Publications

Atrial fibrillation (AF) and heart failure (HF) frequently coexist in patients, with development of AF often preceding HF decompensation. This study evaluated whether daily remote monitoring of implantable cardioverter-defibrillator (ICD) parameters could predict AF occurrence using machine learning techniques in a real-world cohort. Data from patients with primary prevention ICDs transmitted daily to the Northwell centralized remote monitoring center between 2012 and 2021 were analyzed. An XGBoost model was trained to predict AF occurrence with a 3-day time horizon using a 14-day data collection sequence in 207 patients (69.0% male, median age 65.0 years, median ejection fraction 30%). The model predicted AF occurrence within the following 3 days in 49 (23.7%) patients after a median of 36 months post-implant with an AUROC of 0.79 and AUPRC of 0.10. Key variables included RV and RA sensing amplitudes and pulse width, suggesting machine learning approaches have potential to predict AF from daily remote ICD monitoring.

The objective was to determine if it is possible to model the response of the carotid blood flow to different chest compression waveforms as a function of time during resuscitation from cardiac arrest. Several approaches were tested to predict the carotid blood flow generated by the next chest compression based on knowledge of the duration of resuscitation, the chest compression rate, and the last compression's carotid blood flow. A single physiological metric, carotid blood flow, combined with information about the duration of resuscitation and the compression rate was sufficient to model and predict carotid blood flow in the next compression. This suggests that closed loop mechanical CPR is a viable medical device target.