Evidence map›Paper›PMID 40669514›Full record

ArticleEuropean heart journal2025

Artificial intelligence-enhanced electrocardiography to predict regurgitant valvular heart diseases: an international study.

Yixiu Liang, Arunashis Sau, Boroumand Zeidaabadi, Joseph Barker, Konstantinos Patlatzoglou, Libor Pastika, Ewa Sieliwonczyk, Zachary Whinnett, Nicholas S Peters, Ziqing Yu and 8 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in European heart journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 3 pooled it
–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

10 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  7. Review
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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

18 authors.

Yixiu LiangDepartment of Cardiology, Zhongshan Hospital of Fudan University, Shanghai, Institute of Cardiovascular Diseases, National Clinical Research Centre for Interventional Medicine, Shanghai 200032, China.ORCID 0000-0001-5467-8958
Arunashis SauNational Heart and Lung Institute, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK.ORCID 0000-0002-0204-7078
Boroumand ZeidaabadiNational Heart and Lung Institute, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK.
Joseph BarkerNational Heart and Lung Institute, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK.
Konstantinos PatlatzoglouNational Heart and Lung Institute, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK.ORCID 0000-0002-5888-8490
Libor PastikaNational Heart and Lung Institute, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK.ORCID 0000-0001-6892-6553
Ewa SieliwonczykNational Heart and Lung Institute, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK.ORCID 0000-0002-8603-7044
Zachary WhinnettNational Heart and Lung Institute, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK.
Nicholas S PetersNational Heart and Lung Institute, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK.
Ziqing YuDepartment of Cardiology, Zhongshan Hospital of Fudan University, Shanghai, Institute of Cardiovascular Diseases, National Clinical Research Centre for Interventional Medicine, Shanghai 200032, China.
Xi LiuDepartment of Cardiology, Zhongshan Hospital of Fudan University, Shanghai, Institute of Cardiovascular Diseases, National Clinical Research Centre for Interventional Medicine, Shanghai 200032, China.
Shuo WangSchool of Basic Medical Sciences, Digital Medical Research Center, Fudan University, Shanghai, China.
Hongyang LuCardiac Rhythm Management, Medtronic Technology Center, Medtronic (Shanghai) Ltd, Shanghai, China.
Daniel B KramerRichard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.ORCID 0000-0003-4241-3586
Jonathan W WaksHarvard Medical School, Beth Israel Deaconess Medical Center, Harvard-Thorndike Electrophysiology Institute, Boston, MA, USA.ORCID 0000-0001-5560-5638
Yangang SuDepartment of Cardiology, Zhongshan Hospital of Fudan University, Shanghai, Institute of Cardiovascular Diseases, National Clinical Research Centre for Interventional Medicine, Shanghai 200032, China.
Junbo GeDepartment of Cardiology, Zhongshan Hospital of Fudan University, Shanghai, Institute of Cardiovascular Diseases, National Clinical Research Centre for Interventional Medicine, Shanghai 200032, China.
Fu Siong NgNational Heart and Lung Institute, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK.

Funding

BHF RE/18/4/34215BHF RG/F/22/110078British Heart Foundation FS/CRTF/21/24183EJP RD Research Mobility FellowshipFudan University AI4S Project FudanX24AI056Imperial's BHF Centre RE/18/4/34215Imperial's BHF Centre RE/24/130023Medical Research Council MR/Y000803/1National Institute for Health Research Imperial Biomedical Research CentreNIHR Academic Clinical LectureshipShanghai Municipal Key Clinical Specialty shslczdzk01701
6 · The paper itself

Abstract

BACKGROUND AND

aimsValvular heart disease (VHD) is a significant source of morbidity and mortality, though early intervention can improve outcomes. This study aims to develop artificial intelligence-enhanced electrocardiography (AI-ECG) models to diagnose and predict future moderate or severe regurgitant VHDs (rVHDs), including mitral regurgitation (MR), tricuspid regurgitation (TR), and aortic regurgitation (AR).

methodsThe AI-ECG models were developed in a data set of 988 618 ECG and transthoracic echocardiogram pairs from 400 882 patients from Zhongshan Hospital, Shanghai, China. The AI-ECG models used a residual convolutional neural network with a discrete-time survival loss function. External evaluation was performed in outpatients from a secondary care data set from Beth Israel Deaconess Medical Center, Boston, USA, consisting of 34 214 patients with linked echocardiography.

resultsIn the internal test set, the AI-ECG models accurately predicted future significant MR [C-index 0.774, 95% confidence interval (CI) 0.753-0.792], AR (0.691, 95% CI 0.657-0.720), and TR (0.793, 95% CI 0.777-0.808). In age- and sex-adjusted Cox models, the highest risk quartile had a hazard ratio (HR) of 7.6 (95% CI 5.8-9.9, P < .0001) for risk of future significant MR, compared with the lowest risk quartile. For future AR and TR, the equivalent HRs were 3.8 (95% CI 2.7-5.5) and 9.9 (95% CI 7.5-13.0), respectively. These findings were confirmed in the transnational external test set. Imaging association analyses demonstrated AI-ECG predictions were associated with subclinical chamber remodelling.

conclusionsThis study developed AI-ECG models to diagnose and predict rVHDs and validated the models in a transnational and ethnically distinct cohort. The AI-ECG models could be utilized to guide surveillance echocardiography in patients at risk of future rVHDs, to facilitate early detection and intervention.

Indexed as

Artificial IntelligenceElectrocardiographyHeart Valve DiseasesMitral Valve InsufficiencyAgedAortic Valve InsufficiencyEchocardiographyFemaleHumansMaleMiddle AgedTricuspid Valve InsufficiencyArtificial intelligenceElectrocardiogramValvular heart disease

Identifiers

PMID40669514
PMCPMC12634117

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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.