Background Emergency surgery has poor outcomes with high mortality. Numerous studies have reported the risk factors for postoperative death in order to stratify risk and improve perioperative care; nevertheless, a predictive model based upon these risk factors is lacking. Objective We aimed to identify the risk factors of postoperative mortality and to construct a new model for predicting mortality and improving patient care. Methods We included adult patients undergoing emergency surgery at Srinagarind Hospital between January 2012 and December 2014. The patients were randomized: 80% to the Training group for model construction and 20% to the Validation group. Patient data were extracted from medical records and then analyzed using univariate and multivariate logistic regression. Results We recruited 758 patients, and the mortality rate was 14.5%. The Training group comprised 596 patients, and the Validation group comprised 162. Based upon a multivariate analysis in the Training group, we constructed a model to predict postoperative mortality—an Emergency Surgery Mortality (ESM) score based on the coefficient of each risk factor from the multivariate analysis. The ESM score comprised 7 risk factors, i.e., coagulopathy, ASA class 5, bicarbonate <15 mEq/L, heart rate >100/min, systolic blood pressure <90 mmHg, renal comorbidity, and general surgery, for a total score of 11. An ESM score ≥4 was predictive of postoperative mortality with an AUC of 0.83. The respective sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, positive predictive value, negative predictive value, and accuracy for an ESM score ≥4 predictive of postoperative mortality was 70.2%, 94.9%, 13.8, 0.3, 69.4%, 95.1%, and 91.4%. The performance of the ESM score in the Validation group was comparable. Conclusions An ESM score comprises 7 risk factors for a total score of 11. An ESM score ≥4 is predictive of postoperative mortality with a high AUC (0.83), sensitivity (70.2%), and specificity (94.9%). Four risk factors are preoperatively manageable for decreasing the probability of postoperative mortality and improving quality of patient care.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.