Diagnosis of hemorrhage in a prehospital trauma population using linear and nonlinear multiparameter analysis of vital signs.
In this study, we analyzed a dataset of time-series vital-signs data collected by standard Propaq travel monitor during helicopter transport of 898 civilian trauma casualties from the scene of injury to a receiving trauma center. The goals of the analysis are two fold. First, to determine which combination of the automatically-collected and -qualified vital signs provides the best discrimination between casualties with and without major hemorrhage. Second, to determine whether nonlinear classifiers provide improved discrimination over simpler, linear classifiers. Major hemorrhage is defined by the presence of injuries consistent with hemorrhage in casualties who received one or more units of blood. We randomly selected a subset of the casualties to train and test the classifiers with multiple combinations of the vital-signs variables, and used the area under the receiver operating characteristic curve (ROC AUC) as a decision metric. Based on the results of 100 simulations, we observe that: (i) the best two features obtained are systolic blood pressure and heart rate (mean AUC = 0.75 from a linear classifier), and (ii) the use of nonlinear classifiers does not improve discrimination. These results support earlier findings that the interaction of systolic blood pressure and heart rate is useful for the identification of trauma hemorrhage and that linear classifiers are adequate for many real-world applications.
Chen
Liangyou
L
Bioinformatics Cell, Telemedicine and Advanced Technology Research Center, USAMRMC, Frederick, MD 21702, USA. lchen@bioanalysis.org
Reisner
Andrew T
AT
McKenna
Thomas M
TM
Gribok
Andrei
A
Reifman
Jaques
J
D017732:Air Ambulances; D000465:Algorithms; D003198:Computer Simulation; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005103:Expert Systems; D006470:Hemorrhage / Q000175:diagnosis* / Q000209:etiology; D016014:Linear Models; D008991:Monitoring, Physiologic / Q000379:methods*; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D014218:Triage / Q000379:methods*; D014481:United States; D014947:Wounds and Injuries / Q000150:complications / Q000175:diagnosis*