基于竞争风险模型的北京市老年人群心血管疾病短期风险评估

A short-term risk assessment for cardiovascular diseases among the elderly in Beijing based on competing risk model

目的在考虑"竞争事件"的前提下,对北京市老年人群心血管疾病(cardiovascular disease,CVD)进行5年的风险评估。方法基于传统的心血管疾病影响因素年龄、糖尿病、吸烟、婚姻状况、体质量指数、血压(blood pressure,BP)、总胆固醇(total cholesterol,TC)、高密度脂蛋白胆固醇(high-density lipoprotein cholesterol,HDL-C),构建竞争风险模型,计算未来5年心血管疾病的绝对风险,通过受试者工作特征曲线(receiver operating characteristic curve,ROC)及依时受试工作者曲线下面积(areas under the ROC curve,AUC)值来衡量模型的判别能力,采用校正图来衡量模型的校正度。经过Bootstrap重抽样技术对风险预测模型进行内部验证以减少过度拟合偏倚。结果 1992年基线检查无心血管疾病者共1 775人,其中男性886人(49.92%),女性889人(50.08%),至2012年末随访结束,共有473人死于心血管疾病,693人死于非心血管疾病,609人存活或失访。男性与女性心血管疾病的风险评分总分分别为65分和62分,最佳切点分别为34分和30分。预测模型具有较好的判别能力与校正度。结论利用心血管疾病的传统危险因素,借助竞争风险模型,构建了北京市老年人群心血管疾病的短期风险评估工具,为有效筛查心血管疾病的高危人群提供了科学依据和技术手段。

Objective The study aimed at developing a 5-year risk prediction model of cardiovascular disease（ CVD） based on competing risk model,and developing a user-friendly risk score tool for risk assessment of cardiovascular disease. Methods We used competing risks model to evaluate the risks of developing a first CVD event. Sub-distribution hazard ratios（ SHR） and 95% confidence intervals（ 95% CI） were computed for the associations between each risk factor and CVD. Time-dependent receiver operating characteristic（ ROC） curve and time-dependent areas under the ROC curves（ AUC） were used to evaluate the discrimination ability of the models,calibration plot was applied to assess the calibration of the models. Internal validation of predictive accuracy was performed via 1 000 times of bootstrap resampling. Results Of the 1 775 subjects without CVD at baseline,473 incident cases of CVD were documented for a median 8-year follow-up. The participants were 886 men and 889 women between the ages of 55 and 96 years old without cardiovascular disease at baseline with 20 years of follow-up. Multivariable risk functions were derived that incorporated age,total cholesterol（ TC）,highdensity lipoprotein cholesterol（ HDL-C）,blood pressure,smoking,diabetes,marital status and overweight or obesity body mass index（ BMI）. The model showed good discrimination and calibration. Male and female cardiovascular disease risk scores were 65 points and 62 points,the best point were 34 points and 30 points. Conclusion A sex-specific multivariable risk factor algorithm based competing risk model was made. The BLSA CVD risk prediction model can be used to predicted an individual’s risk of CVD and provides a useful guide to identify the groups at high risk for CVD among over 55 years old man. A user-friendly risk score tool predicting 5-year probability of cardiovascular disease was developed.