Objectives This study sought to externally validate prediction models for the presence of obstructive coronary artery disease (CAD).

Background A better assessment of the probability of CAD may improve the identification of patients who benefit from noninvasive testing.

Methods Stable chest pain patients from the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) trial with computed tomography angiography (CTA) or invasive coronary angiography (ICA) were included. The authors assumed that patients with CTA showing 0% stenosis and a coronary artery calcium (CAC) score of 0 were free of obstructive CAD (≥50% stenosis) on ICA, and they multiply imputed missing ICA results based on clinical variables and CTA results. Predicted CAD probabilities were calculated using published coefficients for 3 models: basic model (age, sex, chest pain type), clinical model (basic model + diabetes, hypertension, dyslipidemia, and smoking), and clinical + CAC score model. The authors assessed discrimination and calibration, and compared published effects with observed predictor effects.

Results In 3,468 patients (1,805 women; mean 60 years of age; 779 [23%] with obstructive CAD on CTA), the models demonstrated moderate-good discrimination, with C-statistics of 0.69 (95% confidence interval [CI]: 0.67 to 0.72), 0.72 (95% CI: 0.69 to 0.74), and 0.86 (95% CI: 0.85 to 0.88) for the basic, clinical, and clinical + CAC score models, respectively. Calibration was satisfactory although typical chest pain and diabetes were less predictive and CAC score was more predictive than was suggested by the models. Among the 31% of patients for whom the clinical model predicted a low (≤10%) probability of CAD, actual prevalence was 7%; among the 48% for whom the clinical + CAC score model predicted a low probability the observed prevalence was 2%. In 2 sensitivity analyses excluding imputed data, similar results were obtained using CTA as the outcome, whereas in those who underwent ICA the models significantly underestimated CAD probability.

Conclusions Existing clinical prediction models can identify patients with a low probability of obstructive CAD. Obstructive CAD on ICA was imputed for 61% of patients; hence, further validation is necessary.

目的本研究试图从外部验证阻塞性冠状动脉疾病（CAD）的预测模型。

背景技术对CAD可能性的更好评估可能会改善对受益于无创检测的患者的识别。

方法纳入了通过计算机断层血管造影（CTA）或有创冠状动脉造影（ICA）进行的PROMISE（评估胸痛的前瞻性多中心影像研究）试验中稳定的胸痛患者。作者认为，CTA显示0％狭窄且冠状动脉钙（CAC）评分为0的患者在ICA上没有阻塞性CAD（≥50％狭窄），并且他们根据临床变量和CTA结果乘以估算的ICA缺失结果。使用3种模型的公开系数计算预测的CAD概率：基本模型（年龄，性别，胸痛类型），临床模型（基本模型+糖尿病，高血压，血脂异常和吸烟）以及临床+ CAC评分模型。作者评估了歧视和校准，并将已发表的效应与观察到的预测效应进行了比较。

结果在3,468名患者（1,805名女性；平均年龄60岁； 779名[23％]的CTA阻塞性CAD）中，这些模型表现出中度良好的歧视性，其C统计量为0.69（95％置信区间[CI]：0.67至基本，临床和临床+ CAC评分模型分别为0.72、0.72（95％CI：0.69至0.74）和0.86（95％CI：0.85至0.88）。尽管典型的胸痛和糖尿病的预测性较差，CAC评分的预测性较模型建议的高，但校准仍令人满意。在临床模型预测的CAD可能性低（≤10％）的31％的患者中，实际患病率为7％；在临床+ CAC评分模型预测为低概率的48％人群中，观察到的患病率为2％。在2项敏感性分析中（不包括估算数据），使用CTA作为结果获得了相似的结果，

结论现有的临床预测模型可以识别出阻塞性CAD可能性低的患者。61％的患者因ICA阻塞性CAD。因此，有必要进行进一步的验证。