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.

中文翻译：

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心肌梗死的机器学习与指南推荐的诊断途径的比较

背景：急性冠状动脉综合征诊断和评估协作 (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 如何在不同时间点进行连续测量并与依赖于固定阈值和时间点的指南推荐的诊断路径进行比较尚不确定。 方法：在 5 个研究中心的 12 个中心招募了可能患有 MI 但无 ST 段抬高的患者。国家并在 0、1 和 2 小时进行连续高灵敏度心肌肌钙蛋白 I 浓度测量。 CoDE-ACS 模型在每个时间点的诊断性能均针对指数类型 1 MI 进行确定，并与指南推荐的欧洲心脏病学会 (ESC) 0/1 小时相比，之前验证的低概率和高概率评分的有效性， ESC 0/2 小时和 High-STEACS（高敏肌钙蛋白用于评估疑似急性冠状动脉综合征患者）途径。 结果：总共 4105 名患者（平均年龄，61 岁 [四分位数范围，50–74] ; 32% 女性）被纳入其中，其中 575 人（14%）患有 1 型心肌梗死。目前，CoDE-ACS 确定 56% 的患者为低概率患者，阴性预测值和敏感性分别为 99.7%（95% CI，99.5%–99.9%）和 99.0%（98.6%–99.2%），排除了更多可能性患者比 ESC 0 小时和 High-STEACS（25% 和 35%）途径更有效。结合第二次心肌肌钙蛋白测量，CoDE-ACS 在 1 或 2 小时内确定 65% 或 68% 的患者为低概率，相同的阴性预测值为 99.7% (99.5%–99.9%)； 19%或18%为高概率，阳性预测值为64.9%（63.5%–66.4%）和68.8%（67.3%–70.1%）； 16% 或 14% 为中间概率。相比之下，经过系列测量后，ESC 0/1 小时、ESC 0/2 小时和 High-STEACS 途径将 49%、53% 和 71% 的患者识别为低风险，阴性预测值为 100 %（99.9%–100%）、100%（99.9%–100%）和 99.7%（99.5%–99.8%）； 20%、19%或29%为高风险，阳性预测值为61.5%（60.0%–63.0%）、65.8%（64.3%–67.2%）和48.3%（46.8%–49.8%） ，分别导致 31%、28% 或 0% 的患者需要在急诊科进一步观察。 结论：无论连续心肌肌钙蛋白测量的时间如何，CoDE-ACS 的表现始终如一，将更多患者识别为低概率且具有可比性能指南推荐的 MI 途径。以概率为指导的护理是否可以改善 MI 的早期诊断需要前瞻性评估。注册：URL：https://www.clinicaltrials.gov；唯一标识符：NCT00470587。