Patient characteristics associated with false arrhythmia alarms in intensive care.
Patient characteristics associated with false arrhythmia alarms in intensive care.
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DOI：
10.2147/tcrm.s126191
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发表时间：
2017
影响因子：
2.8
通讯作者：
Hu X
中科院分区：
文献类型：
--
作者：
Harris PR;Zègre-Hemsey JK;Schindler D;Bai Y;Pelter MM;Hu X
重症监护病房（ICU）的心律失常假警报率高，导致警报疲劳，脱敏状态，以及由于频繁无效和不可操作的警报而可能不适当地关闭警报，通常称为假警报。本研究的目的是确定患者的特征，如性别、年龄、体重指数和与ICU中频繁的心律失常假警报相关的诊断。这项描述性观察性研究前瞻性地纳入了2013年在城市医疗中心连续入住5个成人icu（77张床位）之一的患者，为期31天。所有监视器报警和连续波形都存储在安全服务器上。具有心脏监测专业知识的护士科学家使用一种标准化的方案来注释六种临床上重要的心律失常警报类型（心搏停止、暂停、心室颤动、室性心动过速、室性心律加速和室性心动过缓）的真假。测量每位患者的总监测时间，并计算这六种报警类型每小时的误报次数。研究人员检查了医疗记录，以获取有关患者特征的数据。共纳入461例独特患者（平均年龄=60±17岁），共产生2,558,760次报警，包括各级心律失常、参数和技术报警。患者监护时间为48404小时，平均总报警率为52次/小时。调查人员记录了12,671例心律失常报警；11345个（89.5%）被判定为错误。250名患者（54%）至少产生了六种标注报警类型中的一种。2例患者发生6940次心律失常报警（55%）。每监测小时患者心律失常注释报警的误报次数为0.0 ~ 7.7次，每小时误报持续时间为0.0 ~ 158.8秒。将患者特征与1)每24小时心律失常假警报的次数和2)持续时间进行比较，使用非参数统计来最小化异常值的影响。显著相关性包括：年龄≥60岁（P=0.013； P=0.034）、精神状态混乱（两组比较均P<0.001）、心血管诊断（两组比较均P<0.001）、心电图（ECG）特征，如束支阻滞（BBB）导致的宽心电图波形对应于心室去极化（称为QRS复核）(P=0.003；P=0.004)或心室节律（两项比较均P=0.002），呼吸诊断（两项比较均P=0.004），以及机械通气支持，包括原发性诊断非呼吸疾病的患者（两项比较均P<0.001）。可能触发较多虚假心律失常警报的患者可能是年龄较大、精神错乱、心血管诊断、心电图特征表明血脑屏障或心室起搏、呼吸诊断和机械通气支持的患者。算法的改进可以集中在更好的降噪（例如，混淆状态的运动伪影）和区分血脑屏障和节奏性心律与室性心律失常。提高对明显触发较高心律失常假警报率的患者状况的认识可能有助于减少不必要的噪音和改进警报管理。
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. 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. 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. 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). 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.