0 605

Cited 6 times in

Prediction of ATLS hypovolemic shock class in rats using the perfusion index and lactate concentration

 Choi, Soo Beom  ;  Park, Jee Soo  ;  Chung, Jai Won  ;  Kim, Sung Woo  ;  Kim, Deok Won 
 SHOCK, Vol.43(4) : 361-368, 2015 
Journal Title
Issue Date
Algorithms ; Animals ; Blood Pressure ; Blood Transfusion ; Lactates/blood* ; Male ; Models, Statistical ; Perfusion* ; ROC Curve ; Rats ; Rats, Sprague-Dawley ; Reproducibility of Results ; Resuscitation/adverse effects* ; Resuscitation/methods ; Shock/pathology* ; Shock, Hemorrhagic/therapy ; Support Vector Machine ; Time Factors ; Trauma Severity Indices
Perfusion ; lactate concentration ; classification accuracy ; triage ; support vector ; neural network
It is necessary to quickly and accurately determine Advanced Trauma Life Support (ATLS) hemorrhagic shock class for triage in cases of acute hemorrhage caused by trauma. However, the ATLS classification has limitations, namely, with regard to primary vital signs. This study identified the optimal variables for appropriate triage of hemorrhage severity, including the peripheral perfusion index and serum lactate concentration in addition to the conventional primary vital signs. To predict the four ATLS classes, three popular machine learning algorithms with four feature selection methods for multicategory classification were applied to a rat model of acute hemorrhage. A total of 78 anesthetized rats were divided into four groups for ATLS classification based on blood loss (in percent). The support vector machine one-versus-one model with the Kruskal-Wallis feature selection method performed best, with 80.8% accuracy, relative classifier information of 0.629, and a kappa index of 0.732. The new hemorrhage-induced severity index (lactate concentration/perfusion index), diastolic blood pressure, mean arterial pressure, and the perfusion index were selected as the optimal variables for predicting the four ATLS classes by support vector machine one-versus-one with the Kruskal-Wallis method. These four variables were also selected for binary classification to predict ATLS classes I and II versus III and IV for blood transfusion requirement. The suggested ATLS classification system would be helpful to first responders by indicating the severity of patients, allowing physicians to prepare suitable resuscitation before hospital arrival, which could hasten treatment initiation.
Full Text
Appears in Collections:
1. College of Medicine (의과대학) > Research Institute (부설연구소) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Urology (비뇨의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Deok Won(김덕원)
Kim, Seong Woo(김성우)
Park, Jee Soo(박지수) ORCID logo https://orcid.org/0000-0001-9976-6599
Chung, Jai Won(정재원)
Choi, Soo Beom(최수범)
사서에게 알리기


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.