Search

Copyright of JBES: Brazilian Journal of Health Economics / Jornal Brasileiro de Economia da Saúde is the property of JBES: Brazilian Journal of Health Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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). 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. 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). 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. NCT05796024. [ABSTRACT FROM AUTHOR]

Background: The development of feasible preoperative risk tools is desirable, especially for low-middle income countries with limited resources and complex surgical settings. This study aimed to derive and validate a preoperative risk model (Ex-Care model) for postoperative mortality and compare its performance with current risk tools., Methods: A multivariable logistic regression model predicting in-hospital mortality was developed using a large Brazilian surgical cohort. Patient and perioperative predictors were considered. Its performance was compared with the Charlson comorbidity index (CCI), Revised Cardiac Risk Index (RCRI), and the Surgical Outcome Risk Tool (SORT)., Results: The derivation cohort included 16 618 patients. In-hospital death occurred in 465 patients (2.8%). Age, with adjusted splines, degree of procedure (major vs non-major), ASA physical status, and urgency were entered in a final model. It showed high discrimination with an area under the receiver operating characteristic curve (AUROC) of 0.926 (95% confidence interval [CI], 0.91-0.93). It had superior accuracy to the RCRI (AUROC, 0.90 vs 0.76; P<0.01) and similar to the CCI (0.90 vs 0.82; P=0.06) and SORT models (0.90 vs 0.92; P=0.2) in the temporal validation cohort of 1173 patients. Calibration was adequate in both development (Hosmer-Lemeshow, 9.26; P=0.41) and temporal validation cohorts (Hosmer-Lemeshow 5.29; P=0.71)., Conclusions: The Ex-Care risk model proved very efficient at identifying high-risk surgical patients. Although multicentre studies are needed, it should have particular value in low resource settings to better inform perioperative health policy and clinical decision-making., (Copyright © 2020 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.)

Study Objective: Studying postoperative in-hospital mortality is crucial to the understanding of the perioperative process failures and to the implementation of strategies to improve patient outcomes. We intend to classify the causes of perioperative deaths up to 30 days after procedures requiring anesthesia and to evaluate the risk factors for early (48 h) or late (30 day) mortality., Design: Retrospective cohort study., Setting: A quaternary University Hospital from South Brazil., Patients: The information related to the perioperative care was collected from surgeries performed between January 2012 and December 2011., Interventions: None (observational study)., Measurements: Three anesthesiologists classified the causes of deaths according to the ANZCA (Australian and New Zealand College of Anesthetists) classification, used in the report of Anesthesia-Related Mortality in Australia since 1985, which defines eight death categories. The risk factors for early or late death were analyzed in a regression model., Main Results: 11.562 surgeries were performed, with a mortality incidence of 2.75% within 30 days (319 deaths). Most deaths were inevitable (50.7%), as they were related to advanced illnesses and would occur regardless of anesthetic or surgical procedures. The second most common cause was related to surgical complications (25%). The death rate having anesthesia as a likely contributor was 1.72:10.000 procedures, and as a potential contributor 7.78:10.000. These deaths occurred significantly earlier (<48 h) when compared to deaths from other causes. Transoperative vasopressor, extremes of age and out-of-hour surgery were independent variables associated to early deaths., Conclusions: The study confirms that postoperative mortality in which anesthesia was involved occurred earlier in the perioperative period. In addition, it was revealed that this involvement of anesthesia as a morbidity contributor shows higher frequency when considering the anesthesiologist perioperative role, and when assessing the mortality in the long term (30 days)., (Copyright © 2018 Elsevier Inc. All rights reserved.)