Validation of the IHE Cohort Model of Type 2 Diabetes and the Impact of Choice of Macrovascular Risk Equations

Lundqvist, Adam; Carlsson, Katarina Steen; Johansen, Pierre; Andersson, Emelie; Willis, Michael

doi:10.1371/journal.pone.0110235

Cited by 36 publications

(59 citation statements)

References 34 publications

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“…The UKPDS OM2 has been shown to accurately predict results for the population in which it was developed (internally validated) (9) and for other diabetes populations (externally validated) (see the Appendix, available at Annals.org, for details) (13). We internally validated our version of the UKPDS OM2 by comparing our results with predicted results for the same UKPDS population.…”

Section: Methodsmentioning

confidence: 99%

“…The internal validation of the model compared its predictive accuracy for patient-level outcomes at 25 years for the same UKPDS participants. The predicted failure curves for diabetes outcomes and mortality were within the 95% CI of the actual cumulative failure curves for all events and death. The UKPDS OM2 was externally validated in a study of the Swedish Institute for Health Economics Cohort Model of Type 2 Diabetes (13). This study included data from 12 clinical trials and observational studies.…”

Section: Ukpds Om2 Internal and External Validationmentioning

confidence: 99%

“…The UKPDS OM2 has been internally and externally validated (9, 13). Internal validation is the performance of a predictive model in a population similar to that from which the sample originated.…”

Section: Ukpds Om2 Internal and External Validationmentioning

confidence: 99%

“…The UKPDS OM2 was externally validated in a study of the Swedish Institute for Health Economics Cohort Model of Type 2 Diabetes (13). This study included data from 12 clinical trials and observational studies.…”

Section: Ukpds Om2 Internal and External Validationmentioning

confidence: 99%

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Individualized Glycemic Control for U.S. Adults With Type 2 Diabetes

et al. 2017

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Background Intensive glycemic control in type 2 diabetes (glycated hemoglobin [HbA1c] level <7%) is an established, cost-effective standard of care. However, guidelines recommend individualizing goals on the basis of age, comorbidity, diabetes duration, and complications. Objective To estimate the cost-effectiveness of individualized control versus uniform intensive control (HbA1c level <7%) for the U.S. population with type 2 diabetes. Design Patient-level Monte Carlo–based Markov model. Data Sources National Health and Nutrition Examination Survey 2011–2012. Target Population The approximately 17.3 million persons in the United States with diabetes diagnosed at age 30 years or older. Time Horizon Lifetime. Perspective Health care sector. Intervention Individualized versus uniform intensive glycemic control. Outcome Measures Average lifetime costs, life-years, and quality-adjusted life-years (QALYs). Results of Base-Case Analysis Individualized control saved $13 547 per patient compared with uniform intensive control ($105 307 vs. $118 854), primarily due to lower medication costs ($34 521 vs. $48 763). Individualized control decreased life expectancy (20.63 vs. 20.73 years) due to an increase in complications but produced more QALYs (16.68 vs. 16.58) due to fewer hypoglycemic events and fewer medications. Results of Sensitivity Analysis Individualized control was cost-saving and generated more QALYs compared with uniform intensive control, except in analyses where the disutility associated with receiving diabetes medications was decreased by at least 60%. Limitation The model did not account for effects of early versus later intensive glycemic control. Conclusion Health policies and clinical programs that encourage an individualized approach to glycemic control for U.S. adults with type 2 diabetes reduce costs and increase quality of life compared with uniform intensive control. Additional research is needed to confirm the risks and benefits of this strategy. Primary Funding Source National Institute of Diabetes and Digestive and Kidney Diseases.

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Section: Methodsmentioning

confidence: 99%

Section: Ukpds Om2 Internal and External Validationmentioning

confidence: 99%

Section: Ukpds Om2 Internal and External Validationmentioning

confidence: 99%

Section: Ukpds Om2 Internal and External Validationmentioning

confidence: 99%

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Individualized Glycemic Control for U.S. Adults With Type 2 Diabetes

et al. 2017

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“…To our knowledge, none of the published, diabetes diseasestate simulation models, [20][21][22][23][24][25][26][27][28][29][30][31][32][33] including the recently published UKPDS Outcomes Model 2, 34 takes into account currently available medical and surgical treatments. For the general population, various CHD policy models exist.…”

mentioning

confidence: 99%

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

Brändle

Brown

et al. 2015

Diabetes Technology & Therapeutics

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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.

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Decision models in type 2 diabetes mellitus: A systematic review

Bao

Chen

et al. 2021

Acta Diabetol

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Aims To reduce the burden of type 2 diabetes (T2DM), the disease decision model plays a vital role in supporting decision-making. Currently, there is no comprehensive summary and assessment of the existing decision models for T2DM. The objective of this review is to provide an overview of the characteristics and capabilities of published decision models for T2DM. We also discuss which models are suitable for different study demands. Materials and methods Four databases (PubMed, Web of Science, Embase, and the Cochrane Library) were electronically searched for papers published from inception to August 2020. Search terms were: “Diabetes-Mellitus, Type 2”, “cost-utility”, “quality-of-life”, and “decision model”. Reference lists of the included studies were manually searched. Two reviewers independently screened the titles and abstracts following the inclusion and exclusion criteria. If there was insufficient information to include or exclude a study, then a full-text version was sought. The extracted information included basic information, study details, population characteristics, basic modeling methodologies, model structure, and data inputs for the included applications, model outcomes, model validation, and uncertainty. Results Fourteen unique decision models for T2DM were identified. Markov chains and risk equations were utilized by four and three models, respectively. Three models utilized both. Except for the Archimedes model, all other models (n = 13) implemented an annual cycle length. The time horizon of most models was flexible. Fourteen models had differences in the division of health states. Ten models emphasized macrovascular and microvascular complications. Six models included adverse events. Majority of the models (n = 11) were patient-level simulation models. Eleven models simulated annual changes in risk factors (body mass index, glycemia, HbA1c, blood pressure (systolic and/or diastolic), and lipids (total cholesterol and/or high-density lipoprotein)). All models reported the main data sources used to develop health states of complications. Most models (n = 11) could deal with the uncertainty of models, which were described in varying levels of detail in the primary studies. Eleven studies reported that one or more validation checks were performed. Conclusions The existing decision models for T2DM are heterogeneous in terms of the level of detail in the classification of health states. Thus, more attention should be focused on balancing the desired level of complexity against the required level of transparency in the development of T2DM decision models.

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Validation of the IHE Cohort Model of Type 2 Diabetes and the Impact of Choice of Macrovascular Risk Equations

Cited by 36 publications

References 34 publications

Individualized Glycemic Control for U.S. Adults With Type 2 Diabetes

Individualized Glycemic Control for U.S. Adults With Type 2 Diabetes

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

Decision models in type 2 diabetes mellitus: A systematic review

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