User login
Monitor Alarms and Response Time
Hospital physiologic monitors can alert clinicians to early signs of physiologic deterioration, and thus have great potential to save lives. However, monitors generate frequent alarms,[1, 2, 3, 4, 5, 6, 7, 8] and most are not relevant to the patient's safety (over 90% of pediatric intensive care unit (PICU)[1, 2] and over 70% of adult intensive care alarms).[5, 6] In psychology experiments, humans rapidly learn to ignore or respond more slowly to alarms when exposed to high false‐alarm rates, exhibiting alarm fatigue.[9, 10] In 2013, The Joint Commission named alarm fatigue the most common contributing factor to alarm‐related sentinel events in hospitals.[11, 12]
Although alarm fatigue has been implicated as a major threat to patient safety, little empirical data support its existence in hospitals. In this study, we aimed to determine if there was an association between nurses' recent exposure to nonactionable physiologic monitor alarms and their response time to future alarms for the same patients. This exploratory work was designed to inform future research in this area, acknowledging that the sample size would be too small for multivariable modeling.
METHODS
Study Definitions
The alarm classification scheme is shown in Figure 1. Note that, for clarity, we have intentionally avoided using the terms true and false alarms because their interpretations vary across studies and can be misleading.
Potentially Critical Alarm
A potentially critical alarm is any alarm for a clinical condition for which a timely response is important to determine if the alarm requires intervention to save the patient's life. This is based on the alarm type alone, including alarms for life‐threatening arrhythmias such as asystole and ventricular tachycardia, as well as alarms for vital signs outside the set limits. Supporting Table 1 in the online version of this article lists the breakdown of alarm types that we defined a priori as potentially and not potentially critical.
| PICU | Ward | |||||||
|---|---|---|---|---|---|---|---|---|
| Alarm type | No. | % of Total | % Valid | % Actionable | No. | % of Total | % Valid | % Actionable |
| ||||||||
| Oxygen saturation | 197 | 19.4 | 82.7 | 38.6 | 590 | 41.2 | 24.4 | 1.9 |
| Heart rate | 194 | 19.1 | 95.4 | 1.0 | 266 | 18.6 | 87.2 | 0.0 |
| Respiratory rate | 229 | 22.6 | 80.8 | 13.5 | 316 | 22.1 | 48.1 | 1.0 |
| Blood pressure | 259 | 25.5 | 83.8 | 5.8 | 11 | 0.8 | 72.7 | 0.0 |
| Critical arrhythmia | 1 | 0.1 | 0.0 | 0.0 | 4 | 0.3 | 0.0 | 0.0 |
| Noncritical arrhythmia | 71 | 7.0 | 2.8 | 0.0 | 244 | 17.1 | 8.6 | 0.0 |
| Central venous pressure | 49 | 4.8 | 0.0 | 0.0 | 0 | 0.0 | N/A | N/A |
| Exhaled carbon dioxide | 14 | 1.4 | 92.9 | 50.0 | 0 | 0.0 | N/A | N/A |
| Total | 1014 | 100.0 | 75.6 | 12.9 | 1,431 | 100.0 | 38.9 | 1.0 |
Valid Alarm
A valid alarm is any alarm that correctly identifies the physiologic status of the patient. Validity was based on waveform quality, lead signal strength indicators, and artifact conditions, referencing each monitor's operator's manual.
Actionable Alarm
An actionable alarm is any valid alarm for a clinical condition that either: (1) leads to a clinical intervention; (2) leads to a consultation with another clinician at the bedside (and thus visible on camera); or (3) is a situation that should have led to intervention or consultation, but the alarm was unwitnessed or misinterpreted by the staff at the bedside.
Nonactionable Alarm
An unactionable alarm is any alarm that does not meet the actionable definition above, including invalid alarms such as those caused by motion artifact, equipment/technical alarms, and alarms that are valid but nonactionable (nuisance alarms).[13]
Response Time
The response time is the time elapsed from when the alarm fired at the bedside to when the nurse entered the room or peered through a window or door, measured in seconds.
Setting and Subjects
We performed this study between August 2012 and July 2013 at a freestanding children's hospital. We evaluated nurses caring for 2 populations: (1) PICU patients with heart and/or lung failure (requiring inotropic support and/or invasive mechanical ventilation), and (2) medical patients on a general inpatient ward. Nurses caring for heart and/or lung failure patients in the PICU typically were assigned 1 to 2 total patients. Nurses on the medical ward typically were assigned 2 to 4 patients. We identified subjects from the population of nurses caring for eligible patients with parents available to provide in‐person consent in each setting. Our primary interest was to evaluate the association between nonactionable alarms and response time, and not to study the epidemiology of alarms in a random sample. Therefore, when alarm data were available prior to screening, we first approached nurses caring for patients in the top 25% of alarm rates for that unit over the preceding 4 hours. We identified preceding alarm rates using BedMasterEx (Excel Medical Electronics, Jupiter, FL).
Human Subjects Protection
This study was approved by the institutional review board of The Children's Hospital of Philadelphia. We obtained written in‐person consent from the patient's parent and the nurse subject. We obtained a Certificate of Confidentiality from the National Institutes of Health to further protect study participants.[14]
Monitoring Equipment
All patients in the PICU were monitored continuously using General Electric (GE) (Fairfield, CT) solar devices. All bed spaces on the wards include GE Dash monitors that are used if ordered. On the ward we studied, 30% to 50% of patients are typically monitored at any given time. In addition to alarming at the bedside, most clinical alarms also generated a text message sent to the nurse's wireless phone listing the room number and the word monitor. Messages did not provide any clinical information about the alarm or patient's status. There were no technicians reviewing alarms centrally.
Physicians used an order set to order monitoring, selecting 1 of 4 available preconfigured profiles: infant <6 months, infant 6 months to 1 year, child, and adult. The parameters for each age group are in Supporting Figure 1, available in the online version of this article. A physician order is required for a nurse to change the parameters. Participating in the study did not affect this workflow.
Primary Outcome
The primary outcome was the nurse's response time to potentially critical monitor alarms that occurred while neither they nor any other clinicians were in the patient's room.
Primary Exposure and Alarm Classification
The primary exposure was the number of nonactionable alarms in the same patient over the preceding 120 minutes (rolling and updated each minute). The alarm classification scheme is shown in Figure 1.
Due to technical limitations with obtaining time‐stamped alarm data from the different ventilators in use during the study period, we were unable to identify the causes of all ventilator alarms. Therefore, we included ventilator alarms that did not lead to clinical interventions as nonactionable alarm exposures, but we did not evaluate the response time to any ventilator alarms.
Data Collection
We combined video recordings with monitor time‐stamp data to evaluate the association between nonactionable alarms and the nurse's response time. Our detailed video recording and annotation methods have been published separately.[15] Briefly, we mounted up to 6 small video cameras in patients' rooms and recorded up to 6 hours per session. The cameras captured the monitor display, a wide view of the room, a close‐up view of the patient, and all windows and doors through which staff could visually assess the patient without entering the room.
Video Processing, Review, and Annotation
The first 5 video sessions were reviewed in a group training setting. Research assistants received instruction on how to determine alarm validity and actionability in accordance with the study definitions. Following the training period, the review workflow was as follows. First, a research assistant entered basic information and a preliminary assessment of the alarm's clinical validity and actionability into a REDCap (Research Electronic Data Capture; Vanderbilt University, Nashville, TN) database.[16] Later, a physician investigator secondarily reviewed all alarms and confirmed the assessments of the research assistants or, when disagreements occurred, discussed and reconciled the database. Alarms that remained unresolved after secondary review were flagged for review with an additional physician or nurse investigator in a team meeting.
Data Analysis
We summarized the patient and nurse subjects, the distributions of alarms, and the response times to potentially critical monitor alarms that occurred while neither the nurse nor any other clinicians were in the patient's room. We explored the data using plots of alarms and response times occurring within individual video sessions as well as with simple linear regression. Hypothesizing that any alarm fatigue effect would be strongest in the highest alarm patients, and having observed that alarms are distributed very unevenly across patients in both the PICU and ward, we made the decision not to use quartiles, but rather to form clinically meaningful categories. We also hypothesized that nurses might not exhibit alarm fatigue unless they were inundated with alarms. We thus divided the nonactionable alarm counts over the preceding 120 minutes into 3 categories: 0 to 29 alarms to represent a low to average alarm rate exhibited by the bottom 50% of the patients, 30 to 79 alarms to represent an elevated alarm rate, and 80+ alarms to represent an extremely high alarm rate exhibited by the top 5%. Because the exposure time was 120 minutes, we conducted the analysis on the alarms occurring after a nurse had been video recorded for at least 120 minutes.
We further evaluated the relationship between nonactionable alarms and nurse response time with Kaplan‐Meier plots by nonactionable alarm count category using the observed response‐time data. The Kaplan‐Meier plots compared response time across the nonactionable alarm exposure group, without any statistical modeling. A log‐rank test stratified by nurse evaluated whether the distributions of response time in the Kaplan‐Meier plots differed across the 3 alarm exposure groups, accounting for within‐nurse clustering.
Accelerated failure‐time regression based on the Weibull distribution then allowed us to compare response time across each alarm exposure group and provided confidence intervals. Accelerated failure‐time models are comparable to Cox models, but emphasize time to event rather than hazards.[17, 18] We determined that the Weibull distribution was suitable by evaluating smoothed hazard and log‐hazard plots, the confidence intervals of the shape parameters in the Weibull models that did not include 1, and by demonstrating that the Weibull model had better fit than an alternative (exponential) model using the likelihood‐ratio test (P<0.0001 for PICU, P=0.02 for ward). Due to the small sample size of nurses and patients, we could not adjust for nurse‐ or patient‐level covariates in the model. When comparing the nonactionable alarm exposure groups in the regression model (029 vs 3079, 3079 vs 80+, and 029 vs 80+), we Bonferroni corrected the critical P value for the 3 comparisons, for a critical P value of 0.05/3=0.0167.
Nurse Questionnaire
At the session's conclusion, nurses completed a questionnaire that included demographics and asked, Did you respond more quickly to monitor alarms during this study because you knew you were being filmed? to measure if nurses would report experiencing a Hawthorne‐like effect.[19, 20, 21]
RESULTS
We performed 40 sessions among 40 patients and 36 nurses over 210 hours. We performed 20 sessions in children with heart and/or lung failure in the PICU and 20 sessions in children on a general ward. Sessions took place on weekdays between 9:00 am and 6:00 pm. There were 3 occasions when we filmed 2 patients cared for by the same nurse at the same time.
Nurses were mostly female (94.4%) and had between 2 months and 28 years of experience (median, 4.8 years). Patients on the ward ranged from 5 days to 5.4 years old (median, 6 months). Patients in the PICU ranged from 5 months to 16 years old (median, 2.5 years). Among the PICU patients, 14 (70%) were receiving mechanical ventilation only, 3 (15%) were receiving vasopressors only, and 3 (15%) were receiving mechanical ventilation and vasopressors.
We observed 5070 alarms during the 40 sessions. We excluded 108 (2.1%) that occurred at the end of video recording sessions with the nurse absent from the room because the nurse's response could not be determined. Alarms per session ranged from 10 to 1430 (median, 75; interquartile range [IQR], 35138). We excluded the outlier PICU patient with 1430 alarms in 5 hours from the analysis to avoid the potential for biasing the results. Figure 2 depicts the data flow.
Following the 5 training sessions, research assistants independently reviewed and made preliminary assessments on 4674 alarms; these alarms were all secondarily reviewed by a physician. Using the physician reviewer as the gold standard, the research assistant's sensitivity (assess alarm as actionable when physician also assesses as actionable) was 96.8% and specificity (assess alarm as nonactionable when physician also assesses as nonactionable) was 96.9%. We had to review 54 of 4674 alarms (1.2%) with an additional physician or nurse investigator to achieve consensus.
Characteristics of the 2445 alarms for clinical conditions are shown in Table 1. Only 12.9% of alarms in heart‐ and/or lung‐failure patients in the PICU were actionable, and only 1.0% of alarms in medical patients on a general inpatient ward were actionable.
Overall Response Times for Out‐of‐Room Alarms
We first evaluated response times without excluding alarms occurring prior to the 120‐minute mark. Of the 2445 clinical condition alarms, we excluded the 315 noncritical arrhythmia types from analysis of response time because they did not meet our definition of potentially critical alarms. Of the 2130 potentially critical alarms, 1185 (55.6%) occurred while neither the nurse nor any other clinician was in the patient's room. We proceeded to analyze the response time to these 1185 alarms (307 in the PICU and 878 on the ward). In the PICU, median response time was 3.3 minutes (IQR, 0.814.4). On the ward, median response time was 9.8 minutes (IQR, 3.222.4).
Response‐Time Association With Nonactionable Alarm Exposure
Next, we analyzed the association between response time to potentially critical alarms that occurred when the nurse was not in the patient's room and the number of nonactionable alarms occurring over the preceding 120‐minute window. This required excluding the alarms that occurred in the first 120 minutes of each session, leaving 647 alarms with eligible response times to evaluate the exposure between prior nonactionable alarm exposure and response time: 219 in the PICU and 428 on the ward. Kaplan‐Meier plots and tabulated response times demonstrated the incremental relationships between each nonactionable alarm exposure category in the observed data, with the effects most prominent as the Kaplan‐Meier plots diverged beyond the median (Figure 3 and Table 2). Excluding the extreme outlier patient had no effect on the results, because 1378 of the 1430 alarms occurred with the nurse present at the bedside, and only 2 of the remaining alarms were potentially critical.
| Observed Data | Accelerated Failure‐Time Model | |||||||
|---|---|---|---|---|---|---|---|---|
| Number of Potentially Critical Alarms | Minutes Elapsed Until This Percentage of Alarms Was Responded to | Modeled Response Time, min | 95% CI, min | P Value* | ||||
| 50% (Median) | 75% | 90% | 95% | |||||
| ||||||||
| PICU | ||||||||
| 029 nonactionable alarms | 70 | 1.6 | 8.0 | 18.6 | 25.1 | 2.8 | 1.9‐3.8 | Reference |
| 3079 nonactionable alarms | 122 | 6.3 | 17.8 | 22.5 | 26.0 | 5.3 | 4.06.7 | 0.001 (vs 029) |
| 80+ nonactionable alarms | 27 | 16.0 | 28.4 | 32.0 | 33.1 | 8.5 | 4.312.7 | 0.009 (vs 029), 0.15 (vs 3079) |
| Ward | ||||||||
| 029 nonactionable alarms | 159 | 9.8 | 17.8 | 25.0 | 28.9 | 7.7 | 6.39.1 | Reference |
| 3079 nonactionable alarms | 211 | 11.6 | 22.4 | 44.6 | 63.2 | 11.5 | 9.613.3 | 0.001 (vs 029) |
| 80+ nonactionable alarms | 58 | 8.3 | 57.6 | 63.8 | 69.5 | 15.6 | 11.020.1 | 0.001 (vs 029), 0.09 (vs 3079) |
Accelerated failure‐time regressions revealed significant incremental increases in the modeled response time as the number of preceding nonactionable alarms increased in both the PICU and ward settings (Table 2).
Hawthorne‐like Effects
Four of the 36 nurses reported that they responded more quickly to monitor alarms because they knew they were being filmed.
DISCUSSION
Alarm fatigue has recently generated interest among nurses,[22] physicians,[23] regulatory bodies,[24] patient safety organizations,[25] and even attorneys,[26] despite a lack of prior evidence linking nonactionable alarm exposure to response time or other adverse patient‐relevant outcomes. This study's main findings were that (1) the vast majority of alarms were nonactionable, (2) response time to alarms occurring while the nurse was out of the room increased as the number of nonactionable alarms over the preceding 120 minutes increased. These findings may be explained by alarm fatigue.
Our results build upon the findings of other related studies. The nonactionable alarm proportions we found were similar to other pediatric studies, reporting greater than 90% nonactionable alarms.[1, 2] One other study has reported a relationship between alarm exposure and response time. In that study, Voepel‐Lewis and colleagues evaluated nurse responses to pulse oximetry desaturation alarms in adult orthopedic surgery patients using time‐stamp data from their monitor notification system.[27] They found that alarm response time was significantly longer for patients in the highest quartile of alarms compared to those in lower quartiles. Our study provides new data suggesting a relationship between nonactionable alarm exposure and nurse response time.
Our study has several limitations. First, as a preliminary study to investigate feasibility and possible association, the sample of patients and nurses was necessarily limited and did not permit adjustment for nurse‐ or patient‐level covariates. A multivariable analysis with a larger sample might provide insight into alternate explanations for these findings other than alarm fatigue, including measures of nurse workload and patient factors (such as age and illness severity). Additional factors that are not as easily measured can also contribute to the complex decision of when and how to respond to alarms.[28, 29] Second, nurses were aware that they were being video recorded as part of a study of nonactionable alarms, although they did not know the specific details of measurement. Although this lack of blinding might lead to a Hawthorne‐like effect, our positive results suggest that this effect, if present, did not fully obscure the association. Third, all sessions took place on weekdays during daytime hours, but effects of nonactionable alarms might vary by time and day. Finally, we suspect that when nurses experience critical alarms that require them to intervene and rescue a patient, their response times to that patient's alarms that occur later in their shift will be quicker due to a heightened concern for the alarm being actionable. We were unable to explore that relationship in this analysis because the number of critical alarms requiring intervention was very small. This is a topic of future study.
CONCLUSIONS
We identified an association between a nurse's prior exposure to nonactionable alarms and response time to future alarms. This finding is consistent with alarm fatigue, but requires further study to more clearly delineate other factors that might confound or modify that relationship.
Disclosures
This project was funded by the Health Research Formula Fund Grant 4100050891 from the Pennsylvania Department of Public Health Commonwealth Universal Research Enhancement Program (awarded to Drs. Keren and Bonafide). Dr. Bonafide is also supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K23HL116427. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors have no financial relationships or conflicts of interest relevant to this article to disclose.
- . Crying wolf: false alarms in a pediatric intensive care unit. Crit Care Med. 1994;22(6):981–985.
- , . Poor prognosis for existing monitors in the intensive care unit. Crit Care Med. 1997;25(4):614–619.
- , , , , . Clinical evaluation of alarm efficiency in intensive care [in French]. Ann Fr Anesth Reanim. 2000;19:459–466.
- , , , . Reducing false alarms of intensive care online‐monitoring systems: an evaluation of two signal extraction algorithms. Comput Math Methods Med. 2011;2011:143480.
- , , , , , . Multicentric study of monitoring alarms in the adult intensive care unit (ICU): a descriptive analysis. Intensive Care Med. 1999;25:1360–1366.
- , , . Improving alarm performance in the medical intensive care unit using delays and clinical context. Anesth Analg. 2009;108:1546–1552.
- , . Monitor alarm fatigue: standardizing use of physiological monitors and decreasing nuisance alarms. Am J Crit Care. 2010;19:28–34.
- , , , , , . Intensive care unit alarms—how many do we need? Crit Care Med. 2010;38:451–456.
- , , , . System operator response to warnings of danger: a laboratory investigation of the effects of the predictive value of a warning on human response time. J Exp Psychol Appl. 1995;1:19–33.
- , , . Human probability matching behaviour in response to alarms of varying reliability. Ergonomics. 1995;38:2300–2312.
- The Joint Commission. Sentinel event alert: medical device alarm safety in hospitals. 2013. Available at: http://www.jointcommission.org/sea_issue_50/. Accessed October 9, 2014.
- . Joint commission warns of alarm fatigue: multitude of alarms from monitoring devices problematic. JAMA. 2013;309(22):2315–2316.
- . Monitor alarm fatigue: an integrative review. Biomed Instrum Technol. 2012;46(4):268–277.
- NIH Certificates of Confidentiality Kiosk. Available at: http://grants.nih.gov/grants/policy/coc/. Accessed April 21, 2014.
- , , , et al. Video methods for evaluating physiologic monitor alarms and alarm responses. Biomed Instrum Technol. 2014;48(3):220–230.
- , , , , , . Research electronic data capture (REDCap)—a metadata‐driven methodology and workflow process for providing translational research informatics support. J Biomed Inf. 2009;42:377–381.
- . Accelerated failure time and other parametric models. In: Modelling Survival Data in Medical Research. 2nd ed. Boca Raton, FL: Chapman 2003:197–229.
- , , , . Parametric models. In: An Introduction to Survival Analysis Using Stata, 3rd ed. College Station, TX: Stata Press; 2010:229–244.
- , . Management and the Worker. Cambridge, MA: Harvard University Press; 1939.
- . What happened at Hawthorne? Science. 1974;183(4128):922–932.
- , , , , . Validation of the Work Observation Method By Activity Timing (WOMBAT) method of conducting time‐motion observations in critical care settings: an observational study. BMC Med Inf Decis Mak. 2011;11:32.
- , . Alarm fatigue: a patient safety concern. AACN Adv Crit Care. 2013;24(4):378–386.
- , . Redesigning hospital alarms for patient safety: alarmed and potentially dangerous. JAMA. 2014;311(12):1199–1200.
- The Joint Commission. The Joint Commission announces 2014 National Patient Safety Goal. Jt Comm Perspect. 2013;33:1–4.
- Top 10 health technology hazards for 2014. Health Devices. 2013;42(11):354–380.
- My Philly Lawyer. Medical malpractice: alarm fatigue threatens patient safety. 2014. Available at: http://www.myphillylawyer.com/Resources/Legal-Articles/Medical-Malpractice-Alarm-Fatigue-Threatens-Patient-Safety.shtml. Accessed April 4, 2014.
- , , , et al. Pulse oximetry desaturation alarms on a general postoperative adult unit: a prospective observational study of nurse response time. Int J Nurs Stud. 2013;50(10):1351–1358.
- , , , , . A description of nurses' decision‐making in managing electrocardiographic monitor alarms [published online ahead of print May 10, 2014]. J Clin Nurs. doi:10.1111/jocn.12625.
- . Nurses' response to frequency and types of electrocardiography alarms in a non‐critical care setting: a descriptive study. Int J Nurs Stud. 2014;51(2):190–197.
Hospital physiologic monitors can alert clinicians to early signs of physiologic deterioration, and thus have great potential to save lives. However, monitors generate frequent alarms,[1, 2, 3, 4, 5, 6, 7, 8] and most are not relevant to the patient's safety (over 90% of pediatric intensive care unit (PICU)[1, 2] and over 70% of adult intensive care alarms).[5, 6] In psychology experiments, humans rapidly learn to ignore or respond more slowly to alarms when exposed to high false‐alarm rates, exhibiting alarm fatigue.[9, 10] In 2013, The Joint Commission named alarm fatigue the most common contributing factor to alarm‐related sentinel events in hospitals.[11, 12]
Although alarm fatigue has been implicated as a major threat to patient safety, little empirical data support its existence in hospitals. In this study, we aimed to determine if there was an association between nurses' recent exposure to nonactionable physiologic monitor alarms and their response time to future alarms for the same patients. This exploratory work was designed to inform future research in this area, acknowledging that the sample size would be too small for multivariable modeling.
METHODS
Study Definitions
The alarm classification scheme is shown in Figure 1. Note that, for clarity, we have intentionally avoided using the terms true and false alarms because their interpretations vary across studies and can be misleading.
Potentially Critical Alarm
A potentially critical alarm is any alarm for a clinical condition for which a timely response is important to determine if the alarm requires intervention to save the patient's life. This is based on the alarm type alone, including alarms for life‐threatening arrhythmias such as asystole and ventricular tachycardia, as well as alarms for vital signs outside the set limits. Supporting Table 1 in the online version of this article lists the breakdown of alarm types that we defined a priori as potentially and not potentially critical.
| PICU | Ward | |||||||
|---|---|---|---|---|---|---|---|---|
| Alarm type | No. | % of Total | % Valid | % Actionable | No. | % of Total | % Valid | % Actionable |
| ||||||||
| Oxygen saturation | 197 | 19.4 | 82.7 | 38.6 | 590 | 41.2 | 24.4 | 1.9 |
| Heart rate | 194 | 19.1 | 95.4 | 1.0 | 266 | 18.6 | 87.2 | 0.0 |
| Respiratory rate | 229 | 22.6 | 80.8 | 13.5 | 316 | 22.1 | 48.1 | 1.0 |
| Blood pressure | 259 | 25.5 | 83.8 | 5.8 | 11 | 0.8 | 72.7 | 0.0 |
| Critical arrhythmia | 1 | 0.1 | 0.0 | 0.0 | 4 | 0.3 | 0.0 | 0.0 |
| Noncritical arrhythmia | 71 | 7.0 | 2.8 | 0.0 | 244 | 17.1 | 8.6 | 0.0 |
| Central venous pressure | 49 | 4.8 | 0.0 | 0.0 | 0 | 0.0 | N/A | N/A |
| Exhaled carbon dioxide | 14 | 1.4 | 92.9 | 50.0 | 0 | 0.0 | N/A | N/A |
| Total | 1014 | 100.0 | 75.6 | 12.9 | 1,431 | 100.0 | 38.9 | 1.0 |
Valid Alarm
A valid alarm is any alarm that correctly identifies the physiologic status of the patient. Validity was based on waveform quality, lead signal strength indicators, and artifact conditions, referencing each monitor's operator's manual.
Actionable Alarm
An actionable alarm is any valid alarm for a clinical condition that either: (1) leads to a clinical intervention; (2) leads to a consultation with another clinician at the bedside (and thus visible on camera); or (3) is a situation that should have led to intervention or consultation, but the alarm was unwitnessed or misinterpreted by the staff at the bedside.
Nonactionable Alarm
An unactionable alarm is any alarm that does not meet the actionable definition above, including invalid alarms such as those caused by motion artifact, equipment/technical alarms, and alarms that are valid but nonactionable (nuisance alarms).[13]
Response Time
The response time is the time elapsed from when the alarm fired at the bedside to when the nurse entered the room or peered through a window or door, measured in seconds.
Setting and Subjects
We performed this study between August 2012 and July 2013 at a freestanding children's hospital. We evaluated nurses caring for 2 populations: (1) PICU patients with heart and/or lung failure (requiring inotropic support and/or invasive mechanical ventilation), and (2) medical patients on a general inpatient ward. Nurses caring for heart and/or lung failure patients in the PICU typically were assigned 1 to 2 total patients. Nurses on the medical ward typically were assigned 2 to 4 patients. We identified subjects from the population of nurses caring for eligible patients with parents available to provide in‐person consent in each setting. Our primary interest was to evaluate the association between nonactionable alarms and response time, and not to study the epidemiology of alarms in a random sample. Therefore, when alarm data were available prior to screening, we first approached nurses caring for patients in the top 25% of alarm rates for that unit over the preceding 4 hours. We identified preceding alarm rates using BedMasterEx (Excel Medical Electronics, Jupiter, FL).
Human Subjects Protection
This study was approved by the institutional review board of The Children's Hospital of Philadelphia. We obtained written in‐person consent from the patient's parent and the nurse subject. We obtained a Certificate of Confidentiality from the National Institutes of Health to further protect study participants.[14]
Monitoring Equipment
All patients in the PICU were monitored continuously using General Electric (GE) (Fairfield, CT) solar devices. All bed spaces on the wards include GE Dash monitors that are used if ordered. On the ward we studied, 30% to 50% of patients are typically monitored at any given time. In addition to alarming at the bedside, most clinical alarms also generated a text message sent to the nurse's wireless phone listing the room number and the word monitor. Messages did not provide any clinical information about the alarm or patient's status. There were no technicians reviewing alarms centrally.
Physicians used an order set to order monitoring, selecting 1 of 4 available preconfigured profiles: infant <6 months, infant 6 months to 1 year, child, and adult. The parameters for each age group are in Supporting Figure 1, available in the online version of this article. A physician order is required for a nurse to change the parameters. Participating in the study did not affect this workflow.
Primary Outcome
The primary outcome was the nurse's response time to potentially critical monitor alarms that occurred while neither they nor any other clinicians were in the patient's room.
Primary Exposure and Alarm Classification
The primary exposure was the number of nonactionable alarms in the same patient over the preceding 120 minutes (rolling and updated each minute). The alarm classification scheme is shown in Figure 1.
Due to technical limitations with obtaining time‐stamped alarm data from the different ventilators in use during the study period, we were unable to identify the causes of all ventilator alarms. Therefore, we included ventilator alarms that did not lead to clinical interventions as nonactionable alarm exposures, but we did not evaluate the response time to any ventilator alarms.
Data Collection
We combined video recordings with monitor time‐stamp data to evaluate the association between nonactionable alarms and the nurse's response time. Our detailed video recording and annotation methods have been published separately.[15] Briefly, we mounted up to 6 small video cameras in patients' rooms and recorded up to 6 hours per session. The cameras captured the monitor display, a wide view of the room, a close‐up view of the patient, and all windows and doors through which staff could visually assess the patient without entering the room.
Video Processing, Review, and Annotation
The first 5 video sessions were reviewed in a group training setting. Research assistants received instruction on how to determine alarm validity and actionability in accordance with the study definitions. Following the training period, the review workflow was as follows. First, a research assistant entered basic information and a preliminary assessment of the alarm's clinical validity and actionability into a REDCap (Research Electronic Data Capture; Vanderbilt University, Nashville, TN) database.[16] Later, a physician investigator secondarily reviewed all alarms and confirmed the assessments of the research assistants or, when disagreements occurred, discussed and reconciled the database. Alarms that remained unresolved after secondary review were flagged for review with an additional physician or nurse investigator in a team meeting.
Data Analysis
We summarized the patient and nurse subjects, the distributions of alarms, and the response times to potentially critical monitor alarms that occurred while neither the nurse nor any other clinicians were in the patient's room. We explored the data using plots of alarms and response times occurring within individual video sessions as well as with simple linear regression. Hypothesizing that any alarm fatigue effect would be strongest in the highest alarm patients, and having observed that alarms are distributed very unevenly across patients in both the PICU and ward, we made the decision not to use quartiles, but rather to form clinically meaningful categories. We also hypothesized that nurses might not exhibit alarm fatigue unless they were inundated with alarms. We thus divided the nonactionable alarm counts over the preceding 120 minutes into 3 categories: 0 to 29 alarms to represent a low to average alarm rate exhibited by the bottom 50% of the patients, 30 to 79 alarms to represent an elevated alarm rate, and 80+ alarms to represent an extremely high alarm rate exhibited by the top 5%. Because the exposure time was 120 minutes, we conducted the analysis on the alarms occurring after a nurse had been video recorded for at least 120 minutes.
We further evaluated the relationship between nonactionable alarms and nurse response time with Kaplan‐Meier plots by nonactionable alarm count category using the observed response‐time data. The Kaplan‐Meier plots compared response time across the nonactionable alarm exposure group, without any statistical modeling. A log‐rank test stratified by nurse evaluated whether the distributions of response time in the Kaplan‐Meier plots differed across the 3 alarm exposure groups, accounting for within‐nurse clustering.
Accelerated failure‐time regression based on the Weibull distribution then allowed us to compare response time across each alarm exposure group and provided confidence intervals. Accelerated failure‐time models are comparable to Cox models, but emphasize time to event rather than hazards.[17, 18] We determined that the Weibull distribution was suitable by evaluating smoothed hazard and log‐hazard plots, the confidence intervals of the shape parameters in the Weibull models that did not include 1, and by demonstrating that the Weibull model had better fit than an alternative (exponential) model using the likelihood‐ratio test (P<0.0001 for PICU, P=0.02 for ward). Due to the small sample size of nurses and patients, we could not adjust for nurse‐ or patient‐level covariates in the model. When comparing the nonactionable alarm exposure groups in the regression model (029 vs 3079, 3079 vs 80+, and 029 vs 80+), we Bonferroni corrected the critical P value for the 3 comparisons, for a critical P value of 0.05/3=0.0167.
Nurse Questionnaire
At the session's conclusion, nurses completed a questionnaire that included demographics and asked, Did you respond more quickly to monitor alarms during this study because you knew you were being filmed? to measure if nurses would report experiencing a Hawthorne‐like effect.[19, 20, 21]
RESULTS
We performed 40 sessions among 40 patients and 36 nurses over 210 hours. We performed 20 sessions in children with heart and/or lung failure in the PICU and 20 sessions in children on a general ward. Sessions took place on weekdays between 9:00 am and 6:00 pm. There were 3 occasions when we filmed 2 patients cared for by the same nurse at the same time.
Nurses were mostly female (94.4%) and had between 2 months and 28 years of experience (median, 4.8 years). Patients on the ward ranged from 5 days to 5.4 years old (median, 6 months). Patients in the PICU ranged from 5 months to 16 years old (median, 2.5 years). Among the PICU patients, 14 (70%) were receiving mechanical ventilation only, 3 (15%) were receiving vasopressors only, and 3 (15%) were receiving mechanical ventilation and vasopressors.
We observed 5070 alarms during the 40 sessions. We excluded 108 (2.1%) that occurred at the end of video recording sessions with the nurse absent from the room because the nurse's response could not be determined. Alarms per session ranged from 10 to 1430 (median, 75; interquartile range [IQR], 35138). We excluded the outlier PICU patient with 1430 alarms in 5 hours from the analysis to avoid the potential for biasing the results. Figure 2 depicts the data flow.
Following the 5 training sessions, research assistants independently reviewed and made preliminary assessments on 4674 alarms; these alarms were all secondarily reviewed by a physician. Using the physician reviewer as the gold standard, the research assistant's sensitivity (assess alarm as actionable when physician also assesses as actionable) was 96.8% and specificity (assess alarm as nonactionable when physician also assesses as nonactionable) was 96.9%. We had to review 54 of 4674 alarms (1.2%) with an additional physician or nurse investigator to achieve consensus.
Characteristics of the 2445 alarms for clinical conditions are shown in Table 1. Only 12.9% of alarms in heart‐ and/or lung‐failure patients in the PICU were actionable, and only 1.0% of alarms in medical patients on a general inpatient ward were actionable.
Overall Response Times for Out‐of‐Room Alarms
We first evaluated response times without excluding alarms occurring prior to the 120‐minute mark. Of the 2445 clinical condition alarms, we excluded the 315 noncritical arrhythmia types from analysis of response time because they did not meet our definition of potentially critical alarms. Of the 2130 potentially critical alarms, 1185 (55.6%) occurred while neither the nurse nor any other clinician was in the patient's room. We proceeded to analyze the response time to these 1185 alarms (307 in the PICU and 878 on the ward). In the PICU, median response time was 3.3 minutes (IQR, 0.814.4). On the ward, median response time was 9.8 minutes (IQR, 3.222.4).
Response‐Time Association With Nonactionable Alarm Exposure
Next, we analyzed the association between response time to potentially critical alarms that occurred when the nurse was not in the patient's room and the number of nonactionable alarms occurring over the preceding 120‐minute window. This required excluding the alarms that occurred in the first 120 minutes of each session, leaving 647 alarms with eligible response times to evaluate the exposure between prior nonactionable alarm exposure and response time: 219 in the PICU and 428 on the ward. Kaplan‐Meier plots and tabulated response times demonstrated the incremental relationships between each nonactionable alarm exposure category in the observed data, with the effects most prominent as the Kaplan‐Meier plots diverged beyond the median (Figure 3 and Table 2). Excluding the extreme outlier patient had no effect on the results, because 1378 of the 1430 alarms occurred with the nurse present at the bedside, and only 2 of the remaining alarms were potentially critical.
| Observed Data | Accelerated Failure‐Time Model | |||||||
|---|---|---|---|---|---|---|---|---|
| Number of Potentially Critical Alarms | Minutes Elapsed Until This Percentage of Alarms Was Responded to | Modeled Response Time, min | 95% CI, min | P Value* | ||||
| 50% (Median) | 75% | 90% | 95% | |||||
| ||||||||
| PICU | ||||||||
| 029 nonactionable alarms | 70 | 1.6 | 8.0 | 18.6 | 25.1 | 2.8 | 1.9‐3.8 | Reference |
| 3079 nonactionable alarms | 122 | 6.3 | 17.8 | 22.5 | 26.0 | 5.3 | 4.06.7 | 0.001 (vs 029) |
| 80+ nonactionable alarms | 27 | 16.0 | 28.4 | 32.0 | 33.1 | 8.5 | 4.312.7 | 0.009 (vs 029), 0.15 (vs 3079) |
| Ward | ||||||||
| 029 nonactionable alarms | 159 | 9.8 | 17.8 | 25.0 | 28.9 | 7.7 | 6.39.1 | Reference |
| 3079 nonactionable alarms | 211 | 11.6 | 22.4 | 44.6 | 63.2 | 11.5 | 9.613.3 | 0.001 (vs 029) |
| 80+ nonactionable alarms | 58 | 8.3 | 57.6 | 63.8 | 69.5 | 15.6 | 11.020.1 | 0.001 (vs 029), 0.09 (vs 3079) |
Accelerated failure‐time regressions revealed significant incremental increases in the modeled response time as the number of preceding nonactionable alarms increased in both the PICU and ward settings (Table 2).
Hawthorne‐like Effects
Four of the 36 nurses reported that they responded more quickly to monitor alarms because they knew they were being filmed.
DISCUSSION
Alarm fatigue has recently generated interest among nurses,[22] physicians,[23] regulatory bodies,[24] patient safety organizations,[25] and even attorneys,[26] despite a lack of prior evidence linking nonactionable alarm exposure to response time or other adverse patient‐relevant outcomes. This study's main findings were that (1) the vast majority of alarms were nonactionable, (2) response time to alarms occurring while the nurse was out of the room increased as the number of nonactionable alarms over the preceding 120 minutes increased. These findings may be explained by alarm fatigue.
Our results build upon the findings of other related studies. The nonactionable alarm proportions we found were similar to other pediatric studies, reporting greater than 90% nonactionable alarms.[1, 2] One other study has reported a relationship between alarm exposure and response time. In that study, Voepel‐Lewis and colleagues evaluated nurse responses to pulse oximetry desaturation alarms in adult orthopedic surgery patients using time‐stamp data from their monitor notification system.[27] They found that alarm response time was significantly longer for patients in the highest quartile of alarms compared to those in lower quartiles. Our study provides new data suggesting a relationship between nonactionable alarm exposure and nurse response time.
Our study has several limitations. First, as a preliminary study to investigate feasibility and possible association, the sample of patients and nurses was necessarily limited and did not permit adjustment for nurse‐ or patient‐level covariates. A multivariable analysis with a larger sample might provide insight into alternate explanations for these findings other than alarm fatigue, including measures of nurse workload and patient factors (such as age and illness severity). Additional factors that are not as easily measured can also contribute to the complex decision of when and how to respond to alarms.[28, 29] Second, nurses were aware that they were being video recorded as part of a study of nonactionable alarms, although they did not know the specific details of measurement. Although this lack of blinding might lead to a Hawthorne‐like effect, our positive results suggest that this effect, if present, did not fully obscure the association. Third, all sessions took place on weekdays during daytime hours, but effects of nonactionable alarms might vary by time and day. Finally, we suspect that when nurses experience critical alarms that require them to intervene and rescue a patient, their response times to that patient's alarms that occur later in their shift will be quicker due to a heightened concern for the alarm being actionable. We were unable to explore that relationship in this analysis because the number of critical alarms requiring intervention was very small. This is a topic of future study.
CONCLUSIONS
We identified an association between a nurse's prior exposure to nonactionable alarms and response time to future alarms. This finding is consistent with alarm fatigue, but requires further study to more clearly delineate other factors that might confound or modify that relationship.
Disclosures
This project was funded by the Health Research Formula Fund Grant 4100050891 from the Pennsylvania Department of Public Health Commonwealth Universal Research Enhancement Program (awarded to Drs. Keren and Bonafide). Dr. Bonafide is also supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K23HL116427. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors have no financial relationships or conflicts of interest relevant to this article to disclose.
Hospital physiologic monitors can alert clinicians to early signs of physiologic deterioration, and thus have great potential to save lives. However, monitors generate frequent alarms,[1, 2, 3, 4, 5, 6, 7, 8] and most are not relevant to the patient's safety (over 90% of pediatric intensive care unit (PICU)[1, 2] and over 70% of adult intensive care alarms).[5, 6] In psychology experiments, humans rapidly learn to ignore or respond more slowly to alarms when exposed to high false‐alarm rates, exhibiting alarm fatigue.[9, 10] In 2013, The Joint Commission named alarm fatigue the most common contributing factor to alarm‐related sentinel events in hospitals.[11, 12]
Although alarm fatigue has been implicated as a major threat to patient safety, little empirical data support its existence in hospitals. In this study, we aimed to determine if there was an association between nurses' recent exposure to nonactionable physiologic monitor alarms and their response time to future alarms for the same patients. This exploratory work was designed to inform future research in this area, acknowledging that the sample size would be too small for multivariable modeling.
METHODS
Study Definitions
The alarm classification scheme is shown in Figure 1. Note that, for clarity, we have intentionally avoided using the terms true and false alarms because their interpretations vary across studies and can be misleading.
Potentially Critical Alarm
A potentially critical alarm is any alarm for a clinical condition for which a timely response is important to determine if the alarm requires intervention to save the patient's life. This is based on the alarm type alone, including alarms for life‐threatening arrhythmias such as asystole and ventricular tachycardia, as well as alarms for vital signs outside the set limits. Supporting Table 1 in the online version of this article lists the breakdown of alarm types that we defined a priori as potentially and not potentially critical.
| PICU | Ward | |||||||
|---|---|---|---|---|---|---|---|---|
| Alarm type | No. | % of Total | % Valid | % Actionable | No. | % of Total | % Valid | % Actionable |
| ||||||||
| Oxygen saturation | 197 | 19.4 | 82.7 | 38.6 | 590 | 41.2 | 24.4 | 1.9 |
| Heart rate | 194 | 19.1 | 95.4 | 1.0 | 266 | 18.6 | 87.2 | 0.0 |
| Respiratory rate | 229 | 22.6 | 80.8 | 13.5 | 316 | 22.1 | 48.1 | 1.0 |
| Blood pressure | 259 | 25.5 | 83.8 | 5.8 | 11 | 0.8 | 72.7 | 0.0 |
| Critical arrhythmia | 1 | 0.1 | 0.0 | 0.0 | 4 | 0.3 | 0.0 | 0.0 |
| Noncritical arrhythmia | 71 | 7.0 | 2.8 | 0.0 | 244 | 17.1 | 8.6 | 0.0 |
| Central venous pressure | 49 | 4.8 | 0.0 | 0.0 | 0 | 0.0 | N/A | N/A |
| Exhaled carbon dioxide | 14 | 1.4 | 92.9 | 50.0 | 0 | 0.0 | N/A | N/A |
| Total | 1014 | 100.0 | 75.6 | 12.9 | 1,431 | 100.0 | 38.9 | 1.0 |
Valid Alarm
A valid alarm is any alarm that correctly identifies the physiologic status of the patient. Validity was based on waveform quality, lead signal strength indicators, and artifact conditions, referencing each monitor's operator's manual.
Actionable Alarm
An actionable alarm is any valid alarm for a clinical condition that either: (1) leads to a clinical intervention; (2) leads to a consultation with another clinician at the bedside (and thus visible on camera); or (3) is a situation that should have led to intervention or consultation, but the alarm was unwitnessed or misinterpreted by the staff at the bedside.
Nonactionable Alarm
An unactionable alarm is any alarm that does not meet the actionable definition above, including invalid alarms such as those caused by motion artifact, equipment/technical alarms, and alarms that are valid but nonactionable (nuisance alarms).[13]
Response Time
The response time is the time elapsed from when the alarm fired at the bedside to when the nurse entered the room or peered through a window or door, measured in seconds.
Setting and Subjects
We performed this study between August 2012 and July 2013 at a freestanding children's hospital. We evaluated nurses caring for 2 populations: (1) PICU patients with heart and/or lung failure (requiring inotropic support and/or invasive mechanical ventilation), and (2) medical patients on a general inpatient ward. Nurses caring for heart and/or lung failure patients in the PICU typically were assigned 1 to 2 total patients. Nurses on the medical ward typically were assigned 2 to 4 patients. We identified subjects from the population of nurses caring for eligible patients with parents available to provide in‐person consent in each setting. Our primary interest was to evaluate the association between nonactionable alarms and response time, and not to study the epidemiology of alarms in a random sample. Therefore, when alarm data were available prior to screening, we first approached nurses caring for patients in the top 25% of alarm rates for that unit over the preceding 4 hours. We identified preceding alarm rates using BedMasterEx (Excel Medical Electronics, Jupiter, FL).
Human Subjects Protection
This study was approved by the institutional review board of The Children's Hospital of Philadelphia. We obtained written in‐person consent from the patient's parent and the nurse subject. We obtained a Certificate of Confidentiality from the National Institutes of Health to further protect study participants.[14]
Monitoring Equipment
All patients in the PICU were monitored continuously using General Electric (GE) (Fairfield, CT) solar devices. All bed spaces on the wards include GE Dash monitors that are used if ordered. On the ward we studied, 30% to 50% of patients are typically monitored at any given time. In addition to alarming at the bedside, most clinical alarms also generated a text message sent to the nurse's wireless phone listing the room number and the word monitor. Messages did not provide any clinical information about the alarm or patient's status. There were no technicians reviewing alarms centrally.
Physicians used an order set to order monitoring, selecting 1 of 4 available preconfigured profiles: infant <6 months, infant 6 months to 1 year, child, and adult. The parameters for each age group are in Supporting Figure 1, available in the online version of this article. A physician order is required for a nurse to change the parameters. Participating in the study did not affect this workflow.
Primary Outcome
The primary outcome was the nurse's response time to potentially critical monitor alarms that occurred while neither they nor any other clinicians were in the patient's room.
Primary Exposure and Alarm Classification
The primary exposure was the number of nonactionable alarms in the same patient over the preceding 120 minutes (rolling and updated each minute). The alarm classification scheme is shown in Figure 1.
Due to technical limitations with obtaining time‐stamped alarm data from the different ventilators in use during the study period, we were unable to identify the causes of all ventilator alarms. Therefore, we included ventilator alarms that did not lead to clinical interventions as nonactionable alarm exposures, but we did not evaluate the response time to any ventilator alarms.
Data Collection
We combined video recordings with monitor time‐stamp data to evaluate the association between nonactionable alarms and the nurse's response time. Our detailed video recording and annotation methods have been published separately.[15] Briefly, we mounted up to 6 small video cameras in patients' rooms and recorded up to 6 hours per session. The cameras captured the monitor display, a wide view of the room, a close‐up view of the patient, and all windows and doors through which staff could visually assess the patient without entering the room.
Video Processing, Review, and Annotation
The first 5 video sessions were reviewed in a group training setting. Research assistants received instruction on how to determine alarm validity and actionability in accordance with the study definitions. Following the training period, the review workflow was as follows. First, a research assistant entered basic information and a preliminary assessment of the alarm's clinical validity and actionability into a REDCap (Research Electronic Data Capture; Vanderbilt University, Nashville, TN) database.[16] Later, a physician investigator secondarily reviewed all alarms and confirmed the assessments of the research assistants or, when disagreements occurred, discussed and reconciled the database. Alarms that remained unresolved after secondary review were flagged for review with an additional physician or nurse investigator in a team meeting.
Data Analysis
We summarized the patient and nurse subjects, the distributions of alarms, and the response times to potentially critical monitor alarms that occurred while neither the nurse nor any other clinicians were in the patient's room. We explored the data using plots of alarms and response times occurring within individual video sessions as well as with simple linear regression. Hypothesizing that any alarm fatigue effect would be strongest in the highest alarm patients, and having observed that alarms are distributed very unevenly across patients in both the PICU and ward, we made the decision not to use quartiles, but rather to form clinically meaningful categories. We also hypothesized that nurses might not exhibit alarm fatigue unless they were inundated with alarms. We thus divided the nonactionable alarm counts over the preceding 120 minutes into 3 categories: 0 to 29 alarms to represent a low to average alarm rate exhibited by the bottom 50% of the patients, 30 to 79 alarms to represent an elevated alarm rate, and 80+ alarms to represent an extremely high alarm rate exhibited by the top 5%. Because the exposure time was 120 minutes, we conducted the analysis on the alarms occurring after a nurse had been video recorded for at least 120 minutes.
We further evaluated the relationship between nonactionable alarms and nurse response time with Kaplan‐Meier plots by nonactionable alarm count category using the observed response‐time data. The Kaplan‐Meier plots compared response time across the nonactionable alarm exposure group, without any statistical modeling. A log‐rank test stratified by nurse evaluated whether the distributions of response time in the Kaplan‐Meier plots differed across the 3 alarm exposure groups, accounting for within‐nurse clustering.
Accelerated failure‐time regression based on the Weibull distribution then allowed us to compare response time across each alarm exposure group and provided confidence intervals. Accelerated failure‐time models are comparable to Cox models, but emphasize time to event rather than hazards.[17, 18] We determined that the Weibull distribution was suitable by evaluating smoothed hazard and log‐hazard plots, the confidence intervals of the shape parameters in the Weibull models that did not include 1, and by demonstrating that the Weibull model had better fit than an alternative (exponential) model using the likelihood‐ratio test (P<0.0001 for PICU, P=0.02 for ward). Due to the small sample size of nurses and patients, we could not adjust for nurse‐ or patient‐level covariates in the model. When comparing the nonactionable alarm exposure groups in the regression model (029 vs 3079, 3079 vs 80+, and 029 vs 80+), we Bonferroni corrected the critical P value for the 3 comparisons, for a critical P value of 0.05/3=0.0167.
Nurse Questionnaire
At the session's conclusion, nurses completed a questionnaire that included demographics and asked, Did you respond more quickly to monitor alarms during this study because you knew you were being filmed? to measure if nurses would report experiencing a Hawthorne‐like effect.[19, 20, 21]
RESULTS
We performed 40 sessions among 40 patients and 36 nurses over 210 hours. We performed 20 sessions in children with heart and/or lung failure in the PICU and 20 sessions in children on a general ward. Sessions took place on weekdays between 9:00 am and 6:00 pm. There were 3 occasions when we filmed 2 patients cared for by the same nurse at the same time.
Nurses were mostly female (94.4%) and had between 2 months and 28 years of experience (median, 4.8 years). Patients on the ward ranged from 5 days to 5.4 years old (median, 6 months). Patients in the PICU ranged from 5 months to 16 years old (median, 2.5 years). Among the PICU patients, 14 (70%) were receiving mechanical ventilation only, 3 (15%) were receiving vasopressors only, and 3 (15%) were receiving mechanical ventilation and vasopressors.
We observed 5070 alarms during the 40 sessions. We excluded 108 (2.1%) that occurred at the end of video recording sessions with the nurse absent from the room because the nurse's response could not be determined. Alarms per session ranged from 10 to 1430 (median, 75; interquartile range [IQR], 35138). We excluded the outlier PICU patient with 1430 alarms in 5 hours from the analysis to avoid the potential for biasing the results. Figure 2 depicts the data flow.
Following the 5 training sessions, research assistants independently reviewed and made preliminary assessments on 4674 alarms; these alarms were all secondarily reviewed by a physician. Using the physician reviewer as the gold standard, the research assistant's sensitivity (assess alarm as actionable when physician also assesses as actionable) was 96.8% and specificity (assess alarm as nonactionable when physician also assesses as nonactionable) was 96.9%. We had to review 54 of 4674 alarms (1.2%) with an additional physician or nurse investigator to achieve consensus.
Characteristics of the 2445 alarms for clinical conditions are shown in Table 1. Only 12.9% of alarms in heart‐ and/or lung‐failure patients in the PICU were actionable, and only 1.0% of alarms in medical patients on a general inpatient ward were actionable.
Overall Response Times for Out‐of‐Room Alarms
We first evaluated response times without excluding alarms occurring prior to the 120‐minute mark. Of the 2445 clinical condition alarms, we excluded the 315 noncritical arrhythmia types from analysis of response time because they did not meet our definition of potentially critical alarms. Of the 2130 potentially critical alarms, 1185 (55.6%) occurred while neither the nurse nor any other clinician was in the patient's room. We proceeded to analyze the response time to these 1185 alarms (307 in the PICU and 878 on the ward). In the PICU, median response time was 3.3 minutes (IQR, 0.814.4). On the ward, median response time was 9.8 minutes (IQR, 3.222.4).
Response‐Time Association With Nonactionable Alarm Exposure
Next, we analyzed the association between response time to potentially critical alarms that occurred when the nurse was not in the patient's room and the number of nonactionable alarms occurring over the preceding 120‐minute window. This required excluding the alarms that occurred in the first 120 minutes of each session, leaving 647 alarms with eligible response times to evaluate the exposure between prior nonactionable alarm exposure and response time: 219 in the PICU and 428 on the ward. Kaplan‐Meier plots and tabulated response times demonstrated the incremental relationships between each nonactionable alarm exposure category in the observed data, with the effects most prominent as the Kaplan‐Meier plots diverged beyond the median (Figure 3 and Table 2). Excluding the extreme outlier patient had no effect on the results, because 1378 of the 1430 alarms occurred with the nurse present at the bedside, and only 2 of the remaining alarms were potentially critical.
| Observed Data | Accelerated Failure‐Time Model | |||||||
|---|---|---|---|---|---|---|---|---|
| Number of Potentially Critical Alarms | Minutes Elapsed Until This Percentage of Alarms Was Responded to | Modeled Response Time, min | 95% CI, min | P Value* | ||||
| 50% (Median) | 75% | 90% | 95% | |||||
| ||||||||
| PICU | ||||||||
| 029 nonactionable alarms | 70 | 1.6 | 8.0 | 18.6 | 25.1 | 2.8 | 1.9‐3.8 | Reference |
| 3079 nonactionable alarms | 122 | 6.3 | 17.8 | 22.5 | 26.0 | 5.3 | 4.06.7 | 0.001 (vs 029) |
| 80+ nonactionable alarms | 27 | 16.0 | 28.4 | 32.0 | 33.1 | 8.5 | 4.312.7 | 0.009 (vs 029), 0.15 (vs 3079) |
| Ward | ||||||||
| 029 nonactionable alarms | 159 | 9.8 | 17.8 | 25.0 | 28.9 | 7.7 | 6.39.1 | Reference |
| 3079 nonactionable alarms | 211 | 11.6 | 22.4 | 44.6 | 63.2 | 11.5 | 9.613.3 | 0.001 (vs 029) |
| 80+ nonactionable alarms | 58 | 8.3 | 57.6 | 63.8 | 69.5 | 15.6 | 11.020.1 | 0.001 (vs 029), 0.09 (vs 3079) |
Accelerated failure‐time regressions revealed significant incremental increases in the modeled response time as the number of preceding nonactionable alarms increased in both the PICU and ward settings (Table 2).
Hawthorne‐like Effects
Four of the 36 nurses reported that they responded more quickly to monitor alarms because they knew they were being filmed.
DISCUSSION
Alarm fatigue has recently generated interest among nurses,[22] physicians,[23] regulatory bodies,[24] patient safety organizations,[25] and even attorneys,[26] despite a lack of prior evidence linking nonactionable alarm exposure to response time or other adverse patient‐relevant outcomes. This study's main findings were that (1) the vast majority of alarms were nonactionable, (2) response time to alarms occurring while the nurse was out of the room increased as the number of nonactionable alarms over the preceding 120 minutes increased. These findings may be explained by alarm fatigue.
Our results build upon the findings of other related studies. The nonactionable alarm proportions we found were similar to other pediatric studies, reporting greater than 90% nonactionable alarms.[1, 2] One other study has reported a relationship between alarm exposure and response time. In that study, Voepel‐Lewis and colleagues evaluated nurse responses to pulse oximetry desaturation alarms in adult orthopedic surgery patients using time‐stamp data from their monitor notification system.[27] They found that alarm response time was significantly longer for patients in the highest quartile of alarms compared to those in lower quartiles. Our study provides new data suggesting a relationship between nonactionable alarm exposure and nurse response time.
Our study has several limitations. First, as a preliminary study to investigate feasibility and possible association, the sample of patients and nurses was necessarily limited and did not permit adjustment for nurse‐ or patient‐level covariates. A multivariable analysis with a larger sample might provide insight into alternate explanations for these findings other than alarm fatigue, including measures of nurse workload and patient factors (such as age and illness severity). Additional factors that are not as easily measured can also contribute to the complex decision of when and how to respond to alarms.[28, 29] Second, nurses were aware that they were being video recorded as part of a study of nonactionable alarms, although they did not know the specific details of measurement. Although this lack of blinding might lead to a Hawthorne‐like effect, our positive results suggest that this effect, if present, did not fully obscure the association. Third, all sessions took place on weekdays during daytime hours, but effects of nonactionable alarms might vary by time and day. Finally, we suspect that when nurses experience critical alarms that require them to intervene and rescue a patient, their response times to that patient's alarms that occur later in their shift will be quicker due to a heightened concern for the alarm being actionable. We were unable to explore that relationship in this analysis because the number of critical alarms requiring intervention was very small. This is a topic of future study.
CONCLUSIONS
We identified an association between a nurse's prior exposure to nonactionable alarms and response time to future alarms. This finding is consistent with alarm fatigue, but requires further study to more clearly delineate other factors that might confound or modify that relationship.
Disclosures
This project was funded by the Health Research Formula Fund Grant 4100050891 from the Pennsylvania Department of Public Health Commonwealth Universal Research Enhancement Program (awarded to Drs. Keren and Bonafide). Dr. Bonafide is also supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K23HL116427. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors have no financial relationships or conflicts of interest relevant to this article to disclose.
- . Crying wolf: false alarms in a pediatric intensive care unit. Crit Care Med. 1994;22(6):981–985.
- , . Poor prognosis for existing monitors in the intensive care unit. Crit Care Med. 1997;25(4):614–619.
- , , , , . Clinical evaluation of alarm efficiency in intensive care [in French]. Ann Fr Anesth Reanim. 2000;19:459–466.
- , , , . Reducing false alarms of intensive care online‐monitoring systems: an evaluation of two signal extraction algorithms. Comput Math Methods Med. 2011;2011:143480.
- , , , , , . Multicentric study of monitoring alarms in the adult intensive care unit (ICU): a descriptive analysis. Intensive Care Med. 1999;25:1360–1366.
- , , . Improving alarm performance in the medical intensive care unit using delays and clinical context. Anesth Analg. 2009;108:1546–1552.
- , . Monitor alarm fatigue: standardizing use of physiological monitors and decreasing nuisance alarms. Am J Crit Care. 2010;19:28–34.
- , , , , , . Intensive care unit alarms—how many do we need? Crit Care Med. 2010;38:451–456.
- , , , . System operator response to warnings of danger: a laboratory investigation of the effects of the predictive value of a warning on human response time. J Exp Psychol Appl. 1995;1:19–33.
- , , . Human probability matching behaviour in response to alarms of varying reliability. Ergonomics. 1995;38:2300–2312.
- The Joint Commission. Sentinel event alert: medical device alarm safety in hospitals. 2013. Available at: http://www.jointcommission.org/sea_issue_50/. Accessed October 9, 2014.
- . Joint commission warns of alarm fatigue: multitude of alarms from monitoring devices problematic. JAMA. 2013;309(22):2315–2316.
- . Monitor alarm fatigue: an integrative review. Biomed Instrum Technol. 2012;46(4):268–277.
- NIH Certificates of Confidentiality Kiosk. Available at: http://grants.nih.gov/grants/policy/coc/. Accessed April 21, 2014.
- , , , et al. Video methods for evaluating physiologic monitor alarms and alarm responses. Biomed Instrum Technol. 2014;48(3):220–230.
- , , , , , . Research electronic data capture (REDCap)—a metadata‐driven methodology and workflow process for providing translational research informatics support. J Biomed Inf. 2009;42:377–381.
- . Accelerated failure time and other parametric models. In: Modelling Survival Data in Medical Research. 2nd ed. Boca Raton, FL: Chapman 2003:197–229.
- , , , . Parametric models. In: An Introduction to Survival Analysis Using Stata, 3rd ed. College Station, TX: Stata Press; 2010:229–244.
- , . Management and the Worker. Cambridge, MA: Harvard University Press; 1939.
- . What happened at Hawthorne? Science. 1974;183(4128):922–932.
- , , , , . Validation of the Work Observation Method By Activity Timing (WOMBAT) method of conducting time‐motion observations in critical care settings: an observational study. BMC Med Inf Decis Mak. 2011;11:32.
- , . Alarm fatigue: a patient safety concern. AACN Adv Crit Care. 2013;24(4):378–386.
- , . Redesigning hospital alarms for patient safety: alarmed and potentially dangerous. JAMA. 2014;311(12):1199–1200.
- The Joint Commission. The Joint Commission announces 2014 National Patient Safety Goal. Jt Comm Perspect. 2013;33:1–4.
- Top 10 health technology hazards for 2014. Health Devices. 2013;42(11):354–380.
- My Philly Lawyer. Medical malpractice: alarm fatigue threatens patient safety. 2014. Available at: http://www.myphillylawyer.com/Resources/Legal-Articles/Medical-Malpractice-Alarm-Fatigue-Threatens-Patient-Safety.shtml. Accessed April 4, 2014.
- , , , et al. Pulse oximetry desaturation alarms on a general postoperative adult unit: a prospective observational study of nurse response time. Int J Nurs Stud. 2013;50(10):1351–1358.
- , , , , . A description of nurses' decision‐making in managing electrocardiographic monitor alarms [published online ahead of print May 10, 2014]. J Clin Nurs. doi:10.1111/jocn.12625.
- . Nurses' response to frequency and types of electrocardiography alarms in a non‐critical care setting: a descriptive study. Int J Nurs Stud. 2014;51(2):190–197.
- . Crying wolf: false alarms in a pediatric intensive care unit. Crit Care Med. 1994;22(6):981–985.
- , . Poor prognosis for existing monitors in the intensive care unit. Crit Care Med. 1997;25(4):614–619.
- , , , , . Clinical evaluation of alarm efficiency in intensive care [in French]. Ann Fr Anesth Reanim. 2000;19:459–466.
- , , , . Reducing false alarms of intensive care online‐monitoring systems: an evaluation of two signal extraction algorithms. Comput Math Methods Med. 2011;2011:143480.
- , , , , , . Multicentric study of monitoring alarms in the adult intensive care unit (ICU): a descriptive analysis. Intensive Care Med. 1999;25:1360–1366.
- , , . Improving alarm performance in the medical intensive care unit using delays and clinical context. Anesth Analg. 2009;108:1546–1552.
- , . Monitor alarm fatigue: standardizing use of physiological monitors and decreasing nuisance alarms. Am J Crit Care. 2010;19:28–34.
- , , , , , . Intensive care unit alarms—how many do we need? Crit Care Med. 2010;38:451–456.
- , , , . System operator response to warnings of danger: a laboratory investigation of the effects of the predictive value of a warning on human response time. J Exp Psychol Appl. 1995;1:19–33.
- , , . Human probability matching behaviour in response to alarms of varying reliability. Ergonomics. 1995;38:2300–2312.
- The Joint Commission. Sentinel event alert: medical device alarm safety in hospitals. 2013. Available at: http://www.jointcommission.org/sea_issue_50/. Accessed October 9, 2014.
- . Joint commission warns of alarm fatigue: multitude of alarms from monitoring devices problematic. JAMA. 2013;309(22):2315–2316.
- . Monitor alarm fatigue: an integrative review. Biomed Instrum Technol. 2012;46(4):268–277.
- NIH Certificates of Confidentiality Kiosk. Available at: http://grants.nih.gov/grants/policy/coc/. Accessed April 21, 2014.
- , , , et al. Video methods for evaluating physiologic monitor alarms and alarm responses. Biomed Instrum Technol. 2014;48(3):220–230.
- , , , , , . Research electronic data capture (REDCap)—a metadata‐driven methodology and workflow process for providing translational research informatics support. J Biomed Inf. 2009;42:377–381.
- . Accelerated failure time and other parametric models. In: Modelling Survival Data in Medical Research. 2nd ed. Boca Raton, FL: Chapman 2003:197–229.
- , , , . Parametric models. In: An Introduction to Survival Analysis Using Stata, 3rd ed. College Station, TX: Stata Press; 2010:229–244.
- , . Management and the Worker. Cambridge, MA: Harvard University Press; 1939.
- . What happened at Hawthorne? Science. 1974;183(4128):922–932.
- , , , , . Validation of the Work Observation Method By Activity Timing (WOMBAT) method of conducting time‐motion observations in critical care settings: an observational study. BMC Med Inf Decis Mak. 2011;11:32.
- , . Alarm fatigue: a patient safety concern. AACN Adv Crit Care. 2013;24(4):378–386.
- , . Redesigning hospital alarms for patient safety: alarmed and potentially dangerous. JAMA. 2014;311(12):1199–1200.
- The Joint Commission. The Joint Commission announces 2014 National Patient Safety Goal. Jt Comm Perspect. 2013;33:1–4.
- Top 10 health technology hazards for 2014. Health Devices. 2013;42(11):354–380.
- My Philly Lawyer. Medical malpractice: alarm fatigue threatens patient safety. 2014. Available at: http://www.myphillylawyer.com/Resources/Legal-Articles/Medical-Malpractice-Alarm-Fatigue-Threatens-Patient-Safety.shtml. Accessed April 4, 2014.
- , , , et al. Pulse oximetry desaturation alarms on a general postoperative adult unit: a prospective observational study of nurse response time. Int J Nurs Stud. 2013;50(10):1351–1358.
- , , , , . A description of nurses' decision‐making in managing electrocardiographic monitor alarms [published online ahead of print May 10, 2014]. J Clin Nurs. doi:10.1111/jocn.12625.
- . Nurses' response to frequency and types of electrocardiography alarms in a non‐critical care setting: a descriptive study. Int J Nurs Stud. 2014;51(2):190–197.
© 2015 Society of Hospital Medicine
Code Status Documentation
In the hospital, cardiopulmonary resuscitation (CPR) is the default treatment for a patient who suffers a cardiac arrest. Clinician assessment of patient preferences regarding resuscitation, with appropriate documentation in the medical record, is therefore essential for patients who do not wish to be resuscitated.[1] In addition, given frequent patient handoffs between physicians, consistent documentation of patient preferences is critical.[2] Unfortunately, multiple deficiencies in the quality of code status documentation have been identified in prior work.[3, 4] In this issue of the Journal of Hospital Medicine, Weinerman and colleagues[5] build on this literature by not only evaluating the completeness of code status documentation in multiple documentation sites, but also its consistency.
In this Canadian multihospital study, the authors found that only 38 of the 187 patients (20%) admitted to 1 of 4 medicine services had complete and consistent documentation of code status. Even more worrisome is that two‐thirds of the patients had inconsistent code status documentation. Although most of these inconsistencies involved missing information in 1 of the 5 sites of documentation (progress note, physician order, electronic resident sign‐out lists, nursing‐care plan, and do‐not‐resuscitate [DNR] face sheet), 31% were deemed clinically significant (eg, DNR in 1 source and full code in another). Such inconsistent documentation represents a serious threat to patient safety, and highlights the need for interventions aimed at improving the quality and reliability of code status documentation.
The authors identified 71 cases where code status documentation in progress notes was missing or inconsistent with documentation in other sites. Sixty of these notes lacked mention of a preference for full code status, 10 lacked documentation of DNR status, and 1 note incorrectly documented full code rather than DNR status. Interpretation of these findings requires consensus on whether the progress note is an appropriate location for code status documentation. With the evolution of the electronic medical record, the role of the progress note has changed, and unfortunately, these notes have become a lengthy chronicle of a patient's hospital course that includes all clinical data, medical problems, and an array of bottom‐of‐the‐list items such as code status. Information is easily added, but rarely removed, and what remains often goes unedited even for high‐stakes issues such as code status. Given the potential for copying and pasting of progress notes day after day, it is critical that clinicians periodically review the code status documented in the patient's notes and update this information as those preferences change. One solution that may minimize the potential for inaccurate documentation in progress notes is for institutions to utilize a separate note for code status documentation that the clinician fills out following any code status discussion. Having this note clearly labeled (eg, Code Status Note) and in a universal place within the electronic record may provide a reliable and efficient way for both physicians and nurses to identify a patient's preferences, while minimizing the inclusion of repetitive information in daily notes. Furthermore, if entered into a discrete field within the electronic record, this information could then autopopulate other sites (eg, sign‐out, nursing forms), thereby maintaining consistency. Use of note templates can provide a way to then help standardize the quality of information that is included in this type of code status note.
An alternate solution that may minimize the potential for inaccurate implementation of code status preferences is to focus on the fact that they are orders. As this study highlights, there is a need to improve both the completeness and consistency of code status documentation and, to this end, orders such as the Medical Orders for Life‐Sustaining Treatment (MOLST) or Physician Orders for Life‐Sustaining Treatment (POLST) may help.[6] Not only do these orders expand upon resuscitation preferences to include broader preferences for treatment in the context of serious illness, but they are also meant to serve as a standard way to document patient care preferences across healthcare settings. Although the MOLST and POLST primarily aim to translate patient preferences into medical orders to be followed outside of the hospital, their implementation into the electronic medical record may provide a more consistent way to document patient preferences in the hospital as well.
Although many studies have identified the need to improve the quality of code status discussions,[7, 8, 9, 10] the work by Weinerman and colleagues reminds us that attention to documentation is also critical. Ensuring that the electronic medical record contains documentation of the patient's resuscitation preferences and overall goals of care, and that this information can be found easily and reliably by physicians and nurses, should drive future quality improvement and research in this area.
Disclosure
Nothing to report.
- , , , et al. Physician understanding of patient resuscitation preferences: insights and clinical implications. J Am Geriatr Soc. 2000;48(5 suppl):S44–S51.
- , , , et al. The patient handoff: a comprehensive curricular blueprint for resident education to improve continuity of care. Acad Med. 2012;87(4):411–418.
- , , , . Documentation quality of inpatient code status discussions. J Pain Symptom Manage. 2014;48(4):632–638.
- , , , et al. Factors associated with discussion of care plans and code status at the time of hospital admission: results from the Multicenter Hospitalist Study. J Hosp Med. 2008;3(6):437–445.
- , , , , , . Frequency and clinical relevance of inconsistent code status documentation. J Hosp Med. 2015;10(8):491–496.
- , , . Use of the Physician Orders for Life‐Sustaining Treatment Program in the clinical setting: a systematic review of the literature. J Am Geriatr Soc. 2015;63(2):341–350.
- The SUPPORT Principal Investigators. A controlled trial to improve care for seriously ill hospitalized patients. JAMA. 1995;274(20):1591–1598.
- , , . How do medical residents discuss resuscitation with patients? J Gen Intern Med. 1995;10(8):436–442.
- , , , , . Code status discussions between attending hospitalist physicians and medical patients at hospital admission. J Gen Intern Med. 2011;26(4):359–366.
- , , , . Code status discussions: agreement between internal medicine residents and hospitalized patients. Teach Learn Med. 2010;22(4):251–256.
In the hospital, cardiopulmonary resuscitation (CPR) is the default treatment for a patient who suffers a cardiac arrest. Clinician assessment of patient preferences regarding resuscitation, with appropriate documentation in the medical record, is therefore essential for patients who do not wish to be resuscitated.[1] In addition, given frequent patient handoffs between physicians, consistent documentation of patient preferences is critical.[2] Unfortunately, multiple deficiencies in the quality of code status documentation have been identified in prior work.[3, 4] In this issue of the Journal of Hospital Medicine, Weinerman and colleagues[5] build on this literature by not only evaluating the completeness of code status documentation in multiple documentation sites, but also its consistency.
In this Canadian multihospital study, the authors found that only 38 of the 187 patients (20%) admitted to 1 of 4 medicine services had complete and consistent documentation of code status. Even more worrisome is that two‐thirds of the patients had inconsistent code status documentation. Although most of these inconsistencies involved missing information in 1 of the 5 sites of documentation (progress note, physician order, electronic resident sign‐out lists, nursing‐care plan, and do‐not‐resuscitate [DNR] face sheet), 31% were deemed clinically significant (eg, DNR in 1 source and full code in another). Such inconsistent documentation represents a serious threat to patient safety, and highlights the need for interventions aimed at improving the quality and reliability of code status documentation.
The authors identified 71 cases where code status documentation in progress notes was missing or inconsistent with documentation in other sites. Sixty of these notes lacked mention of a preference for full code status, 10 lacked documentation of DNR status, and 1 note incorrectly documented full code rather than DNR status. Interpretation of these findings requires consensus on whether the progress note is an appropriate location for code status documentation. With the evolution of the electronic medical record, the role of the progress note has changed, and unfortunately, these notes have become a lengthy chronicle of a patient's hospital course that includes all clinical data, medical problems, and an array of bottom‐of‐the‐list items such as code status. Information is easily added, but rarely removed, and what remains often goes unedited even for high‐stakes issues such as code status. Given the potential for copying and pasting of progress notes day after day, it is critical that clinicians periodically review the code status documented in the patient's notes and update this information as those preferences change. One solution that may minimize the potential for inaccurate documentation in progress notes is for institutions to utilize a separate note for code status documentation that the clinician fills out following any code status discussion. Having this note clearly labeled (eg, Code Status Note) and in a universal place within the electronic record may provide a reliable and efficient way for both physicians and nurses to identify a patient's preferences, while minimizing the inclusion of repetitive information in daily notes. Furthermore, if entered into a discrete field within the electronic record, this information could then autopopulate other sites (eg, sign‐out, nursing forms), thereby maintaining consistency. Use of note templates can provide a way to then help standardize the quality of information that is included in this type of code status note.
An alternate solution that may minimize the potential for inaccurate implementation of code status preferences is to focus on the fact that they are orders. As this study highlights, there is a need to improve both the completeness and consistency of code status documentation and, to this end, orders such as the Medical Orders for Life‐Sustaining Treatment (MOLST) or Physician Orders for Life‐Sustaining Treatment (POLST) may help.[6] Not only do these orders expand upon resuscitation preferences to include broader preferences for treatment in the context of serious illness, but they are also meant to serve as a standard way to document patient care preferences across healthcare settings. Although the MOLST and POLST primarily aim to translate patient preferences into medical orders to be followed outside of the hospital, their implementation into the electronic medical record may provide a more consistent way to document patient preferences in the hospital as well.
Although many studies have identified the need to improve the quality of code status discussions,[7, 8, 9, 10] the work by Weinerman and colleagues reminds us that attention to documentation is also critical. Ensuring that the electronic medical record contains documentation of the patient's resuscitation preferences and overall goals of care, and that this information can be found easily and reliably by physicians and nurses, should drive future quality improvement and research in this area.
Disclosure
Nothing to report.
In the hospital, cardiopulmonary resuscitation (CPR) is the default treatment for a patient who suffers a cardiac arrest. Clinician assessment of patient preferences regarding resuscitation, with appropriate documentation in the medical record, is therefore essential for patients who do not wish to be resuscitated.[1] In addition, given frequent patient handoffs between physicians, consistent documentation of patient preferences is critical.[2] Unfortunately, multiple deficiencies in the quality of code status documentation have been identified in prior work.[3, 4] In this issue of the Journal of Hospital Medicine, Weinerman and colleagues[5] build on this literature by not only evaluating the completeness of code status documentation in multiple documentation sites, but also its consistency.
In this Canadian multihospital study, the authors found that only 38 of the 187 patients (20%) admitted to 1 of 4 medicine services had complete and consistent documentation of code status. Even more worrisome is that two‐thirds of the patients had inconsistent code status documentation. Although most of these inconsistencies involved missing information in 1 of the 5 sites of documentation (progress note, physician order, electronic resident sign‐out lists, nursing‐care plan, and do‐not‐resuscitate [DNR] face sheet), 31% were deemed clinically significant (eg, DNR in 1 source and full code in another). Such inconsistent documentation represents a serious threat to patient safety, and highlights the need for interventions aimed at improving the quality and reliability of code status documentation.
The authors identified 71 cases where code status documentation in progress notes was missing or inconsistent with documentation in other sites. Sixty of these notes lacked mention of a preference for full code status, 10 lacked documentation of DNR status, and 1 note incorrectly documented full code rather than DNR status. Interpretation of these findings requires consensus on whether the progress note is an appropriate location for code status documentation. With the evolution of the electronic medical record, the role of the progress note has changed, and unfortunately, these notes have become a lengthy chronicle of a patient's hospital course that includes all clinical data, medical problems, and an array of bottom‐of‐the‐list items such as code status. Information is easily added, but rarely removed, and what remains often goes unedited even for high‐stakes issues such as code status. Given the potential for copying and pasting of progress notes day after day, it is critical that clinicians periodically review the code status documented in the patient's notes and update this information as those preferences change. One solution that may minimize the potential for inaccurate documentation in progress notes is for institutions to utilize a separate note for code status documentation that the clinician fills out following any code status discussion. Having this note clearly labeled (eg, Code Status Note) and in a universal place within the electronic record may provide a reliable and efficient way for both physicians and nurses to identify a patient's preferences, while minimizing the inclusion of repetitive information in daily notes. Furthermore, if entered into a discrete field within the electronic record, this information could then autopopulate other sites (eg, sign‐out, nursing forms), thereby maintaining consistency. Use of note templates can provide a way to then help standardize the quality of information that is included in this type of code status note.
An alternate solution that may minimize the potential for inaccurate implementation of code status preferences is to focus on the fact that they are orders. As this study highlights, there is a need to improve both the completeness and consistency of code status documentation and, to this end, orders such as the Medical Orders for Life‐Sustaining Treatment (MOLST) or Physician Orders for Life‐Sustaining Treatment (POLST) may help.[6] Not only do these orders expand upon resuscitation preferences to include broader preferences for treatment in the context of serious illness, but they are also meant to serve as a standard way to document patient care preferences across healthcare settings. Although the MOLST and POLST primarily aim to translate patient preferences into medical orders to be followed outside of the hospital, their implementation into the electronic medical record may provide a more consistent way to document patient preferences in the hospital as well.
Although many studies have identified the need to improve the quality of code status discussions,[7, 8, 9, 10] the work by Weinerman and colleagues reminds us that attention to documentation is also critical. Ensuring that the electronic medical record contains documentation of the patient's resuscitation preferences and overall goals of care, and that this information can be found easily and reliably by physicians and nurses, should drive future quality improvement and research in this area.
Disclosure
Nothing to report.
- , , , et al. Physician understanding of patient resuscitation preferences: insights and clinical implications. J Am Geriatr Soc. 2000;48(5 suppl):S44–S51.
- , , , et al. The patient handoff: a comprehensive curricular blueprint for resident education to improve continuity of care. Acad Med. 2012;87(4):411–418.
- , , , . Documentation quality of inpatient code status discussions. J Pain Symptom Manage. 2014;48(4):632–638.
- , , , et al. Factors associated with discussion of care plans and code status at the time of hospital admission: results from the Multicenter Hospitalist Study. J Hosp Med. 2008;3(6):437–445.
- , , , , , . Frequency and clinical relevance of inconsistent code status documentation. J Hosp Med. 2015;10(8):491–496.
- , , . Use of the Physician Orders for Life‐Sustaining Treatment Program in the clinical setting: a systematic review of the literature. J Am Geriatr Soc. 2015;63(2):341–350.
- The SUPPORT Principal Investigators. A controlled trial to improve care for seriously ill hospitalized patients. JAMA. 1995;274(20):1591–1598.
- , , . How do medical residents discuss resuscitation with patients? J Gen Intern Med. 1995;10(8):436–442.
- , , , , . Code status discussions between attending hospitalist physicians and medical patients at hospital admission. J Gen Intern Med. 2011;26(4):359–366.
- , , , . Code status discussions: agreement between internal medicine residents and hospitalized patients. Teach Learn Med. 2010;22(4):251–256.
- , , , et al. Physician understanding of patient resuscitation preferences: insights and clinical implications. J Am Geriatr Soc. 2000;48(5 suppl):S44–S51.
- , , , et al. The patient handoff: a comprehensive curricular blueprint for resident education to improve continuity of care. Acad Med. 2012;87(4):411–418.
- , , , . Documentation quality of inpatient code status discussions. J Pain Symptom Manage. 2014;48(4):632–638.
- , , , et al. Factors associated with discussion of care plans and code status at the time of hospital admission: results from the Multicenter Hospitalist Study. J Hosp Med. 2008;3(6):437–445.
- , , , , , . Frequency and clinical relevance of inconsistent code status documentation. J Hosp Med. 2015;10(8):491–496.
- , , . Use of the Physician Orders for Life‐Sustaining Treatment Program in the clinical setting: a systematic review of the literature. J Am Geriatr Soc. 2015;63(2):341–350.
- The SUPPORT Principal Investigators. A controlled trial to improve care for seriously ill hospitalized patients. JAMA. 1995;274(20):1591–1598.
- , , . How do medical residents discuss resuscitation with patients? J Gen Intern Med. 1995;10(8):436–442.
- , , , , . Code status discussions between attending hospitalist physicians and medical patients at hospital admission. J Gen Intern Med. 2011;26(4):359–366.
- , , , . Code status discussions: agreement between internal medicine residents and hospitalized patients. Teach Learn Med. 2010;22(4):251–256.
HHS secretary tells how to combat drug abuse
More people are dying of drug overdoses in the United States than in car crashes, Nicholas Garlow writes in a blog entry for the U.S. Department of Health & Human Services.
Sylvia Mathews Burwell, secretary of the HHS, announced ways to combat opioid abuse during a speech at the 4th annual National Rx Drug Abuse Summit in Atlanta.
The strategies Ms. Burwell cited are:
• Provide the training, tools, and educational resources that health care professionals need to make more informed prescribing decisions.
• Increase the use of naloxone, a drug that can reverse opioid overdose*.
• Use medication-assisted treatment to help lift people from opioid addiction.
Drug overdose is the leading cause of injury death in the country. In fact, the number of drug overdoses resulting in deaths has increased fivefold since 1980, Mr. Garlow writes.
*Correction, 4/16/2015: An earlier version of this article misstated naloxone's indication.
More people are dying of drug overdoses in the United States than in car crashes, Nicholas Garlow writes in a blog entry for the U.S. Department of Health & Human Services.
Sylvia Mathews Burwell, secretary of the HHS, announced ways to combat opioid abuse during a speech at the 4th annual National Rx Drug Abuse Summit in Atlanta.
The strategies Ms. Burwell cited are:
• Provide the training, tools, and educational resources that health care professionals need to make more informed prescribing decisions.
• Increase the use of naloxone, a drug that can reverse opioid overdose*.
• Use medication-assisted treatment to help lift people from opioid addiction.
Drug overdose is the leading cause of injury death in the country. In fact, the number of drug overdoses resulting in deaths has increased fivefold since 1980, Mr. Garlow writes.
*Correction, 4/16/2015: An earlier version of this article misstated naloxone's indication.
More people are dying of drug overdoses in the United States than in car crashes, Nicholas Garlow writes in a blog entry for the U.S. Department of Health & Human Services.
Sylvia Mathews Burwell, secretary of the HHS, announced ways to combat opioid abuse during a speech at the 4th annual National Rx Drug Abuse Summit in Atlanta.
The strategies Ms. Burwell cited are:
• Provide the training, tools, and educational resources that health care professionals need to make more informed prescribing decisions.
• Increase the use of naloxone, a drug that can reverse opioid overdose*.
• Use medication-assisted treatment to help lift people from opioid addiction.
Drug overdose is the leading cause of injury death in the country. In fact, the number of drug overdoses resulting in deaths has increased fivefold since 1980, Mr. Garlow writes.
*Correction, 4/16/2015: An earlier version of this article misstated naloxone's indication.
Ablation during mitral valve surgery offers up mixed results
SAN DIEGO – Surgical ablation of atrial fibrillation at the time of mitral valve surgery provides significantly greater rhythm control than mitral valve surgery alone, a study showed.
Freedom from atrial fibrillation (AF) at both 6 months and 1 year was 63% in patients undergoing mitral valve surgery (MVS) plus ablation and 29% in those undergoing MVS alone, a statistically significant difference.
However, patients who had ablation plus MVS were 2.5 times more likely to have a permanent pacemaker implanted than were those who had MVS alone, at 21.5% and 8.1%, respectively, also a significant difference.
Ablation did not increase mortality or major adverse cardiac or cerebrovascular events, Dr. A. Marc Gillinov said at the annual meeting of the American College of Cardiology.
Preoperative AF is present in up to 50% of patients undergoing mitral valve operations and is associated with an increased risk of death and stroke.
The study enrolled 260 relatively elderly patients (mean age 69 years) with AF that was persistent (non–self-terminating for at least 7 days) or long-standing persistent (continuous for at least a year), in addition to mitral valve disease. A total of 133 patients were randomly assigned to MVS plus ablation and 127 to MVS alone. The ablation group was further randomized to pulmonary vein isolation or a biatrial maze procedure; all underwent closure of the left atrial appendage.
There was no significant difference in freedom from AF at 6 months and 1 year between patients who had pulmonary vein isolation or a biatrial maze procedure, at 61% and 66%, respectively, said Dr. Gillinov, a cardiac surgeon at Cleveland Clinic.
One-year mortality was similar among all patients undergoing MVS plus ablation vs. MVS alone, at 6.8% and 8.7%.
The two groups also had similar Short Form-12 questionnaire scores for physical function and mental function, although AF occurring at least once daily was significantly less common with ablation, at 19.8%, compared with 45.2% in the MVS-alone patients, he said.
The heart rhythm endpoint was “stringent,” with 3-day Holter monitors obtained at both 6 and 12 months and repeat ablation procedures and death considered treatment failures, Dr. Gillinov said.
He acknowledged that 20% of patients did not have data for the primary endpoint and that the endpoint was not a clinical one, but said a trial with mortality or stroke as the endpoint would require more than 1,000 patients and many years follow-up.
Regarding whether ablation should now be performed routinely, “the glass is half full or half empty,” remarked discussant Dr. Bernard Gersh of Mayo Clinic in Rochester, Minn. “On one hand, you have shown less atrial fibrillation [with ablation], but no effect on quality of life, and the price to be paid was a higher rate of pacemaker implantation,” he said.
The pacemaker implantation rate was higher than expected – 17% in-hospital – and does represent a potential cost, but he would routinely do a maze procedure, Dr. Gillinov said.
Discussant Dr. Alice Jacobs of the Cardiovascular Center at Boston Medical Center, said she expected Dr. Gillinov to say the procedure should not be used in everyone given the lack of benefit in stroke, probably because they tied off the left atrium appendage, and the increase in pacemaker implantations.
About half of the pacemaker implantations were due to atrioventricular block, possibly a consequence of the valve surgery, and one-third to sinus-node dysfunction, which is common in elderly patients, Dr. Gillinov explained.
The study was funded by the National Institutes of Health and the Canadian Institutes of Health Research. Dr. Gillinov reported serving as a consultant/speaker for AtriCure, Medtronic, On-X, Edwards, and Tendyne; research funding from St. Jude Medical; an equity interest in Clear Catheter; and that his institution receives royalties from AtriCure for a left atrial appendage occlusion device.
SAN DIEGO – Surgical ablation of atrial fibrillation at the time of mitral valve surgery provides significantly greater rhythm control than mitral valve surgery alone, a study showed.
Freedom from atrial fibrillation (AF) at both 6 months and 1 year was 63% in patients undergoing mitral valve surgery (MVS) plus ablation and 29% in those undergoing MVS alone, a statistically significant difference.
However, patients who had ablation plus MVS were 2.5 times more likely to have a permanent pacemaker implanted than were those who had MVS alone, at 21.5% and 8.1%, respectively, also a significant difference.
Ablation did not increase mortality or major adverse cardiac or cerebrovascular events, Dr. A. Marc Gillinov said at the annual meeting of the American College of Cardiology.
Preoperative AF is present in up to 50% of patients undergoing mitral valve operations and is associated with an increased risk of death and stroke.
The study enrolled 260 relatively elderly patients (mean age 69 years) with AF that was persistent (non–self-terminating for at least 7 days) or long-standing persistent (continuous for at least a year), in addition to mitral valve disease. A total of 133 patients were randomly assigned to MVS plus ablation and 127 to MVS alone. The ablation group was further randomized to pulmonary vein isolation or a biatrial maze procedure; all underwent closure of the left atrial appendage.
There was no significant difference in freedom from AF at 6 months and 1 year between patients who had pulmonary vein isolation or a biatrial maze procedure, at 61% and 66%, respectively, said Dr. Gillinov, a cardiac surgeon at Cleveland Clinic.
One-year mortality was similar among all patients undergoing MVS plus ablation vs. MVS alone, at 6.8% and 8.7%.
The two groups also had similar Short Form-12 questionnaire scores for physical function and mental function, although AF occurring at least once daily was significantly less common with ablation, at 19.8%, compared with 45.2% in the MVS-alone patients, he said.
The heart rhythm endpoint was “stringent,” with 3-day Holter monitors obtained at both 6 and 12 months and repeat ablation procedures and death considered treatment failures, Dr. Gillinov said.
He acknowledged that 20% of patients did not have data for the primary endpoint and that the endpoint was not a clinical one, but said a trial with mortality or stroke as the endpoint would require more than 1,000 patients and many years follow-up.
Regarding whether ablation should now be performed routinely, “the glass is half full or half empty,” remarked discussant Dr. Bernard Gersh of Mayo Clinic in Rochester, Minn. “On one hand, you have shown less atrial fibrillation [with ablation], but no effect on quality of life, and the price to be paid was a higher rate of pacemaker implantation,” he said.
The pacemaker implantation rate was higher than expected – 17% in-hospital – and does represent a potential cost, but he would routinely do a maze procedure, Dr. Gillinov said.
Discussant Dr. Alice Jacobs of the Cardiovascular Center at Boston Medical Center, said she expected Dr. Gillinov to say the procedure should not be used in everyone given the lack of benefit in stroke, probably because they tied off the left atrium appendage, and the increase in pacemaker implantations.
About half of the pacemaker implantations were due to atrioventricular block, possibly a consequence of the valve surgery, and one-third to sinus-node dysfunction, which is common in elderly patients, Dr. Gillinov explained.
The study was funded by the National Institutes of Health and the Canadian Institutes of Health Research. Dr. Gillinov reported serving as a consultant/speaker for AtriCure, Medtronic, On-X, Edwards, and Tendyne; research funding from St. Jude Medical; an equity interest in Clear Catheter; and that his institution receives royalties from AtriCure for a left atrial appendage occlusion device.
SAN DIEGO – Surgical ablation of atrial fibrillation at the time of mitral valve surgery provides significantly greater rhythm control than mitral valve surgery alone, a study showed.
Freedom from atrial fibrillation (AF) at both 6 months and 1 year was 63% in patients undergoing mitral valve surgery (MVS) plus ablation and 29% in those undergoing MVS alone, a statistically significant difference.
However, patients who had ablation plus MVS were 2.5 times more likely to have a permanent pacemaker implanted than were those who had MVS alone, at 21.5% and 8.1%, respectively, also a significant difference.
Ablation did not increase mortality or major adverse cardiac or cerebrovascular events, Dr. A. Marc Gillinov said at the annual meeting of the American College of Cardiology.
Preoperative AF is present in up to 50% of patients undergoing mitral valve operations and is associated with an increased risk of death and stroke.
The study enrolled 260 relatively elderly patients (mean age 69 years) with AF that was persistent (non–self-terminating for at least 7 days) or long-standing persistent (continuous for at least a year), in addition to mitral valve disease. A total of 133 patients were randomly assigned to MVS plus ablation and 127 to MVS alone. The ablation group was further randomized to pulmonary vein isolation or a biatrial maze procedure; all underwent closure of the left atrial appendage.
There was no significant difference in freedom from AF at 6 months and 1 year between patients who had pulmonary vein isolation or a biatrial maze procedure, at 61% and 66%, respectively, said Dr. Gillinov, a cardiac surgeon at Cleveland Clinic.
One-year mortality was similar among all patients undergoing MVS plus ablation vs. MVS alone, at 6.8% and 8.7%.
The two groups also had similar Short Form-12 questionnaire scores for physical function and mental function, although AF occurring at least once daily was significantly less common with ablation, at 19.8%, compared with 45.2% in the MVS-alone patients, he said.
The heart rhythm endpoint was “stringent,” with 3-day Holter monitors obtained at both 6 and 12 months and repeat ablation procedures and death considered treatment failures, Dr. Gillinov said.
He acknowledged that 20% of patients did not have data for the primary endpoint and that the endpoint was not a clinical one, but said a trial with mortality or stroke as the endpoint would require more than 1,000 patients and many years follow-up.
Regarding whether ablation should now be performed routinely, “the glass is half full or half empty,” remarked discussant Dr. Bernard Gersh of Mayo Clinic in Rochester, Minn. “On one hand, you have shown less atrial fibrillation [with ablation], but no effect on quality of life, and the price to be paid was a higher rate of pacemaker implantation,” he said.
The pacemaker implantation rate was higher than expected – 17% in-hospital – and does represent a potential cost, but he would routinely do a maze procedure, Dr. Gillinov said.
Discussant Dr. Alice Jacobs of the Cardiovascular Center at Boston Medical Center, said she expected Dr. Gillinov to say the procedure should not be used in everyone given the lack of benefit in stroke, probably because they tied off the left atrium appendage, and the increase in pacemaker implantations.
About half of the pacemaker implantations were due to atrioventricular block, possibly a consequence of the valve surgery, and one-third to sinus-node dysfunction, which is common in elderly patients, Dr. Gillinov explained.
The study was funded by the National Institutes of Health and the Canadian Institutes of Health Research. Dr. Gillinov reported serving as a consultant/speaker for AtriCure, Medtronic, On-X, Edwards, and Tendyne; research funding from St. Jude Medical; an equity interest in Clear Catheter; and that his institution receives royalties from AtriCure for a left atrial appendage occlusion device.
AT ACC 15
Key clinical point: Surgical ablation of atrial fibrillation during mitral valve surgery decreases AF at 6 months and 1 year, but increases pacemaker implantations.
Major finding: Freedom from AF at both 6 months and 1 year was 63% with mitral valve surgery plus ablation and 29% for MVS alone.
Data source: Prospective, randomized study in 260 patients with persistent or longstanding persistent AF who required mitral valve surgery.
Disclosures: The study was funded by the National Institutes of Health and the Canadian Institutes of Health Research. Dr. Gillinov reported serving as a consultant/speaker for AtriCure, Medtronic, On-X, Edwards, and Tendyne; research funding from St. Jude Medical; an equity interest in Clear Catheter; and that his institution receives royalties from AtriCure for a left atrial appendage occlusion device.
Three factors boost dabigatran adherence
Patient adherence to dabigatran therapy varied enormously in a nationwide study of pharmacy data, and three factors were identified that markedly enhanced adherence, according to a report published online April 14 in JAMA.
After research suggested that adherence to dabigatran was suboptimal among patients taking the drug for nonvalvular atrial fibrillation, investigators explored variations in adherence across thousands of sites using a Veterans Health Administration pharmacy database. They found that among 4,863 patients filling dabigatran prescriptions at 67 pharmacies during a 2-year period, adherence ranged from a low of 42% to a high of 93%, said Dr. Supriya Shore, a cardiology fellow at Emory University, Atlanta, and her associates.
The single most important factor that influenced dabigatran adherence was appropriate patient selection before dispensing the drug. This was defined as the pharmacist assessing the indication for treatment and ruling out contraindications after the physician ordered the prescription, as well as checking the patient’s adherence to other medications. Pharmacist monitoring of dabigatran use and adverse events, either alone or in collaboration with the treating clinician, also boosted adherence.
In addition, pharmacists working with clinicians to identify and address nonadherence also enhanced patient adherence. Prescribing physicians may not be able to routinely monitor adherence because of their large workload and limited time during clinic visits. Having a pharmacist do so mitigated patients’ tendency to stop taking the drug when minor adverse effects developed, the investigators reported (JAMA 2015 April 14 [doi:101001/jama.2015.2761]).
“These findings suggest that such site-level practices provide modifiable targets to improve patient adherence to dabigatran, as opposed to patient characteristics that frequently cannot be modified,” Dr. Shore and her associates wrote.
This study was funded in part by VA Health Services Research & Development, an American Heart Association National Scientist Development Grant, and a Gilead Sciences Cardiovascular Research Scholars Program award. Dr. Shore reported having no financial disclosures; one of her associates reported serving as a consultant to Precision Health Economics, Medtronic, and St. Jude Medical.
Patient adherence to dabigatran therapy varied enormously in a nationwide study of pharmacy data, and three factors were identified that markedly enhanced adherence, according to a report published online April 14 in JAMA.
After research suggested that adherence to dabigatran was suboptimal among patients taking the drug for nonvalvular atrial fibrillation, investigators explored variations in adherence across thousands of sites using a Veterans Health Administration pharmacy database. They found that among 4,863 patients filling dabigatran prescriptions at 67 pharmacies during a 2-year period, adherence ranged from a low of 42% to a high of 93%, said Dr. Supriya Shore, a cardiology fellow at Emory University, Atlanta, and her associates.
The single most important factor that influenced dabigatran adherence was appropriate patient selection before dispensing the drug. This was defined as the pharmacist assessing the indication for treatment and ruling out contraindications after the physician ordered the prescription, as well as checking the patient’s adherence to other medications. Pharmacist monitoring of dabigatran use and adverse events, either alone or in collaboration with the treating clinician, also boosted adherence.
In addition, pharmacists working with clinicians to identify and address nonadherence also enhanced patient adherence. Prescribing physicians may not be able to routinely monitor adherence because of their large workload and limited time during clinic visits. Having a pharmacist do so mitigated patients’ tendency to stop taking the drug when minor adverse effects developed, the investigators reported (JAMA 2015 April 14 [doi:101001/jama.2015.2761]).
“These findings suggest that such site-level practices provide modifiable targets to improve patient adherence to dabigatran, as opposed to patient characteristics that frequently cannot be modified,” Dr. Shore and her associates wrote.
This study was funded in part by VA Health Services Research & Development, an American Heart Association National Scientist Development Grant, and a Gilead Sciences Cardiovascular Research Scholars Program award. Dr. Shore reported having no financial disclosures; one of her associates reported serving as a consultant to Precision Health Economics, Medtronic, and St. Jude Medical.
Patient adherence to dabigatran therapy varied enormously in a nationwide study of pharmacy data, and three factors were identified that markedly enhanced adherence, according to a report published online April 14 in JAMA.
After research suggested that adherence to dabigatran was suboptimal among patients taking the drug for nonvalvular atrial fibrillation, investigators explored variations in adherence across thousands of sites using a Veterans Health Administration pharmacy database. They found that among 4,863 patients filling dabigatran prescriptions at 67 pharmacies during a 2-year period, adherence ranged from a low of 42% to a high of 93%, said Dr. Supriya Shore, a cardiology fellow at Emory University, Atlanta, and her associates.
The single most important factor that influenced dabigatran adherence was appropriate patient selection before dispensing the drug. This was defined as the pharmacist assessing the indication for treatment and ruling out contraindications after the physician ordered the prescription, as well as checking the patient’s adherence to other medications. Pharmacist monitoring of dabigatran use and adverse events, either alone or in collaboration with the treating clinician, also boosted adherence.
In addition, pharmacists working with clinicians to identify and address nonadherence also enhanced patient adherence. Prescribing physicians may not be able to routinely monitor adherence because of their large workload and limited time during clinic visits. Having a pharmacist do so mitigated patients’ tendency to stop taking the drug when minor adverse effects developed, the investigators reported (JAMA 2015 April 14 [doi:101001/jama.2015.2761]).
“These findings suggest that such site-level practices provide modifiable targets to improve patient adherence to dabigatran, as opposed to patient characteristics that frequently cannot be modified,” Dr. Shore and her associates wrote.
This study was funded in part by VA Health Services Research & Development, an American Heart Association National Scientist Development Grant, and a Gilead Sciences Cardiovascular Research Scholars Program award. Dr. Shore reported having no financial disclosures; one of her associates reported serving as a consultant to Precision Health Economics, Medtronic, and St. Jude Medical.
FROM JAMA
Key clinical point: Three modifiable factors improved AF patients’ adherence to dabigatran therapy.
Major finding: Among 4,863 patients filling dabigatran prescriptions at 67 pharmacies across the country during a 2-year period, adherence ranged from 42% to 93%.
Data source: A retrospective quantitative analysis and a cross-sectional qualitative analysis of data concerning 4,863 patients who filled dabigatran prescriptions at 67 sites.
Disclosures: This study was funded in part by VA Health Services Research & Development, an American Heart Association National Scientist Development Grant, and a Gilead Sciences Cardiovascular Research Scholars Program award. Dr. Shore reported having no financial disclosures; one of her associates reported serving as a consultant to Precision Health Economics, Medtronic, and St. Jude Medical.
Make the Diagnosis - April 2015
Diagnosis: Lichen Planus
Lichen planus is a common inflammatory condition involving the skin, nails, mucous membranes, and hair follicles. It has no racial predilection, and often affects men and women aged 20-60 years. It is less common in children, who account for only 4% of cases. The lesions are often atypical.
Clinically, patients often present with erythematous to violaceous small, flat-topped, polygonal papules that may coalesce into plaques. Lesions are generally pruritic, and may be tender or painful. Older lesions may be hyperpigmented. White streaks known as Wickham striae can cross the surface of lesions. These striae also can be present orally, such as in the patient described here. Oral lesions also may be atrophic or erosive.
Common body locations with involvement are the inner wrists, legs, torso, or genitals (glans penis). The face is rarely involved. Nail changes, such as longitudinal ridging and splitting, onycholysis, red lunula, yellow nail syndrome, and pterygium formation, can occur.
Lichen planus often spontaneously resolves on its own, with 2/3 of patients resolving in a year. Mucous membrane disease tends to be more chronic. The etiology of lichen planus is unknown. It may have an autoimmune mechanism in which T cells induce keratinocytes to undergo apoptosis. Between 4% and 60% of lichen planus patients also have hepatitis C infections. The differential diagnosis for cutaneous lesions include lichenoid drug eruption, guttate psoriasis, syphilis, and pityriasis lichenoides et varioliformis acuta. Oral lesions may resemble candidiasis, leukoplakia, malignancies, and bullous disease.
Topical and intralesional steroids are often effective for localized disease. Systemic corticosteroids can be useful when lesions are widespread. Phototherapy, isotretinoin, acitretin, hydroxychloroquine, and oral immunosuppressive agents (such as cyclosporine and mycophenolate mofetil) all have been described in the treatment of lichen planus.
Diagnosis: Lichen Planus
Lichen planus is a common inflammatory condition involving the skin, nails, mucous membranes, and hair follicles. It has no racial predilection, and often affects men and women aged 20-60 years. It is less common in children, who account for only 4% of cases. The lesions are often atypical.
Clinically, patients often present with erythematous to violaceous small, flat-topped, polygonal papules that may coalesce into plaques. Lesions are generally pruritic, and may be tender or painful. Older lesions may be hyperpigmented. White streaks known as Wickham striae can cross the surface of lesions. These striae also can be present orally, such as in the patient described here. Oral lesions also may be atrophic or erosive.
Common body locations with involvement are the inner wrists, legs, torso, or genitals (glans penis). The face is rarely involved. Nail changes, such as longitudinal ridging and splitting, onycholysis, red lunula, yellow nail syndrome, and pterygium formation, can occur.
Lichen planus often spontaneously resolves on its own, with 2/3 of patients resolving in a year. Mucous membrane disease tends to be more chronic. The etiology of lichen planus is unknown. It may have an autoimmune mechanism in which T cells induce keratinocytes to undergo apoptosis. Between 4% and 60% of lichen planus patients also have hepatitis C infections. The differential diagnosis for cutaneous lesions include lichenoid drug eruption, guttate psoriasis, syphilis, and pityriasis lichenoides et varioliformis acuta. Oral lesions may resemble candidiasis, leukoplakia, malignancies, and bullous disease.
Topical and intralesional steroids are often effective for localized disease. Systemic corticosteroids can be useful when lesions are widespread. Phototherapy, isotretinoin, acitretin, hydroxychloroquine, and oral immunosuppressive agents (such as cyclosporine and mycophenolate mofetil) all have been described in the treatment of lichen planus.
Diagnosis: Lichen Planus
Lichen planus is a common inflammatory condition involving the skin, nails, mucous membranes, and hair follicles. It has no racial predilection, and often affects men and women aged 20-60 years. It is less common in children, who account for only 4% of cases. The lesions are often atypical.
Clinically, patients often present with erythematous to violaceous small, flat-topped, polygonal papules that may coalesce into plaques. Lesions are generally pruritic, and may be tender or painful. Older lesions may be hyperpigmented. White streaks known as Wickham striae can cross the surface of lesions. These striae also can be present orally, such as in the patient described here. Oral lesions also may be atrophic or erosive.
Common body locations with involvement are the inner wrists, legs, torso, or genitals (glans penis). The face is rarely involved. Nail changes, such as longitudinal ridging and splitting, onycholysis, red lunula, yellow nail syndrome, and pterygium formation, can occur.
Lichen planus often spontaneously resolves on its own, with 2/3 of patients resolving in a year. Mucous membrane disease tends to be more chronic. The etiology of lichen planus is unknown. It may have an autoimmune mechanism in which T cells induce keratinocytes to undergo apoptosis. Between 4% and 60% of lichen planus patients also have hepatitis C infections. The differential diagnosis for cutaneous lesions include lichenoid drug eruption, guttate psoriasis, syphilis, and pityriasis lichenoides et varioliformis acuta. Oral lesions may resemble candidiasis, leukoplakia, malignancies, and bullous disease.
Topical and intralesional steroids are often effective for localized disease. Systemic corticosteroids can be useful when lesions are widespread. Phototherapy, isotretinoin, acitretin, hydroxychloroquine, and oral immunosuppressive agents (such as cyclosporine and mycophenolate mofetil) all have been described in the treatment of lichen planus.
This case and photo were submitted by Dr. Damon McClain, a dermatologist in Camp Lejeune, N.C. A 34-year-old male presented with a 1-month history of an itchy rash on his penis and feet. Upon physical examination, these lesions were seen orally. Blood work, including hepatitis serologies, was negative. His skin lesions improved with topical steroids.
Gene appears key to HSC regulation
in the bone marrow
The gene Ash1l plays a key role in regulating the maintenance and self-renewal of hematopoietic stem cells (HSCs), according to a study published in The Journal of Clinical Investigation.
The research provides new insight into how the body creates and maintains a healthy blood supply and immune system. It also opens new lines of inquiry about Ash1l’s potential role in cancers—like leukemia—that involve other members of the same gene family.
“If we find that Ash1l plays a role [in leukemia], that would open up avenues to try to block or slow down its activity pharmacologically,” said study author Ivan Maillard, MD, of the University of Michigan Medical School in Ann Arbor.
The Ash1l gene regulates the expression of multiple downstream homeotic genes, which help ensure the correct anatomical structure of a developing organism. And Ash1l is part of a family of genes that includes MLL1.
The researchers found that both Ash1l and MLL1 contribute to blood renewal. They observed mild defects in mice missing one gene or the other, but lacking both genes led to catastrophic deficiencies.
“We now have clear evidence that these genes cooperate to develop a healthy blood system,” Dr Maillard said.
He and his colleagues also found that Ash1l-deficient mice had normal numbers of HSCs during early development but a lack of HSCs in maturity—an indication the cells were not able to properly maintain themselves in the bone marrow.
Ash1l-deficient HSCs were unable to establish normal blood renewal after an HSC transplant. Moreover, Ash1l-deficient stem cells competed poorly with normal HSCs in the bone marrow and could easily be dislodged.
“By continuing to investigate the basic, underlying mechanisms [of blood renewal], we are helping to untangle the complex machinery . . . that may lay the foundation for new human treatments 5, 10, or 20 years from now,” Dr Maillard said. 
in the bone marrow
The gene Ash1l plays a key role in regulating the maintenance and self-renewal of hematopoietic stem cells (HSCs), according to a study published in The Journal of Clinical Investigation.
The research provides new insight into how the body creates and maintains a healthy blood supply and immune system. It also opens new lines of inquiry about Ash1l’s potential role in cancers—like leukemia—that involve other members of the same gene family.
“If we find that Ash1l plays a role [in leukemia], that would open up avenues to try to block or slow down its activity pharmacologically,” said study author Ivan Maillard, MD, of the University of Michigan Medical School in Ann Arbor.
The Ash1l gene regulates the expression of multiple downstream homeotic genes, which help ensure the correct anatomical structure of a developing organism. And Ash1l is part of a family of genes that includes MLL1.
The researchers found that both Ash1l and MLL1 contribute to blood renewal. They observed mild defects in mice missing one gene or the other, but lacking both genes led to catastrophic deficiencies.
“We now have clear evidence that these genes cooperate to develop a healthy blood system,” Dr Maillard said.
He and his colleagues also found that Ash1l-deficient mice had normal numbers of HSCs during early development but a lack of HSCs in maturity—an indication the cells were not able to properly maintain themselves in the bone marrow.
Ash1l-deficient HSCs were unable to establish normal blood renewal after an HSC transplant. Moreover, Ash1l-deficient stem cells competed poorly with normal HSCs in the bone marrow and could easily be dislodged.
“By continuing to investigate the basic, underlying mechanisms [of blood renewal], we are helping to untangle the complex machinery . . . that may lay the foundation for new human treatments 5, 10, or 20 years from now,” Dr Maillard said.
in the bone marrow
The gene Ash1l plays a key role in regulating the maintenance and self-renewal of hematopoietic stem cells (HSCs), according to a study published in The Journal of Clinical Investigation.
The research provides new insight into how the body creates and maintains a healthy blood supply and immune system. It also opens new lines of inquiry about Ash1l’s potential role in cancers—like leukemia—that involve other members of the same gene family.
“If we find that Ash1l plays a role [in leukemia], that would open up avenues to try to block or slow down its activity pharmacologically,” said study author Ivan Maillard, MD, of the University of Michigan Medical School in Ann Arbor.
The Ash1l gene regulates the expression of multiple downstream homeotic genes, which help ensure the correct anatomical structure of a developing organism. And Ash1l is part of a family of genes that includes MLL1.
The researchers found that both Ash1l and MLL1 contribute to blood renewal. They observed mild defects in mice missing one gene or the other, but lacking both genes led to catastrophic deficiencies.
“We now have clear evidence that these genes cooperate to develop a healthy blood system,” Dr Maillard said.
He and his colleagues also found that Ash1l-deficient mice had normal numbers of HSCs during early development but a lack of HSCs in maturity—an indication the cells were not able to properly maintain themselves in the bone marrow.
Ash1l-deficient HSCs were unable to establish normal blood renewal after an HSC transplant. Moreover, Ash1l-deficient stem cells competed poorly with normal HSCs in the bone marrow and could easily be dislodged.
“By continuing to investigate the basic, underlying mechanisms [of blood renewal], we are helping to untangle the complex machinery . . . that may lay the foundation for new human treatments 5, 10, or 20 years from now,” Dr Maillard said.
How telomere length affects cancer mortality
with telomeres in green
Image by Claus Azzalin
New research suggests telomere length is associated with cancer mortality—but not in the way researchers expected.
The study showed that short telomeres in peripheral blood leukocytes were associated with high mortality from all causes.
But genetically determined short telomeres were associated with low cancer mortality.
Line Rode, MD, PhD, of Herlev Hospital in Denmark, and colleagues reported these findings in JNCI: Journal of the National Cancer Institute.
Some previous studies have suggested an association between short telomeres and high mortality, including cancer mortality, while others have not. A possible explanation for the conflicting evidence may be that the association was correlational, but other factors that were not adjusted for (such as age and lifestyle) were the real causes.
Genetic variation in genes associated with telomere length (TERC, TERT, and OBFC1) is independent of age and lifestyle factors. So researchers speculated that a genetic analysis called a Mendelian randomization could eliminate some of the confounding and allow them to confirm the association between telomere length and cancer mortality.
To perform this analysis, the team used data from 2 prospective cohort studies. The Copenhagen City Heart Study and the Copenhagen General Population Study included 64,637 individuals who were followed from 1991 to 2011.
Participants completed a questionnaire, underwent a physical examination, and had blood drawn for biochemistry, genotyping, and telomere length assays.
For each subject, the researchers had information on physical characteristics such as body mass index (BMI), blood pressure, and cholesterol measurements, as well as smoking status, alcohol consumption, physical activity, and socioeconomic variables.
In addition to measuring telomere length for each subject, the researchers used 3 single nucleotide polymorphisms of TERC, TERT, and OBFC1 to construct a score for the presence of telomere-shortening alleles.
A total of 7607 individuals died during the study period, 2420 of cancer. Overall, decreasing telomere length was associated with age, variables such as BMI and smoking, and death from all causes, including cancer.
In contrast, a higher genetic score for telomere shortening was associated with decreased cancer mortality but not with any other causes of death.
The researchers said this suggests the slightly shorter telomeres in cancer patients with the higher genetic score for telomere shortening might be beneficial because uncontrolled cancer cell replication is reduced.
And long telomeres may confer a survival advantage for cancer cells, as they allow for multiple cell divisions that lead to high cancer mortality.
with telomeres in green
Image by Claus Azzalin
New research suggests telomere length is associated with cancer mortality—but not in the way researchers expected.
The study showed that short telomeres in peripheral blood leukocytes were associated with high mortality from all causes.
But genetically determined short telomeres were associated with low cancer mortality.
Line Rode, MD, PhD, of Herlev Hospital in Denmark, and colleagues reported these findings in JNCI: Journal of the National Cancer Institute.
Some previous studies have suggested an association between short telomeres and high mortality, including cancer mortality, while others have not. A possible explanation for the conflicting evidence may be that the association was correlational, but other factors that were not adjusted for (such as age and lifestyle) were the real causes.
Genetic variation in genes associated with telomere length (TERC, TERT, and OBFC1) is independent of age and lifestyle factors. So researchers speculated that a genetic analysis called a Mendelian randomization could eliminate some of the confounding and allow them to confirm the association between telomere length and cancer mortality.
To perform this analysis, the team used data from 2 prospective cohort studies. The Copenhagen City Heart Study and the Copenhagen General Population Study included 64,637 individuals who were followed from 1991 to 2011.
Participants completed a questionnaire, underwent a physical examination, and had blood drawn for biochemistry, genotyping, and telomere length assays.
For each subject, the researchers had information on physical characteristics such as body mass index (BMI), blood pressure, and cholesterol measurements, as well as smoking status, alcohol consumption, physical activity, and socioeconomic variables.
In addition to measuring telomere length for each subject, the researchers used 3 single nucleotide polymorphisms of TERC, TERT, and OBFC1 to construct a score for the presence of telomere-shortening alleles.
A total of 7607 individuals died during the study period, 2420 of cancer. Overall, decreasing telomere length was associated with age, variables such as BMI and smoking, and death from all causes, including cancer.
In contrast, a higher genetic score for telomere shortening was associated with decreased cancer mortality but not with any other causes of death.
The researchers said this suggests the slightly shorter telomeres in cancer patients with the higher genetic score for telomere shortening might be beneficial because uncontrolled cancer cell replication is reduced.
And long telomeres may confer a survival advantage for cancer cells, as they allow for multiple cell divisions that lead to high cancer mortality.
with telomeres in green
Image by Claus Azzalin
New research suggests telomere length is associated with cancer mortality—but not in the way researchers expected.
The study showed that short telomeres in peripheral blood leukocytes were associated with high mortality from all causes.
But genetically determined short telomeres were associated with low cancer mortality.
Line Rode, MD, PhD, of Herlev Hospital in Denmark, and colleagues reported these findings in JNCI: Journal of the National Cancer Institute.
Some previous studies have suggested an association between short telomeres and high mortality, including cancer mortality, while others have not. A possible explanation for the conflicting evidence may be that the association was correlational, but other factors that were not adjusted for (such as age and lifestyle) were the real causes.
Genetic variation in genes associated with telomere length (TERC, TERT, and OBFC1) is independent of age and lifestyle factors. So researchers speculated that a genetic analysis called a Mendelian randomization could eliminate some of the confounding and allow them to confirm the association between telomere length and cancer mortality.
To perform this analysis, the team used data from 2 prospective cohort studies. The Copenhagen City Heart Study and the Copenhagen General Population Study included 64,637 individuals who were followed from 1991 to 2011.
Participants completed a questionnaire, underwent a physical examination, and had blood drawn for biochemistry, genotyping, and telomere length assays.
For each subject, the researchers had information on physical characteristics such as body mass index (BMI), blood pressure, and cholesterol measurements, as well as smoking status, alcohol consumption, physical activity, and socioeconomic variables.
In addition to measuring telomere length for each subject, the researchers used 3 single nucleotide polymorphisms of TERC, TERT, and OBFC1 to construct a score for the presence of telomere-shortening alleles.
A total of 7607 individuals died during the study period, 2420 of cancer. Overall, decreasing telomere length was associated with age, variables such as BMI and smoking, and death from all causes, including cancer.
In contrast, a higher genetic score for telomere shortening was associated with decreased cancer mortality but not with any other causes of death.
The researchers said this suggests the slightly shorter telomeres in cancer patients with the higher genetic score for telomere shortening might be beneficial because uncontrolled cancer cell replication is reduced.
And long telomeres may confer a survival advantage for cancer cells, as they allow for multiple cell divisions that lead to high cancer mortality.
Oral anticoagulants overprescribed in AF
Results of a large study indicate that patients with atrial fibrillation (AF) and a low risk of thromboembolism are sometimes prescribed oral
anticoagulants even though guidelines recommend against it.
“The irony is that there is a general push to get providers to prescribe these drugs, and they are also generally underprescribed among many AF
patients who actually need them,” said study author Gregory Marcus, MD, of the University of California San Francisco.
“Our study suggests people are trying to do the right thing but, due to a lack of understanding of some of the critical nuances, go too far in that direction in low-risk patients.”
Dr Marcus and his colleagues described this study in JAMA Internal Medicine.
The team noted that previous AF guidelines recommend against the use of oral anticoagulants in patients younger than 60 years of age without heart disease or other known risk factors for thromboembolism, and updated guidelines do not recommend the use of oral anticoagulants in patients without any established risk factor for stroke.
The researchers wanted to determine the frequency with which oral anticoagulant prescriptions are made outside of guideline recommendations. So they assessed 10,995 AF patients ages 60 and under from the overall Practice Innovation and Clinical Excellence (PINNACLE) Registry of the National Cardiovascular Data Registry who were treated in the US between 2008 and 2012.
About 23% (n=2561) of patients with a CHADS2 score of 0 and about 27% (n=1787) of patients with a CHA2DS2-VASc score of 0 were prescribed oral anticoagulant therapy contrary to guideline recommendations.
In a multivariable analysis of patients with a CHADS2 score of 0, several factors were associated with a higher likelihood of being prescribed oral anticoagulants. These included older age (relative risk [RR]=1.48 per 10 years; P<0.001), male sex (RR=1.34; P<0.001), higher body mass index (RR=1.18 per 5kg/m2; P<0.001), and having Medicare rather than private insurance (RR=1.32; P<0.001).
On the other hand, being treated in the South rather than the Northeast was associated with a lower likelihood of being prescribed oral anticoagulants (RR=0.69; P=0.04).
The researchers observed similar results in a multivariable analysis of patients with a CHA2DS2-VASc score of 0. Being treated in the South rather than the Northeast was associated with a lower likelihood of being prescribed oral anticoagulants (RR=0.67; P=0.03).
And older age (RR=1.44 per 10 years; P<0.001), higher body mass index (RR=1.19 per 5 kg/m2; P<0.001), having Medicare rather than private insurance (RR=1.29; P<0.001), and having no insurance rather than private insurance (RR=1.19; P=0.02) were all associated with a higher likelihood of being prescribed oral anticoagulants.
The researchers said these results suggest providers may not be fully aware of the potential risks of these drugs or the particularly low risk of thromboembolism in certain populations.
“Practitioners who prescribe blood thinners need to be diligent about weighing the risks and benefits of these medications,” said study author Jonathan C. Hsu, MD, of the University of California San Diego.
“In those patients with no risk factors for stroke, the risk of bleeding likely outweighs the benefit of stroke reduction. The fact that blood thinners were prescribed to so many patients with no risk factors for stroke is a wake-up call that we need to do better for our patients.”
Results of a large study indicate that patients with atrial fibrillation (AF) and a low risk of thromboembolism are sometimes prescribed oral
anticoagulants even though guidelines recommend against it.
“The irony is that there is a general push to get providers to prescribe these drugs, and they are also generally underprescribed among many AF
patients who actually need them,” said study author Gregory Marcus, MD, of the University of California San Francisco.
“Our study suggests people are trying to do the right thing but, due to a lack of understanding of some of the critical nuances, go too far in that direction in low-risk patients.”
Dr Marcus and his colleagues described this study in JAMA Internal Medicine.
The team noted that previous AF guidelines recommend against the use of oral anticoagulants in patients younger than 60 years of age without heart disease or other known risk factors for thromboembolism, and updated guidelines do not recommend the use of oral anticoagulants in patients without any established risk factor for stroke.
The researchers wanted to determine the frequency with which oral anticoagulant prescriptions are made outside of guideline recommendations. So they assessed 10,995 AF patients ages 60 and under from the overall Practice Innovation and Clinical Excellence (PINNACLE) Registry of the National Cardiovascular Data Registry who were treated in the US between 2008 and 2012.
About 23% (n=2561) of patients with a CHADS2 score of 0 and about 27% (n=1787) of patients with a CHA2DS2-VASc score of 0 were prescribed oral anticoagulant therapy contrary to guideline recommendations.
In a multivariable analysis of patients with a CHADS2 score of 0, several factors were associated with a higher likelihood of being prescribed oral anticoagulants. These included older age (relative risk [RR]=1.48 per 10 years; P<0.001), male sex (RR=1.34; P<0.001), higher body mass index (RR=1.18 per 5kg/m2; P<0.001), and having Medicare rather than private insurance (RR=1.32; P<0.001).
On the other hand, being treated in the South rather than the Northeast was associated with a lower likelihood of being prescribed oral anticoagulants (RR=0.69; P=0.04).
The researchers observed similar results in a multivariable analysis of patients with a CHA2DS2-VASc score of 0. Being treated in the South rather than the Northeast was associated with a lower likelihood of being prescribed oral anticoagulants (RR=0.67; P=0.03).
And older age (RR=1.44 per 10 years; P<0.001), higher body mass index (RR=1.19 per 5 kg/m2; P<0.001), having Medicare rather than private insurance (RR=1.29; P<0.001), and having no insurance rather than private insurance (RR=1.19; P=0.02) were all associated with a higher likelihood of being prescribed oral anticoagulants.
The researchers said these results suggest providers may not be fully aware of the potential risks of these drugs or the particularly low risk of thromboembolism in certain populations.
“Practitioners who prescribe blood thinners need to be diligent about weighing the risks and benefits of these medications,” said study author Jonathan C. Hsu, MD, of the University of California San Diego.
“In those patients with no risk factors for stroke, the risk of bleeding likely outweighs the benefit of stroke reduction. The fact that blood thinners were prescribed to so many patients with no risk factors for stroke is a wake-up call that we need to do better for our patients.”
Results of a large study indicate that patients with atrial fibrillation (AF) and a low risk of thromboembolism are sometimes prescribed oral
anticoagulants even though guidelines recommend against it.
“The irony is that there is a general push to get providers to prescribe these drugs, and they are also generally underprescribed among many AF
patients who actually need them,” said study author Gregory Marcus, MD, of the University of California San Francisco.
“Our study suggests people are trying to do the right thing but, due to a lack of understanding of some of the critical nuances, go too far in that direction in low-risk patients.”
Dr Marcus and his colleagues described this study in JAMA Internal Medicine.
The team noted that previous AF guidelines recommend against the use of oral anticoagulants in patients younger than 60 years of age without heart disease or other known risk factors for thromboembolism, and updated guidelines do not recommend the use of oral anticoagulants in patients without any established risk factor for stroke.
The researchers wanted to determine the frequency with which oral anticoagulant prescriptions are made outside of guideline recommendations. So they assessed 10,995 AF patients ages 60 and under from the overall Practice Innovation and Clinical Excellence (PINNACLE) Registry of the National Cardiovascular Data Registry who were treated in the US between 2008 and 2012.
About 23% (n=2561) of patients with a CHADS2 score of 0 and about 27% (n=1787) of patients with a CHA2DS2-VASc score of 0 were prescribed oral anticoagulant therapy contrary to guideline recommendations.
In a multivariable analysis of patients with a CHADS2 score of 0, several factors were associated with a higher likelihood of being prescribed oral anticoagulants. These included older age (relative risk [RR]=1.48 per 10 years; P<0.001), male sex (RR=1.34; P<0.001), higher body mass index (RR=1.18 per 5kg/m2; P<0.001), and having Medicare rather than private insurance (RR=1.32; P<0.001).
On the other hand, being treated in the South rather than the Northeast was associated with a lower likelihood of being prescribed oral anticoagulants (RR=0.69; P=0.04).
The researchers observed similar results in a multivariable analysis of patients with a CHA2DS2-VASc score of 0. Being treated in the South rather than the Northeast was associated with a lower likelihood of being prescribed oral anticoagulants (RR=0.67; P=0.03).
And older age (RR=1.44 per 10 years; P<0.001), higher body mass index (RR=1.19 per 5 kg/m2; P<0.001), having Medicare rather than private insurance (RR=1.29; P<0.001), and having no insurance rather than private insurance (RR=1.19; P=0.02) were all associated with a higher likelihood of being prescribed oral anticoagulants.
The researchers said these results suggest providers may not be fully aware of the potential risks of these drugs or the particularly low risk of thromboembolism in certain populations.
“Practitioners who prescribe blood thinners need to be diligent about weighing the risks and benefits of these medications,” said study author Jonathan C. Hsu, MD, of the University of California San Diego.
“In those patients with no risk factors for stroke, the risk of bleeding likely outweighs the benefit of stroke reduction. The fact that blood thinners were prescribed to so many patients with no risk factors for stroke is a wake-up call that we need to do better for our patients.”
Gene therapy superior to partially matched HSCT for SCID-X1
Photo by Chad McNeeley
Children with X-linked severe combined immunodeficiency (SCID-X1) who undergo gene therapy have fewer infections and hospitalizations than those who receive a hematopoietic stem cell transplant (HSCT) from a partially matched donor, according to a study published in Blood.
“Over the last decade, gene therapy has emerged as a viable alternative to a partial matched stem cell transplant for infants with SCID-X1,” said study author Fabien Touzot, MD, PhD, of Necker Children’s Hospital in Paris, France.
“To ensure that we are providing the best alternative therapy possible, we wanted to compare outcomes among infants treated with gene therapy and infants receiving partial matched transplants.”
Dr Touzot and his colleagues studied the medical records of 27 children who received either a partially matched HSCT (n=13) or gene therapy (n=14) for SCID-X1 at Necker Children’s Hospital between 1999 and 2013.
The children receiving half-matched transplants and the children receiving gene therapy had been followed for a median of 6 years and 12 years, respectively.
The researchers compared T-cell development among the patients, as well as key clinical outcomes such as infections and hospitalization.
The 14 children in the gene therapy group developed healthy immune cells faster than the 13 children in the half-matched transplant group. In fact, in the first 6 months after therapy, T-cell counts had reached normal values in 78% of the gene therapy patients, compared to 26% of the HSCT patients.
The more rapid growth of the immune system in gene therapy patients was also associated with faster resolution of disseminated BDG infections. These infections resolved in a median of 11 months in the gene therapy group, compared to 25.5 months in the half-matched transplant group.
Gene therapy patients also had fewer infection-related hospitalizations—3 hospitalizations in 3 patients, compared to 15 hospitalizations in 5 patients from the half-matched HSCT group.
“Our analysis suggests that gene therapy can put these incredibly sick children on the road to defending themselves against infection faster than a half-matched transplant,” Dr Touzot said. “These results suggest that, for patients without a fully matched stem cell donor, gene therapy is the next-best approach.”
Photo by Chad McNeeley
Children with X-linked severe combined immunodeficiency (SCID-X1) who undergo gene therapy have fewer infections and hospitalizations than those who receive a hematopoietic stem cell transplant (HSCT) from a partially matched donor, according to a study published in Blood.
“Over the last decade, gene therapy has emerged as a viable alternative to a partial matched stem cell transplant for infants with SCID-X1,” said study author Fabien Touzot, MD, PhD, of Necker Children’s Hospital in Paris, France.
“To ensure that we are providing the best alternative therapy possible, we wanted to compare outcomes among infants treated with gene therapy and infants receiving partial matched transplants.”
Dr Touzot and his colleagues studied the medical records of 27 children who received either a partially matched HSCT (n=13) or gene therapy (n=14) for SCID-X1 at Necker Children’s Hospital between 1999 and 2013.
The children receiving half-matched transplants and the children receiving gene therapy had been followed for a median of 6 years and 12 years, respectively.
The researchers compared T-cell development among the patients, as well as key clinical outcomes such as infections and hospitalization.
The 14 children in the gene therapy group developed healthy immune cells faster than the 13 children in the half-matched transplant group. In fact, in the first 6 months after therapy, T-cell counts had reached normal values in 78% of the gene therapy patients, compared to 26% of the HSCT patients.
The more rapid growth of the immune system in gene therapy patients was also associated with faster resolution of disseminated BDG infections. These infections resolved in a median of 11 months in the gene therapy group, compared to 25.5 months in the half-matched transplant group.
Gene therapy patients also had fewer infection-related hospitalizations—3 hospitalizations in 3 patients, compared to 15 hospitalizations in 5 patients from the half-matched HSCT group.
“Our analysis suggests that gene therapy can put these incredibly sick children on the road to defending themselves against infection faster than a half-matched transplant,” Dr Touzot said. “These results suggest that, for patients without a fully matched stem cell donor, gene therapy is the next-best approach.”
Photo by Chad McNeeley
Children with X-linked severe combined immunodeficiency (SCID-X1) who undergo gene therapy have fewer infections and hospitalizations than those who receive a hematopoietic stem cell transplant (HSCT) from a partially matched donor, according to a study published in Blood.
“Over the last decade, gene therapy has emerged as a viable alternative to a partial matched stem cell transplant for infants with SCID-X1,” said study author Fabien Touzot, MD, PhD, of Necker Children’s Hospital in Paris, France.
“To ensure that we are providing the best alternative therapy possible, we wanted to compare outcomes among infants treated with gene therapy and infants receiving partial matched transplants.”
Dr Touzot and his colleagues studied the medical records of 27 children who received either a partially matched HSCT (n=13) or gene therapy (n=14) for SCID-X1 at Necker Children’s Hospital between 1999 and 2013.
The children receiving half-matched transplants and the children receiving gene therapy had been followed for a median of 6 years and 12 years, respectively.
The researchers compared T-cell development among the patients, as well as key clinical outcomes such as infections and hospitalization.
The 14 children in the gene therapy group developed healthy immune cells faster than the 13 children in the half-matched transplant group. In fact, in the first 6 months after therapy, T-cell counts had reached normal values in 78% of the gene therapy patients, compared to 26% of the HSCT patients.
The more rapid growth of the immune system in gene therapy patients was also associated with faster resolution of disseminated BDG infections. These infections resolved in a median of 11 months in the gene therapy group, compared to 25.5 months in the half-matched transplant group.
Gene therapy patients also had fewer infection-related hospitalizations—3 hospitalizations in 3 patients, compared to 15 hospitalizations in 5 patients from the half-matched HSCT group.
“Our analysis suggests that gene therapy can put these incredibly sick children on the road to defending themselves against infection faster than a half-matched transplant,” Dr Touzot said. “These results suggest that, for patients without a fully matched stem cell donor, gene therapy is the next-best approach.”