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
Cardiovascular Research Institute Basel (CRIB) and Department of Cardiology (J.B., P.L.-A., L.K., K.W., T.N., R.B., I.S., M.R.G., C.M.), University Hospital Basel, University of Basel, Switzerland.
BHF/University Centre for Cardiovascular Science (J.B., D.D., K.K.L., A.B., Z.L., A.V.F., C.T., A.A., N.L.M.), University of Edinburgh, UK.
Circulation. 2024 Apr 2;149(14):1090-1101. doi: 10.1161/CIRCULATIONAHA.123.066917. Epub 2024 Feb 12.
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.
急性冠状动脉综合征诊断与评估协作研究（CoDE-ACS）是一种经过验证的临床决策支持工具，它使用机器学习，在灵活的时间点进行或不进行连续心肌肌钙蛋白测量，以计算心肌梗死（MI）的概率。CoDE-ACS在不同时间点进行连续测量时的表现如何，以及与依赖固定阈值和时间点的指南推荐诊断途径相比如何，目前尚不确定。
在5个国家的12个地点招募了可能患有MI但无ST段抬高的患者，并在0、1和2小时进行连续高敏心肌肌钙蛋白I浓度测量。确定CoDE-ACS模型在每个时间点对1型MI的诊断性能，以及与指南推荐的欧洲心脏病学会（ESC）0/1小时、ESC 0/2小时和High-STEACS（疑似急性冠状动脉综合征患者评估中的高敏肌钙蛋白）途径相比，先前验证的低概率和高概率评分的有效性。
总共纳入了4105例患者（平均年龄61岁[四分位间距，50 - 74岁]；32%为女性），其中575例（14%）患有1型MI。就诊时，CoDE-ACS将56%的患者识别为低概率，阴性预测值和敏感度分别为99.7%（95%CI，99.5% - 99.9%）和99.0%（98.6% - 99.2%），排除的患者比ESC 0小时和High-STEACS途径（分别为25%和35%）更多。纳入第二次心肌肌钙蛋白测量后，CoDE-ACS在1小时或2小时将65%或
