ABSTRACT: To date, no studies have attempted to utilize data from a combination of vital signs, heart rate variability and complexity (HRV, HRC), as well as machine learning (ML), for identifying the need for lifesaving interventions (LSIs) in trauma patients. The objectives of this study were to examine the utility of the above for identifying LSI needs and compare different LSI-associated models, with the hypothesis that an ML model would be superior in performance over multivariate logistic regression models. One hundred four patients transported from the injury scene via helicopter were selected for the study. A wireless vital signs monitor was attached to the patient’s arm and used to capture physiologic data, including HRV and HRC. The power of vital sign measurements, HRV, HRC, and Glasgow Coma Scale score (GCS) to identify patients requiring LSIs was estimated using multivariate logistic regression and ML. Receiver operating characteristic (ROC) curves were also obtained. Thirty-two patients underwent 75 LSIs. After logistic regression, ROC curves demonstrated better identification for LSIs using heart rate (HR) and HRC (area under the curve [AUC] of 0.81) than using HR alone (AUC of 0.73). Likewise, ROC curves demonstrated better identification for LSIs using GCS and HRC (AUC of 0.94) than using GCS and HR (AUC of 0.92). Importantly, ROC curves demonstrated that an ML model using HR, GCS, and HRC (AUC of 0.99) had superior performance over multivariate logistic regression models for identifying the need for LSIs in trauma patients. Development of computer decision support systems should utilize vital signs, HRC, and ML in order to achieve more accurate diagnostic capabilities, such as identification of needs for LSIs in trauma patients.
*US Army Institute of Surgical Research, Fort Sam Houston; and †Center for Translational Injury Research, Department of Surgery, University of Texas Health Science Center at Houston, Houston, Texas; and ‡Athena GTX, Inc, Des Moines, Iowa
Received 20 Feb 2014; first review completed 17 Mar 2014; accepted in final form 31 Mar 2014
Address reprint requests to Nehemiah T. Liu, MS, US Army Institute of Surgical Research, 3698 Chambers Pass, JBSA Fort Sam Houston, TX 78234-6315. E-mail: email@example.com.
Author Contributions: N.T.L. contributed to study design, data analysis, data interpretation, writing, and critical revision. J.B.H., C.E.W., and M.I.D. contributed to study design and critical revision. J.S. contributed to study design, writing, and critical revision.
This work was supported by the National Trauma Institute, the Combat Casualty Care Research Program, and the State of Texas Emerging Technology Fund.
The authors declare no conflicts of interest.
This study was conducted under a protocol reviewed and approved by the University of Texas Health Science Center at Houston and in accordance with the approved protocol. The opinions or assertions contained herein are the private views of the authors and are not to be construed as official or as reflecting the views of the Department of the Army or the Department of Defense.