Usefulness of Trends in Continuous Electrocardiographic Telemetry Monitoring to Predict In-Hospital Cardiac Arrest

Abstract Survival from in-hospital cardiac arrest (IHCA) due to pulseless electrical activity (PEA)/asystole remains poor. We aimed to evaluate whether electrocardiographic changes provide predictive information for risk of IHCA from PEA/asystole. We conducted a retrospective case-control study, utilizing continuous electrocardiographic data from case and control patients. We selected three consecutive 3-hour blocks (block 3, 2, 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 vs. control patients (case block 1 vs. control block 1), and temporal relationship to cardiac arrest (case block 2 vs. case block 1). Ninety-one cases (age 63.0±17.6, 58% male) and 1783 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 AUC of 0.829, 63.2% sensitivity, 94.6% specificity at differentiating case vs. control block 1 on a validation set, and AUC 0.954, 91.2% sensitivity, 83.5% specificity at differentiating case block 1 vs. case block 2. In conclusion, trends in electrocardiographic parameters during the 3-hour window immediately preceding in-hospital cardiac arrest differ significantly from other time periods, and provide robust predictive information.