Type 2 diabetes mellitus and related complications are prevalent and result in heavy economic and disease burdens both within the US healthcare system and globally.

This study developed predictive risk models for coronary heart disease, heart failure and stroke tailored to an integrated delivery health system patient population with type 2 diabetes and compared the performance of the locally fitted model to the QRisk3, RECODE and ASCVD risk equations.

The locally fitted model performed significantly better than the other three models for predicting incident cardiovascular disease in the health system population.

Use of population-specific clinical data and application of machine learning methods can transform existing general predictive models to locally fitted models that perform better in local populations.