AI Model Predicts Heart Failure Risk Using Single-Lead ECGs, Offering Potential for Wearable Technology Integration

A study led by Rohan Khera, MD, MS of Yale University developed an AI model that estimates heart failure risk using lead I electrocardiograms (ECGs). Tested across multinational cohorts, the noise-adapted AI-ECG model suggests a potential strategy for heart failure risk stratification. The research highlights the need for prospective studies to evaluate its use with wearable and portable ECG devices.

The study introduces an AI model designed to estimate heart failure risk using single-lead ECGs, specifically lead I. This innovative approach leverages noise-adapted technology to enhance accuracy across diverse datasets, reflecting its applicability in real-world clinical settings.

The research was conducted across multinational cohorts, underscoring the model’s robustness and generalizability. By analyzing data from varied populations, the study highlights the potential for widespread application of this AI-driven risk assessment tool.

The findings suggest that wearable and portable ECG devices could play a pivotal role in future prospective studies. This integration could facilitate continuous monitoring and early detection of heart failure risks, offering significant benefits for patient care and management strategies.

For further details or access to the full study, interested parties can contact Rohan Khera, MD, MS, at rohan.khera@yale.edu or visit the provided link for embargoed content.

Interested parties can also visit the specified URL to obtain further information, including author contributions, affiliations, conflicts of interest, and financial disclosures. This resource ensures transparency and provides comprehensive context for understanding the research methodology and implications.

The study demonstrates how an AI-enabled model can estimate heart failure risk using single-lead ECGs, specifically lead I, with noise-adapted technology to improve accuracy across diverse datasets. This approach offers potential for real-world clinical applications by addressing variability in ECG signal quality.

By analyzing data from multinational cohorts, the research highlights the robustness and generalizability of the AI model, suggesting its suitability for widespread use. The findings indicate that wearable and portable ECG devices could be valuable tools in prospective studies, enabling continuous monitoring and early detection of heart failure risks.

The integration of such technology into clinical practice could enhance risk stratification efforts, potentially improving patient outcomes through timely interventions. Further research is needed to validate these results in larger, diverse populations and to explore the practical implementation of AI-driven ECG analysis in routine care settings.