Potential Value of Identifying Type 2 Diabetes Subgroups for Guiding Intensive Treatment: A Comparison of Novel Data-Driven Clustering With Risk-Driven Subgroups
Authors' conclusion
Risk-driven subgroups better discriminated prognosis. Both stratification methods supported stratified treatment intensification, with the risk-driven subgroups being somewhat 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.
Study context
- Population
- Newly diagnosed individuals with T2D from the Hoorn Diabetes Care System (DCS) cohort
- Participants
- 2,935 participants
- Exposure
- Data-driven clustering (RHAPSODY) vs risk-driven subgroups for guiding treatment intensification vs care as usual
- Outcome
- Discounted expected lifetime complication costs and QALYs
- Follow-up
- Lifetime
Methodological detailssimulation using the UKPDS Outcomes Model 2 in a cohort, UK Prospective Diabetes Study Outcomes Model 2, lifetime horizon, discounted costs and quality-adjusted life-years (QALYs), sensitivity analysis based on Ahlqvist subgroups
- Simulation using the UKPDS Outcomes Model 2 in a cohort
- UK Prospective Diabetes Study Outcomes Model 2
- Lifetime horizon
- Discounted costs and QALYs
- Sensitivity analysis based on Ahlqvist subgroups
Main finding
As reported in the abstract
Prognosis range, RHAPSODY data-driven subgroups
7.9 to 12.6 QALYs
Prognosis range, risk-driven subgroups
6.8 to 12.0 QALYs
Vs homogenous T2D, high-risk subgroup treatment could cost more and still be cost effective: data-driven subgroups
22.0%
Vs homogenous T2D, high-risk subgroup treatment could cost more and still be cost effective: risk-driven subgroups
25.3%
Observational data: associations, not effects.
Further findings
Targeting BMI and LDL in addition to HbA1c
- Might deliver up to 10-fold increases in QALYs gained
Original publication
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