Development and validation of a neural network survival prediction model for ischemic heart disease

Peter C Holm¹, Amalie D Haue^{1

2}, David Westergaard^{1

3

4}, Timo Röder¹, Karina Banasik^{1

3}, Vinicius Tragante⁵, Christian H Johansen¹, Alex H Christensen^{2

6

7}, Laurent Thomas^{8

9}, Therese H Nøst⁹, Anne-Heidi Skogholt⁹, Kasper K Iversen^{6

7}, Frants Pedersen^{2

6}, Dan E Høfsten^{2

6}, Ole B Pedersen^{6

10}, Sisse Rye Ostrowski^{6

11}, Henrik Ullum^{6

11

12}, Mette N Svendsen¹³, Iben M Gjødsbøl¹³, Thorarinn Gudnason¹⁴, Daníel F Guðbjartsson⁵, Anna Helgadottir⁵, Kristian Hveem⁹, Lars V Køber^{2

6}, Hilma Holm⁵, Kari Stefansson^{5

15}, Søren Brunak^#^{16

17}, Henning Bundgaard^#^{2

6}

Affiliations

¹ Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark.
² Department of Cardiology, The Heart Center, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.
³ Department Obstetrics and Gynecology, Copenhagen University Hospital, Kettegård Alle 30, 2650, Hvidovre, Denmark.
⁴ Methods and Analysis, Statistics Denmark, Sejrøgade 11, 2100, Copenhagen, Denmark.
⁵ deCODE Genetics, Sturlugata 8, 102, Reykjavik, Iceland.
⁶ Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark.
⁷ Department of Cardiology, Copenhagen University Hospital, Herlev-Gentofte Hospital, Borgmester Ib Juuls Vej 1, 2730, Herlev, Denmark.
⁸ Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, 7491, Trondheim, Norway.
⁹ K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, Norwegian University of Science and Technology, 7491, Trondheim, Norway.
¹⁰ Department of Clinical Immunology, Zealand University Hospital, 4600, Køge, Denmark.
¹¹ Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.
¹² Statens Serum Institut, Artillerivej 5, 2300, Copenhagen, Denmark.
¹³ Department of Public Health, University of Copenhagen, Øster Farimagsgade 5, 1353, Copenhagen, Denmark.
¹⁴ Laeknasetrid Cardiology Clinic, Thonglabakka 1, 109, Reykjavík, Iceland.
¹⁵ Faculty of Medicine, University of Iceland, Vatnsmyrarvegur 16, Reykjavik, 101, Iceland.
¹⁶ Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark. soren.brunak@cpr.ku.dk.
¹⁷ Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark. soren.brunak@cpr.ku.dk.

^# Contributed equally.

PMID: 41593634
DOI: 10.1186/s12933-026-03078-3

Free article

Development and validation of a neural network survival prediction model for ischemic heart disease

Peter C Holm et al. Cardiovasc Diabetol. 2026.

Free article

. 2026 Jan 28.

doi: 10.1186/s12933-026-03078-3. Online ahead of print.

Authors

Affiliations

¹ Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark.
² Department of Cardiology, The Heart Center, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.
³ Department Obstetrics and Gynecology, Copenhagen University Hospital, Kettegård Alle 30, 2650, Hvidovre, Denmark.
⁴ Methods and Analysis, Statistics Denmark, Sejrøgade 11, 2100, Copenhagen, Denmark.
⁵ deCODE Genetics, Sturlugata 8, 102, Reykjavik, Iceland.
⁶ Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark.
⁷ Department of Cardiology, Copenhagen University Hospital, Herlev-Gentofte Hospital, Borgmester Ib Juuls Vej 1, 2730, Herlev, Denmark.
⁸ Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, 7491, Trondheim, Norway.
⁹ K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health, Norwegian University of Science and Technology, 7491, Trondheim, Norway.
¹⁰ Department of Clinical Immunology, Zealand University Hospital, 4600, Køge, Denmark.
¹¹ Department of Clinical Immunology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.
¹² Statens Serum Institut, Artillerivej 5, 2300, Copenhagen, Denmark.
¹³ Department of Public Health, University of Copenhagen, Øster Farimagsgade 5, 1353, Copenhagen, Denmark.
¹⁴ Laeknasetrid Cardiology Clinic, Thonglabakka 1, 109, Reykjavík, Iceland.
¹⁵ Faculty of Medicine, University of Iceland, Vatnsmyrarvegur 16, Reykjavik, 101, Iceland.
¹⁶ Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark. soren.brunak@cpr.ku.dk.
¹⁷ Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark. soren.brunak@cpr.ku.dk.

^# Contributed equally.

PMID: 41593634
DOI: 10.1186/s12933-026-03078-3

Abstract

Background: Current risk prediction models for ischemic heart disease in clinical use are relatively simple and use a limited collection of well-known risk factors. Using machine learning to integrate a broader panel of features from electronic health records (EHRs) may improve post-angiography prognostication.

Methods: This retrospective model development and validation study was based on Danish EHR data. Icelandic EHR data were used for external test. Patients with a coronary angiography-confirmed diagnosis of coronary atherosclerosis between 2006 and 2016 were included for model development (n = 39,746). Time to all-cause mortality, the prediction target, was tracked until 2019, or up to 5 years, whichever came first. To model time-to-event data and deal with censoring, neural network-based discrete-time survival models were used. The model, PMHnet, uses 584 different features including clinical characteristics, laboratory tests, and diagnosis and procedure codes. Model performance was evaluated using time-dependent AUC (tdAUC) and the Brier score. PMHnet was benchmarked against the updated GRACE2.0 risk score and less feature-rich neural network models. Models were evaluated using hold-out data (n = 5000) and external validation data from Iceland. Feature importance and model explainability were assessed using SHAP analysis.

Results: On the test set (n = 5000), the tdAUC of PMHnet was 0.88 [ 0.86-0.90] (case count = 196) at six months, 0.88 [0.86-0.90] (cc = 261) at one year, 0.84 [0.82-0.86] (cc = 395) at three years, and 0.82 [0.80-0.84] (cc = 763) at five years. PMHnet showed similar performance in the Icelandic data. Compared to the GRACE2.0 score and intermediate models limited to GRACE2.0 features or single data modalities, PMHnet had significantly better model discrimination across all evaluated prediction timepoints.

Conclusions: More complex and feature-rich machine learning models can better predict all-cause mortality in ischemic heart disease and may be used by clinicians and patients to inform and guide treatment and management.

Keywords: Atherosclerosis; artificial intelligence; electronic health records; prognostication; risk score; time-to-event.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: The study was approved by The National Ethics Committee (1708829, ‘Genetics of CVD’—a genome-wide association study on repository samples from CHB), The Danish Data ProtectionAgency (ref: 514-0255/18-3000, 514-0254/18-3000, SUND-2016-50), The Danish Health DataAuthority (ref: FSEID-00003724 and FSEID-00003092), and The Danish Patient Safety Authority (3-3013-1731/1/). The National Danish Ethics Committee have waived the need for informed consent due to the societal value of the study. Danish personal identifiers were pseudonymised prior to any analysis. The validation part of the study was approved by the Data Protection Authority of Iceland and the National Bioethics Committee of Iceland (VSN-15-114). Icelandic participants that donated biological samples provided informed consent. Personal identities of the participants were encrypted with a third-party system provided by the Data Protection Authority of Iceland. Study design, methods, and results were reported in agreement with the TRIPOD statement [62, 63] and following the STROBE recommendations [64]. A filled-out TRIPOD checklist is included in the Supplementary Information document. Competing interests: Lars V. Køber reports speakers honorarium from AstraZeneca, Boehringer, Novartis and Novo Nordisk. Søren Brunak reports ownerships in Hoba Therapeutics, Novo Nordisk, Eli Lilly & Co., andLundbeck, and has received lecture fees from MDS, Bayer, LEO Pharma, Roche and Astellas. Henning Bundgaard reports ownership in Novo Nordisk and has received lecture fees from Amgen, BMS, MSD and Sanofi. The following co-authors are or were employed by deCODE genetics/Amgen, Inc: Vinicius Tragante, Daníel F. Guðbjartsson, Anna Helgadottir, Hilma Holm, and Kari Stefansson. The rest of the authors declare no competing interests.

References

1. Collet J-P, et al. 2020 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation: the task force for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation of the European Society of Cardiology (ESC). Eur Heart J. 2021;42:1289–367.
1. Byrne RA, et al. 2023 ESC guidelines for the management of acute coronary syndromes. Eur Heart J. 2023. https://doi.org/10.1093/eurheartj/ehad191.
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Development and validation of a neural network survival prediction model for ischemic heart disease

Affiliations

Development and validation of a neural network survival prediction model for ischemic heart disease

Authors

Affiliations

Abstract

Conflict of interest statement

References

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