The Potential Value of Identifying Type 2 Diabetes Subgroups in the CPRD Cohort for Guiding Intensive Treatment: A HTx Study Comparing Novel Data-Driven Clustering to Traditional Risk-Driven Subgroups
Speaker(s)
Li X1, Wang J2, Altunkaya J3, Somolinos Simon FJ4, Van Giessen A5, Jiu L6, Tapia-Galisteo J4, García-Sáez G4, Hernando ME4, Leal J3, Goettsch W2, Feenstra T7
1Utrecht University, Groningen, Netherlands, 2Utrecht University, Utrecht, Netherlands, 3University of Oxford, Oxford, Oxford, UK, 4Universidad Politécnica de Madrid, Madrid, Madrid, Spain, 5National Institute for Public Health and the Environment, Bilthoven, Utrecht, Netherlands, 6Utrecht University, Amersfoort, UT, Netherlands, 7University of Groningen, Groningen, Groningen, Netherlands
Presentation Documents
OBJECTIVES: To estimate the impact on lifetime health and economic outcomes of different methods of stratifying individuals with type 2 diabetes, followed by guideline-based treatment intensification, targeting BMI and LDL in addition to HbA1c.
METHODS: We divided 90,374 newly diagnosed individuals from the Clinical Practice Research Datalink (CPRD) into three data-driven clustering subgroups (based on unsupervised learning for age at diagnosis, BMI, HbA1c, and metabolic score for insulin resistance), as well as four risk-driven subgroups (based on fixed cut-offs for HbA1c and the risk of cardiovascular disease according to guidelines). The UKPDS Outcomes Model 2 estimated discounted expected lifetime complication costs and quality-adjusted life-years (QALYs) for each subgroup and across all individuals. Gains from treatment intensification were compared to “care-as-usual” as observed in CPRD. To alleviate the computational load of the simulations, a random sample of 2,000 individuals was selected.
RESULTS: Under care-as-usual, prognosis in the HTx data-driven subgroups ranged from 9.78 to 12.31 QALYs. Prognosis in the risk-driven subgroups ranged from 6.43 to 12.74 QALYs. Compared to homogenous type 2 diabetes, treatment for individuals in high-risk subgroups could cost up to 22% and 62% more and still be cost-effective for data-driven and risk-driven subgroups respectively. Targeting BMI and LDL in addition to HbA1c might deliver up to more than ten-fold increases in QALYs gained.
CONCLUSIONS: Risk-driven subgroups better discriminated regarding prognosis. Both stratification methods supported stratified treatment intensification, with the risk-driven subgroups being better in identifying individuals with the most potential to benefit from intensive treatment. Irrespective of stratification approach, better cholesterol and weight control showed substantial potential for health gains.
Code
EE627
Topic
Economic Evaluation, Methodological & Statistical Research, Study Approaches
Topic Subcategory
Cost-comparison, Effectiveness, Utility, Benefit Analysis, Decision Modeling & Simulation
Disease
Diabetes/Endocrine/Metabolic Disorders (including obesity), No Additional Disease & Conditions/Specialized Treatment Areas