A five-drug class model using routinely available clinical features to optimise prescribing in type 2 diabetes: a prediction model development and validation study

作者：John Dennis, Katherine G Young, Pedro Cardoso, Laura M. Güdemann, Andrew McGovern, Andrew Farmer, Rury R. Holman, N Sattar, Trevelyan J. McKinley, Ewan R. Pearson, Angus G. Jones, Beverley M. Shields, Andrew T. Hattersley · 发表于：The Lancet · 年份：2025 · DOI：10.1016/s0140-6736(24)02617-5 · 被引用次数：48 · 研究领域：Diabetes Treatment and Management、Diabetes, Cardiovascular Risks, and Lipoproteins、Advanced Causal Inference Techniques

Background Data to support individualised choice of optimal glucose-lowering therapy are scarce for people with type 2 diabetes. We aimed to establish whether routinely available clinical features can be used to predict the relative glycaemic effectiveness of five glucose-lowering drug classes. Methods We developed and validated a five-drug class model to predict the relative glycaemic effectiveness, in terms of absolute 12-month glycated haemoglobin (HbA 1c ), for initiating dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 receptor agonists, sodium–glucose co-transporter-2 inhibitors, sulfonylureas, and thiazolidinediones. The model used nine routinely available clinical features of people with type 2 diabetes at drug initiation as predictive factors (age, duration of diabetes, sex, and baseline HbA 1c , BMI, estimated glomerular filtration rate, HDL cholesterol, total cholesterol, and alanine aminotransferase). The model was developed and validated with observational data from England (Clinical Practice Research Datalink [CPRD] Aurum), in people with type 2 diabetes aged 18–79 years initiating one of the five drug classes between Jan 1, 2004, and Oct 14, 2020, with holdback validation according to geographical region and calendar period. The model was further validated in individual-level data from three published randomised drug trials in type 2 diabetes (TriMaster three-drug crossover trial and two parallel-arm trials [NCT00622284 and NCT01167881]). For validati...