Machine learning for myocardial infarction compared to guideline recommended diagnostic pathways

Jasper Boeddinghaus, Dimitrios Doudesis, Pedro Lopez-Ayala, Kuan Ken Lee, Luca Koechlin, Karin Wildi, Thomas Nestelberger, Raphael Borer, Òscar Miró, F. Martín-Sánchez, Ivo Strebel, Maria Rubini Gimenez, Dagmar I. Keller, Michael Christ, Anda Bularga, Ziwen L., Amy Ferry, Chris Tuck, Atul Anand, Alasdair GrayNicholas L Mills*, Christian Mueller

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Background: CoDE-ACS is a validated clinical decision-support tool that uses machine learning with or without serial cardiac troponin measurements at a flexible timepoint to calculate the probability of myocardial infarction (MI). How CoDE-ACS performs at different timepoints for serial measurement and compares with guideline recommended diagnostic pathways that rely on fixed thresholds and timepoints is uncertain.

Methods: Patients with possible MI without ST-segment elevation were enrolled at 12 sites in five countries and underwent serial high-sensitivity cardiac troponin I concentration measurement at 0, 1 and 2 hours. Diagnostic performance of the CoDE-ACS model at each timepoint was determined for index type 1 MI and the effectiveness of previously validated low- and high-probability scores compared with guideline recommended ESC 0/1h, ESC 0/2h and High-STEACS pathways.

Results: In total 4,105 patients (age 61 [50-74] years, 32% women) were included where 575 (14%) had type 1 MI. At presentation, CoDE-ACS identified 56% of patients as low-probability, with a negative predictive value and sensitivity of 99.7% (95% confidence interval [CI] 99.5-99.9%) and 99.0% (98.6-99.2%), ruling out more patients than the ESC 0h and High-STEACS (25% and 35%) pathways. CoDE-ACS incorporating a second cTn measurement identified 65% or 68% of patients as low probability at 1 or 2 hours, for an identical negative predictive value of 99.7% (99.5-99.9%), 19% or 18% as high-probability with a positive predictive value of 64.9% (63.5-66.4%) and 68.8% (67.3-70.1%), and 16% or 14% as intermediate probability. In comparison, the ESC 0/1h, ESC 0/2h and High-STEACS pathways after serial measurements identified 49%, 53% and 71% of patients as low-risk with a negative predictive value of 100% (99.9-100%), 100% (99.9-100%) and 99.7% (99.5-99.8%), and 20%, 19% or 29% as high-risk with a positive predictive value of 61.5% (60.0-63.0%), 65.8% (64.3-67.2%), and 48.3% (46.8-49.8%) resulting in 31%, 28% or 0% who require further observation in the Emergency Department, respectively.

Conclusions: CoDE-ACS performs consistently irrespective of the timing of serial cardiac troponin measurement identifying more patients as low-probability with comparable performance to guideline recommended pathways for MI. Whether care guided by probabilities can improve the early diagnosis of MI requires prospective evaluation.
Original languageEnglish
Publication statusPublished - 12 Feb 2024


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