OBJECTIVE 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. RESEARCH DESIGN AND METHODS We divided 2,935 newly diagnosed individuals from the Hoorn Diabetes Care System (DCS) cohort into five Risk Assessment and Progression of Diabetes (RHAPSODY) data-driven clustering subgroups (based on age, BMI, HbA1c, C-peptide, and HDL) and four risk-driven subgroups by using fixed cutoffs for HbA1c and risk of cardiovascular disease based on guidelines. The UK Prospective Diabetes Study 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 with care as usual as observed in DCS. A sensitivity analysis was conducted based on Ahlqvist subgroups. RESULTS Under care as usual, prognosis in the RHAPSODY data-driven subgroups ranged from 7.9 to 12.6 QALYs. Prognosis in the risk-driven subgroups ranged from 6.8 to 12.0 QALYs. Compared with homogenous type 2 diabetes, treatment for individuals in the high-risk subgroups could cost 22.0% and 25.3% 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 10-fold increases in QALYs gained. CONCLUSIONS 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.

Aim Diabetes health economic (HE) models play important roles in decision making. For most HE models of diabetes 2 diabetes (T2D), the core model concerns the prediction of complications. However, reviews of HE models pay little attention to the incorporation of prediction models. The objective of the current review is to investigate how prediction models have been incorporated into HE models of T2D and to identify challenges and possible solutions. Methods PubMed, Web of Science, Embase, and Cochrane were searched from January 1, 1997, to November 15, 2022, to identify published HE models for T2D. All models that participated in The Mount Hood Diabetes Simulation Modeling Database or previous challenges were manually searched. Data extraction was performed by two independent authors. Characteristics of HE models, their underlying prediction models, and methods of incorporating prediction models were investigated. Results The scoping review identified 34 HE models, including a continuous-time object-oriented model (n = 1), discrete-time state transition models (n = 18), and discrete-time discrete event simulation models (n = 15). Published prediction models were often applied to simulate complication risks, such as the UKPDS (n = 20), Framingham (n = 7), BRAVO (n = 2), NDR (n = 2), and RECODe (n = 2). Four methods were identified to combine interdependent prediction models for different complications, including random order evaluation (n = 12), simultaneous evaluation (n = 4), the ‘sunflower method’ (n = 3), and pre-defined order (n = 1). The remaining studies did not consider interdependency or reported unclearly. Conclusions The methodology of integrating prediction models in HE models requires further attention, especially regarding how prediction models are selected, adjusted, and ordered.

Objective Sodium–glucose cotransporter 2 inhibitors (SGLT2i) have been shown to reduce the risk of cardiovascular complications, which largely drive diabetes’ health and economic burdens. Trial results indicated that SGLT2i are cost effective. However, these findings may not be generalizable to the real-world target population. This study aims to evaluate the cost effectiveness of SGLT2i in a routine care type 2 diabetes population that meets Dutch reimbursement criteria using the MICADO model. Methods Individuals from the Hoorn Diabetes Care System cohort ( N = 15,392) were filtered to satisfy trial inclusion criteria (including EMPA-REG, CANVAS, and DECLARE-TIMI58) or satisfy the current Dutch reimbursement criteria for SGLT2i. We validated a health economic model (MICADO) by comparing simulated and observed outcomes regarding the relative risks of events in the intervention and comparator arm from three trials, and used the validated model to evaluate the long-term health outcomes using the filtered cohorts’ baseline characteristics and treatment effects from trials and a review of observational studies. The incremental cost-effectiveness ratio (ICER) of SGLT2i, compared with care-as-usual, was assessed from a third-party payer perspective, measured in euros (2021 price level), using a discount rate of 4% for costs and 1.5% for effects. Results From Dutch individuals with diabetes in routine care, 15.8% qualify for the current Dutch reimbursement criteria for SGLT2i. Their characteristics were significantly different (lower HbA1c, higher age, and generally more preexisting complications) than trial populations. After validating the MICADO model, we found that lifetime ICERs of SGLT2i, when compared with usual care, were favorable (< €20,000/QALY) for all filtered cohorts, resulting in an ICER of €5440/QALY using trial-based treatment effect estimates in reimbursed population. Several pragmatic scenarios were tested, the ICERs remained favorable. Conclusions Although the Dutch reimbursement indications led to a target group that deviates from trial populations, SGLT2i are likely to be cost effective when compared with usual care. Supplementary Information The online version contains supplementary material available at 10.1007/s40273-023-01286-3.