Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, United States.
Center for Statistical Sciences and Department of Biostatistics, Brown University School of Public Health, Providence, RI, United States.
J Cardiovasc Comput Tomogr. 2017 Jul-Aug;11(4):309-316. doi: 10.1016/j.jcct.2017.04.011. Epub 2017 Apr 27.
We sought to derive and validate a model for identifying suspected ACS patients harboring undiagnosed significant coronary artery disease (CAD).
This was a secondary analysis of data from a randomized control trial (RCT). Patients randomized to the CTA arm of an RCT examining a CTA-based strategy for ruling-out acute coronary syndrome (ACS) constitute the derivation cohort, which was randomly divided into a training dataset (2/3, used for model derivation) and a test dataset (1/3, used for internal validation (IV)). ED patients from a different center receiving CTA to evaluate for suspected ACS constitute the external validation (EV) cohort. Primary outcome was CTA-assessed significant CAD (stenosis of ≥50% in a major coronary artery).
In the derivation cohort, 11.2% (76/679) of subjects had CTA-assessed significant CAD, and in the EV cohort, 8.2% of subjects (87/1056) had CTA-assessed significant CAD. Age was the strongest predictor of significant CAD among the clinical risk factors examined. Predictor variables included in the derived logistic regression model were: age, sex, tobacco use, diabetes, and race. This model exhibited an area under the receiver operating characteristic curve (ROC AUC) of 0.72 (95% CI: 0.61-0.83) based on IV, and 0.76 (95% CI: 0.70, 0.82) based on EV. The derived random forest model based on clinical risk factors yielded improved but not sufficient discrimination of significant CAD (ROC AUC = 0.76 [95% CI: 0.67-0.85] based on IV). Coronary artery calcium score was a more accurate predictor of significant CAD than any combination of clinical risk factors (ROC AUC = 0.85 [95% CI: 0.76-0.94] based on IV; ROC AUC = 0.92 [95% CI: 0.88-0.95] based on EV).
Clinical risk factors, either individually or in combination, are insufficient for accurately identifying suspected ACS patients harboring undiagnosed significant coronary artery disease.
我们旨在建立并验证一种模型，以识别疑似急性冠脉综合征（ACS）患者中存在未诊断的严重冠状动脉疾病（CAD）。
这是一项随机对照试验（RCT）数据的二次分析。被随机分配至 RCT 中 CT 血管成像（CTA）臂的患者构成了推导队列，该队列被随机分为训练数据集（2/3，用于模型推导）和测试数据集（1/3，用于内部验证（IV））。来自另一个中心的接受 CTA 以评估疑似 ACS 的 ED 患者构成了外部验证（EV）队列。主要结局是 CTA 评估的严重 CAD（主要冠状动脉狭窄≥50%）。
在推导队列中，11.2%（76/679）的患者有 CTA 评估的严重 CAD，在 EV 队列中，8.2%的患者（87/1056）有 CTA 评估的严重 CAD。在检查的临床危险因素中，年龄是严重 CAD 的最强预测因素。纳入推导逻辑回归模型的预测变量包括：年龄、性别、吸烟、糖尿病和种族。该模型在 IV 中显示出 0.72（95%置信区间：0.61-0.83）的接受者操作特征曲线（ROC）下面积，在 EV 中显示出 0.76（95%置信区间：0.70，0.82）的 ROC 下面积。基于临床危险因素的推导随机森林模型提高了但不能充分区分严重 CAD（IV 中的 ROC AUC=0.76 [95% CI：0.67-0.85]）。冠状动脉钙评分是严重 CAD 的更准确预测指标，优于任何临床危险因素组合（IV 中的 ROC AUC=0.85 [95% CI：0.76-0.94]；EV 中的 ROC AUC=0.92 [95% CI：0.88-0.95]）。
临床危险因素单独或联合使用，不足以准确识别疑似 ACS 患者中存在未诊断的严重冠状动脉疾病。
