ArticleEuropean heart journal. Digital health2026
Artificial intelligence-derived electrocardiographic age gap as a predictor of mortality after coronary revascularization: prognostic value and short-term intra-patient variability.
Article in European heart journal. Digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: The artificial intelligence (AI)-derived electrocardiographic (ECG) age gap-the difference between AI-predicted ECG age and chronological age-is an emerging biomarker of biological ageing linked to mortality. This study assessed its prognostic value for short- and long-term mortality after coronary artery bypass grafting (CABG) or percutaneous coronary intervention (PCI), addressing model bias in ageing cohorts and short-term intra-patient variability. Methods and results: A residual neural network was trained on 532 301 retrospective ECGs and optimized with a distribution-aware loss function to reduce age-imbalance bias (mean absolute error 6.87 years, Conclusion: The AI-derived ECG age gap independently predicts mortality after revascularization, but substantial short-term variability necessitates serial monitoring for reliable clinical use.
Indexed as
Identifiers
What OpenQuestion holds
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.