Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Fukuoka, 812-8582, Japan.
Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Fukuoka, 812-8582, Japan; Center for Cohort Studies, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka City, Fukuoka, 812-8582, Japan.
Atherosclerosis. 2018 Dec;279:38-44. doi: 10.1016/j.atherosclerosis.2018.10.014. Epub 2018 Oct 17.
Predicting cardiovascular events is of practical benefit for disease prevention. The aim of this study was to develop and evaluate an updated risk prediction model for cardiovascular diseases and its subtypes.
A total of 2462 community residents aged 40-84 years were followed up for 24 years. A Cox proportional hazards regression model was used to develop risk prediction models for cardiovascular diseases, and separately for stroke and coronary heart diseases. The risk assessment ability of the developed model was evaluated, and a bootstrapping method was used for internal validation. The predicted risk was translated into a simplified scoring system. A decision curve analysis was used to evaluate clinical usefulness.
The multivariable model for cardiovascular diseases included age, sex, systolic blood pressure, hemoglobin A1c, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, smoking habits, and regular exercise as predictors. The models for stroke and coronary heart diseases incorporated both shared and unique variables. The developed models showed good discrimination with little evidence of overfitting (optimism-corrected Harrell's C statistics 0.726-0.777) and calibrations (Hosmer-Lemeshow test, p = 0.44-0.90). The decision curve analysis revealed that the predicted risk-based decision-making would have higher net benefit than either a CVD intervention strategy for all individuals or no individuals.
The developed risk prediction models showed a good performance and satisfactory internal validity, which may help understand individual risk and setting personalized goals, and promote risk stratification in public health strategies for CVD prevention.
预测心血管事件对疾病预防具有实际意义。本研究旨在建立和评估一种更新的心血管疾病及其亚型的风险预测模型。
对 2462 名年龄在 40-84 岁的社区居民进行了 24 年的随访。采用 Cox 比例风险回归模型建立心血管疾病、卒中及冠心病的风险预测模型。评估所建立模型的风险评估能力，并采用自举法进行内部验证。将预测风险转化为简化评分系统。采用决策曲线分析评估临床实用性。
心血管疾病的多变量模型包括年龄、性别、收缩压、糖化血红蛋白、高密度脂蛋白胆固醇、低密度脂蛋白胆固醇、吸烟习惯和规律运动作为预测因素。卒中及冠心病模型纳入了共同和独特的变量。所建立的模型具有良好的区分度，且证据表明没有过度拟合（校正后的 Harrell C 统计量为 0.726-0.777）和校准（Hosmer-Lemeshow 检验，p 值为 0.44-0.90）。决策曲线分析表明，基于预测风险的决策比针对所有个体或不针对任何个体的 CVD 干预策略具有更高的净获益。
所建立的风险预测模型具有良好的性能和令人满意的内部有效性，有助于了解个体风险和制定个性化目标，并促进公共卫生策略中 CVD 预防的风险分层。
