ArticleTherapeutics and clinical risk management2017
Patient characteristics associated with false arrhythmia alarms in intensive care.
Article in Therapeutics and clinical risk management, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 38 citations in OpenAlex.
- Patient characteristics and monitor alarms in a cardiac ICU: A retrospective cross-sectional study.Journal of clinical monitoring and computing · 2026Article
- Role of a Deep-Learning Based Convolutional Neural Network Model for Real-Time Ventricular Tachycardia Alarm Classification.Journal of cardiovascular electrophysiology · 2026Article
- Patterns of Alarm Fatigue and Their Predictors Among Nurses in Intensive Care Units: A Latent Profile Analysis.Nursing & health sciences · 2026Article
- Reducing False Alarm Rates and Workload in ICUs by Improving Arrhythmia Detection Algorithms of Patient Monitoring Systems.Medical science monitor : international medical journal of experimental and clinical research · 2025Article
- Impact of Alarm Management Training on Adult ICU Nurses' Knowledge, Behaviour, and Fatigue: A Quasi-Experimental Study.Journal of evaluation in clinical practice · 2025Article
- Developing a Scalable Annotation Method for Large Datasets That Enhances Alarms With Actionability Data to Increase Informativeness: Mixed Methods Approach.Journal of medical Internet research · 2025Article
- Performance comparison of 6 in-hospital patient monitoring systems in the detection and alarm of ventricular cardiac arrhythmias.Cardiovascular digital health journal · 2024Article
- Alarm fatigue and sleep quality in medical staff-a Polish-Czech-Slovak study on workplace ergonomics.Frontiers in public health · 2024Article
- Ventricular tachycardia and in-hospital mortality in the intensive care unit.Heart rhythm O2 · 2023Article
- An annotated ventricular tachycardia (VT) alarm database: Toward a uniform standard for optimizing automated VT identification in hospitalized patients.Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc · 2023Article
- Pediatric Characteristics Associated With Higher Rates of Monitor Alarms.Biomedical instrumentation & technology · 2023Article
- Actionable Ventricular Tachycardia During In-Hospital ECG Monitoring and Its Impact on Alarm Fatigue.Critical pathways in cardiology · 2020Observational
- Contribution of Electrocardiographic Accelerated Ventricular Rhythm Alarms to Alarm Fatigue.American journal of critical care : an official publication, American Association of Critical-Care Nurses · 2019Article
- Technical article: Overview of hospital-based data capture systems that acquire continuous ECG and physiologic data.Journal of electrocardiologyArticle
- A call to alarms: Current state and future directions in the battle against alarm fatigue.Journal of electrocardiologyReview
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
introductionA 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.
objectiveThe 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.
methodsThis 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.
resultsA 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 (
conclusionPatients 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.