However, the differences in the estimates were modest and there was no other way to model quitting smoking without relapse. Here, we report models in which we forced the values to remain constant for 40 years, which tends to increase estimates of benefits from risk factor control by preventing regression to the mean. The UKPDS outcomes model allows the researcher to specify risk factor values for up to the subsequent 40 years, or the program can model them based on the current values (and project regression toward the mean). 10 We report here a comparison of output from these 2 models for a set of test cases that we undertook when trying to decide which model to use in a study of communication with diabetic patients about their multiple risks and risk reduction options. The 2 most readily available models that can be used to predict risks of multiple outcomes for persons with diabetes and model the effects of risk factor modification are the United Kingdom Prospective Diabetes Study (UKPDS) outcomes model 8 and the Diabetes Personal Health Decisions (PHD) tool, available from the American Diabetes Association 9 and based on the Archimedes Model. If patients are to make informed choices about where to focus their efforts for risk reduction, access to accurate, personal estimates of risk and effects of risk reduction activities is a logical first step. Historically, diabetes treatment has been very glucocentric, 2, 3 despite the fact that premature morbidity and mortality in diabetes are more strongly affected by cardiovascular risk factors than glucose control. 1 Diabetes increases the risks of multiple adverse health outcomes and is frequently accompanied by other cardiovascular risk factors. The prevalence of diabetes among the American population has increased dramatically in the last 2 decades. Patients and providers considering risk estimates from such models need to understand their substantial uncertainty and risk of misclassification. A substantial majority of the Diabetes PHD point estimates fell outside of the UKPDS outcomes model's 95% CIs.Ĭonclusions:These models produced markedly different predictions. Relative to their predictions for white men, the UKPDS model predicted much lower MI and stroke risks for women and Afro-Caribbean men than the Diabetes PHD model did for women and black men. Predictions for renal failure all differed by more than 2-fold but in a complicated pattern varying by time frame and specific risk factors. In contrast, the UKPDS model predicted risks of amputation and blindness ranging from 2-fold to infinitely higher than the Diabetes PHD model. Results:The Diabetes PHD model predicted substantially higher risks of MI and stroke in most cases, particularly for stroke and for 20-year outcomes. Methods:Ours was a simulation study comparing 10-year and 20-year model predictions for risks of myocardial infarction (MI), stroke, amputation, blindness, and renal failure for representative test cases. We compared predictions from 2 prominent models for diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) outcomes model and the Archimedes-based Diabetes Personal Health Decisions (PHD) model.

Background:Predictive models are increasingly used in guidelines and informed decision-making interventions.

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