Dataset Information

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

ABSTRACT: 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.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.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.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.

SUBMITTER: Ye W

PROVIDER: S-EPMC4696433 | biostudies-other | 2015 Oct

REPOSITORIES: biostudies-other

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

Ye Wen W Brandle Michael M Brown Morton B MB Herman William H WH

Diabetes technology & therapeutics 20150729 10

<h4>Objectives</h4>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.<h4>Research design and methods</h4>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, a ...[more]

PMID: 26222704

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Project description:Many studies had established the chronic kidney disease (CKD) prediction models, but most of them were conducted on the general population and not on patients with type 2 diabetes, especially in Asian populations. This study aimed to develop a risk prediction model for CKD in patients with type 2 diabetes from the Diabetes Care Management Program (DCMP) in Taiwan. This research was a retrospective cohort study. We used the DCMP database to set up a cohort of 4,601 patients with type 2 diabetes without CKD aged 40-92 years enrolled in the DCMP program of a Taichung medical center in 2002-2016. All patients were followed up until incidences of CKD, death, and loss to follow-up or 2016. The dataset for participants of national DCMP in 2002-2004 was used as external validation. The incident CKD cases were defined as having one of the following three conditions: ACR data greater than or equal to 300 (mg/g); both eGFR data less than 60 (ml/min/1.73 m2) and ACR data greater than or equal to 30 (mg/g); and eGFR data less than 45 (ml/min/1.73 m2). The study subjects were randomly allocated to derivation and validation sets at a 2:1 ratio. Cox proportional hazards regression model was used to identify the risk factors of CKD in the derivation set. Time-varying area under receiver operating characteristics curve (AUC) was used to evaluate the performance of the risk model. After an average of 3.8 years of follow-up period, 3,067 study subjects were included in the derivation set, and 786 (25.63%) were newly diagnosed CKD cases. A total of 1,534 participants were designated to the validation set, and 378 (24.64%) were newly diagnosed CKD cases. The final CKD risk factors consisted of age, duration of diabetes, insulin use, estimated glomerular filtration rate, albumin-to-creatinine ratio, high-density lipoprotein cholesterol, triglyceride, diabetes retinopathy, variation in HbA1c, variation in FPG, and hypertension drug use. The AUC values of 1-, 3-, and 5-year CKD risks were 0.74, 0.76, and 0.77 in the validation set, respectively, and were 0.76, 0.77, and 0.76 in the sample for external validation, respectively. The value of Harrell's c-statistics was 0.76 (0.74, 0.78). The proposed model is the first CKD risk prediction model for type 2 diabetes patients in Taiwan. The 1-, 3-, and 5-year CKD risk prediction models showed good prediction accuracy. The model can be used as a guide for clinicians to develop medical plans for future CKD preventive intervention in Chinese patients with type 2 diabetes.

Dataset Information

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

Publications

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

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