ArticleNPJ digital medicine2025
Determinants of artificial intelligence electrocardiogram-derived age and its association with cardiovascular events and mortality: a systematic review and meta-analysis.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence-Derived ECG-Age as a Predictor of Mortality and Cardiovascular Events: A Systematic Review and Meta-Analysis.Arquivos brasileiros de cardiologia · 2026Pooled it
- Prediction of Atrial Fibrillation Risk Through the Integration of Genetic Information and Artificial Intelligence-Based Electrocardiogram Data.Journal of arrhythmia · 2026Article
- Metabolic and Bariatric Surgery and Change in the AI-ECG-Derived Biological-Age Gap: A Longitudinal Cohort Study.Obesity surgery · 2026Article
- Discharge AI-ECG-Derived Age and Incidence of Cardiac Allograft Vasculopathy at 3 Years: A Pilot Study.Journal of cardiovascular development and disease · 2026Article
- Wearable device derived electrocardiographic age and its association with atrial fibrillation.NPJ digital medicine · 2026Article
- Phenotypic Selectivity of Artificial Intelligence-Enhanced Electrocardiography in Cardiovascular Diagnosis and Risk Prediction.Circulation · 2025Article
- Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI)-ECG-derived age (AI-ECG age) and Heart Delta Age (HDA)-the difference between AI-ECG and chronological age-are emerging tools for assessing cardiovascular health. We systematically searched PubMed, Embase, Web of Science, and Scopus from inception through September 2024. Seventeen original studies utilizing AI algorithms to measure HDA and cardiovascular risk factors, outcomes, or mortality were included. Data were pooled using random- and fixed-effects models for meta-analysis. Hypertension and diabetes mellitus emerged as the most prevalent factors contributing to higher HDA, while cardiac diseases including myocardial infarction and heart failure demonstrated the most significant impact. Pooled analysis revealed a significant association between elevated HDA and increased risks of all-cause mortality (hazard ratio [HR] 1.62, 95% confidence interval [CI] 1.49-1.77) and cardiovascular mortality (HR 2.12, 95% CI 1.71-2.63). HDA could enhance existing risk models and play a critical role in primary healthcare prevention.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.