Diagnostic or prognostic study
Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations
- Diagnostic accuracy study
- machine learning model development
- external validation in seven cohorts
Study context
- Population
- Patients assessed for possible MIexternal validation, 7 cohorts
- Participants
- 10,286 patients
- Comparison
- Collaboration for the Diagnosis and Evaluation of Acute Coronary Syndrome (CoDE-ACS) score vs fixed cardiac troponin thresholds
- Outcome
- Discrimination for MI
Performance
As reported in the abstract
MI diagnosis · at presentation
| Test or model | AUC | Identified as low probability | Identified as high probability |
|---|---|---|---|
| CoDE-ACS | 0.953 (95% CI 0.947-0.958) | 61% | 10% |
| Fixed cardiac troponin thresholds | 27% | 16% |
Further findings
Cardiac death by CoDE-ACS probability category · 30 days and 1 year
30 days
1 year
- Cardiac death by CoDE-ACS probability category: P < 0.001 for both
Original publication
Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations. Nat Med. Published 11 May 2023. doi:10.1038/s41591-023-02325-4
Summarised from the published abstract; every number is checked against it