⚡ Quick Summary
A recent study demonstrated that an artificial intelligence (AI) algorithm can effectively predict the risk of heart failure (HF) using single-lead electrocardiograms (ECGs). This breakthrough suggests a scalable approach for community-based risk assessment, potentially transforming how we monitor heart health. ❤️
🔍 Key Details
- 📊 Dataset: 192,667 YNHHS patients, 42,141 UKB participants, 13,454 ELSA-Brasil participants
- 🧩 Features used: Single-lead ECGs
- ⚙️ Technology: Noise-adapted AI-ECG model
- 🏆 Performance: Discrimination for new-onset HF: 0.723 to 0.828 across cohorts
🔑 Key Takeaways
- 📈 AI-ECG model predicts HF risk from noisy single-lead ECGs.
- 💡 Positive AI-ECG screening indicates a 3- to 7-fold higher risk for HF.
- 🔍 Each 0.1 increment in model probability correlates with a 27% to 65% higher hazard for HF.
- 🏥 AI-ECG’s discrimination for new-onset HF ranged from 0.723 to 0.828 across different cohorts.
- 📊 Incorporating AI-ECG improved risk assessment metrics significantly compared to traditional methods.
- 🌍 Study conducted across multinational cohorts, enhancing its relevance.
- 🔮 Future research is needed to validate findings using wearable ECG devices.
📚 Background
Heart failure remains a significant global health challenge, despite the availability of various disease-modifying therapies. Traditional methods of risk stratification often lack scalability and accessibility. The advent of portable devices capable of recording single-lead ECGs presents a promising opportunity for large-scale community-based risk assessment, potentially enabling earlier interventions and better patient outcomes.
🗒️ Study
This retrospective cohort study involved individuals without heart failure at baseline, utilizing data from the Yale New Haven Health System (YNHHS), the UK Biobank (UKB), and the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). The researchers aimed to evaluate the effectiveness of an AI-ECG model in predicting left ventricular systolic dysfunction (LVSD) and subsequent heart failure.
📈 Results
The study included a substantial number of participants, with 3697 individuals developing heart failure in YNHHS over a median follow-up of 4.6 years. The AI-ECG model demonstrated impressive discrimination capabilities, with a Harrel C statistic ranging from 0.723 to 0.828 across cohorts. Notably, the incorporation of AI-ECG predictions alongside traditional risk scores resulted in significant improvements in risk assessment metrics.
🌍 Impact and Implications
The findings from this study could revolutionize heart failure risk stratification. By leveraging AI technology and single-lead ECGs, healthcare providers may be able to implement more effective and accessible screening strategies. This could lead to earlier detection and intervention for individuals at risk of heart failure, ultimately improving patient outcomes and reducing healthcare costs.
🔮 Conclusion
This study highlights the remarkable potential of AI in predicting heart failure risk using single-lead ECGs. As we move towards a future where wearable and portable ECG devices become commonplace, the integration of AI-driven risk assessment tools could significantly enhance our ability to monitor heart health. Continued research in this area is essential to validate these findings and explore their practical applications in clinical settings.
💬 Your comments
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Artificial Intelligence-Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms.
Abstract
IMPORTANCE: Despite the availability of disease-modifying therapies, scalable strategies for heart failure (HF) risk stratification remain elusive. Portable devices capable of recording single-lead electrocardiograms (ECGs) may enable large-scale community-based risk assessment.
OBJECTIVE: To evaluate whether an artificial intelligence (AI) algorithm can predict HF risk from noisy single-lead ECGs.
DESIGN, SETTING, AND PARTICIPANTS: A retrospective cohort study of individuals without HF at baseline was conducted among individuals with conventionally obtained outpatient ECGs in the integrated Yale New Haven Health System (YNHHS) and prospective population-based cohorts of the UK Biobank (UKB) and the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Data analysis was performed from September 2023 to February 2025.
EXPOSURE: AI-ECG-defined risk of left ventricular systolic dysfunction (LVSD).
MAIN OUTCOMES AND MEASURES: Among individuals with ECGs, lead I ECGs were isolated and a noise-adapted AI-ECG model (to simulate ECG signals from wearable devices) trained to identify LVSD was deployed. The association of the model probability with new-onset HF, defined as the first HF hospitalization, was evaluated. The discrimination of AI-ECG was compared against 2 risk scores for new-onset HF (Pooled Cohort Equations to Prevent Heart Failure [PCP-HF] and Predicting Risk of Cardiovascular Disease Events [PREVENT] equations) using the Harrel C statistic, integrated discrimination improvement, and net reclassification improvement.
RESULTS: There were 192 667 YNHHS patients (median [IQR] age, 56 [41-69] years; 111 181 women [57.7%]), 42 141 UKB participants (median [IQR] age, 65 [59-71] years; 21 795 women [51.7%]), and 13 454 ELSA-Brasil participants (median [IQR] age, 51 [45-58] years; 7348 women [54.6%]) with baseline ECGs. A total of 3697 (1.9%) developed HF in YNHHS over a median (IQR) of 4.6 (2.8-6.6) years, 46 (0.1%) in UKB over a median (IQR) of 3.1 (2.1-4.5) years, and 31 (0.2%) in ELSA-Brasil over a median (IQR) of 4.2 (3.7-4.5) years. A positive AI-ECG screening result for LVSD was associated with a 3- to 7-fold higher risk for HF, and each 0.1 increment in the model probability was associated with a 27% to 65% higher hazard across cohorts, independent of age, sex, comorbidities, and competing risk of death. AI-ECG’s discrimination for new-onset HF was 0.723 (95% CI, 0.694-0.752) in YNHHS, 0.736 (95% CI, 0.606-0.867) in UKB, and 0.828 (95% CI, 0.692-0.964) in ELSA-Brasil. Across cohorts, incorporating AI-ECG predictions alongside PCP-HF and PREVENT equations was associated with a higher Harrel C statistic (difference in addition to PCP-HF, 0.080-0.107; difference in addition to PREVENT, 0.069-0.094). AI-ECG had an integrated discrimination improvement of 0.091 to 0.205 vs PCP-HF and 0.068 to 0.192 vs PREVENT; it had a net reclassification improvement of 18.2% to 47.2% vs PCP-HF and 11.8% to 47.5% vs PREVENT.
CONCLUSIONS AND RELEVANCE: Across multinational cohorts, a noise-adapted AI-ECG model estimated HF risk using lead I ECGs, suggesting a potential HF risk-stratification strategy requiring prospective study using wearable and portable ECG devices.
Author: [‘Dhingra LS’, ‘Aminorroaya A’, ‘Pedroso AF’, ‘Khunte A’, ‘Sangha V’, ‘McIntyre D’, ‘Chow CK’, ‘Asselbergs FW’, ‘Brant LCC’, ‘Barreto SM’, ‘Ribeiro ALP’, ‘Krumholz HM’, ‘Oikonomou EK’, ‘Khera R’]
Journal: JAMA Cardiol
Citation: Dhingra LS, et al. Artificial Intelligence-Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms. Artificial Intelligence-Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms. 2025; (unknown volume):(unknown pages). doi: 10.1001/jamacardio.2025.0492