TL;DR
While ECG-Age provides incremental predictive value beyond sex and CA alone, it does not provide additional predictive value when ECG features are included in the feature set for predicting adverse cardiac outcomes.
Abstract Biological age reflects the functional state of the heart and can differ from chronological age, providing a useful indicator of cardiovascular health. This difference, estimated from electrocardiogram (ECG) signals as ECGderived biological age, has emerged as a promising digital biomarker for cardiovascular r...
ECG-RISK, a multitask survival model for 67 incident three-character ICD-10 endpoints using ECG waveforms, demographic characteristics and routinely collected laboratory data from 86,673 MIMIC-IV patients, supports the feasibility of ECG-based longitudinal risk prediction across multiple diseases.
This review critically analyzes recent ECG-based ML/DL models for CVD prediction, with particular attention to model performance, dataset characteristics, preprocessing strategies, and the integration of explainable artificial intelligence (XAI).
BACKGROUND Interpretable ECG Heart Age models estimate the Heart Age Gap, the difference between ECG-predicted and chronological age. Whether a single measurement of accumulated burden predicts the future pace of electrical aging is unknown. OBJECTIVES To derive and internally validate an interpretable ECG Heart Age...
Objective. Cardiovascular disease remains one of the leading causes of morbidity and mortality worldwide, highlighting the need for accurate risk prediction. Atrial fibrillation burden (AFB), premature ventricular contraction burden (PVCB), and T-wave alternans (TWA) have been individually associated with adverse outco...
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