OBJECTIVE 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. RESEARCH DESIGN AND METHODS 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/m2 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. RESULTS 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. CONCLUSIONS 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.

中文翻译：

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预测有或没有代谢手术的 2 型糖尿病终末器官并发症的 10 年风险：机器学习方法。

目的 构建并内部验证预测模型，以估计 2 型糖尿病和肥胖患者的长期终末器官并发症和死亡率的风险，该模型可用于为正在考虑进行代谢手术的患者和从业者提供治疗决策信息。研究设计和方法 1998 年至 2017 年间，克利夫兰诊所医疗系统中总共 2,287 名接受代谢手术的 2 型糖尿病患者与 11,435 名 BMI ≥ 30 kg/m2 且接受代谢手术的 2 型糖尿病患者进行了 1:5 倾向匹配。接受常规护理并进行随访直至 2018 年 12 月。建立了多变量事件时间回归和随机森林机器学习模型，并使用五重交叉验证进行内部验证，以预测四种感兴趣结果的 10 年风险。预测模型经过编程，可构建用户友好的基于网络和智能手机的个体化糖尿病并发症 (IDC) 风险评分应用程序，供临床使用。结果 预测工具分别证明了手术组和非手术组 10 年时基于受试者工作特征曲线下面积（1 = 完美辨别，0.5 = 机会）的以下辨别能力：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）以及肾病（0.73 和 0.76）。当患者的数据输入 IDC 应用程序时，它会估计接受或不接受代谢手术的个体化 10 年发病和死亡风险。结论 IDC 风险评分可以根据 2 型糖尿病和肥胖患者当前的肥胖、糖尿病和相关心脏代谢状况状况，为他们提供关于是否接受代谢手术的未来心血管结局和死亡率的个性化循证风险信息。