• Duc H Do, Alan Kuo, Edward S Lee, David Mortara, David Elashoff, Xiao Hu, and Noel G Boyle.
• UCLA Cardiac Arrhythmia Center, David Geffen School of Medicine at UCLA, Los Angeles, California. Electronic address: ddo@mednet.ucla.edu.
• Am. J. Cardiol. 2019 Oct 1; 124 (7): 1149-1158.

AbstractSurvival from in-hospital cardiac arrest (IHCA) due to pulseless electrical activity/asystole remains poor. We aimed to evaluate whether electrocardiographic changes provide predictive information for risk of IHCA from pulseless electrical activity/asystole. We conducted a retrospective case-control study, utilizing continuous electrocardiographic data from case and control patients. We selected 3 consecutive 3-hour blocks (block 3, 2, and 1 in that order); block 1 immediately preceded cardiac arrest in cases, whereas block 1 was chosen at random in controls. In each block, we measured dominant positive and negative trends in electrocardiographic parameters, evaluated for arrhythmias, and compared these between consecutive blocks. We created random forest and logistic regression models, and tested them on differentiating case versus control patients (case block 1 vs control block 1), and temporal relation to cardiac arrest (case block 2 vs case block 1). Ninety-one cases (age 63.0 ± 17.6, 58% male) and 1,783 control patients (age 63.5 ± 14.8, 67% male) were evaluated. We found significant differences in electrocardiographic trends between case and control block 1, particularly in QRS duration, QTc, RR, and ST. New episodes of atrial fibrillation and bradyarrhythmias were more common before IHCA. The optimal model was the random forest, achieving an area under the curve of 0.829, 63.2% sensitivity, 94.6% specificity at differentiating case versus control block 1 on a validation set, and area under the curve 0.954, 91.2% sensitivity, 83.5% specificity at differentiating case block 1 versus case block 2. In conclusion, trends in electrocardiographic parameters during the 3-hour window immediately preceding IHCA differ significantly from other time periods, and provide robust predictive information.Copyright © 2019 Elsevier Inc. All rights reserved.

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