Background Cardiovascular disease (CVD) risk prediction models for individuals with type 2 diabetes are important tools to guide intensification of interventions for CVD prevention. We aimed to assess the added value of incorporating risk factors variability in CVD risk prediction for people with type 2 diabetes. Methods We used electronic health records (EHRs) data from 83 910 adults with type 2 diabetes but without pre-existing CVD from the UK Clinical Practice Research Datalink for 2004–2017. Using a landmark-modelling approach, we developed and validated sex-specific Cox models, incorporating conventional predictors and trajectories plus variability of systolic blood pressure (SBP), total and high-density lipoprotein (HDL) cholesterol, and glycated haemoglobin (HbA1c). Such models were compared against simpler models using single last observed values or means. Results The standard deviations (SDs) of SBP, HDL cholesterol and HbA1c were associated with higher CVD risk (P < 0.05). Models incorporating trajectories and variability of continuous predictors demonstrated improvement in risk discrimination (C-index = 0.659, 95% CI: 0.654–0.663) as compared with using last observed values (C-index = 0.651, 95% CI: 0.646–0.656) or means (C-index = 0.650, 95% CI: 0.645–0.655). Inclusion of SDs of SBP yielded the greatest improvement in discrimination (C-index increase = 0.005, 95% CI: 0.004–0.007) in comparison to incorporating SDs of total cholesterol (C-index increase = 0.002, 95% CI: 0.000–0.003), HbA1c (C-index increase = 0.002, 95% CI: 0.000–0.003) or HDL cholesterol (C-index increase= 0.003, 95% CI: 0.002–0.005). Conclusion Incorporating variability of predictors from EHRs provides a modest improvement in CVD risk discrimination for individuals with type 2 diabetes. Given that repeat measures are readily available in EHRs especially for regularly monitored patients with diabetes, this improvement could easily be achieved.

中文翻译：

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危险因素变异性对 2 型糖尿病患者心血管风险预测的增量价值：英国初级保健电子健康记录的结果


背景 2 型糖尿病患者的心血管疾病 (CVD) 风险预测模型是指导强化 CVD 预防干预措施的重要工具。我们的目的是评估在 2 型糖尿病患者的 CVD 风险预测中纳入风险因素变异性的附加值。方法 我们使用了 2004-2017 年英国临床实践研究数据链中 83910 名患有 2 型糖尿病但没有既往患有 CVD 的成人的电子健康记录 (EHR) 数据。使用具有里程碑意义的建模方法，我们开发并验证了性别特异性 Cox 模型，结合了传统的预测因子和轨迹以及收缩压 (SBP)、总胆固醇和高密度脂蛋白 (HDL) 胆固醇以及糖化血红蛋白 (HbA1c) 的变异性。使用单个最后观察值或平均值将此类模型与更简单的模型进行比较。结果 SBP、HDL 胆固醇和 HbA1c 的标准差 (SD) 与较高的 CVD 风险相关 (P < 0.05)。与使用最后观察值（C 指数 = 0.651，95% CI：0.646–0.656）相比，包含连续预测变量的轨迹和变异性的模型证明风险辨别能力有所改善（C 指数 = 0.659，95% CI：0.654–0.663）或平均值（C 指数 = 0.650，95% CI：0.645–0.655）。与纳入总胆固醇的标准差（C 指数增加 = 0.002，95% CI：0.000–0.003）相比，纳入 SBP 的标准差在区分度方面产生了最大的改善（C 指数增加 = 0.005，95% CI：0.004–0.007） ）、HbA1c（C 指数增加 = 0.002，95% CI：0.000–0.003）或 HDL 胆固醇（C 指数增加 = 0.003，95% CI：0.002–0.005）。结论 纳入 EHR 预测变量的变异性可适度改善 2 型糖尿病患者的 CVD 风险辨别。 鉴于电子病历中可以随时进行重复测量，特别是对于定期监测的糖尿病患者，这种改进很容易实现。