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

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.

≥69 mmol/mol), all-cause mortality, major adverse cardiovascular events or heart failure (MACE-HF) outcomes, renal progression, and microvascular complications using Cox proportional hazards regression adjusting for relevant demographic and clinical covariates.