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.
Methods:
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.
Results:
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.
Conclusions:
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.
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