Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study.

Summary

An AI model applied to 12-lead ECG images predicted incident heart failure across three cohorts, with hazard ratios 3.9–23.5 and C-statistics up to 0.81. Adding AI-ECG to PCP-HF improved discrimination, supporting ECG-based digital biomarkers for HF risk stratification.

Key Findings

• AI-ECG positivity was associated with markedly higher incident HF risk across YNHHS, UKB, and ELSA-Brasil (HRs 3.88–23.50).
• Discrimination ranged from 0.718 to 0.810; integrating AI-ECG with PCP-HF significantly improved risk prediction.
• Risk increased monotonically with higher AI-ECG probabilities, robust to comorbidities and competing risk of death.

Clinical Implications

AI-ECG probability could triage patients for echocardiography, intensify risk modification, and augment PCP-HF in primary care and health systems.

Limitations

• Observational design limits causal inference about interventions triggered by AI-ECG
• Model transportability to other healthcare systems and ECG acquisition workflows requires further testing

Future Directions

Prospective impact studies to test AI-ECG-triggered pathways, calibration in underrepresented groups, and integration with imaging and biomarkers.