Toward a hemorrhagic trauma severity score: fusing five physiological biomarkers

Journal of Translational Medicine

Ankita Bhat¹  John R. Aggas¹  Brandon K. Walther²  Anthony Guiseppi-Elie³  Daria Podstawczyk⁴  Kevin R. Ward⁵  David Machado-Aranda⁶ 

[1] Center for Bioelectronics, Biosensors and Biochips (C3B®), Department of Biomedical Engineering, Texas A&M University, 77843, College Station, TX, USA;Center for Bioelectronics, Biosensors and Biochips (C3B®), Department of Biomedical Engineering, Texas A&M University, 77843, College Station, TX, USA;Department of Cardiovascular Sciences, Houston Methodist Institute for Academic Medicine and Houston Methodist Research Institute, 6670 Bertner Ave, 77030, Houston, TX, USA;Center for Bioelectronics, Biosensors and Biochips (C3B®), Department of Biomedical Engineering, Texas A&M University, 77843, College Station, TX, USA;Department of Cardiovascular Sciences, Houston Methodist Institute for Academic Medicine and Houston Methodist Research Institute, 6670 Bertner Ave, 77030, Houston, TX, USA;Department of Electrical and Computer Engineering, Texas A&M University, 77843, College Station, TX, USA;ABTECH Scientific, Inc, Biotechnology Research Park, 800 East Leigh Street, 23219, Richmond, VA, USA;Department of Process Engineering and Technology of Polymer and Carbon Materials, Wroclaw University of Science and Technology, Norwida 4/6, 50-373, Wroclaw, Poland;Department of Surgery, Division of Acute Care Surgery, University of Michigan, 48109, Ann Arbor, MI, USA;Departments of Emergency Medicine and Biomedical Engineering, Michigan Center for Integrative Research in Critical Care, University of Michigan, 48109, Ann Arbor, MI, USA;Department of Surgery, Division of Acute Care Surgery, University of Michigan, 48109, Ann Arbor, MI, USA;

关键词: Decision-making;    Hemorrhage;    Trauma care;    DATA fusion;    Risk stratification;    Triage;   

DOI  :  10.1186/s12967-020-02516-4

来源: Springer

【 摘 要 】

BackgroundTo introduce the Hemorrhage Intensive Severity and Survivability (HISS) score, based on the fusion of multi-biomarker data; glucose, lactate, pH, potassium, and oxygen tension, to serve as a patient-specific attribute in hemorrhagic trauma.Materials and methodsOne hundred instances of Sensible Fictitious Rationalized Patient (SFRP) data were synthetically generated and the HISS score assigned by five clinically active physician experts (100 [5]). The HISS score stratifies the criticality of the trauma patient as; low(0), guarded(1), elevated(2), high(3) and severe(4). Standard classifier algorithms; linear support vector machine (SVM-L), multi-class ensemble bagged decision tree (EBDT), artificial neural network with bayesian regularization (ANN:BR) and possibility rule-based using function approximation (PRBF) were evaluated for their potential to similarly classify and predict a HISS score.ResultsSVM-L, EBDT, ANN:BR and PRBF generated score predictions with testing accuracies (majority vote) corresponding to 0.91 ± 0.06, 0.93 ± 0.04, 0.92 ± 0.07, and 0.92 ± 0.03, respectively, with no statistically significant difference (p > 0.05). Targeted accuracies of 0.99 and 0.999 could be achieved with SFRP data size and clinical expert scores of 147[7](0.99) and 154[9](0.999), respectively.ConclusionsThe predictions of the data-driven model in conjunction with an adjunct multi-analyte biosensor intended for point-of-care continual monitoring of trauma patients, can aid in patient stratification and triage decision-making.

【 授权许可】

CC BY