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
JournalCirculation
DOIs
Publication statusPublished - 12 Feb 2024

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