Skip to main content

Research Repository

Advanced Search

Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations

Doudesis, Dimitrios; Lee, Kuan Ken; Boeddinghaus, Jasper; Bularga, Anda; Ferry, Amy V.; Tuck, Chris; Lowry, Matthew T.H.; Lopez-Ayala, Pedro; Nestelberger, Thomas; Koechlin, Luca; Bernabeu, Miguel O.; Neubeck, Lis; Anand, Atul; Schulz, Karen; Apple, Fred S.; Parsonage, William; Greenslade, Jaimi H.; Cullen, Louise; Pickering, John W.; Than, Martin P.; Gray, Alasdair; Mueller, Christian; Mills, Nicholas L.; CoDE-ACS Investigators

Authors

Dimitrios Doudesis

Kuan Ken Lee

Jasper Boeddinghaus

Anda Bularga

Amy V. Ferry

Chris Tuck

Matthew T.H. Lowry

Pedro Lopez-Ayala

Thomas Nestelberger

Luca Koechlin

Miguel O. Bernabeu

Atul Anand

Karen Schulz

Fred S. Apple

William Parsonage

Jaimi H. Greenslade

Louise Cullen

John W. Pickering

Martin P. Than

Alasdair Gray

Christian Mueller

Nicholas L. Mills

CoDE-ACS Investigators



Abstract

Although guidelines recommend fixed cardiac troponin thresholds for the diagnosis of myocardial infarction, troponin concentrations are influenced by age, sex, comorbidities and time from symptom onset. To improve diagnosis, we developed machine learning models that integrate cardiac troponin concentrations at presentation or on serial testing with clinical features and compute the CoDE-ACS score (0-100) that corresponds to an individual’s probability of myocardial infarction. The models were trained on data from 10,038 patients (48% women) and their performance was externally validated using data from 10,286 patients (35% women) from seven cohorts. CoDE-ACS had excellent discrimination for myocardial infarction (area under curve 0.953, 95% confidence interval 0.947-0.958), performed well across subgroups, and identified more patients at presentation as low59 probability as having myocardial infarction than fixed cardiac troponin thresholds (61% versus 27%) with a similar negative predictive value, and fewer as high-probability for having myocardial infarction (10% versus 16%) with a greater positive predictive value. Patients identified as having a low-probability of myocardial infarction had a lower rate of cardiac death than those with intermediate- or high-probability 30-days (0.1% versus 0.5% and 1.8%) and one year (0.3% versus 2.8% and 4.2%; P<0.001 for both) from patient presentation. CoDE-ACS used as a clinical decision support system has the potential to reduce hospital admissions and have major benefits for patients and healthcare providers.

Journal Article Type Article
Acceptance Date Mar 29, 2023
Online Publication Date May 11, 2023
Publication Date 2023
Deposit Date Mar 30, 2023
Publicly Available Date May 11, 2023
Journal Nature Medicine
Print ISSN 1078-8956
Electronic ISSN 1546-170X
Publisher Nature Research
Peer Reviewed Peer Reviewed
Volume 29
Issue 5
Pages 1201-1210
DOI https://doi.org/10.1038/s41591-023-02325-4

Files







You might also like



Downloadable Citations