作者
Jasper Boeddinghaus,Dimitrios Doudesis,Pedro Lopez-Ayala,Kuan Ken Lee,Luca Koechlin,Karin Wildi,Thomas Nestelberger,RC Borer,Òscar Miró,Francisco Javier Martín‐Sánchez,Ivo Strebel,María Rubini Giménez,Dagmar I. Keller,Michael Christ,Anda Bularga,Ziwen Li,Amy V. Ferry,Chris Tuck,Atul Anand,Alasdair Gray,Nicholas L. Mills,Christian Müller,A. Mark Richards,Chris J. Pemberton,Richard W. Troughton,Sally Aldous,Anthony Brown,Emily Dalton,C. Hammett,Tracey Hawkins,Shanen O’Kane,Kelly Parke,Kimberley Ryan,Jessica Schluter,Stephanie Barker,Jennifer Blades,Andrew R. Chapman,Takeshi Fujisawa,Dorien M Kimenai,Michael McDermott,David E. Newby,Stacey D. Schulberg,Anoop Shah,Andrew Sorbie,Geoff Soutar,Fiona E. Strachan,Caelan Taggart,Daniel P. Vicencio,Yiqing Wang,Ryan Wereski,Kelly L. Williams,Christopher J. Weir,Colin Berry,Alan Reid,Donogh Maguire,Paul Collinson,Yader Sandoval,Stephen W. Smith,Desiree Wussler,Tamar Muench-Gerber,Jonas Glaeser,Carlos C. Spagnuolo,Gabrielle Huré,Juliane Gehrke,Christian Puelacher,Danielle Menosi Gualandro,Samyut Shrestha,Damian Kawecki,Beata Morawiec,Piotr Muzyk,Franz Buergler,Andreas Buser,Katharina Rentsch,Raphael Twerenbold,Beatriz López,Gemma Martínez‐Nadal,Esther Rodríguez Adrada,Jiří Pařenica,Arnold von Eckardstein
摘要
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.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.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.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.URL: https://www.clinicaltrials.gov; Unique identifier: NCT00470587.