Evidence map›Paper›PMID 41624565›Full record

ArticleEuropean heart journal. Digital health2026

Artificial intelligence-predicted ECG age gap as a biomarker: bias-adjusted correlation with mortality and cardiovascular risk factors.

Myrte Barthels, Elisa Verhofstadt, Inigo Bermejo Delgado, Henri Gruwez, Laurent Pison, Noëlla Pierlet, Pieter Vandervoort

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PubMed Full text at PMC Publisher
In one paragraph

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. 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. Article
  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

7 authors.

Myrte BarthelsLimburg Clinical Research Centre/Mobile Health Unit, Faculty of Medicine and Life Sciences, Hasselt University, Martelarenlaan 42, Hasselt 3500, Belgium.ORCID https://orcid.org/0009-0008-0565-5870
Elisa VerhofstadtData Science Institute, Hasselt University, Martelarenlaan 43, Hasselt 3500, Belgium.
Inigo Bermejo DelgadoData Science Institute, Hasselt University, Martelarenlaan 43, Hasselt 3500, Belgium.
Henri GruwezLimburg Clinical Research Centre/Mobile Health Unit, Faculty of Medicine and Life Sciences, Hasselt University, Martelarenlaan 42, Hasselt 3500, Belgium.ORCID https://orcid.org/0000-0002-9169-265X
Laurent PisonLimburg Clinical Research Centre/Mobile Health Unit, Faculty of Medicine and Life Sciences, Hasselt University, Martelarenlaan 42, Hasselt 3500, Belgium.ORCID https://orcid.org/0000-0002-8518-3330
Noëlla PierletLimburg Clinical Research Centre/Mobile Health Unit, Faculty of Medicine and Life Sciences, Hasselt University, Martelarenlaan 42, Hasselt 3500, Belgium.
Pieter VandervoortLimburg Clinical Research Centre/Mobile Health Unit, Faculty of Medicine and Life Sciences, Hasselt University, Martelarenlaan 42, Hasselt 3500, Belgium.ORCID https://orcid.org/0000-0001-7031-6500

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: Artificial intelligence models can estimate a person's age from ECG. The gap between the predicted ECG age and chronological age, predicted age deviation ( Objectives: To investigate the bias introduced by age on Methods and results: ECG and cardiovascular risk data from Ziekenhuis Oost-Limburg (2002-23) were linked to mortality data from the Belgian National Registry. A neural network was trained to predict age from ECGs. Conclusion:

Indexed as

Age predictionBias correctionBiological ageDeep learningElectrocardiogramSurvival analysis

Identifiers

PMID41624565
PMCPMC12853119

What Socratic holds

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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.