Department of Medical Informatic, School of Biomedical Engineering and Informatics, Nanjing Medical University, CN.
Women's College Research Institute, Women's College Hospital, Toronto, CA.
Glob Heart. 2022 Jul 28;17(1):46. doi: 10.5334/gh.1131. eCollection 2022.
To use routinely collected data to develop a five-year cardiovascular disease (CVD) risk prediction model for Chinese adults with type 2 diabetes with validation of its performance in a population of European ancestry.
People with incident type 2 diabetes and no history of CVD at diagnosis of diabetes between 2008 and 2017 were included in derivation and validation cohorts. The derivation cohort was identified from a pseudonymized research extract of data from the First Affiliated Hospital of Nanjing Medical University (NMU). Five-year risk of CVD was estimated using basic and extended Cox proportional hazards regression models including 6 and 11 predictors respectively. The risk prediction models were internally validated and externally validated in a Scottish population-based cohort with CVD events identified from linked hospital records. Discrimination and calibration were assessed using Harrell's C-statistic and calibration plots, respectively.
Mean age of the derivation and validation cohorts were 58.4 and 59.2 years, respectively, with 53.5% and 56.9% men. During a median follow-up time of 4.75 [2.67, 7.42] years, 18,827 (22.25%) of the 84,630 people in the NMU-Diabetes cohort and 8,763 (7.31%) of the Scottish cohort of 119,891 people developed CVD. The extended model had a C-statistic of 0.723 [0.721-0.724] in internal validation and 0.716 [0.713-0.719] in external validation.
It is possible to generate a risk prediction model with moderate discriminative power in internal and external validation derived from routinely collected Chinese hospital data. The proposed risk score could be used to improve CVD prevention in people with diabetes.
利用常规收集的数据为中国成年 2 型糖尿病患者建立一个 5 年心血管疾病（CVD）风险预测模型，并在欧洲人群中验证其性能。
纳入了 2008 年至 2017 年间诊断为 2 型糖尿病且无 CVD 病史的患者。推导队列来自南京医科大学第一附属医院（NMU）的匿名研究数据摘录。使用包括 6 个和 11 个预测因子的基本和扩展 Cox 比例风险回归模型来估计 CVD 风险。使用来自链接医院记录的苏格兰人群队列中的 CVD 事件来对风险预测模型进行内部验证和外部验证。使用 Harrell 的 C 统计量和校准图分别评估区分度和校准度。
推导和验证队列的平均年龄分别为 58.4 岁和 59.2 岁，分别有 53.5%和 56.9%的男性。在中位数为 4.75 年[2.67 年至 7.42 年]的随访期间，在 NMU-Diabetes 队列的 84630 人中，有 18827 人（22.25%）发生了 CVD，在苏格兰队列的 119891 人中，有 8763 人（7.31%）发生了 CVD。扩展模型在内部验证中的 C 统计量为 0.723[0.721-0.724]，在外部验证中的 C 统计量为 0.716[0.713-0.719]。
从常规收集的中国医院数据中推导和验证，有可能生成具有中等区分能力的风险预测模型。该风险评分可用于改善糖尿病患者的 CVD 预防。
