Developing new predictive alarms based on ECG metrics for bradyasystolic cardiac arrest. (26th October 2015)

• Record Type: Journal Article
• Authors: Ding, Quan; Bai, Yong; Tinoco, Adelita; Mortara, David; Do, Duc; Boyle, Noel G; Pelter, Michele M; Hu, Xiao
• Is Part Of: Physiological measurement. Volume 36:Number 12(2015:Dec.)
• Page Start: 2405
• Page End: 2422
• Publication Date: 2015-10-26
• Subjects: ECG -- cardiac arrest -- predictive alarm; Physiology -- Measurement -- Periodicals; Patient monitoring -- Periodicals; 612
• DOI: 10.1088/0967-3334/36/12/2405
• Languages: English
• ISSNs: 0967-3334

Abstract

Abstract: 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 µ V (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.