• Sávio C Passos, Stela M de Jezus Castro, Adriene Stahlschmidt, Paulo C da Silva Neto, Paulo J Irigon Pereira, da Cunha LealPlínioPHospital São Domingos, São Luís, Brazil., Maristela B Lopes, Luiz F Dos Reis Falcão, Vera L F de Azevedo, Eric B Lineburger, Florentino F Mendes, Ramon M Vilela, Liana M T de Araújo Azi, Fabrício D Antunes, Leandro G Braz, and Luciana C Stefani.
• Graduate Program in Medical Sciences, School of Medicine, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Brazil; Anesthesiology and Perioperative Medicine Service, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil.
• Br J Anaesth. 2024 Jul 1; 133 (1): 125134125-134.

BackgroundSurgical risk stratification is crucial for enhancing perioperative assistance and allocating resources efficiently. However, existing models may not capture the complexity of surgical care in Brazil. Using data from various healthcare settings nationwide, we developed a new risk model for 30-day in-hospital mortality (the Ex-Care BR model).MethodsA retrospective cohort study was conducted in 10 hospitals from different geographic regions in Brazil. Data were analysed using multilevel logistic regression models. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), Brier score, and calibration plots. Derivation and validation cohorts were randomly assigned.ResultsA total of 107,372 patients were included, and 30-day in-hospital mortality was 2.1% (n=2261). The final risk model comprised four predictors related to the patient and surgery (age, ASA physical status classification, surgical urgency, and surgical size), and the random effect related to hospitals. The model showed excellent discrimination (AUROC=0.93, 95% confidence interval [CI], 0.93-0.94), calibration, and overall performance (Brier score=0.017) in the derivation cohort (n=75,094). Similar results were observed in the validation cohort (n=32,278) (AUROC=0.93, 95% CI, 0.92-0.93).ConclusionsThe Ex-Care BR is the first model to consider regional and organisational peculiarities of the Brazilian surgical scene, in addition to patient and surgical factors. It is particularly useful for identifying high-risk surgical patients in situations demanding efficient allocation of limited resources. However, a thorough exploration of mortality variations among hospitals is essential for a comprehensive understanding of risk.Clinical Trial RegistrationNCT05796024.Copyright © 2024 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.

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