Are population-based diabetes models useful for individual risk estimation?

Background: predictive models are increasingly used in guidelines and informed decision-making interventions. 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. 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. results: the diabetes phd model predicted substantially higher risks of mi and stroke in most cases,particularly for stroke and for 20-year outcomes. in contrast,the ukpds model predicted risks of amputation and blindness ranging from 2-fold to infinitely higher than the diabetes phd model. predictions for renal failure all differed by more than 2-fold but in a complicated pattern varying by time frame and specific risk factors. 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. a substantial majority of the diabetes phd point estimates fell outside of the ukpds outcomes model's 95% cis. conclusions: these models produced markedly different predictions. patients and providers considering risk estimates from such models need to understand their substantial uncertainty and risk of misclassification.