Computational approaches to alleviate alarm fatigue in intensive care medicine: A systematic literature review

Patient monitoring technology has been used to guide therapy and alert staff when a vital sign leaves a predefined range in the intensive care unit (ICU) for decades. However, large amounts of technically false or clinically irrelevant alarms provoke alarm fatigue in staff leading to desensitisation towards critical alarms. With this systematic review, we are following the Preferred Reporting Items for Systematic Reviews (PRISMA) checklist in order to summarise scientific efforts that aimed to develop IT systems to reduce alarm fatigue in ICUs. 69 peer-reviewed publications were included. The majority of publications targeted the avoidance of technically false alarms, while the remainder focused on prediction of patient deterioration or alarm presentation. The investigated alarm types were mostly associated with heart rate or arrhythmia, followed by arterial blood pressure, oxygen saturation, and respiratory rate. Most publications focused on the development of software solutions, some on wearables, smartphones, or headmounted displays for delivering alarms to staff. The most commonly used statistical models were tree-based. In conclusion, we found strong evidence that alarm fatigue can be alleviated by IT-based solutions. However, future efforts should focus more on the avoidance of clinically non-actionable alarms which could be accelerated by improving the data availability

Patient Characteristics Associated with False Arrhythmia Alarms in Intensive Care

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\u3c0.001 for both comparisons), cardiovascular diagnoses (P\u3c0.001 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\u3c0.001 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

Patient characteristics associated with false arrhythmia alarms in intensive care

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

Alarmes clínicos em terapia intensiva: implicações da fadiga de alarmes para a segurança do paciente

OBJETIVOS: identificar o número de alarmes dos equipamentos eletromédicos numa unidade coronariana, caracterizar o tipo e analisar as implicações para a segurança do paciente na perspectiva da fadiga de alarmes. MÉTODO: trata-se de estudo quantitativo observacional descritivo, não participante, desenvolvido numa unidade coronariana de um hospital de cardiologia, com capacidade para 170 leitos. RESULTADOS: registrou-se o total de 426 sinais de alarmes, sendo 227 disparados por monitores multiparamétricos e 199 alarmes disparados por outros equipamentos (bombas infusoras, hemodiálise, ventiladores mecânicos e balão intra-aórtico), nas 40h, numa média total de 10,6 alarmes/hora. CONCLUSÃO: os resultados encontrados reforçam a importância da configuração de variáveis fisiológicas, do volume e dos parâmetros de alarmes dos monitores multiparamétricos à rotina das unidades de terapia intensiva. Os alarmes dos equipamentos destinados a proteger os pacientes têm conduzido ao aumento do ruído na unidade, à fadiga de alarmes, a distrações e interrupções no fluxo de trabalho e à falsa sensação de segurança.OBJETIVOS: identificar el número de alarmas de los equipamientos electromédicos en una unidad coronariana, caracterizar el tipo y analizar las implicaciones para la seguridad del paciente en la perspectiva de fatiga de alarmas. MÉTODO: se trata de un estudio cuantitativo, observacional, descriptivo, no participante, desarrollado en una unidad coronariana de un hospital de cardiología, con capacidad de 170 camas. RESULTADOS: se registró un total de 426 señales de alarmas, siendo 227 disparadas por monitores multiparamétricos y 199 disparadas por otros equipamientos (bombas de infusión, hemodiálisis, ventiladores mecánicos y balón intraaórtico), durante 40h, con un promedio total de 10,6 alarmas/hora. CONCLUSIÓN: los resultados encontrados refuerzan la importancia de la configuración de las variables fisiológicas, del volumen y de los parámetros de alarma de los monitores multiparamétricos, a la rutina de las unidades de terapia intensiva. Las alarmas de los equipamientos destinados a proteger a los pacientes, han llevado al aumento del ruido en la unidad, a la fatiga de alarmas, a las distracciones e interrupciones en el flujo de trabajo y a una falsa sensación de seguridad.OBJECTIVES: to identify the number of electro-medical pieces of equipment in a coronary care unit, characterize their types, and analyze implications for the safety of patients from the perspective of alarm fatigue. METHOD: this quantitative, observational, descriptive, non-participatory study was conducted in a coronary care unit of a cardiology hospital with 170 beds. RESULTS: a total of 426 alarms were recorded in 40 hours of observation: 227 were triggered by multi-parametric monitors and 199 were triggered by other equipment (infusion pumps, dialysis pumps, mechanical ventilators, and intra-aortic balloons); that is an average of 10.6 alarms per hour. CONCLUSION: the results reinforce the importance of properly configuring physiological variables, the volume and parameters of alarms of multi-parametric monitors within the routine of intensive care units. The alarms of equipment intended to protect patients have increased noise within the unit, the level of distraction and interruptions in the workflow, leading to a false sense of security

Single-modal and Multi-modal False Arrhythmia Alarm Reduction using Attention-based Convolutional and Recurrent Neural Networks

This study proposes a deep learning model that effectively suppresses the false alarms in the intensive care units (ICUs) without ignoring the true alarms using single- and multimodal biosignals. Most of the current work in the literature are either rule-based methods, requiring prior knowledge of arrhythmia analysis to build rules, or classical machine learning approaches, depending on hand-engineered features. In this work, we apply convolutional neural networks to automatically extract time-invariant features, an attention mechanism to put more emphasis on the important regions of the input segmented signal(s) that are more likely to contribute to an alarm, and long short-term memory units to capture the temporal information presented in the signal segments. We trained our method efficiently using a two-step training algorithm (i.e., pre-training and fine-tuning the proposed network) on the dataset provided by the PhysioNet computing in cardiology challenge 2015. The evaluation results demonstrate that the proposed method obtains better results compared to other existing algorithms for the false alarm reduction task in ICUs. The proposed method achieves a sensitivity of 93.88% and a specificity of 92.05% for the alarm classification, considering three different signals. In addition, our experiments for 5 separate alarm types leads significant results, where we just consider a single-lead ECG (e.g., a sensitivity of 90.71%, a specificity of 88.30%, an AUC of 89.51 for alarm type of Ventricular Tachycardia arrhythmia