Development and validation of a neural network-based survival model for mortality in ischemic heart disease

Background Current risk prediction models for ischemic heart disease (IHD) use a limited set of established risk factors and are based on classical statistical techniques. Using machine-learning techniques and including a broader panel of features from electronic health records (EHRs) may improve prognostication. Objectives Developing and externally validating a neural network-based time-to-event model (PMHnet) for prediction of all-cause mortality in IHD. Methods We included 39,746 patients (training: 34,746, test: 5,000) with IHD from the Eastern Danish Heart Registry, who underwent coronary angiography (CAG) between 2006-2016. Clinical and genetic features were extracted from national registries, EHRs, and biobanks. The feature-selection process identified 584 features, including prior diagnosis and procedure codes, laboratory test results, and clinical measurements. Model performance was evaluated using time-dependent AUC (tdAUC) and the Brier score. PMHnet was benchmarked against GRACE Risk Score 2.0 (GRACE2.0), and externally validated using data from Iceland (n=8,287). Feature importance and model explainability were assessed using SHAP analysis. Findings On the test set, the tdAUC was 0.88 (95% CI 0.86-0.90, case count, cc=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. On the same data, GRACE2.0 had a lower performance: 0.77 (0.73-0.80) at six months, 0.77(0.74-0.80) at one year, and 0.73(0.70-0.75) at three years. PMHnet showed similar performance in the Icelandic data. Conclusion PMHnet significantly improved survival prediction in patients with IHD compared to GRACE2.0. Our findings support the use of deep phenotypic data as precision medicine tools in modern healthcare systems. ### Competing Interest Statement Søren Brunak reports ownerships in Intomics, Hoba Therapeutics, Novo Nordisk, Lundbeck, and ALK; and managing board memberships in Proscion and Intomics. Henning Bundgaard reports ownership in Novo Nordisk and has received lecture fees from Amgen, BMS, MSD and Sanofi. The following co-authors are employed by deCODE genetics/Amgen, Inc: Vinicius Tragante, Daníel F. Guðbjartsson, Anna Helgadottir, Hilma Holm, and Kari Stefansson. ### Funding Statement Novo Nordisk Foundation (grant agreements: NNF14CC0001 and NNF17OC0027594) ? Hellerup, Denmark; NordForsk (PM Heart; grant agreement: 90580) ? Oslo, Norge; and the Innovation Foundation (BigTempHealth; grant agreement: 5153-00002B) ? Aarhus, Denmark. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: 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 Protection Agency (ref: 514-0255/18-3000, 514-0254/18-3000, SUND-2016-50), The Danish Health Data Authority (ref: FSEID-00003724 and FSEID-00003092), and The Danish Patient Safety Authority (3-3013-1731/1/). Danish personal identifiers were pseudonymised prior to any analysis. 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. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Due to national and EU regulations, the datasets used for model development and validation cannot be made publicly available. Research groups with access to secure and dedicated computing environments can request access to the source data registries via application to the Danish Health Data Authority.