Dynamic Risk Stratification of worsening heart failure using a Deep learning enabled Implanted Ambulatory Single lead ECG

Abstract

Abstract Background and Aims Implantable loop recorders (ILRs) provide continuous single-lead ambulatory electrocardiogram (aECG) monitoring. It is unknown whether these aECGs could be used to identify worsening heart failure. Methods and results We linked ILR aECG from Medtronic device database to the LVEF measurements in Optum® de-identified electronic health record dataset. We trained an AI algorithm (aECG-CNN) on a dataset of 35,741 aECGs from 2247 patients to identify left ventricular ejection fraction (LVEF) ≤ 40% and assessed its performance using the area under the receiver operating characteristic curve (AUROC). aECG-CNN was then used to identify patients with increasing risk of heart-failure hospitalization in a real-world cohort of 909 patients with prior heart failure diagnosis. This dataset provided 12,467 follow up monthly evaluations, with 201 heart failure hospitalizations. For every month, time series features from these predictions were used to categorize patients into high and low risk groups and predict heart failure hospitalization in the next month. The risk of heart-failure hospitalization in the next 30 days was significantly higher in the cohort that aECG-CNN identified as high risk (hazard ratio 1·89; 95% confidence interval 1·28-2·79; p = 0·001) compared to low risk, even after adjusting patient demographics. (Hazard ratio 1·88, 1.27 to 2·79 p = 0·002). Conclusion An AI algorithm trained to detect LVEF ≤40% using ILR aECGs can also readily identify patients at increased risk for HF hospitalizations by monitoring changes in the probability of heart failure over 30 days.