TY  - JOUR
AU  - Cho, Youngjin
AU  - Yoon, Minjae
AU  - Kim, Joonghee
AU  - Lee, Ji Hyun
AU  - Oh, Il-Young
AU  - Lee, Chan Joo
AU  - Kang, Seok-Min
AU  - Choi, Dong-Ju
PY  - 2024
DA  - 2024/7/3
TI  - Artificial Intelligence–Based Electrocardiographic Biomarker for Outcome Prediction in Patients With Acute Heart Failure: Prospective Cohort Study
JO  - J Med Internet Res
SP  - e52139
VL  - 26
KW  - acute heart failure
KW  - electrocardiography
KW  - artificial intelligence
KW  - deep learning
AB  - 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 
SN  - 1438-8871
UR  - https://www.jmir.org/2024/1/e52139
UR  - https://doi.org/10.2196/52139
DO  - 10.2196/52139
ID  - info:doi/10.2196/52139
ER  -