1 Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, Massachusetts.
2 Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts.
Diabetes Technol Ther. 2019 Jun;21(6):344-355. doi: 10.1089/dia.2018.0393.
Type 2 diabetes mellitus (T2DM) affects ∼30 million people in the United States and ∼400 million people worldwide, numbers likely to increase due to the rising prevalence of obesity. We sought to design, develop, and validate PREDICT-DM (PRojection and Evaluation of Disease Interventions, Complications, and Treatments-Diabetes Mellitus), a state-transition microsimulation model of T2DM, incorporating recent data. PREDICT-DM is populated with natural history, risk factor, and outcome data from large-scale cohort studies and randomized clinical trials. The model projects diabetes-relevant outcomes, including cardiovascular and renal disease outcomes, and 5/10-year survival. We assessed the model validity against 62 endpoints from ACCORD (Action to Control Cardiovascular Risk in Diabetes), VADT (Veterans Affairs Diabetes Trial), and Look AHEAD trials via several comparative statistical methods, including mean absolute percentage error (MAPE), Bland-Altman graphs, and Kaplan-Meier curves. For the comparison between simulated and observed outcomes of the intervention/control arms of the trial, the MAPE was 19%/25% (ACCORD), 29%/20% (VADT), and 42%/10% (Look AHEAD). The Bland-Altman's 95% limit of agreement was 0.02 (ACCORD), 0.03 (VADT), and 0.01 (Look AHEAD), and the mean difference (95% confidence interval) for the comparison between PREDICT-DM and trial endpoints was 0.0025 (-0.0018 to 0.0070) for ACCORD, -0.0067 (-0.0137 to 0.0002) for VADT, and -0.0033 (-0.0067 to 0.00002) for Look AHEAD, indicating an adequate model fit to the data. The model-driven Kaplan-Meier curves were similarly close to those previously published. PREDICT-DM can reasonably predict clinical outcomes from ACCORD and other clinical trials of U.S. patients with T2DM. This model may be leveraged to inform clinical strategy questions related to the management and care of T2DM in the United States.
2 型糖尿病（T2DM）影响了美国约 3000 万人和全世界约 4 亿人，由于肥胖的流行，这个数字还在不断增加。我们旨在设计、开发和验证 PREDICT-DM（疾病干预、并发症和治疗的预测和评估-糖尿病），这是一个包含最新数据的 T2DM 状态转换微观模拟模型。PREDICT-DM 采用了来自大型队列研究和随机临床试验的自然史、风险因素和结果数据。该模型预测与糖尿病相关的结果，包括心血管和肾脏疾病的结果以及 5/10 年的生存率。我们通过几种比较统计方法，包括平均绝对百分比误差（MAPE）、Bland-Altman 图和 Kaplan-Meier 曲线，对模型的有效性进行了评估，评估对象为 ACCORD（控制心血管风险的行动中的糖尿病）、VADT（退伍军人事务糖尿病试验）和 LOOK AHEAD 试验的 62 个终点。对于试验干预/对照组的模拟和观察结果之间的比较，MAPE 为 19%/25%（ACCORD）、29%/20%（VADT）和 42%/10%（LOOK AHEAD）。Bland-Altman 的 95%一致性限为 0.02（ACCORD）、0.03（VADT）和 0.01（LOOK AHEAD），PREDICT-DM 与试验终点之间比较的平均值差异（95%置信区间）为 0.0025（-0.0018 至 0.0070）用于 ACCORD，-0.0067（-0.0137 至 0.0002）用于 VADT，-0.0033（-0.0067 至 0.00002）用于 LOOK AHEAD，表明模型对数据有足够的拟合度。模型驱动的 Kaplan-Meier 曲线也与之前发表的曲线非常接近。PREDICT-DM 可以合理地预测来自 ACCORD 和其他美国 T2DM 临床试验的临床结果。该模型可用于为美国 2 型糖尿病管理和护理相关的临床策略问题提供信息。
