Evidence map›Paper›PMID 41267837›Full record

ArticleEuropean heart journal. Digital health2025

AI-ECG-derived biological age as a predictor of mortality in cardiovascular and acute care patients.

Daniel Pavluk, Fabian Theurl, Samuel Proell, Michael Schreinlecher, Florian Hofer, Patrick Rockenschaub, Angus Nicolson, Mercedes Gauthier, Sebastian Reinstadler, Clemens Dlaska and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European heart journal. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Daniel PavlukUniversity Clinic of Internal Medicine III, Cardiology and Angiology-Medical University Innsbruck, Anichstraße 35, Innsbruck AT-6020, Austria.ORCID https://orcid.org/0009-0004-2243-6902
Fabian TheurlUniversity Clinic of Internal Medicine III, Cardiology and Angiology-Medical University Innsbruck, Anichstraße 35, Innsbruck AT-6020, Austria.ORCID https://orcid.org/0000-0002-0190-5958
Samuel ProellDigital Cardiology Lab, University Clinic of Internal Medicine III-Medical University Innsbruck, Andreas-Hofer-Straße 4, Innsbruck AT-6020, Austria.ORCID https://orcid.org/0000-0003-3074-8637
Michael SchreinlecherUniversity Clinic of Internal Medicine III, Cardiology and Angiology-Medical University Innsbruck, Anichstraße 35, Innsbruck AT-6020, Austria.ORCID https://orcid.org/0000-0001-5251-6971
Florian HoferUniversity Clinic of Internal Medicine III, Cardiology and Angiology-Medical University Innsbruck, Anichstraße 35, Innsbruck AT-6020, Austria.
Patrick RockenschaubInstitute of Clinical Epidemiology, Public Health, Health Economics, Medical Statistics and Informatics-Medical University Innsbruck, Austria.ORCID https://orcid.org/0000-0002-6499-7933
Angus NicolsonDigital Cardiology Lab, University Clinic of Internal Medicine III-Medical University Innsbruck, Andreas-Hofer-Straße 4, Innsbruck AT-6020, Austria.ORCID https://orcid.org/0009-0008-5128-9680
Mercedes GauthierUniversity Clinic of Internal Medicine III, Cardiology and Angiology-Medical University Innsbruck, Anichstraße 35, Innsbruck AT-6020, Austria.ORCID https://orcid.org/0000-0003-1431-0045
Sebastian ReinstadlerUniversity Clinic of Internal Medicine III, Cardiology and Angiology-Medical University Innsbruck, Anichstraße 35, Innsbruck AT-6020, Austria.ORCID https://orcid.org/0000-0002-7700-1357
Clemens DlaskaUniversity Clinic of Internal Medicine III, Cardiology and Angiology-Medical University Innsbruck, Anichstraße 35, Innsbruck AT-6020, Austria.ORCID https://orcid.org/0000-0002-8798-0827
Axel BauerUniversity Clinic of Internal Medicine III, Cardiology and Angiology-Medical University Innsbruck, Anichstraße 35, Innsbruck AT-6020, Austria.ORCID https://orcid.org/0000-0001-9201-8555

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: Artificial Intelligence (AI) models applied to standard 12-lead ECGs enable estimation of biological age (AI-ECG age), which has shown prognostic value in general populations. However, its clinical utility in high-risk patients with cardiovascular disease (CVD) or acute medical conditions remains insufficiently explored. Methods and results: We analysed the first ECG of 48 950 consecutive patients presenting to a tertiary care centre with CVD or acute illness between 2000 and 2021. AI-ECG age was derived using a validated deep learning model. Δ-age, defined as the difference between AI-ECG and chronological age, was analysed categorically (±8 years) and continuously using multivariable Cox models adjusted for clinical and ECG variables. Primary endpoint was long-term total mortality (up to 10 years). Saliency map analysis was performed to identify input regions that the model was most sensitive to. AI-ECG age correlated strongly with chronological age ( Conclusion: AI-ECG age is a strong and independent predictor of long-term mortality in cardiovascular and acute care patients.

Indexed as

Artificial intelligenceBiological ageCardiovascular diseaseElectrocardiogram (ECG)Mortality predictionRisk stratification

Identifiers

PMID41267837
PMCPMC12629650

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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 Socratic graph.