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.

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.

Lung Metabolic Activation as an Early Biomarker of Acute Respiratory Distress Syndrome and Local Gene Expression Heterogeneity.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is an inflammatory condition comprising diffuse lung edema and alveolar damage. ARDS frequently results from regional injury mechanisms. However, it is unknown whether detectable inflammation precedes lung edema and opacification and whether topographically differential gene expression consistent with heterogeneous injury occurs in early ARDS. The authors aimed to determine the temporal relationship between pulmonary metabolic activation and density in a large animal model of early ARDS and to assess gene expression in differentially activated regions. METHODS: The authors produced ARDS in sheep with intravenous lipopolysaccharide (10 ng ⋅ kg ⋅ h) and mechanical ventilation for 20 h. Using positron emission tomography, the authors assessed regional cellular metabolic activation with 2-deoxy-2-[(18)F]fluoro-D-glucose, perfusion and ventilation with NN-saline, and aeration using transmission scans. Species-specific microarray technology was used to assess regional gene expression. RESULTS: Metabolic activation preceded detectable increases in lung density (as required for clinical diagnosis) and correlated with subsequent histologic injury, suggesting its predictive value for severity of disease progression. Local time courses of metabolic activation varied, with highly perfused and less aerated dependent lung regions activated earlier than nondependent regions. These regions of distinct metabolic trajectories demonstrated differential gene expression for known and potential novel candidates for ARDS pathogenesis. CONCLUSIONS: Heterogeneous lung metabolic activation precedes increases in lung density in the development of ARDS due to endotoxemia and mechanical ventilation. Local differential gene expression occurs in these early stages and reveals molecular pathways relevant to ARDS biology and of potential use as treatment targets.