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Evolving the UKPDS-OM2: Advancing Predictions and Generalizability to Better Support T2DM Management

  • Chronic non-communicable diseases

Background

The UKPDS Outcomes Model 2 (UKPDS-OM2) is the diabetes simulation model preferred by the National Institute for Health and Care Excellence (NICE) and is widely used internationally to inform reimbursement decisions. However, it was estimated from data collected between 1977 to 2007 and may have three growing limitations: it overestimates contemporary cardiovascular and mortality risks because it does not capture secular improvements in diabetes care; its trial-based derivation cohort may not represent routine-care patients; and it treats type 2 diabetes as homogeneous, despite evidence that data-driven subgroups differ in risk and treatment response.

Approach

Using up to 44 years of UKPDS follow-up data (1977 to 2021), the project addresses these gaps in three strands. The first integrates secular trends (eg calendar year) into the UKPDS-OM2 risk equations and quantifies the impact on simulation and cost-effectiveness. The second investigates generalisability by estimating standardised mortality ratios (SMRs) against UK general population life tables and benchmarking against a meta-analysis of routine-care SMRs to quantify the "healthy participant effect". The third replicates the Ahlqvist data-driven subgroups in UKPDS, characterises their treatment and legacy effects, and tests whether incorporating subgroup membership improves the predictive and cost-effectiveness performance of UKPDS-OM2.

Findings

Preliminary findings suggest that incorporating secular trends meaningfully alters projected outcomes and cost-effectiveness conclusions, with implications for reimbursement decisions. The SMR analyses indicate evidence consistent with a healthy participant effect in the early years of follow-up, varying by participant characteristics. The subgroups analyses are expected to clarify whether data-driven subgroups show distinct long-term risk trajectories, differential treatment responses, and durable legacy effects, and whether incorporating subgroup membership materially improves UKPDS-OM2.

Impact

Together, this project indicates that ignoring temporal change, trial selection effects, and population heterogeneity can systematically bias the cost-effectiveness estimates underpinning reimbursement decisions. The methods developed are parsimonious and transferable to other disease-area simulation models, strengthening the evidence base for NICE and other decision-makers.