An Approach for Combining Clinical Judgment with Machine Learning to Inform Medical Decision Making: Analysis of Nonemergency Surgery Strategies for Acute Appendicitis in Patients with Multiple Long-Term Conditions.

Background: Machine learning (ML) methods can identify complex patterns of treatment effect heterogeneity. However, before ML can help to personalize decision making, transparent approaches must be developed that draw on clinical judgment. We develop an approach that combines clinical judgment with ML to generate appropriate comparative effectiveness evidence for informing decision making.

Critical care unit bed availability and postoperative outcomes: a multinational cohort study.

Background: Critical care beds are a limited resource, yet research indicates that recommendations for postoperative critical care admission based on patient-level risk stratification are not followed. It is unclear how prioritisation decisions are made in real-world settings and the effect of this prioritisation on outcomes.

Methods: This was a prespecified analysis of an observational cohort study of adult patients undergoing inpatient surgery, conducted in 274 hospitals across the UK and Australasia during 2017.

Risk factors for complications after emergency surgery for paediatric appendicitis: a national prospective observational cohort study.

Appendicectomy is a common procedure in children with a low risk of mortality, however, complication rates and risk factors are largely unknown. This study aimed to characterise the incidence and epidemiology of postoperative complications in children undergoing appendicectomy in the UK. This multicentre prospective observational cohort study, which included children aged 1-16 y who underwent surgery for suspected appendicitis, was conducted between November 2019 and January 2022.