Abstract 10125: Continuous Heart Rate Dynamics Preceding In-Hospital Cardiac Arrest of Respiratory Etiology

Abstract

Introduction: Respiratory failure is a common cause of pulseless electrical activity (PEA) arrest in hospitalized patients, but how pathophysiologic changes in these conditions affect heart rate (HR) pre-arrest is not well described. We describe HR dynamics prior to in-hospital cardiac arrest (IHCA) among PEA/asystole arrest patients with respiratory etiology. Methods: In this retrospective descriptive study, we evaluated 67 patients with ≥3 hours of continuous ECG data recorded immediately preceding PEA/asystole IHCA in a single institution from 2010-2014. We identified respiratory arrest cases (eg. pneumonia, aspiration, pulmonary embolism, acute respiratory distress syndrome) by chart review and evaluated ECG patterns up to 24 hours prior to arrest to identify patterns of HR increase, HR decrease, sinus arrest, and escape rhythms. Results: We identified 31/67 patients with respiratory etiology (age 59±17 years, 52% male, 83% return of spontaneous circulation, 41% survived to discharge); of these 23/31(74%) fit an a priori model of HR response (Figure). Twelve cases demonstrated clear onset of HR increase at a median of 44 (IQR 28-507) minutes prior to arrest, while the remaining 11 cases started the monitoring period in sinus tachycardia. The mean peak HR was 120±20 bpm. An abrupt onset of HR decrease occurred at a median of 3.4 (IQR 2.3-5.9) minutes prior to arrest. Sinus arrest occurred during the HR decrease phase in 18/23 cases; the first escape rhythm was atrial in 11 (61%), junctional in 2 (11%) and ventricular in 3 (17%) cases. Conclusion: The majority of IHCA due to respiratory etiology (74%) follow a typical model of HR increase due to physiologic compensation to hypoxia, followed by rapid HR decrease leading to PEA arrest, likely from the vagal effect of hypoxia and sinus node suppression from acidosis. Understanding HR trends can aid clinical management as well as development of artificial intelligence models for prediction of IHCA.