Teressa Getu, Bhasin Varun, Noack Pamela, Poon Michael
From the Department of Internal Medicine, Stony Brook Medicine, Stony Brook, NY.
Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY.
Crit Pathw Cardiol. 2019 Sep;18(3):125-129. doi: 10.1097/HPC.0000000000000184.
The objective of this study was to compare the History, Electrocardiogram, Age, Risk factors, and Troponin (HEART) score and clinical coronary artery disease (CAD) consortium (CADC) model for predicting obstructive CAD (≥50% stenosis on coronary computed tomographic angiography) and 30-day major adverse cardiovascular events (MACE, composite of acute myocardial infarction, revascularization, and mortality). We studied 1981 patients with no known CAD who presented with acute chest pain and had negative initial troponin and electrocardiogram. Chest pain was classified as typical, atypical, and nonanginal and used to score the history component of the modified HEART score. The C-statistic for predicting obstructive CAD was 0.747 [95% confidence interval (CI), 0.712-0.783] for the HEART score and 0.792 (95% CI, 0.762-0.823) for the CADC model (P = 0.0005). The C-statistic for predicting 30-day MACE was 0.820 (95% CI, 0.774-0.864) for the HEART score and 0.850 (95% CI, 0.800-0.891) for the CADC model (P = 0.11). Among the 48.3% of patients for whom the CADC model predicted ≤5% probability of obstructive CAD, the observed 30-day MACE was 0.6%; among the 48.9% of patients for whom the HEART score was ≤2, the 30-day MACE was 0.6%. In conclusion, the CADC model was more effective at predicting obstructive CAD compared to the HEART score. The HEART score and CADC model were equally effective to safely identify low-risk patients by achieving <1% missed 30-day MACE.
本研究的目的是比较用于预测阻塞性冠心病(冠状动脉计算机断层血管造影显示狭窄≥50%)和30天主要不良心血管事件(MACE,急性心肌梗死、血运重建和死亡的复合事件)的病史、心电图、年龄、危险因素和肌钙蛋白(HEART)评分与临床冠状动脉疾病(CAD)联盟(CADC)模型。我们研究了1981例无已知CAD且因急性胸痛就诊、初始肌钙蛋白和心电图均为阴性的患者。胸痛分为典型、非典型和非心绞痛性,并用于对改良HEART评分的病史部分进行评分。HEART评分预测阻塞性CAD的C统计量为0.747 [95%置信区间(CI),0.712 - 0.783],CADC模型为0.792(95%CI,0.762 - 0.823)(P = 0.0005)。HEART评分预测30天MACE的C统计量为0.820(95%CI,0.774 - 0.864),CADC模型为0.850(95%CI,0.800 - 0.891)(P = 0.11)。在CADC模型预测阻塞性CAD概率≤5%的48.3%患者中,观察到的30天MACE为0.6%;在HEART评分≤2的48.9%患者中,30天MACE为0.6%。总之,与HEART评分相比,CADC模型在预测阻塞性CAD方面更有效。HEART评分和CADC模型在安全识别30天MACE漏诊率<1%的低风险患者方面同样有效。