Abstract
ObjectivesThe UK Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS-OM) developed using 30-year (1977-2007) data from the UKPDS is widely used for health outcomes’ projections and economic evaluations of therapies for patients with type 2 diabetes (T2D). Nevertheless, its reliability for contemporary UK T2D populations is unclear. We assessed the performance of version 2 of the model (UKPDS-OM2) using data from A Study of Cardiovascular Events in Diabetes (ASCEND), which followed participants with diabetes in the UK between 2005 and 2017. MethodsThe UKPDS-OM2 was used to predict the occurrence of myocardial infarction (MI), other ischemic heart disease, stroke, cardiovascular (CV) death, and other death among the 14 569 participants with T2D in ASCEND, all without previous CV disease at study entry. Calibration (comparison of predicted and observed year-on-year cumulative incidence over 10 years) and discrimination (c-statistics) of the model were assessed for each endpoint. The percentage error in event rates at year 7 (mean duration of follow up) was used to quantify model bias. ResultsThe UKPDS-OM2 substantially overpredicted MI, stroke, CV death, and other death over the 10-year follow-up period (by 149%, 42%, 269%, and 52%, respectively, at year 7). Discrimination of the model for MI and other ischemic heart disease (c-statistics 0.58 and 0.60, respectively) was poorer than that for other outcomes (c-statistics ranging from 0.66 to 0.72). ConclusionsThe UKPDS-OM2 substantially overpredicted risks of key CV outcomes and death in people with T2D in ASCEND. Appropriate adjustments or a new model may be required for assessments of long-term effects of treatments in contemporary T2D cohorts.
Highlights
Decision-analytic modeling is commonly used in healthcare to synthesize clinical and economic data to evaluate health outcomes and costs of healthcare interventions.[1]
These models are used as decision-support tools to inform decisions on reimbursement of new drugs, allocation of resources between competing interventions, estimation of the budget impact of new interventions, and other health policy questions. Such models can be used to extrapolate short-term clinical trial data to evaluate the long-term effects of interventions on health outcomes and healthcare costs and to inform cost-effectiveness analyses.[2,3]. This is especially relevant for type 2 diabetes (T2D), a chronic disease associated with a range of macrovascular and microvascular complications that develop over a lifetime and are major drivers of long-term survival, deterioration in quality of life, and healthcare costs.[4,5,6]
Among A Study of Cardiovascular Events in Diabetes (ASCEND) participants, only those with T2D formed the validation cohort because UK Prospective Diabetes Study (UKPDS)-OM2 was developed for predicting outcomes in patients with T2D
Summary
Decision-analytic modeling is commonly used in healthcare to synthesize clinical and economic data to evaluate health outcomes and costs of healthcare interventions.[1]. Such models can be used to extrapolate short-term clinical trial data to evaluate the long-term effects of interventions on health outcomes and healthcare costs and to inform cost-effectiveness analyses.[2,3] This is especially relevant for type 2 diabetes (T2D), a chronic disease associated with a range of macrovascular and microvascular complications that develop over a lifetime and are major drivers of long-term survival, deterioration in quality of life, and healthcare costs.[4,5,6] such decision-analytic models need to provide accurate outcome estimates and, warrant validation, as recommended by published guidelines.[2,7] External validation of a model is important to ensure its suitability for patient cohorts other than the one used to develop the model.[8,9]
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