The Michigan Model for Coronary Heart Disease in Type 2 Diabetes: Development and Validation

Abstract

Objectives: The aim of this study was to develop and validate a computer simulation model for coronary heart disease (CHD) in type 2 diabetes mellitus (T2DM) that reflects current medical and surgical treatments. Research Design and Methods: We modified the structure of the CHD submodel in the Michigan Model for Diabetes to allow for revascularization procedures before and after first myocardial infarction, for repeat myocardial infarctions and repeat revascularization procedures, and for congestive heart failure. Transition probabilities that reflect the direct effects of medical and surgical therapies on outcomes were derived from the literature and calibrated to recently published population-based epidemiologic studies and randomized controlled clinical trials. Monte Carlo techniques were used to implement a discrete-state and discrete-time multistate microsimulation model. Performance of the model was assessed using internal and external validation. Simple regression analysis (simulated outcome = b 0 + b 1 · published outcome) was used to evaluate the validation results. Results: For the 21 outcomes in the six studies used for internal validation, R 2 was 0.99, and the slope of the regression line was 0.98. For the 16 outcomes in the five studies used for external validation, R 2 was 0.81, and the slope was 0.84. Conclusions: Our new computer simulation model predicted the progression of CHD in patients with T2DM and will be incorporated into the Michigan Model for Diabetes to assess the cost-effectiveness of alternative strategies to prevent and treat T2DM.

Development and Validation of PREDICT-DM: A New Microsimulation Model to Project and Evaluate Complications and Treatments of Type 2 Diabetes Mellitus

“…In comparison with other T2DM models, the MAPE measure for PREDICT-DM versus ACCORD endpoints was 22% (combined over intensive and standard control arms), lower than the 33% error reported by one model 58 and the 83% error of another model. 67 Similarly, the MAPE measure for PREDICT-DM compared with VADT endpoints was 24%, lower than the 45% reported elsewhere 58 and similar to the 24% error of a previous model. 67 We could not find validation results against Look AHEAD for other T2DM models.…”

“…67 Similarly, the MAPE measure for PREDICT-DM compared with VADT endpoints was 24%, lower than the 45% reported elsewhere 58 and similar to the 24% error of a previous model. 67 We could not find validation results against Look AHEAD for other T2DM models. Our ICC values were ‡89% for different outcomes; an ICC above 90% (70%) is often regarded as excellent (good) agreement between model and trial.…”

TECH-VER: A Verification Checklist to Reduce Errors in Models and Improve Their Credibility

“…Of those, 3383 records were excluded based on their title and/or abstract. Sixty-eight articles were screened in full-text and 15 articles were included after applying the inclusion and exclusion criteria [2, 4–16, 24]. The resulting PRISMA diagram can be seen in Fig.…”

“…Among the 15 included articles, the majority of the studies ( n = 10) were model-based cost-effectiveness analysis studies, which reported some limited amount of verification efforts [7–16]. The reporting of the verification efforts in none of these studies was systematic.…”

Estimating risk factor progression equations for the UKPDS Outcomes Model 2 (UKPDS 90)

“…Different treatment and management strategies are evaluated through their impact on risk factor levels. Elements of UKPDS‐OM1 and UKPDS‐OM2 have been widely used in many other prediabetes and diabetes simulation models 13–18 …”