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Published on: June 30, 2023
Artificial Intelligence-Based Electrocardiographic Biomarker for Outcome Prediction in Patients With Acute Heart
Youngjin Cho1,2, Minjae Yoon1, Joonghee Kim2,3
1Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Gyeonggi-do, Republic of Korea.
Insights
An artificial intelligence (AI) algorithm analyzing electrocardiograms (ECGs) can predict outcomes in acute heart failure (HF) patients. This AI-based ECG score may serve as a novel, cost-effective biomarker for HF management.
Area of Science:
- Cardiology
- Artificial Intelligence
- Biomarker Discovery
Background:
- Existing heart failure (HF) biomarkers face limitations in cost and availability for routine clinical use.
- There is a need for accessible and reliable methods for predicting HF patient outcomes.
Purpose of the Study:
- To evaluate the utility of an artificial intelligence (AI) algorithm for outcome prediction in acute heart failure (HF) patients using electrocardiograms (ECGs).
- To determine if an AI-derived ECG score can serve as a novel biomarker for HF prognosis.
Main Methods:
- Retrospective analysis of prospectively collected data from 1254 acute HF patients at two Korean tertiary centers.
- Utilized a deep-learning system (Quantitative ECG - QCG) to analyze baseline ECGs, trained to identify critical conditions.
- The QCG-Critical score was calculated and assessed for its predictive value regarding in-hospital cardiac death and long-term mortality.
Main Results:
- The QCG-Critical score was significantly higher in patients who experienced in-hospital cardiac death compared to survivors (P<.001).
- The QCG-Critical score independently predicted in-hospital cardiac death, even after adjustments for clinical factors and echocardiographic data (aOR 1.68, P<.001).
- Higher QCG-Critical scores were associated with increased long-term mortality (aHR 2.69, P<.001).
Conclusions:
- Predicting outcomes in acute heart failure patients using the AI-based QCG-Critical score is feasible.
- The AI-derived QCG-Critical score shows potential as a novel and accessible biomarker for heart failure prognosis.
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.
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