Evidence map›Paper›PMID 42553749›Full record

ArticleEuropean heart journal. Digital health2026

Deep learning-derived biological age from preoperative chest radiographs predicts mortality following surgical and transcatheter procedures for structural heart disease beyond EuroSCORE II.

Era Stambollxhiu, Miriam Kumpf, Maximilian-Niklas Bonk, Lisa C Adams, Marcus Makowski, Martin Hadamitzky, Markus Krane, Keno K Bressem, Oliver Deutsch

Abstract read
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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. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Era StambollxhiuInstitute for Diagnostic and Interventional Radiology, TUM School of Medicine and Health, Klinikum Rechts der Isar, TUM University Hospital, Ismaninger Str. 22, Munich 81675, Germany.ORCID https://orcid.org/0009-0000-5202-7887
Miriam KumpfInstitute for Diagnostic and Interventional Radiology, TUM School of Medicine and Health, Klinikum Rechts der Isar, TUM University Hospital, Ismaninger Str. 22, Munich 81675, Germany.
Maximilian-Niklas BonkDepartment of Cardiovascular Surgery, TUM School of Medicine and Health, German Heart Centre Munich, Munich, Germany.
Lisa C AdamsInstitute for Diagnostic and Interventional Radiology, TUM School of Medicine and Health, Klinikum Rechts der Isar, TUM University Hospital, Ismaninger Str. 22, Munich 81675, Germany.
Marcus MakowskiInstitute for Diagnostic and Interventional Radiology, TUM School of Medicine and Health, Klinikum Rechts der Isar, TUM University Hospital, Ismaninger Str. 22, Munich 81675, Germany.ORCID https://orcid.org/0000-0002-1922-0826
Martin HadamitzkyInstitute for Cardiovascular Radiology and Nuclear Medicine, TUM School of Medicine and Health, German Heart Center Munich, Munich, Germany.
Markus KraneDepartment of Cardiovascular Surgery, TUM School of Medicine and Health, German Heart Centre Munich, Munich, Germany.ORCID https://orcid.org/0000-0002-5466-9290
Keno K BressemInstitute for Diagnostic and Interventional Radiology, TUM School of Medicine and Health, Klinikum Rechts der Isar, TUM University Hospital, Ismaninger Str. 22, Munich 81675, Germany.
Oliver DeutschDepartment of Cardiovascular Surgery, TUM School of Medicine and Health, German Heart Centre Munich, Munich, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: Accurate risk stratification before structural heart disease interventions is essential for clinical decision-making. Traditional risk models, such as the European System for Cardiac Operative Risk Evaluation II (EuroSCORE II) and Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM), were designed for surgical patients and show inconsistent performance in transcatheter cohorts. Biological age, reflecting cumulative physiological decline, may offer prognostic value beyond chronological age and established risk scores. Methods and results: In this retrospective study of 1269 patients [non-transcatheter aortic valve implantation (TAVI) Conclusion: Biological age distinguished risk across both populations, suggesting that deep learning-based biological age estimation from routine chest radiographs could serve as an automated, accessible complement to existing risk models.

Indexed as

Artificial intelligenceBiological ageChest radiographyEuroSCORE IIRisk stratificationStructural heart diseaseSTS-PROMTranscatheter aortic valve implantation

Identifiers

PMID42553749
PMCPMC13436687

What Socratic holds

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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 Socratic graph.