Conclusions: The 4AT is a pragmatic screening test for delirium in a medical space that does not require special training to administer. The use of this instrument may help to improve delirium detection as a part of routine clinical care in hospitalized older adults.

 

 

Commentary

Delirium is an acute confusional state marked by fluctuating mental status, inattention, disorganized thinking, and altered level of consciousness. It is exceedingly common in older patients in both surgical and medical settings and is associated with increased morbidity, mortality, hospital length of stay, institutionalization, and health care costs. Delirium is frequently underdiagnosed in the hospitalized setting, perhaps due to a combination of its waxing and waning nature and a lack of pragmatic and easily implementable screening tools that can be readily administered by clinicians and nonclinicians alike.¹ While the CAM is a well-validated instrument to diagnose delirium, it requires specific training in the rating of each of the cardinal features ascertained through a brief cognitive assessment and takes 5 to 10 minutes to complete. Taken together, given the high patient load for clinicians in the hospital setting, the validation and application of brief delirium screening instruments that can be reliably administered by nonphysicians and nonclinicians may enhance delirium detection in vulnerable patients and consequently improve their outcomes.

In Study 1, Oberhaus et al approach the challenge of underdiagnosing delirium in the postoperative setting by investigating whether the widely accepted long-form CAM and an abbreviated 3-minute version, the 3D-CAM, provide similar delirium detection in older surgical patients. The authors found that both instruments were reliable tests individually (high interrater reliability) and had good overall agreement. However, the 3D-CAM was more likely to yield a positive diagnosis of delirium compared to the long-form CAM, consistent with its purpose as a screening tool with a high sensitivity. It is important to emphasize that the 3D-CAM takes less time to administer, but also requires less extensive training and clinical knowledge than the long-form CAM. Therefore, this instrument meets the prerequisite of a brief screening test that can be rapidly administered by nonclinicians, and if affirmative, followed by a more extensive confirmatory test performed by a clinician. Limitations of this study include a lack of a reference standard structured interview conducted by a physician-rater to better determine the true diagnostic accuracy of both 3D-CAM and CAM assessments, and the use of convenience sampling at a single center, which reduces the generalizability of its findings.

In a similar vein, Shenkin et al in Study 2 attempt to evaluate the utility of the 4AT instrument in diagnosing delirium in older medical inpatients by testing the diagnostic accuracy of the 4AT against a reference standard (ie, DSM-IV–based evaluation by physicians) as well as comparing it to CAM. The 4AT takes less time (~2 minutes) and requires less knowledge and training to administer as compared to the CAM. The study showed that the abbreviated 4AT, compared to CAM, had a higher sensitivity (76% vs 40%) and lower specificity (94% vs 100%) in delirium detection. Thus, akin to the application of 3D-CAM in the postoperative setting, 4AT possesses key characteristics of a brief delirium screening test for older patients in the acute medical setting. In contrast to the Oberhaus et al study, a major strength of this study was the utilization of a reference standard that was validated by expert consensus. This allowed the 4AT and CAM assessments to be compared to a more objective standard, thereby directly testing their diagnostic performance in detecting delirium.

Application for Clinical Practice and System Implementation

The findings from both Study 1 and 2 suggest that using an abbreviated delirium instrument in both surgical and acute medical settings may provide a pragmatic and sensitive method to detect delirium in older patients. The brevity of administration of 3D-CAM (~3 minutes) and 4AT (~2 minutes), combined with their higher sensitivity for detecting delirium compared to CAM, make these instruments potentially effective rapid screening tests for delirium in hospitalized older patients. Importantly, the utilization of such instruments might be a feasible way to mitigate the issue of underdiagnosing delirium in the hospital.

Several additional aspects of these abbreviated delirium instruments increase their suitability for clinical application. Specifically, the 3D-CAM and 4AT require less extensive training and clinical knowledge to both administer and interpret the results than the CAM.² For instance, a multistage, multiday training for CAM is a key factor in maintaining its diagnostic accuracy.^3,4 In contrast, the 3D-CAM requires only a 1- to 2-hour training session, and the 4AT can be administered by a nonclinician without the need for instrument-specific training. Thus, implementation of these instruments can be particularly pragmatic in clinical settings in which the staff involved in delirium screening cannot undergo the substantial training required to administer CAM. Moreover, these abbreviated tests enable nonphysician care team members to assume the role of delirium screener in the hospital. Taken together, the adoption of these abbreviated instruments may facilitate brief screenings of delirium in older patients by caregivers who see them most often—nurses and certified nursing assistants—thereby improving early detection and prevention of delirium-related complications in the hospital.

The feasibility of using abbreviated delirium screening instruments in the hospital setting raises a system implementation question—if these instruments are designed to be administered by those with limited to no training, could nonclinicians, such as hospital volunteers, effectively take on delirium screening roles in the hospital? If volunteers are able to take on this role, the integration of hospital volunteers into the clinical team can greatly expand the capacity for delirium screening in the hospital setting. Further research is warranted to validate the diagnostic accuracy of 3D-CAM and 4AT by nonclinician administrators in order to more broadly adopt this approach to delirium screening.

Practice Points

• Abbreviated delirium screening tools such as 3D-CAM and 4AT may be pragmatic instruments to improve delirium detection in surgical and hospitalized older patients, respectively.
• Further studies are warranted to validate the diagnostic accuracy of 3D-CAM and 4AT by nonclinician administrators in order to more broadly adopt this approach to delirium screening.

Jared Doan, BS, and Fred Ko, MD
Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai

Study 1 Overview (Oberhaus et al)

Objective: To compare the 3-Minute Diagnostic Confusion Assessment Method (3D-CAM) to the long-form Confusion Assessment Method (CAM) in detecting postoperative delirium.

Design: Prospective concurrent comparison of 3D-CAM and CAM evaluations in a cohort of postoperative geriatric patients.

Setting and participants: Eligible participants were patients aged 60 years or older undergoing major elective surgery at Barnes Jewish Hospital (St. Louis, Missouri) who were enrolled in ongoing clinical trials (PODCAST, ENGAGES, SATISFY-SOS) between 2015 and 2018. Surgeries were at least 2 hours in length and required general anesthesia, planned extubation, and a minimum 2-day hospital stay. Investigators were extensively trained in administering 3D-CAM and CAM instruments. Participants were evaluated 2 hours after the end of anesthesia care on the day of surgery, then daily until follow-up was completed per clinical trial protocol or until the participant was determined by CAM to be nondelirious for 3 consecutive days. For each evaluation, both 3D-CAM and CAM assessors approached the participant together, but the evaluation was conducted such that the 3D-CAM assessor was masked to the additional questions ascertained by the long-form CAM assessment. The 3D-CAM or CAM assessor independently scored their respective assessments blinded to the results of the other assessor.

Main outcome measures: Participants were concurrently evaluated for postoperative delirium by both 3D-CAM and long-form CAM assessments. Comparisons between 3D-CAM and CAM scores were made using Cohen κ with repeated measures, generalized linear mixed-effects model, and Bland-Altman analysis.

Main results: Sixteen raters performed 471 concurrent 3D-CAM and CAM assessments in 299 participants (mean [SD] age, 69 [6.5] years). Of these participants, 152 (50.8%) were men, 263 (88.0%) were White, and 211 (70.6%) underwent noncardiac surgery. Both instruments showed good intraclass correlation (0.98 for 3D-CAM, 0.84 for CAM) with good overall agreement (Cohen κ = 0.71; 95% CI, 0.58-0.83). The mixed-effects model indicated a significant disagreement between the 3D-CAM and CAM assessments (estimated difference in fixed effect, –0.68; 95% CI, –1.32 to –0.05; P = .04). The Bland-Altman analysis showed that the probability of a delirium diagnosis with the 3D-CAM was more than twice that with the CAM (probability ratio, 2.78; 95% CI, 2.44-3.23).

Conclusion: The high degree of agreement between 3D-CAM and long-form CAM assessments suggests that the former may be a pragmatic and easy-to-administer clinical tool to screen for postoperative delirium in vulnerable older surgical patients.

Study 2 Overview (Shenkin et al)

Objective: To assess the accuracy of the 4 ‘A’s Test (4AT) for delirium detection in the medical inpatient setting and to compare the 4AT to the CAM.

Design: Prospective randomized diagnostic test accuracy study.

Setting and participants: This study was conducted in emergency departments and acute medical wards at 3 UK sites (Edinburgh, Bradford, and Sheffield) and enrolled acute medical patients aged 70 years or older without acute life-threatening illnesses and/or coma. Assessors administering the delirium evaluation were nurses or graduate clinical research associates who underwent systematic training in delirium and delirium assessment. Additional training was provided to those administering the CAM but not to those administering the 4AT as the latter is designed to be administered without special training. First, all participants underwent a reference standard delirium assessment using Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition) (DSM-IV) criteria to derive a final definitive diagnosis of delirium via expert consensus (1 psychiatrist and 2 geriatricians). Then, the participants were randomized to either the 4AT or the comparator CAM group using computer-generated pseudo-random numbers, stratified by study site, with block allocation. All assessments were performed by pairs of independent assessors blinded to the results of the other assessment.

Main outcome measures: All participants were evaluated by the reference standard (DSM-IV criteria for delirium) and by either 4AT or CAM instruments for delirium. The accuracy of the 4AT instrument was evaluated by comparing its positive and negative predictive values, sensitivity, and specificity to the reference standard and analyzed via the area under the receiver operating characteristic curve. The diagnostic accuracy of 4AT, compared to the CAM, was evaluated by comparing positive and negative predictive values, sensitivity, and specificity using Fisher’s exact test. The overall performance of 4AT and CAM was summarized using Youden’s Index and the diagnostic odds ratio of sensitivity to specificity.

Results: All 843 individuals enrolled in the study were randomized and 785 were included in the analysis (23 withdrew, 3 lost contact, 32 indeterminate diagnosis, 2 missing outcome). Of the participants analyzed, the mean age was 81.4 [6.4] years, and 12.1% (95/785) had delirium by reference standard assessment, 14.3% (56/392) by 4AT, and 4.7% (18/384) by CAM. The 4AT group had an area under the receiver operating characteristic curve of 0.90 (95% CI, 0.84-0.96), a sensitivity of 76% (95% CI, 61%-87%), and a specificity of 94% (95% CI, 92%-97%). In comparison, the CAM group had a sensitivity of 40% (95% CI, 26%-57%) and a specificity of 100% (95% CI, 98%-100%).

Conclusions: The 4AT is a pragmatic screening test for delirium in a medical space that does not require special training to administer. The use of this instrument may help to improve delirium detection as a part of routine clinical care in hospitalized older adults.

 

 

Commentary

Delirium is an acute confusional state marked by fluctuating mental status, inattention, disorganized thinking, and altered level of consciousness. It is exceedingly common in older patients in both surgical and medical settings and is associated with increased morbidity, mortality, hospital length of stay, institutionalization, and health care costs. Delirium is frequently underdiagnosed in the hospitalized setting, perhaps due to a combination of its waxing and waning nature and a lack of pragmatic and easily implementable screening tools that can be readily administered by clinicians and nonclinicians alike.¹ While the CAM is a well-validated instrument to diagnose delirium, it requires specific training in the rating of each of the cardinal features ascertained through a brief cognitive assessment and takes 5 to 10 minutes to complete. Taken together, given the high patient load for clinicians in the hospital setting, the validation and application of brief delirium screening instruments that can be reliably administered by nonphysicians and nonclinicians may enhance delirium detection in vulnerable patients and consequently improve their outcomes.

In Study 1, Oberhaus et al approach the challenge of underdiagnosing delirium in the postoperative setting by investigating whether the widely accepted long-form CAM and an abbreviated 3-minute version, the 3D-CAM, provide similar delirium detection in older surgical patients. The authors found that both instruments were reliable tests individually (high interrater reliability) and had good overall agreement. However, the 3D-CAM was more likely to yield a positive diagnosis of delirium compared to the long-form CAM, consistent with its purpose as a screening tool with a high sensitivity. It is important to emphasize that the 3D-CAM takes less time to administer, but also requires less extensive training and clinical knowledge than the long-form CAM. Therefore, this instrument meets the prerequisite of a brief screening test that can be rapidly administered by nonclinicians, and if affirmative, followed by a more extensive confirmatory test performed by a clinician. Limitations of this study include a lack of a reference standard structured interview conducted by a physician-rater to better determine the true diagnostic accuracy of both 3D-CAM and CAM assessments, and the use of convenience sampling at a single center, which reduces the generalizability of its findings.

In a similar vein, Shenkin et al in Study 2 attempt to evaluate the utility of the 4AT instrument in diagnosing delirium in older medical inpatients by testing the diagnostic accuracy of the 4AT against a reference standard (ie, DSM-IV–based evaluation by physicians) as well as comparing it to CAM. The 4AT takes less time (~2 minutes) and requires less knowledge and training to administer as compared to the CAM. The study showed that the abbreviated 4AT, compared to CAM, had a higher sensitivity (76% vs 40%) and lower specificity (94% vs 100%) in delirium detection. Thus, akin to the application of 3D-CAM in the postoperative setting, 4AT possesses key characteristics of a brief delirium screening test for older patients in the acute medical setting. In contrast to the Oberhaus et al study, a major strength of this study was the utilization of a reference standard that was validated by expert consensus. This allowed the 4AT and CAM assessments to be compared to a more objective standard, thereby directly testing their diagnostic performance in detecting delirium.

Application for Clinical Practice and System Implementation

The findings from both Study 1 and 2 suggest that using an abbreviated delirium instrument in both surgical and acute medical settings may provide a pragmatic and sensitive method to detect delirium in older patients. The brevity of administration of 3D-CAM (~3 minutes) and 4AT (~2 minutes), combined with their higher sensitivity for detecting delirium compared to CAM, make these instruments potentially effective rapid screening tests for delirium in hospitalized older patients. Importantly, the utilization of such instruments might be a feasible way to mitigate the issue of underdiagnosing delirium in the hospital.

Several additional aspects of these abbreviated delirium instruments increase their suitability for clinical application. Specifically, the 3D-CAM and 4AT require less extensive training and clinical knowledge to both administer and interpret the results than the CAM.² For instance, a multistage, multiday training for CAM is a key factor in maintaining its diagnostic accuracy.^3,4 In contrast, the 3D-CAM requires only a 1- to 2-hour training session, and the 4AT can be administered by a nonclinician without the need for instrument-specific training. Thus, implementation of these instruments can be particularly pragmatic in clinical settings in which the staff involved in delirium screening cannot undergo the substantial training required to administer CAM. Moreover, these abbreviated tests enable nonphysician care team members to assume the role of delirium screener in the hospital. Taken together, the adoption of these abbreviated instruments may facilitate brief screenings of delirium in older patients by caregivers who see them most often—nurses and certified nursing assistants—thereby improving early detection and prevention of delirium-related complications in the hospital.

The feasibility of using abbreviated delirium screening instruments in the hospital setting raises a system implementation question—if these instruments are designed to be administered by those with limited to no training, could nonclinicians, such as hospital volunteers, effectively take on delirium screening roles in the hospital? If volunteers are able to take on this role, the integration of hospital volunteers into the clinical team can greatly expand the capacity for delirium screening in the hospital setting. Further research is warranted to validate the diagnostic accuracy of 3D-CAM and 4AT by nonclinician administrators in order to more broadly adopt this approach to delirium screening.

Practice Points

• Abbreviated delirium screening tools such as 3D-CAM and 4AT may be pragmatic instruments to improve delirium detection in surgical and hospitalized older patients, respectively.
• Further studies are warranted to validate the diagnostic accuracy of 3D-CAM and 4AT by nonclinician administrators in order to more broadly adopt this approach to delirium screening.

Jared Doan, BS, and Fred Ko, MD
Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai

Study 1 Overview (Oberhaus et al)

Objective: To compare the 3-Minute Diagnostic Confusion Assessment Method (3D-CAM) to the long-form Confusion Assessment Method (CAM) in detecting postoperative delirium.

Design: Prospective concurrent comparison of 3D-CAM and CAM evaluations in a cohort of postoperative geriatric patients.

Setting and participants: Eligible participants were patients aged 60 years or older undergoing major elective surgery at Barnes Jewish Hospital (St. Louis, Missouri) who were enrolled in ongoing clinical trials (PODCAST, ENGAGES, SATISFY-SOS) between 2015 and 2018. Surgeries were at least 2 hours in length and required general anesthesia, planned extubation, and a minimum 2-day hospital stay. Investigators were extensively trained in administering 3D-CAM and CAM instruments. Participants were evaluated 2 hours after the end of anesthesia care on the day of surgery, then daily until follow-up was completed per clinical trial protocol or until the participant was determined by CAM to be nondelirious for 3 consecutive days. For each evaluation, both 3D-CAM and CAM assessors approached the participant together, but the evaluation was conducted such that the 3D-CAM assessor was masked to the additional questions ascertained by the long-form CAM assessment. The 3D-CAM or CAM assessor independently scored their respective assessments blinded to the results of the other assessor.

Main outcome measures: Participants were concurrently evaluated for postoperative delirium by both 3D-CAM and long-form CAM assessments. Comparisons between 3D-CAM and CAM scores were made using Cohen κ with repeated measures, generalized linear mixed-effects model, and Bland-Altman analysis.

Main results: Sixteen raters performed 471 concurrent 3D-CAM and CAM assessments in 299 participants (mean [SD] age, 69 [6.5] years). Of these participants, 152 (50.8%) were men, 263 (88.0%) were White, and 211 (70.6%) underwent noncardiac surgery. Both instruments showed good intraclass correlation (0.98 for 3D-CAM, 0.84 for CAM) with good overall agreement (Cohen κ = 0.71; 95% CI, 0.58-0.83). The mixed-effects model indicated a significant disagreement between the 3D-CAM and CAM assessments (estimated difference in fixed effect, –0.68; 95% CI, –1.32 to –0.05; P = .04). The Bland-Altman analysis showed that the probability of a delirium diagnosis with the 3D-CAM was more than twice that with the CAM (probability ratio, 2.78; 95% CI, 2.44-3.23).

Conclusion: The high degree of agreement between 3D-CAM and long-form CAM assessments suggests that the former may be a pragmatic and easy-to-administer clinical tool to screen for postoperative delirium in vulnerable older surgical patients.

Study 2 Overview (Shenkin et al)

Objective: To assess the accuracy of the 4 ‘A’s Test (4AT) for delirium detection in the medical inpatient setting and to compare the 4AT to the CAM.

Design: Prospective randomized diagnostic test accuracy study.

Setting and participants: This study was conducted in emergency departments and acute medical wards at 3 UK sites (Edinburgh, Bradford, and Sheffield) and enrolled acute medical patients aged 70 years or older without acute life-threatening illnesses and/or coma. Assessors administering the delirium evaluation were nurses or graduate clinical research associates who underwent systematic training in delirium and delirium assessment. Additional training was provided to those administering the CAM but not to those administering the 4AT as the latter is designed to be administered without special training. First, all participants underwent a reference standard delirium assessment using Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition) (DSM-IV) criteria to derive a final definitive diagnosis of delirium via expert consensus (1 psychiatrist and 2 geriatricians). Then, the participants were randomized to either the 4AT or the comparator CAM group using computer-generated pseudo-random numbers, stratified by study site, with block allocation. All assessments were performed by pairs of independent assessors blinded to the results of the other assessment.

Main outcome measures: All participants were evaluated by the reference standard (DSM-IV criteria for delirium) and by either 4AT or CAM instruments for delirium. The accuracy of the 4AT instrument was evaluated by comparing its positive and negative predictive values, sensitivity, and specificity to the reference standard and analyzed via the area under the receiver operating characteristic curve. The diagnostic accuracy of 4AT, compared to the CAM, was evaluated by comparing positive and negative predictive values, sensitivity, and specificity using Fisher’s exact test. The overall performance of 4AT and CAM was summarized using Youden’s Index and the diagnostic odds ratio of sensitivity to specificity.

Results: All 843 individuals enrolled in the study were randomized and 785 were included in the analysis (23 withdrew, 3 lost contact, 32 indeterminate diagnosis, 2 missing outcome). Of the participants analyzed, the mean age was 81.4 [6.4] years, and 12.1% (95/785) had delirium by reference standard assessment, 14.3% (56/392) by 4AT, and 4.7% (18/384) by CAM. The 4AT group had an area under the receiver operating characteristic curve of 0.90 (95% CI, 0.84-0.96), a sensitivity of 76% (95% CI, 61%-87%), and a specificity of 94% (95% CI, 92%-97%). In comparison, the CAM group had a sensitivity of 40% (95% CI, 26%-57%) and a specificity of 100% (95% CI, 98%-100%).

Conclusions: The 4AT is a pragmatic screening test for delirium in a medical space that does not require special training to administer. The use of this instrument may help to improve delirium detection as a part of routine clinical care in hospitalized older adults.

 

 

Commentary

Delirium is an acute confusional state marked by fluctuating mental status, inattention, disorganized thinking, and altered level of consciousness. It is exceedingly common in older patients in both surgical and medical settings and is associated with increased morbidity, mortality, hospital length of stay, institutionalization, and health care costs. Delirium is frequently underdiagnosed in the hospitalized setting, perhaps due to a combination of its waxing and waning nature and a lack of pragmatic and easily implementable screening tools that can be readily administered by clinicians and nonclinicians alike.¹ While the CAM is a well-validated instrument to diagnose delirium, it requires specific training in the rating of each of the cardinal features ascertained through a brief cognitive assessment and takes 5 to 10 minutes to complete. Taken together, given the high patient load for clinicians in the hospital setting, the validation and application of brief delirium screening instruments that can be reliably administered by nonphysicians and nonclinicians may enhance delirium detection in vulnerable patients and consequently improve their outcomes.

In Study 1, Oberhaus et al approach the challenge of underdiagnosing delirium in the postoperative setting by investigating whether the widely accepted long-form CAM and an abbreviated 3-minute version, the 3D-CAM, provide similar delirium detection in older surgical patients. The authors found that both instruments were reliable tests individually (high interrater reliability) and had good overall agreement. However, the 3D-CAM was more likely to yield a positive diagnosis of delirium compared to the long-form CAM, consistent with its purpose as a screening tool with a high sensitivity. It is important to emphasize that the 3D-CAM takes less time to administer, but also requires less extensive training and clinical knowledge than the long-form CAM. Therefore, this instrument meets the prerequisite of a brief screening test that can be rapidly administered by nonclinicians, and if affirmative, followed by a more extensive confirmatory test performed by a clinician. Limitations of this study include a lack of a reference standard structured interview conducted by a physician-rater to better determine the true diagnostic accuracy of both 3D-CAM and CAM assessments, and the use of convenience sampling at a single center, which reduces the generalizability of its findings.

In a similar vein, Shenkin et al in Study 2 attempt to evaluate the utility of the 4AT instrument in diagnosing delirium in older medical inpatients by testing the diagnostic accuracy of the 4AT against a reference standard (ie, DSM-IV–based evaluation by physicians) as well as comparing it to CAM. The 4AT takes less time (~2 minutes) and requires less knowledge and training to administer as compared to the CAM. The study showed that the abbreviated 4AT, compared to CAM, had a higher sensitivity (76% vs 40%) and lower specificity (94% vs 100%) in delirium detection. Thus, akin to the application of 3D-CAM in the postoperative setting, 4AT possesses key characteristics of a brief delirium screening test for older patients in the acute medical setting. In contrast to the Oberhaus et al study, a major strength of this study was the utilization of a reference standard that was validated by expert consensus. This allowed the 4AT and CAM assessments to be compared to a more objective standard, thereby directly testing their diagnostic performance in detecting delirium.

Application for Clinical Practice and System Implementation

The findings from both Study 1 and 2 suggest that using an abbreviated delirium instrument in both surgical and acute medical settings may provide a pragmatic and sensitive method to detect delirium in older patients. The brevity of administration of 3D-CAM (~3 minutes) and 4AT (~2 minutes), combined with their higher sensitivity for detecting delirium compared to CAM, make these instruments potentially effective rapid screening tests for delirium in hospitalized older patients. Importantly, the utilization of such instruments might be a feasible way to mitigate the issue of underdiagnosing delirium in the hospital.

Several additional aspects of these abbreviated delirium instruments increase their suitability for clinical application. Specifically, the 3D-CAM and 4AT require less extensive training and clinical knowledge to both administer and interpret the results than the CAM.² For instance, a multistage, multiday training for CAM is a key factor in maintaining its diagnostic accuracy.^3,4 In contrast, the 3D-CAM requires only a 1- to 2-hour training session, and the 4AT can be administered by a nonclinician without the need for instrument-specific training. Thus, implementation of these instruments can be particularly pragmatic in clinical settings in which the staff involved in delirium screening cannot undergo the substantial training required to administer CAM. Moreover, these abbreviated tests enable nonphysician care team members to assume the role of delirium screener in the hospital. Taken together, the adoption of these abbreviated instruments may facilitate brief screenings of delirium in older patients by caregivers who see them most often—nurses and certified nursing assistants—thereby improving early detection and prevention of delirium-related complications in the hospital.

The feasibility of using abbreviated delirium screening instruments in the hospital setting raises a system implementation question—if these instruments are designed to be administered by those with limited to no training, could nonclinicians, such as hospital volunteers, effectively take on delirium screening roles in the hospital? If volunteers are able to take on this role, the integration of hospital volunteers into the clinical team can greatly expand the capacity for delirium screening in the hospital setting. Further research is warranted to validate the diagnostic accuracy of 3D-CAM and 4AT by nonclinician administrators in order to more broadly adopt this approach to delirium screening.

Practice Points

• Abbreviated delirium screening tools such as 3D-CAM and 4AT may be pragmatic instruments to improve delirium detection in surgical and hospitalized older patients, respectively.
• Further studies are warranted to validate the diagnostic accuracy of 3D-CAM and 4AT by nonclinician administrators in order to more broadly adopt this approach to delirium screening.

Jared Doan, BS, and Fred Ko, MD
Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai

Corresponding author: Ebrahim Barkoudah, MD, MPH, Department of Medicine, Brigham and Women’s Hospital, Boston, MA; [email protected]

Process improvement in any industry sector aims to increase the efficiency of resource utilization and delivery methods (cost) and the quality of the product (outcomes), with the goal of ultimately achieving continuous development.¹ In the health care industry, variation in processes and outcomes along with inefficiency in resource use that result in changes in value (the product of outcomes/costs) are the general targets of quality improvement (QI) efforts employing various implementation methodologies.² When the ultimate aim is to serve the patient (customer), best clinical practice includes both maintaining high quality (individual care delivery) and controlling costs (efficient care system delivery), leading to optimal delivery (value-based care). High-quality individual care and efficient care delivery are not competing concepts, but when working to improve both health care outcomes and cost, traditional and nontraditional barriers to system QI often arise.³

The possible scenarios after a QI intervention include backsliding (regression to the mean over time), steady-state (minimal fixed improvement that could sustain), and continuous improvement (tangible enhancement after completing the intervention with legacy effect).⁴ The scalability of results can be considered during the process measurement and the intervention design phases of all QI projects; however, the complex nature of barriers in the health care environment during each level of implementation should be accounted for to prevent failure in the scalability phase.⁵

The barriers to optimal QI outcomes leading to continuous improvement are multifactorial and are related to intrinsic and extrinsic factors.⁶ These factors include 3 fundamental levels: (1) individual level inertia/beliefs, prior personal knowledge, and team-related factors^7,8; (2) intervention-related and process-specific barriers and clinical practice obstacles; and (3) organizational level challenges and macro-level and population-level barriers (Figure). The obstacles faced during the implementation phase will likely include 2 of these levels simultaneously, which could add complexity and hinder or prevent the implementation of a tangible successful QI process and eventually lead to backsliding or minimal fixed improvement rather than continuous improvement. Furthermore, a patient-centered approach to QI would contribute to further complexity in design and execution, given the importance of reaching sustainable, meaningful improvement by adding elements of patient’s preferences, caregiver engagement, and the shared decision-making processes.⁹

Overcoming these multidomain barriers and reaching resilience and sustainability requires thoughtful planning and execution through a multifaceted approach.¹⁰ A meaningful start could include addressing the clinical inertia for the individual and the team by promoting open innovation and allowing outside institutional collaborations and ideas through networks.¹¹ On the individual level, encouraging participation and motivating health care workers in QI to reach a multidisciplinary operation approach will lead to harmony in collaboration. Concurrently, the organization should support the QI capability and scalability by removing competing priorities and establishing effective leadership that ensures resource allocation, communicates clear value-based principles, and engenders a psychological safety environment.

A continuous improvement state is the optimal QI target, a target that can be attained by removing obstacles and paving a clear pathway to implementation. Focusing on the 3 levels of barriers will position the organization for meaningful and successful QI phases to achieve continuous improvement.

Corresponding author: Ebrahim Barkoudah, MD, MPH, Department of Medicine, Brigham and Women’s Hospital, Boston, MA; [email protected]

Process improvement in any industry sector aims to increase the efficiency of resource utilization and delivery methods (cost) and the quality of the product (outcomes), with the goal of ultimately achieving continuous development.¹ In the health care industry, variation in processes and outcomes along with inefficiency in resource use that result in changes in value (the product of outcomes/costs) are the general targets of quality improvement (QI) efforts employing various implementation methodologies.² When the ultimate aim is to serve the patient (customer), best clinical practice includes both maintaining high quality (individual care delivery) and controlling costs (efficient care system delivery), leading to optimal delivery (value-based care). High-quality individual care and efficient care delivery are not competing concepts, but when working to improve both health care outcomes and cost, traditional and nontraditional barriers to system QI often arise.³

The possible scenarios after a QI intervention include backsliding (regression to the mean over time), steady-state (minimal fixed improvement that could sustain), and continuous improvement (tangible enhancement after completing the intervention with legacy effect).⁴ The scalability of results can be considered during the process measurement and the intervention design phases of all QI projects; however, the complex nature of barriers in the health care environment during each level of implementation should be accounted for to prevent failure in the scalability phase.⁵

The barriers to optimal QI outcomes leading to continuous improvement are multifactorial and are related to intrinsic and extrinsic factors.⁶ These factors include 3 fundamental levels: (1) individual level inertia/beliefs, prior personal knowledge, and team-related factors^7,8; (2) intervention-related and process-specific barriers and clinical practice obstacles; and (3) organizational level challenges and macro-level and population-level barriers (Figure). The obstacles faced during the implementation phase will likely include 2 of these levels simultaneously, which could add complexity and hinder or prevent the implementation of a tangible successful QI process and eventually lead to backsliding or minimal fixed improvement rather than continuous improvement. Furthermore, a patient-centered approach to QI would contribute to further complexity in design and execution, given the importance of reaching sustainable, meaningful improvement by adding elements of patient’s preferences, caregiver engagement, and the shared decision-making processes.⁹

Overcoming these multidomain barriers and reaching resilience and sustainability requires thoughtful planning and execution through a multifaceted approach.¹⁰ A meaningful start could include addressing the clinical inertia for the individual and the team by promoting open innovation and allowing outside institutional collaborations and ideas through networks.¹¹ On the individual level, encouraging participation and motivating health care workers in QI to reach a multidisciplinary operation approach will lead to harmony in collaboration. Concurrently, the organization should support the QI capability and scalability by removing competing priorities and establishing effective leadership that ensures resource allocation, communicates clear value-based principles, and engenders a psychological safety environment.

A continuous improvement state is the optimal QI target, a target that can be attained by removing obstacles and paving a clear pathway to implementation. Focusing on the 3 levels of barriers will position the organization for meaningful and successful QI phases to achieve continuous improvement.

Corresponding author: Ebrahim Barkoudah, MD, MPH, Department of Medicine, Brigham and Women’s Hospital, Boston, MA; [email protected]

Process improvement in any industry sector aims to increase the efficiency of resource utilization and delivery methods (cost) and the quality of the product (outcomes), with the goal of ultimately achieving continuous development.¹ In the health care industry, variation in processes and outcomes along with inefficiency in resource use that result in changes in value (the product of outcomes/costs) are the general targets of quality improvement (QI) efforts employing various implementation methodologies.² When the ultimate aim is to serve the patient (customer), best clinical practice includes both maintaining high quality (individual care delivery) and controlling costs (efficient care system delivery), leading to optimal delivery (value-based care). High-quality individual care and efficient care delivery are not competing concepts, but when working to improve both health care outcomes and cost, traditional and nontraditional barriers to system QI often arise.³

The possible scenarios after a QI intervention include backsliding (regression to the mean over time), steady-state (minimal fixed improvement that could sustain), and continuous improvement (tangible enhancement after completing the intervention with legacy effect).⁴ The scalability of results can be considered during the process measurement and the intervention design phases of all QI projects; however, the complex nature of barriers in the health care environment during each level of implementation should be accounted for to prevent failure in the scalability phase.⁵

The barriers to optimal QI outcomes leading to continuous improvement are multifactorial and are related to intrinsic and extrinsic factors.⁶ These factors include 3 fundamental levels: (1) individual level inertia/beliefs, prior personal knowledge, and team-related factors^7,8; (2) intervention-related and process-specific barriers and clinical practice obstacles; and (3) organizational level challenges and macro-level and population-level barriers (Figure). The obstacles faced during the implementation phase will likely include 2 of these levels simultaneously, which could add complexity and hinder or prevent the implementation of a tangible successful QI process and eventually lead to backsliding or minimal fixed improvement rather than continuous improvement. Furthermore, a patient-centered approach to QI would contribute to further complexity in design and execution, given the importance of reaching sustainable, meaningful improvement by adding elements of patient’s preferences, caregiver engagement, and the shared decision-making processes.⁹

Overcoming these multidomain barriers and reaching resilience and sustainability requires thoughtful planning and execution through a multifaceted approach.¹⁰ A meaningful start could include addressing the clinical inertia for the individual and the team by promoting open innovation and allowing outside institutional collaborations and ideas through networks.¹¹ On the individual level, encouraging participation and motivating health care workers in QI to reach a multidisciplinary operation approach will lead to harmony in collaboration. Concurrently, the organization should support the QI capability and scalability by removing competing priorities and establishing effective leadership that ensures resource allocation, communicates clear value-based principles, and engenders a psychological safety environment.

A continuous improvement state is the optimal QI target, a target that can be attained by removing obstacles and paving a clear pathway to implementation. Focusing on the 3 levels of barriers will position the organization for meaningful and successful QI phases to achieve continuous improvement.

Purpose

Ensuring that patients/families are engaged as partners in their health care is an effective way to measure the quality of patient care. Self-reported patient data, such as symptom burden, provides an accurate and effective way to measure patient-reported outcomes. Our team reviewed 20 patient charts, randomly, to assess for documentation of at least 3 or more domains of toxicities of immune checkpoint inhibitors. The baseline comprehensive documentation rate was 50%. Our goal is to improve the documentation rate to 80% for our first process improvement cycle.

Aim Statement

Increase documentation of 3 or more toxicities immune checkpoint inhibitors to a goal rate of 80%.

Methods

A free online patient monitoring checklist tool was printed and provided to patients receiving immune checkpoint inhibitors (on their infusion day) during the check-in process. The patients were instructed to complete the tool prior to the provider clinic visit, while in the waiting area. The completed tool was given to the provider on the day of their visit. Prior to the start of this Plan-Do-Study-Act (PDSA) cycle, all providers were “reminded”/ instructed to ensure documentation of 3 or more toxicities immune checkpoint inhibitors. The cycle lasted for 3 weeks. At the end of the 3 weeks, our team reviewed the charts of those patients.

Results

Documentation rate of 3 or more toxicities increased from 50% to 90%.

Conclusions

When completed patient monitoring tools were provided to the providers during the clinic visit, the providers increased their documentation rate of the toxicities. There is literature supporting improving patient satisfaction using self-reported symptoms monitoring tools. Also, given the burden of documentation and shorter visit times, providers found this to be an easy way to address patient symptoms. While electronic patient-reported outcome (e-PRO) tools are ideal for ongoing symptom monitoring, this is a simple way to address the same in low-resourced communities. For our next cycle, we plan on using patient feedback to improve the documentation form incorporating larger fonts for patients with low vision.

Purpose

Ensuring that patients/families are engaged as partners in their health care is an effective way to measure the quality of patient care. Self-reported patient data, such as symptom burden, provides an accurate and effective way to measure patient-reported outcomes. Our team reviewed 20 patient charts, randomly, to assess for documentation of at least 3 or more domains of toxicities of immune checkpoint inhibitors. The baseline comprehensive documentation rate was 50%. Our goal is to improve the documentation rate to 80% for our first process improvement cycle.

Aim Statement

Increase documentation of 3 or more toxicities immune checkpoint inhibitors to a goal rate of 80%.

Methods

A free online patient monitoring checklist tool was printed and provided to patients receiving immune checkpoint inhibitors (on their infusion day) during the check-in process. The patients were instructed to complete the tool prior to the provider clinic visit, while in the waiting area. The completed tool was given to the provider on the day of their visit. Prior to the start of this Plan-Do-Study-Act (PDSA) cycle, all providers were “reminded”/ instructed to ensure documentation of 3 or more toxicities immune checkpoint inhibitors. The cycle lasted for 3 weeks. At the end of the 3 weeks, our team reviewed the charts of those patients.

Results

Documentation rate of 3 or more toxicities increased from 50% to 90%.

Conclusions

When completed patient monitoring tools were provided to the providers during the clinic visit, the providers increased their documentation rate of the toxicities. There is literature supporting improving patient satisfaction using self-reported symptoms monitoring tools. Also, given the burden of documentation and shorter visit times, providers found this to be an easy way to address patient symptoms. While electronic patient-reported outcome (e-PRO) tools are ideal for ongoing symptom monitoring, this is a simple way to address the same in low-resourced communities. For our next cycle, we plan on using patient feedback to improve the documentation form incorporating larger fonts for patients with low vision.

Purpose

Ensuring that patients/families are engaged as partners in their health care is an effective way to measure the quality of patient care. Self-reported patient data, such as symptom burden, provides an accurate and effective way to measure patient-reported outcomes. Our team reviewed 20 patient charts, randomly, to assess for documentation of at least 3 or more domains of toxicities of immune checkpoint inhibitors. The baseline comprehensive documentation rate was 50%. Our goal is to improve the documentation rate to 80% for our first process improvement cycle.

Aim Statement

Increase documentation of 3 or more toxicities immune checkpoint inhibitors to a goal rate of 80%.

Methods

A free online patient monitoring checklist tool was printed and provided to patients receiving immune checkpoint inhibitors (on their infusion day) during the check-in process. The patients were instructed to complete the tool prior to the provider clinic visit, while in the waiting area. The completed tool was given to the provider on the day of their visit. Prior to the start of this Plan-Do-Study-Act (PDSA) cycle, all providers were “reminded”/ instructed to ensure documentation of 3 or more toxicities immune checkpoint inhibitors. The cycle lasted for 3 weeks. At the end of the 3 weeks, our team reviewed the charts of those patients.

Results

Documentation rate of 3 or more toxicities increased from 50% to 90%.

Conclusions

When completed patient monitoring tools were provided to the providers during the clinic visit, the providers increased their documentation rate of the toxicities. There is literature supporting improving patient satisfaction using self-reported symptoms monitoring tools. Also, given the burden of documentation and shorter visit times, providers found this to be an easy way to address patient symptoms. While electronic patient-reported outcome (e-PRO) tools are ideal for ongoing symptom monitoring, this is a simple way to address the same in low-resourced communities. For our next cycle, we plan on using patient feedback to improve the documentation form incorporating larger fonts for patients with low vision.