Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, Netherlands.
BMJ. 2012 Jun 12;344:e3485. doi: 10.1136/bmj.e3485.
To develop prediction models that better estimate the pretest probability of coronary artery disease in low prevalence populations.
Retrospective pooled analysis of individual patient data.
18 hospitals in Europe and the United States.
Patients with stable chest pain without evidence for previous coronary artery disease, if they were referred for computed tomography (CT) based coronary angiography or catheter based coronary angiography (indicated as low and high prevalence settings, respectively).
Obstructive coronary artery disease (≥ 50% diameter stenosis in at least one vessel found on catheter based coronary angiography). Multiple imputation accounted for missing predictors and outcomes, exploiting strong correlation between the two angiography procedures. Predictive models included a basic model (age, sex, symptoms, and setting), clinical model (basic model factors and diabetes, hypertension, dyslipidaemia, and smoking), and extended model (clinical model factors and use of the CT based coronary calcium score). We assessed discrimination (c statistic), calibration, and continuous net reclassification improvement by cross validation for the four largest low prevalence datasets separately and the smaller remaining low prevalence datasets combined.
We included 5677 patients (3283 men, 2394 women), of whom 1634 had obstructive coronary artery disease found on catheter based coronary angiography. All potential predictors were significantly associated with the presence of disease in univariable and multivariable analyses. The clinical model improved the prediction, compared with the basic model (cross validated c statistic improvement from 0.77 to 0.79, net reclassification improvement 35%); the coronary calcium score in the extended model was a major predictor (0.79 to 0.88, 102%). Calibration for low prevalence datasets was satisfactory.
Updated prediction models including age, sex, symptoms, and cardiovascular risk factors allow for accurate estimation of the pretest probability of coronary artery disease in low prevalence populations. Addition of coronary calcium scores to the prediction models improves the estimates.
开发能够更好地估计低患病率人群中冠状动脉疾病的术前概率的预测模型。
回顾性个体患者数据的汇总分析。
欧洲和美国的 18 家医院。
患有稳定型胸痛且无先前冠状动脉疾病证据的患者，如果他们接受基于计算机断层扫描（CT）的冠状动脉造影或基于导管的冠状动脉造影（分别表示为低患病率和高患病率环境）。
阻塞性冠状动脉疾病（在基于导管的冠状动脉造影中发现至少一条血管≥50%的直径狭窄）。缺失预测因子和结果采用多重插补法进行处理，充分利用了两种血管造影程序之间的强相关性。预测模型包括基本模型（年龄、性别、症状和环境）、临床模型（基本模型因素和糖尿病、高血压、血脂异常和吸烟）和扩展模型（临床模型因素和使用 CT 冠状动脉钙评分）。我们分别对四个最大的低患病率数据集以及较小的剩余低患病率数据集进行交叉验证，以评估四个最大的低患病率数据集和较小的剩余低患病率数据集的判别能力（c 统计量）、校准和连续净重新分类改善情况。
我们纳入了 5677 例患者（3283 例男性，2394 例女性），其中 1634 例患者在基于导管的冠状动脉造影中发现阻塞性冠状动脉疾病。在单变量和多变量分析中，所有潜在的预测因子与疾病的存在均具有显著相关性。与基本模型相比，临床模型提高了预测能力（交叉验证的 c 统计量从 0.77 提高到 0.79，净重新分类改善 35%）；扩展模型中的冠状动脉钙评分是一个主要的预测因子（从 0.79 提高到 0.88，增加 102%）。对于低患病率数据集，校准效果令人满意。
更新的预测模型包括年龄、性别、症状和心血管危险因素，可以准确估计低患病率人群中冠状动脉疾病的术前概率。将冠状动脉钙评分加入预测模型可提高估计值。
