Department of General Surgery, Bariatric & Metabolic Institute, Cleveland Clinic, Cleveland, OH
Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH.
Diabetes Care. 2020 Apr;43(4):852-859. doi: 10.2337/dc19-2057. Epub 2020 Feb 6.
To construct and internally validate prediction models to estimate the risk of long-term end-organ complications and mortality in patients with type 2 diabetes and obesity that can be used to inform treatment decisions for patients and practitioners who are considering metabolic surgery.
A total of 2,287 patients with type 2 diabetes who underwent metabolic surgery between 1998 and 2017 in the Cleveland Clinic Health System were propensity-matched 1:5 to 11,435 nonsurgical patients with BMI ≥30 kg/m and type 2 diabetes who received usual care with follow-up through December 2018. Multivariable time-to-event regression and random forest machine learning models were built and internally validated using fivefold cross-validation to predict the 10-year risk for four outcomes of interest. The prediction models were programmed to construct user-friendly web-based and smartphone applications of Individualized Diabetes Complications (IDC) Risk Scores for clinical use.
The prediction tools demonstrated the following discrimination ability based on the area under the receiver operating characteristic curve (1 = perfect discrimination and 0.5 = chance) at 10 years in the surgical and nonsurgical groups, respectively: all-cause mortality (0.79 and 0.81), coronary artery events (0.66 and 0.67), heart failure (0.73 and 0.75), and nephropathy (0.73 and 0.76). When a patient's data are entered into the IDC application, it estimates the individualized 10-year morbidity and mortality risks with and without undergoing metabolic surgery.
The IDC Risk Scores can provide personalized evidence-based risk information for patients with type 2 diabetes and obesity about future cardiovascular outcomes and mortality with and without metabolic surgery based on their current status of obesity, diabetes, and related cardiometabolic conditions.
构建并内部验证预测模型，以估计 2 型糖尿病合并肥胖患者发生长期终末器官并发症和死亡的风险，为考虑代谢手术的患者和临床医生提供信息，以辅助治疗决策。
克利夫兰诊所医疗系统在 1998 年至 2017 年间对 2287 例 2 型糖尿病患者实施了代谢手术，按照 1:5 的比例与接受常规治疗且 BMI≥30kg/m 且患有 2 型糖尿病的 11435 例非手术患者进行倾向评分匹配，随访至 2018 年 12 月。使用五重交叉验证构建并内部验证多变量时间事件回归和随机森林机器学习模型，以预测四个感兴趣结局的 10 年风险。预测模型被编程为构建用于临床使用的个性化糖尿病并发症（IDC）风险评分的用户友好型网络和智能手机应用程序。
在手术组和非手术组中，预测工具在 10 年时基于接受者操作特征曲线下面积（1 表示完美区分，0.5 表示机会）显示出以下区分能力：全因死亡率（0.79 和 0.81）、冠状动脉事件（0.66 和 0.67）、心力衰竭（0.73 和 0.75）和肾病（0.73 和 0.76）。当患者数据输入 IDC 应用程序时，它会根据患者当前的肥胖、糖尿病和相关心血管代谢状况，估算出是否接受代谢手术的 10 年发病率和死亡率的个体化风险。
IDC 风险评分可以为 2 型糖尿病合并肥胖患者提供基于当前肥胖、糖尿病和相关心血管代谢状况的未来心血管结局和死亡率的个体化、基于循证的风险信息，包括是否接受代谢手术。
