@Article{info:doi/10.2196/52139,
author="Cho, Youngjin
and Yoon, Minjae
and Kim, Joonghee
and Lee, Ji Hyun
and Oh, Il-Young
and Lee, Chan Joo
and Kang, Seok-Min
and Choi, Dong-Ju",
title="Artificial Intelligence--Based Electrocardiographic Biomarker for Outcome Prediction in Patients With Acute Heart Failure: Prospective Cohort Study",
journal="J Med Internet Res",
year="2024",
month="Jul",
day="3",
volume="26",
pages="e52139",
keywords="acute heart failure; electrocardiography; artificial intelligence; deep learning",
abstract="Background: Although several biomarkers exist for patients with heart failure (HF), their use in routine clinical practice is often constrained by high costs and limited availability. Objective: We examined the utility of an artificial intelligence (AI) algorithm that analyzes printed electrocardiograms (ECGs) for outcome prediction in patients with acute HF. Methods: We retrospectively analyzed prospectively collected data of patients with acute HF at two tertiary centers in Korea. Baseline ECGs were analyzed using a deep-learning system called Quantitative ECG (QCG), which was trained to detect several urgent clinical conditions, including shock, cardiac arrest, and reduced left ventricular ejection fraction (LVEF). Results: Among the 1254 patients enrolled, in-hospital cardiac death occurred in 53 (4.2{\%}) patients, and the QCG score for critical events (QCG-Critical) was significantly higher in these patients than in survivors (mean 0.57, SD 0.23 vs mean 0.29, SD 0.20; P<.001). The QCG-Critical score was an independent predictor of in-hospital cardiac death after adjustment for age, sex, comorbidities, HF etiology/type, atrial fibrillation, and QRS widening (adjusted odds ratio [OR] 1.68, 95{\%} CI 1.47-1.92 per 0.1 increase; P<.001), and remained a significant predictor after additional adjustments for echocardiographic LVEF and N-terminal prohormone of brain natriuretic peptide level (adjusted OR 1.59, 95{\%} CI 1.36-1.87 per 0.1 increase; P<.001). During long-term follow-up, patients with higher QCG-Critical scores (>0.5) had higher mortality rates than those with low QCG-Critical scores (<0.25) (adjusted hazard ratio 2.69, 95{\%} CI 2.14-3.38; P<.001). Conclusions: Predicting outcomes in patients with acute HF using the QCG-Critical score is feasible, indicating that this AI-based ECG score may be a novel biomarker for these patients. Trial Registration: ClinicalTrials.gov NCT01389843; https://clinicaltrials.gov/study/NCT01389843 ",
issn="1438-8871",
doi="10.2196/52139",
url="https://www.jmir.org/2024/1/e52139",
url="https://doi.org/10.2196/52139"
}