Electrocardiographic-Driven artificial intelligence Model: A new approach to predicting One-Year mortality in heart failure with reduced ejection fraction patients

Abstract

Background

Despite the proliferation of heart failure (HF) mortality prediction models, their practical utility is limited. Addressing this, we utilized a significant dataset to develop and validate a deep learning artificial intelligence (AI) model for predicting one-year mortality in heart failure with reduced ejection fraction (HFrEF) patients. The study’s focus was to assess the effectiveness of an AI algorithm, trained on an extensive collection of ECG data, in predicting one-year mortality in HFrEF patients.

Methods

We selected HFrEF patients who had high-quality baseline ECGs from two hospital visits between September 2016 and May 2021. A total of 3,894 HFrEF patients (64% male, mean age 64.3, mean ejection fraction 29.8%) were included. Using this ECG data, we developed a deep learning model and evaluated its performance using the area under the receiver operating characteristic curve (AUROC).

Results

The model, validated against 16,228 independent ECGs from the original cohort, achieved an AUROC of 0.826 (95 % CI, 0.794–0.859). It displayed a high sensitivity of 99.0 %, positive predictive value of 16.6 %, and negative predictive value of 98.4 %. Importantly, the deep learning algorithm emerged as an independent predictor of 1-yr mortality of HFrEF patients with an adjusted hazards ratio of 4.12 (95 % CI 2.32–7.33, p < 0.001).

Conclusions

The depth and quality of our dataset and our AI-driven ECG analysis model significantly enhance the prediction of one-year mortality in HFrEF patients. This promises a more personalized, future-focused approach in HF patient management.