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ATLS Hypovolemic Shock Classification by Prediction of Blood Loss in Rats Using Regression Models.

Authors
 Soo Beom Choi  ;  Joon Yul Choi  ;  Jee Soo Park  ;  Deok Won Kim 
Citation
 SHOCK, Vol.46(1) : 92-98, 2016 
Journal Title
 SHOCK 
ISSN
 1073-2322 
Issue Date
2016
MeSH
Advanced Trauma Life Support Care ; Animals ; Heart Rate/physiology ; Hemorrhage/complications ; Male ; Models, Theoretical ; Rats ; Rats, Sprague-Dawley ; Respiratory Rate/physiology ; Shock/classification* ; Support Vector Machine ; Trauma Severity Indices ; Vital Signs
Keywords
Lactate concentration ; linear regression ; multicategory ; perfusion ; support vector regression ; triage
Abstract
In our previous study, our input data set consisted of 78 rats, the blood loss in percent as a dependent variable, and 11 independent variables (heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, respiration rate, temperature, perfusion index, lactate concentration, shock index, and new index (lactate concentration/perfusion)). The machine learning methods for multicategory classification were applied to a rat model in acute hemorrhage to predict the four Advanced Trauma Life Support (ATLS) hypovolemic shock classes for triage in our previous study. However, multicategory classification is much more difficult and complicated than binary classification. We introduce a simple approach for classifying ATLS hypovolaemic shock class by predicting blood loss in percent using support vector regression and multivariate linear regression (MLR). We also compared the performance of the classification models using absolute and relative vital signs. The accuracies of support vector regression and MLR models with relative values by predicting blood loss in percent were 88.5% and 84.6%, respectively. These were better than the best accuracy of 80.8% of the direct multicategory classification using the support vector machine one-versus-one model in our previous study for the same validation data set. Moreover, the simple MLR models with both absolute and relative values could provide possibility of the future clinical decision support system for ATLS classification. The perfusion index and new index were more appropriate with relative changes than absolute values.
Full Text
http://ovidsp.ovid.com/ovidweb.cgi?T=JS&CSC=Y&NEWS=N&PAGE=fulltext&AN=00024382-201607000-00013&LSLINK=80&D=ovft
DOI
10.1097/SHK.0000000000000574
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers
Yonsei Authors
Kim, Deok Won(김덕원)
Park, Jee Soo(박지수) ORCID logo https://orcid.org/0000-0001-9976-6599
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/147140
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