Machine Learning for Myocardial Infarction Compared With Guideline-Recommended Diagnostic Pathways
Authors: Boeddinghaus Jasper , Doudesis Dimitrios , Lopez-Ayala Pedro , Lee Kuan Ken , Koechlin Luca , Wildi Karin , Nestelberger Thomas , Borer Raphael , Miró Òscar , Martin-Sanchez F. Javier , Strebel Ivo , Rubini Giménez Maria , Keller Dagmar I. , Christ Michael , Bularga Anda , Li Ziwen , Ferry Amy V. , Tuck Chris , Anand Atul , Gray Alasdair , Mills Nicholas L. , Mueller Christian , Richards A. Mark , Pemberton Chris , Troughton Richard W. , Aldous Sally J. , Brown Anthony F.T. , Dalton Emily , Hammett Chris , Hawkins Tracey , O’Kane Shanen , Parke Kate , Ryan Kimberley , Schluter Jessica , Barker Stephanie , Blades Jennifer , Chapman Andrew R. , Fujisawa Takeshi , Kimenai Dorien M. , McDermott Michael , Newby David E. , Schulberg Stacey D. , Shah Anoop S.V. , Sorbie Andrew , Soutar Grace , Strachan Fiona E. , Taggart Caelan , Vicencio Daniel Perez , Wang Yiqing , Wereski Ryan , Williams Kelly , Weir Christopher J. , Berry Colin , Reid Alan , Maguire Donogh , Collinson Paul O. , Sandoval Yader , Smith Stephen W. , Wussler Desiree , Muench-Gerber Tamar , Glaeser Jonas , Spagnuolo Carlos , Huré Gabrielle , Gehrke Juliane , Puelacher Christian , Gualandro Danielle M. , Shrestha Samyut , Kawecki Damian , Morawiec Beata , Muzyk Piotr , Buergler Franz , Buser Andreas , Rentsch Katharina , Twerenbold Raphael , López Beatriz , Martinez-Nadal Gemma , Adrada Esther Rodriguez , Parenica Jiri , von Eckardstein Arnold 
Published: February 2024

BACKGROUND:

Collaboration for the Diagnosis and Evaluation of Acute Coronary Syndrome (CoDE-ACS) is a validated clinical decision support tool that uses machine learning with or without serial cardiac troponin measurements at a flexible time point to calculate the probability of myocardial infarction (MI). How CoDE-ACS performs at different time points for serial measurement and compares with guideline-recommended diagnostic pathways that rely on fixed thresholds and time points is uncertain.

METHODS:

Patients with possible MI without ST-segment–elevation were enrolled at 12 sites in 5 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 time point was determined for index type 1 MI and the effectiveness of previously validated low- and high-probability scores compared with guideline-recommended European Society of Cardiology (ESC) 0/1-hour, ESC 0/2-hour, and High-STEACS (High-Sensitivity Troponin in the Evaluation of Patients With Suspected Acute Coronary Syndrome) pathways.

RESULTS:

In total, 4105 patients (mean age, 61 years [interquartile range, 50–74]; 32% women) were included, among whom 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% CI, 99.5%–99.9%) and 99.0% (98.6%–99.2%), ruling out more patients than the ESC 0-hour and High-STEACS (25% and 35%) pathways. Incorporating a second cardiac troponin measurement, CoDE-ACS 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, after serial measurements, the ESC 0/1-hour, ESC 0/2-hour, and High-STEACS pathways 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.

REGISTRATION:

URL:
https://www.clinicaltrials.gov
; Unique identifier: NCT00470587.