Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations

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

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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 low-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
Original languageEnglish
JournalNature Medicine
Publication statusPublished - 11 May 2023


Dive into the research topics of 'Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations'. Together they form a unique fingerprint.

Cite this