Evidence map›Paper›PMID 39846063›Full record

ArticleEuropean heart journal. Digital health2025

Advanced electrocardiography heart age: a prognostic, explainable machine learning approach applicable to sinus and non-sinus rhythms.

Zaidon S Al-Falahi, Todd T Schlegel, Israel Palencia-Lamela, Annie Li, Erik B Schelbert, Louise Niklasson, Maren Maanja, Thomas Lindow, Martin Ugander

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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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Zaidon S Al-FalahiKolling Institute, Royal North Shore Hospital, University of Sydney, St Leonards, Sydney, NSW 2065, Australia.ORCID https://orcid.org/0000-0003-1343-8530
Todd T SchlegelDepartment of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, SE-17176 Stockholm, Sweden.ORCID https://orcid.org/0000-0003-1997-3227
Israel Palencia-LamelaKolling Institute, Royal North Shore Hospital, University of Sydney, St Leonards, Sydney, NSW 2065, Australia.
Annie LiKolling Institute, Royal North Shore Hospital, University of Sydney, St Leonards, Sydney, NSW 2065, Australia.
Erik B SchelbertMinneapolis Heart Institute East, United Hospital, Minneapolis, MN 55407, USA.ORCID https://orcid.org/0000-0003-0356-4437
Louise NiklassonMinneapolis Heart Institute East, United Hospital, Minneapolis, MN 55407, USA.ORCID https://orcid.org/0009-0007-8848-4036
Maren MaanjaDepartment of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, SE-17176 Stockholm, Sweden.ORCID https://orcid.org/0000-0001-8606-0634
Thomas LindowKolling Institute, Royal North Shore Hospital, University of Sydney, St Leonards, Sydney, NSW 2065, Australia.ORCID https://orcid.org/0000-0002-2943-0034
Martin UganderKolling Institute, Royal North Shore Hospital, University of Sydney, St Leonards, Sydney, NSW 2065, Australia.ORCID https://orcid.org/0000-0003-3665-2038

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: An explainable advanced electrocardiography (A-ECG) Heart Age gap is the difference between A-ECG Heart Age and chronological age. This gap is an estimate of accelerated cardiovascular aging expressed in years of healthy human aging, and can intuitively communicate cardiovascular risk to the general population. However, existing A-ECG Heart Age requires sinus rhythm. We aim to develop and prognostically validate a revised, explainable A-ECG Heart Age applicable to both sinus and non-sinus rhythms. Methods and results: An A-ECG Heart Age excluding P-wave measures was derived from the 10-s 12-lead ECG in a derivation cohort using multivariable regression machine learning with Bayesian 5-min 12-lead A-ECG Heart Age as reference. The Heart Age was externally validated in a separate cohort of patients referred for cardiovascular magnetic resonance imaging by describing its association with heart failure hospitalization or death using Cox regression, and its association with comorbidities. In the derivation cohort ( Conclusion: An explainable A-ECG Heart Age gap applicable to both sinus and non-sinus rhythm associates with cardiovascular risk, cardiovascular morbidity, and survival.

Indexed as

Accelerated agingAdvanced ECG analysisECGMachine learningRisk prediction

Identifiers

PMID39846063
PMCPMC11750191

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Registered trials

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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.