Validation of the CORE Diabetes Model against epidemiological and clinical studies.

CORE--Center for Outcomes Research, Basel, Switzerland.
Current Medical Research and Opinion (Impact Factor: 2.37). 09/2004; 20 Suppl 1:S27-40. DOI: 10.1185/030079904X2006
Source: PubMed

ABSTRACT The aim of this study was to assess the validity of the CORE Diabetes Model by comparing results from model simulations with observed outcomes from published epidemiological and clinical studies in type 1 and type 2 diabetes.
A total of 66 second- (internal) and third- (external) order validation analyses were performed across a range of complications and outcomes simulated by the CORE Diabetes Model (amputation, cataract, hypoglycaemia, ketoacidosis, macular oedema, myocardial infarction, nephropathy, neuropathy, retinopathy, stroke and mortality). Published studies were reproduced in the model by recreating cohorts in terms of demographics, baseline risk factors and complications, treatment patterns and patient management strategies, and simulating the progress of the cohort to an equivalent time horizon.
Correlation analysis on results from 66 validation simulations produced an R2 value of 0.9224 (perfect fit = 1). A correlation plot of published study data versus values simulated by the CORE Diabetes Model had a trend line with a gradient of 1.0187 (perfect fit = 1). Validation analyses in type 1 and type 2 diabetes were associated with R2 values of 0.9778 and 0.8861 respectively. Correlation of second-order validation analyses (model predictions versus observed outcomes in studies used to construct the model) produced an R2 value of 0.9574, and the value for third-order analyses (model predictions versus observed outcomes in studies not used to construct the model) was 0.9023.
The CORE Diabetes Model provides an accurate representation of patient outcomes when compared to 66 studies of diabetes and its complications. Model flexibility ensures it can be used to compare diabetes management strategies in different cohorts across a variety of clinical settings.

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    ABSTRACT: BackgroundTo evaluate the long-term cost-effectiveness of liraglutide versus sitagliptin or exenatide, added to oral antidiabetic drug mono- or combination therapy respectively, in patients with Type 2 diabetes in Greece.MethodsThe CORE Diabetes Model, a validated computer simulation model, was adapted to the Greek healthcare setting. Patient and intervention effects data were gathered from a clinical trial comparing liraglutide 1.2 mg once daily vs. sitagliptin 100 mg once daily, both combined with metformin, and a clinical trial comparing liraglutide 1.8 mg once daily vs. exenatide 10 μg twice daily, both as add-on to metformin, glimepiride or both. Direct costs were reported in 2013 Euros and calculated based on published and local sources. All future outcomes were discounted at 3.5% per annum, and the analysis was conducted from the perspective of a third-party payer in Greece.ResultsOver a patient’s lifetime, treatment with liraglutide 1.2 mg vs. sitagliptin drove a mean increase in discounted life expectancy of 0.13 (SD 0.23) years and in discounted quality-adjusted life expectancy of 0.19 (0.16) quality-adjusted life years (QALYs), whereas therapy with liraglutide 1.8 mg vs. exenatide yielded increases of 0.14 (0.23) years and 0.19 (0.16) QALYs respectively. As regards lifetime direct costs, liraglutide 1.2 mg resulted in greater costs of €2797 (€1468) versus sitagliptin, and so did liraglutide 1.8 mg compared with exenatide (€1302 [€1492]). Liraglutide 1.2 and 1.8 mg doses were associated with incremental cost effectiveness ratios of €15101 and €6818 per QALY gained, respectively.ConclusionsLiraglutide is likely to be a cost-effective option for the treatment of Type 2 diabetes in a Greek setting.
    BMC Health Services Research 09/2014; 14(1):419. DOI:10.1186/1472-6963-14-419 · 1.66 Impact Factor
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    ABSTRACT: Background Health-economic models of diabetes are complex since the disease is chronic, progressive and there are many diabetic complications. External validation of these models helps building trust and satisfies demands from decision makers. We evaluated the external validity of the IHE Cohort Model of Type 2 Diabetes; the impact of using alternative macrovascular risk equations; and compared the results to those from microsimulation models. Methods The external validity of the model was analysed from 12 clinical trials and observational studies by comparing 167 predicted microvascular, macrovascular and mortality outcomes to the observed study outcomes. Concordance was examined using visual inspection of scatterplots and regression-based analysis, where an intercept of 0 and a slope of 1 indicate perfect concordance. Additional subgroup analyses were conducted on ‘dependent’ vs. ‘independent’ endpoints and microvascular vs. macrovascular vs. mortality endpoints. Results Visual inspection indicates that the model predicts outcomes well. The UKPDS-OM1 equations showed almost perfect concordance with observed values (slope 0.996), whereas Swedish NDR (0.952) and UKPDS-OM2 (0.899) had a slight tendency to underestimate. The R2 values were uniformly high (>0.96). There were no major differences between ‘dependent’ and ‘independent’ outcomes, nor for microvascular and mortality outcomes. Macrovascular outcomes tended to be underestimated, most so for UKPDS-OM2 and least so for NDR risk equations. Conclusions External validation indicates that the IHE Cohort Model of Type 2 Diabetes has predictive accuracy in line with microsimulation models, indicating that the trade-off in accuracy using cohort simulation might not be that large. While the choice of risk equations was seen to matter, each were associated with generally reasonable results, indicating that the choice must reflect the specifics of the application. The largest variation was observed for macrovascular outcomes. There, NDR performed best for relatively recent and well-treated patients, while UKPDS-OM1 performed best for the older UKPDS cohort.
    PLoS ONE 10/2014; 9(10):e110235. DOI:10.1371/journal.pone.0110235 · 3.53 Impact Factor
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    Diabetes Research and Clinical Practice 09/2014; 106(2). DOI:10.1016/j.diabres.2014.08.024 · 2.54 Impact Factor