Few and feasible preoperative variables can identify high-risk surgical patients: derivation and validation of the Ex-Care risk model

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...

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Bibliographic Details
Published in:	British journal of anaesthesia : BJA Vol. 126; no. 2; p. 525
Main Authors:	Gutierrez, Claudia S, Passos, Sávio C, Castro, Stela M J, Okabayashi, Lucas S M, Berto, Mariana L, Lorenzen, Marina B, Caumo, Wolnei, Stefani, Luciana C
Format:	Journal Article
Language:	English
Published:	England 01-02-2021
Subjects:	Adolescent Adult Aged Aged, 80 and over Brazil Clinical Decision-Making Decision Support Techniques Feasibility Studies Female Hospital Mortality Humans Male Middle Aged Predictive Value of Tests Reproducibility of Results Risk Assessment Risk Factors Surgical Procedures, Operative - mortality Treatment Outcome Young Adult Brazil validation model mortality preoperative risk assessment risk assessment postoperative mortality surgery
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Summary:	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. 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). 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). 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.
ISSN:	1471-6771
DOI:	10.1016/j.bja.2020.09.036