title: Using individualized predictive disease modeling to identify patients with the potential to benefit from a disease management program for diabetes mellitus.
abstract: 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.
authors.affiliation: Institute for Medical Informatics and Biostatistics, Basel, Switzerland. weber@imib.ch
authors.forename: Christian
authors.initials: C
authors.lastname: Weber
authors.forename: Kurt
authors.initials: K
authors.lastname: Neeser
mesh_terms: D003365:Costs and Cost Analysis; D048909:Diabetes Complications; D003924:Diabetes Mellitus, Type 2 / Q000191:economics / Q000628:therapy*; D019468:Disease Management; D005260:Female; D005858:Germany; D006801:Humans; D008297:Male; D008390:Markov Chains; D018579:Patient Selection; D011237:Predictive Value of Tests; D018709:Statistics, Nonparametric