Using individualized predictive disease modeling to identify patients with the potential to benefit from a disease...

Diabetes is an increasing health problem, but efforts to handle this pandemic by disease management programs (DMP) have shown conflicting results. Our hypothesis is that, in addition to a program's content and setting, the choice of the right patients is crucial to a program's efficacy and effectiveness. We used individualized predictive disease modeling (IPDM) on a cohort of 918 patients with type 2 diabetes to identify those patients with the greatest potential to benefit from inclusion in a DMP. A portion of the patients (4.7%) did not have even a theoretical potential for an increase in life expectancy and would therefore be unlikely to benefit from a DMP. Approximately 16.1% had an increase in life expectancy of less than half a year. Stratification of the entire cohort by surrogate parameters like preventable 10-year costs or gain in life expectancy was much more effective than stratification by classical clinical parameters such as high HbA1c level. Preventable costs increased up to 50.6% (or 1,010 per patient (1 = US dollars 1.28), p < 0.01) and life expectancy increased up to 54.8% (or 2.3 years, p < 0.01). IPDM is a valuable strategy to identify those patients with the greatest potential to avoid diabetes-related complications and thus can improve the overall effectiveness and efficacy of DMPs for diabetes mellitus.