Predicting opioid consumption after surgical discharge: a multinational derivation and validation study using a foundation model

Abstract

Opioids are frequently overprescribed after surgery. We applied a tabular foundation model to predict the risk of post-discharge opioid consumption. The model was trained and internally validated on an 80:20 training/test split of the 'Opioid PrEscRiptions and usage After Surgery' (ACTRN12621001451897p) study cohort, including adult patients undergoing general, orthopaedic, gynaecological and urological operations (n = 4267), with external validation in a distinct cohort of patients discharged after general surgical procedures (n = 826). The area under the receiver operator curve was 0.84 (95% confidence interval [CI] 0.81-0.88) at internal testing and 0.77 (95% CI 0.74-0.80) at external validation. Brier scores were 0.13 (95% CI 0.12-0.14) and 0.19 (95% CI 0.17-0.2). Patients with a <50% predicted risk of opioid consumption consumed a median of 0 oral morphine equivalents in the first week after surgery. Applying this model would reduce opioid prescriptions by 4.5% globally, and counterfactual modelling suggests without increasing time in severe pain (-4.3%, 95% CI -17.7 to 8.6).