Clinical Epidemiology Unit, Department of Cardiology, Erasmus Medical Center, Rotterdam, The Netherlands.
Am J Cardiol. 2013 Jul 1;112(1):27-33. doi: 10.1016/j.amjcard.2013.02.049. Epub 2013 Apr 1.
Appropriate risk stratification of patients with established, stable coronary artery disease could contribute to the prevention of recurrent cardiovascular events. The purpose of the present study was to develop and validate risk prediction models for various cardiovascular end points in the EURopean trial On reduction of cardiac events with Perindopril in stable coronary Artery disease (EUROPA) database, consisting of 12,218 patients with established coronary artery disease, with a median follow-up of 4.1 years. Cox proportional hazards models were used for model development. The end points examined were cardiovascular mortality, noncardiovascular mortality, nonfatal myocardial infarction, coronary artery bypass grafting, percutaneous coronary intervention, resuscitated cardiac arrest, and combinations of these end points. The performance measures included Nagelkerke's R², time-dependent area under the receiver operating characteristic curves, and calibration plots. Backward selection resulted in a prediction model for cardiovascular mortality (464 events) containing age, current smoking, diabetes mellitus, total cholesterol, body mass index, previous myocardial infarction, history of congestive heart failure, peripheral vessel disease, previous revascularization, and previous stroke. The model performance was adequate for this end point, with a Nagelkerke R² of 12%, and an area under the receiver operating characteristic curve of 0.73. However, the performance of models constructed for nonfatal and combined end points was considerably worse, with an area under the receiver operating characteristic curve of about 0.6. In conclusion, in patients with established coronary artery disease, the risk of cardiovascular mortality during longer term follow-up can be adequately predicted using the clinical characteristics available at baseline. However, the prediction of nonfatal outcomes, both separately and combined with fatal outcomes, poses major challenges for clinicians and model developers.
对于已确诊、稳定的冠心病患者，进行适当的风险分层有助于预防心血管事件的再次发生。本研究旨在利用包含 12218 例已确诊冠心病患者的 EURopean trial On reduction of cardiac events with Perindopril in stable coronary Artery disease（EUROPA）数据库，建立并验证适用于各种心血管终点的风险预测模型，中位随访时间为 4.1 年。采用 Cox 比例风险模型进行模型建立。所检测的终点包括心血管死亡率、非心血管死亡率、非致死性心肌梗死、冠状动脉旁路移植术、经皮冠状动脉介入治疗、心脏复苏性骤停以及这些终点的组合。评价指标包括 Nagelkerke 的 R²、时间依赖性接受者操作特征曲线下面积和校准图。逐步向后选择得出了心血管死亡率（464 例事件）预测模型，包含年龄、当前吸烟状况、糖尿病、总胆固醇、体重指数、既往心肌梗死、充血性心力衰竭史、外周血管疾病、既往血运重建和既往卒中等因素。该模型在预测心血管死亡率方面表现良好，Nagelkerke R²为 12%，接受者操作特征曲线下面积为 0.73。然而，构建非致死性和复合终点模型的性能则明显较差，接受者操作特征曲线下面积约为 0.6。总之，对于已确诊的冠心病患者，使用基线时可获得的临床特征可以充分预测长期随访期间的心血管死亡率风险。然而，对于非致死性结局，无论是单独预测还是与致死性结局结合预测，都对临床医生和模型开发者提出了重大挑战。
