Development and validation of the Ex-Care BR model: a multicentre initiative for identifying Brazilian surgical patients at risk of 30-day in-hospital mortality - Passos, Sávio C.; de Jezus Castro, Stela M.; Stahlschmidt, Adriene; da Silva Neto, Paulo C.; Irigon Pereira, Paulo J.; da Cunha Leal, Plínio; Lopes, Maristela B.; dos Reis Falcão, Luiz F.; de Azevedo, Vera L.F.; Lineburger, Eric B.; Mendes, Florentino F.; Vilela, Ramon M.; et al. (Elsevier, 2024)
Background: Surgical 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). Methods: A 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. Results: A 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). Conclusions: The 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 registration: NCT05796024.
Keywords: mortality; perioperative death; postoperative outcome; prediction model; risk assessment; risk factors