Ding Quan, Bai Yong, Tinoco Adelita, Mortara David, Do Duc, Boyle Noel G, Pelter Michele M, Hu Xiao
Department of Physiological Nursing, School of Nursing, University of California, 2 Koret Way, Nursing, San Francisco, CA 94143, USA.
Physiol Meas. 2015 Dec;36(12):2405-22. doi: 10.1088/0967-3334/36/12/2405. Epub 2015 Oct 26.
We investigated 17 metrics derived from four leads of electrocardiographic (ECG) signals from hospital patient monitors to develop new ECG alarms for predicting adult bradyasystolic cardiac arrest events.A retrospective case-control study was designed to analyze 17 ECG metrics from 27 adult bradyasystolic and 304 control patients. The 17 metrics consisted of PR interval (PR), P-wave duration (Pdur), QRS duration (QRSdur), RR interval (RR), QT interval (QT), estimate of serum K + using only frontal leads (SerumK2), T-wave complexity (T Complex), ST segment levels for leads I, II, V (ST I, ST II, ST V), and 7 heart rate variability (HRV) metrics. These 7 HRV metrics were standard deviation of normal to normal intervals (SDNN), total power, very low frequency power, low frequency power, high frequency power, normalized low frequency power, and normalized high frequency power. Controls were matched by gender, age (±5 years), admission to the same hospital unit within the same month, and the same major diagnostic category. A research ECG analysis software program developed by co-author D M was used to automatically extract the metrics. The absolute value for each ECG metric, and the duration, terminal value, and slope of the dominant trend for each ECG metric, were derived and tested as the alarm conditions. The maximal true positive rate (TPR) of detecting cardiac arrest at a prescribed maximal false positive rate (FPR) based on the trending conditions was reported. Lead time was also recorded as the time between the first time alarm condition was triggered and the event of cardiac arrest.While conditions based on the absolute values of ECG metrics do not provide discriminative information to predict bradyasystolic cardiac arrest, the trending conditions can be useful. For example, with a max FPR = 5.0%, some derived alarms conditions are: trend duration of PR > 2.8 h (TPR = 48.2%, lead time = 10.0 ± 6.6 h), trend duration of QRSdur > 2.7 h (TPR = 40.7%, lead time = 8.8 ± 6.2 h), trend duration of RR > 3.5 h (TPR = 51.9%, lead time = 6.4 ± 5.5 h), trend duration of T Complex > 2.9 h (TPR = 40.7%, lead time = 6.8 ± 5.5 h), trend duration of ST I > 3.0 h (TPR of 51.9%, lead time = 8.4 ± 8.0 h), trend duration of SDNN > 3.6 h (TPR of 40.7%, lead time = 11.0 ± 8.6 h), trend duration of HRV total power > 3.0 h (TPR of 25.9%, lead time = 7.5 ± 8.1 h), terminal value of ST I < -56 µV (TPR = 22.2%, lead time = 12.8 ± 8.3 h), and slope of QR > 19.4 ms h(-1) (TPR = 25.9%, lead time = 6.7 ± 6.9 h). Eleven trend duration alarms, eight terminal value alarms, and ten slope alarms, achieved a positive TPR with zero FPR. Furthermore, these alarms conditions with zero PFR can be combined by the 'OR'logic could further improve the TPR without increasing the FPR.The trend duration, terminal value, and slope of the dominant trend of the ECG metrics considered in this study are able to predict a subset of patients with bradyasystolic cardiac arrests with low or even zero FPR, which can be used for developing new ECG alarms.
我们研究了从医院患者监护仪的四导联心电图(ECG)信号中得出的17个指标,以开发用于预测成人缓慢性心搏停止心脏骤停事件的新型ECG警报。设计了一项回顾性病例对照研究,以分析来自27例成人缓慢性心搏停止患者和304例对照患者的17个ECG指标。这17个指标包括PR间期(PR)、P波时限(Pdur);QRS时限(QRSdur)、RR间期(RR)、QT间期(QT)、仅使用额面导联估算血清钾离子浓度(SerumK2)、T波复杂性(T Complex)、I、II、V导联的ST段水平(ST I、ST II、ST V)以及7个心率变异性(HRV)指标。这7个HRV指标分别是正常到正常间期的标准差(SDNN)、总功率、极低频功率、低频功率、高频功率、归一化低频功率和归一化高频功率。对照患者按性别、年龄(±5岁)、同月入住同一医院科室以及相同主要诊断类别进行匹配。由共同作者D M开发的一个研究用ECG分析软件程序用于自动提取这些指标。得出每个ECG指标的绝对值,以及每个ECG指标的主导趋势持续时间、终末值和斜率,并将其作为警报条件进行测试。报告了基于趋势条件在规定的最大假阳性率(FPR)下检测心脏骤停的最大真阳性率(TPR)。预警时间也记录为首次触发警报条件到心脏骤停事件之间的时间。虽然基于ECG指标绝对值的条件不能提供预测缓慢性心搏停止心脏骤停的判别信息,但趋势条件可能有用。例如,在最大FPR = 5.0%时,一些得出的警报条件为:PR的趋势持续时间>2.8小时(TPR = 48.2%,预警时间 = 10.0 ± 6.6小时)、QRSdur的趋势持续时间>2.7小时(TPR = 40.7%,预警时间 = 8.8 ± 6.2小时)、RR的趋势持续时间>3.5小时(TPR = 51.9%,预警时间 = 6.4 ± 5.5小时)、T Complex的趋势持续时间>2.9小时(TPR = 40.7%,预警时间 = 6.8 ± 5.5小时)、ST I的趋势持续时间>3.0小时(TPR为51.9%,预警时间 = 8.4 ± 8.0小时)、SDNN的趋势持续时间>3.6小时(TPR为40.7%,预警时间 = 11.0 ± 8.6小时)、HRV总功率的趋势持续时间>3.0小时(TPR为25.9%,预警时间 = 7.5 ± 8.1小时)、ST I的终末值<-56 µV(TPR = 22.2%,预警时间 = 12.8 ± 8.3小时)以及QR的斜率>19.4 ms h(-1)(TPR = 25.9%,预警时间 = 6.7 ± 6.9小时)。11个趋势持续时间警报、8个终末值警报和10个斜率警报实现了零FPR的正TPR。此外,这些零PFR的警报条件可以通过“或”逻辑进行组合,可在不增加FPR的情况下进一步提高TPR。本研究中考虑的ECG指标的趋势持续时间、终末值和主导趋势斜率能够以低FPR甚至零FPR预测一部分缓慢性心搏停止心脏骤停患者,可用于开发新型ECG警报。