Predicting opioid consumption after surgical discharge: a multinational derivation and validation study using a foundation model Chris Varghese, Luke Peters, Lorane Gaborit, William Xu, Kaviya Kalyanasundaram, et al. Npj Digital Medicine, 2025 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).
Contextual challenges in implementing artificial intelligence for healthcare in low-resource environments: insights from the SPEC-AI Nigeria trial Demilade A. Adedinsewo, Damilola Onietan, Andrea Carolina Morales-Lara, Serin Moideen Sheriff, Bosede B. Afolabi, et al. Frontiers in Cardiovascular Medicine, 2025 Nigeria is the most populous country in Africa with the highest gross domestic product (GDP) as of 2022. However, Nigeria is burdened by significant health challenges including an extremely high maternal mortality ratio, inadequate human resources, poor healthcare infrastructure, and population-level poverty rates as high as 40%. Nigeria also has the highest reported prevalence of peripartum cardiomyopathy worldwide which contributes to maternal mortality. Unfortunately, the diagnosis of peripartum cardiomyopathy is often delayed and mortality rates following diagnosis are extremely high (approximately 50%). Thus, there is a huge unmet need for simple, effective, and accessible solutions for cardiomyopathy detection in this population. To address maternal mortality through screening and early diagnosis, we designed and conducted a randomized controlled clinical trial (NCT05438576) of an artificial intelligence (AI) technology in Nigeria. The objective of the study was to evaluate the impact of AI-guided screening on cardiomyopathy detection in obstetric patients. The study findings showed AI-guided screening doubled the detection of cardiomyopathy (defined as left ventricular ejection fraction &lt;50%) when compared to usual care with a number needed to screen of 47. As we explore next steps in relation to deploying this technology for clinical use in Nigeria, we sought to gather contextual information and broadly share lessons learned from the recently completed trial. To that end, we convened a round table discussion with all study site investigators aimed at identifying site-specific contextual challenges related to the development and conduct of the study. The SPEC-AI Nigeria study is the first published randomized controlled clinical trial of a health AI intervention in Nigeria. Insights gained from this study can inform future AI intervention studies in clinical care, guide the development of implementation strategies to ensure effective interventions are successfully incorporated into clinical care, and provide a roadmap for key stakeholders to consider when evaluating AI-technologies for use in low-resource settings.
Patterns of opioid use after surgical discharge: a multicentre, prospective cohort study in 25 countries Anaesthesia, 2024 SummaryBackgroundExcessive opioid prescribing following surgery contributes to the growing opioid crisis. Prescribing practices are modifiable, yet data to guide appropriate prescription of opioids at surgical discharge remain sparse. This study aimed to evaluate factors associated with opioid consumption following discharge from surgery.MethodsAn international prospective multicentre cohort study was performed recruiting adult patients undergoing common general, orthopaedic, gynaecological and urological surgery, with follow‐up 7 days after discharge. The primary outcome measures were the quantities of prescribed and consumed opioids in oral morphine milligram equivalents. Descriptive and multivariable analyses were performed to investigate factors associated with the primary outcome measures.ResultsThis analysis included 4273 patients from 144 hospitals in 25 countries. Overall, 1311 (30.7%) patients were prescribed opioids at discharge. For those patients prescribed opioids, mean (SD) 179 (240) oral morphine milligram equivalents were prescribed, yet only 81 (145) oral morphine milligram equivalents were consumed within the first 7 days after discharge. An increased dose of opioids prescribed at discharge was associated with an increased dose of opioids consumed during the follow‐up period (β = 0.33 (95%CI 0.31–0.34), p < 0.001). The risk of prescribing more opioids than patients consumed increased as quantities of opioids prescribed at discharge exceeded 100 oral morphine milligram equivalents, independent of patient comorbidity, procedure and pain. Patients were prescribed more than twice the quantity of opioids they consumed in the first 7 days following discharge from surgery.ConclusionsOur data suggest that the current quantities of opioids provided at discharge exceed patient needs and may contribute to increasing community opioid use and circulation.
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