• Yinghui Huang, Lei Zhang, Guan Lian, Rixing Zhan, Rufu Xu, Yan Huang, Biswadev Mitra, Jun Wu, and Gaoxing Luo.
• Institute of Burn Research, Southwest Hospital, Third Military Medical University, Chongqing, China; Institute of Combined Injury, State Key Laboratory of Trauma, Burns and Combined Injury, Chongqing Engineering Research Center for Nanomedicine, College of Preventive Medicine, Third Military Medical University, Chongqing, China; Department of Biochemistry and Molecular Biology, Third Military Medical University, Chongqing, China. Electronic address: ikkyhuang@163.com.
• Burns. 2016 Mar 1; 42 (2): 291-9.

ObjectiveTo develop a mathematical model of predicting mortality based on the admission characteristics of 6220 burn cases.MethodsData on all the burn patients presenting to Institute of Burn Research, Southwest Hospital, Third Military Medical University from January of 1999 to December of 2008 were extracted from the departmental registry. The distributions of burn cases were scattered by principal component analysis. Univariate associations with mortality were identified and independent associations were derived from multivariate logistic regression analysis. Using variables independently and significantly associated with mortality, a mathematical model to predict mortality was developed using the support vector machine (SVM) model. The predicting ability of this model was evaluated and verified.ResultsThe overall mortality in this study was 1.8%. Univariate associations with mortality were identified and independent associations were derived from multivariate logistic regression analysis. Variables at admission independently associated with mortality were gender, age, total burn area, full thickness burn area, inhalation injury, shock, period before admission and others. The sensitivity and specificity of logistic model were 99.75% and 85.84% respectively, with an area under the receiver operating curve of 0.989 (95% CI: 0.979-1.000; p<0.01). The model correctly classified 99.50% of cases. The subsequently developed support vector machine (SVM) model correctly classified nearly 100% of test cases, which could not only predict adult group but also pediatric group, with pretty high robustness (92%-100%).ConclusionA mathematical model based on logistic regression and SVM could be used to predict the survival prognosis according to the admission characteristics.Copyright © 2015 Elsevier Ltd and ISBI. All rights reserved.

29 keywords...

hide…