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Predicting two-year survival versus non-survival after first myocardial infarction using machine learning and Swedish national register data
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, Clinical Psychology in Healthcare. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences.ORCID iD: 0000-0002-1473-4916
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences.
Department of Psychology, Umeå University.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, UCR-Uppsala Clinical Research Center. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.ORCID iD: 0000-0001-9402-7404
2017 (English)In: BMC Medical Informatics and Decision Making, ISSN 1472-6947, E-ISSN 1472-6947, Vol. 17, 1-11 p., 99Article in journal (Refereed) Published
Abstract [en]

Background: Machine learning algorithms hold potential for improved prediction of all-cause mortality in cardiovascular patients, yet have not previously been developed with high-quality population data. This study compared four popular machine learning algorithms trained on unselected, nation-wide population data from Sweden to solve the binary classification problem of predicting survival versus non-survival 2 years after first myocardial infarction (MI).

Methods: This prospective national registry study for prognostic accuracy validation of predictive models used data from 51,943 complete first MI cases as registered during 6 years (2006–2011) in the national quality register SWEDEHEART/RIKS-HIA (90% coverage of all MIs in Sweden) with follow-up in the Cause of Death register (> 99% coverage). Primary outcome was AUROC (C-statistic) performance of each model on the untouched test set (40% of cases) after model development on the training set (60% of cases) with the full (39) predictor set. Model AUROCs were bootstrapped and compared, correcting the P-values for multiple comparisons with the Bonferroni method. Secondary outcomes were derived when varying sample size (1–100% of total) and predictor sets (39, 10, and 5) for each model. Analyses were repeated on 79,869 completed cases after multivariable imputation of predictors.

Results: A Support Vector Machine with a radial basis kernel developed on 39 predictors had the highest complete cases performance on the test set (AUROC = 0.845, PPV = 0.280, NPV = 0.966) outperforming Boosted C5.0 (0.845 vs. 0. 841, P = 0.028) but not significantly higher than Logistic Regression or Random Forest. Models converged to the point of algorithm indifference with increased sample size and predictors. Using the top five predictors also produced good classifiers. Imputed analyses had slightly higher performance.

Conclusions: Improved mortality prediction at hospital discharge after first MI is important for identifying high-risk individuals eligible for intensified treatment and care. All models performed accurately and similarly and because of the superior national coverage, the best model can potentially be used to better differentiate new patients, allowing for improved targeting of limited resources. Future research should focus on further model development and investigate possibilities for implementation. 

Place, publisher, year, edition, pages
2017. Vol. 17, 1-11 p., 99
Keyword [en]
Cardiovascular disease, Classification, Coronary Artery Syndrome, Prognostic Modelling, Myocardial infarction, Registries, Supervised machine learning
National Category
Cardiac and Cardiovascular Systems
Identifiers
URN: urn:nbn:se:uu:diva-326336DOI: 10.1186/s12911-017-0500-yISI: 000404803900003PubMedID: 28679442OAI: oai:DiVA.org:uu-326336DiVA: diva2:1120545
Funder
Forte, Swedish Research Council for Health, Working Life and Welfare, 2014-4947Vårdal Foundation, 2014-0114U‐Care: Better Psychosocial Care at Lower Cost? Evidence-based assessment and Psychosocial Care via Internet, a Swedish Example, 2009-1093
Available from: 2017-07-06 Created: 2017-07-06 Last updated: 2017-11-29Bibliographically approved

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Wallert, JohnHeld, Claes
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Clinical Psychology in HealthcareDepartment of Public Health and Caring SciencesUCR-Uppsala Clinical Research CenterDepartment of Medical Sciences
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