Patient characteristics associated with false arrhythmia alarms in intensive care
Received 31 October 2016
Accepted for publication 6 February 2017
Published 19 April 2017 Volume 2017:13 Pages 499—513
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Garry Walsh
Patricia R Harris,1,2 Jessica K Zègre-Hemsey,3,4 Daniel Schindler,5 Yong Bai,6 Michele M Pelter,2,7 Xiao Hu2,8
1Department of Nursing, School of Health and Natural Sciences, Dominican University of California, San Rafael, 2Department of Physiological Nursing, School of Nursing, University of California, San Francisco, CA, 3School of Nursing, 4Department of Emergency Medicine, School of Medicine, University of North Carolina at Chapel Hill, NC, 5Intensive Care Unit, The Neuroscience Center, Sutter Eden Medical Center, Castro Valley, 6Hu Research Laboratory, Department of Physiological Nursing, School of Nursing, University of California, San Francisco, 7ECG Monitoring Research Lab, Department of Physiological Nursing, School of Nursing, 8Physiological Nursing and Neurological Surgery, Affiliate Faculty of Institute for Computational Health Sciences Core Faculty UCB/UCSF Joint Bio-Engineering Graduate Program, University of California, San Francisco, CA, USA
Introduction: A high rate of false arrhythmia alarms in the intensive care unit (ICU) leads to alarm fatigue, the condition of desensitization and potentially inappropriate silencing of alarms due to frequent invalid and nonactionable alarms, often referred to as false alarms.
Objective: The aim of this study was to identify patient characteristics, such as gender, age, body mass index, and diagnosis associated with frequent false arrhythmia alarms in the ICU.
Methods: This descriptive, observational study prospectively enrolled patients who were consecutively admitted to one of five adult ICUs (77 beds) at an urban medical center over a period of 31 days in 2013. All monitor alarms and continuous waveforms were stored on a secure server. Nurse scientists with expertise in cardiac monitoring used a standardized protocol to annotate six clinically important types of arrhythmia alarms (asystole, pause, ventricular fibrillation, ventricular tachycardia, accelerated ventricular rhythm, and ventricular bradycardia) as true or false. Total monitoring time for each patient was measured, and the number of false alarms per hour was calculated for these six alarm types. Medical records were examined to acquire data on patient characteristics.
Results: A total of 461 unique patients (mean age =60±17 years) were enrolled, generating a total of 2,558,760 alarms, including all levels of arrhythmia, parameter, and technical alarms. There were 48,404 hours of patient monitoring time, and an average overall alarm rate of 52 alarms/hour. Investigators annotated 12,671 arrhythmia alarms; 11,345 (89.5%) were determined to be false. Two hundred and fifty patients (54%) generated at least one of the six annotated alarm types. Two patients generated 6,940 arrhythmia alarms (55%). The number of false alarms per monitored hour for patients’ annotated arrhythmia alarms ranged from 0.0 to 7.7, and the duration of these false alarms per hour ranged from 0.0 to 158.8 seconds. Patient characteristics were compared in relation to 1) the number and 2) the duration of false arrhythmia alarms per 24-hour period, using nonparametric statistics to minimize the influence of outliers. Among the significant associations were the following: age ≥60 years (P=0.013; P=0.034), confused mental status (P<0.001 for both comparisons), cardiovascular diagnoses (P<0.001 for both comparisons), electrocardiographic (ECG) features, such as wide ECG waveforms that correspond to ventricular depolarization known as QRS complex due to bundle branch block (BBB) (P=0.003; P=0.004) or ventricular paced rhythm (P=0.002 for both comparisons), respiratory diagnoses (P=0.004 for both comparisons), and support with mechanical ventilation, including those with primary diagnoses other than respiratory ones (P<0.001 for both comparisons).
Conclusion: Patients likely to trigger a higher number of false arrhythmia alarms may be those with older age, confusion, cardiovascular diagnoses, and ECG features that indicate BBB or ventricular pacing, respiratory diagnoses, and mechanical ventilatory support. Algorithm improvements could focus on better noise reduction (eg, motion artifact with confused state) and distinguishing BBB and paced rhythms from ventricular arrhythmias. Increasing awareness of patient conditions that apparently trigger a higher rate of false arrhythmia alarms may be useful for reducing unnecessary noise and improving alarm management.
Keywords: alarm fatigue, electrocardiography, patient safety
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