Abstract 15255: Predicting the Risk of In-Hospital Cardiac Arrest in Real Time by Identification of Changes on Continuous Electrocardiographic Telemetry Monitoring Using Automated Algorithms

Introduction: Despite advances, survival to discharge following in-hospital cardiac arrest (IHCA) remains less than 25%. Patients at risk of IHCA are often on continuous ECG monitoring, but no automated methods exist to leverage the extensive real-time physiologic information that it contains. Hypothesis: We hypothesize that identification of physiologic changes, particularly over a patient’s hospital course, as manifest on continuous ECG, by automated algorithms can help predict the risk of IHCA in real time. Methods: We conducted a retrospective study of 77 IHCA cases (PEA and asystole) and 1763 matched control patients. Continuous ECG data was processed automatically to derive signal-averaged metrics for PR, QRS duration (QRSd), ST, QTc, RR, QRS amplitude, and also presence of atrial fibrillation (AF) and pauses. We selected 2 consecutive 3-hour blocks (blocks 1 and 2) for each case and control: for cases these were selected from the 6-hour period immediately preceding IHCA (with block 2 immediately pr...