학술논문
Machine Learning for Myocardial Infarction Compared With Guideline-Recommended Diagnostic Pathways
Document Type
Academic Journal
Author
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
Source
Circulation. Apr 02, 2024 149(14):1090-1101
Subject
Language
English
ISSN
0009-7322
Abstract
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.