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Prediction of venous thromboembolism using clinical and serum biomarker data from a military cohort of trauma patients
  1. Matthew Bradley1,
  2. A Shi1,
  3. V Khatri2,
  4. S Schobel2,
  5. E Silvius1,
  6. A Kirk3,
  7. T Buchman4,
  8. J Oh5 and
  9. E Elster1,6
  1. 1 Surgery, Walter Reed National Military Medical Center, Bethesda, Maryland, USA
  2. 2 Surgery, Uniformed Services University, Bethesda, Maryland, USA
  3. 3 Surgery, Duke University, Durham, North Carolina, USA
  4. 4 Surgery, Emory University, Atlanta, Georgia, USA
  5. 5 Surgery, Walter Reed National Military Medical Center, Bethesda, MD, United States
  6. 6 Surgery, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
  1. Correspondence to Dr Matthew Bradley, Surgery, Walter Reed National Military Medical Center, Bethesda, MD 20889, USA; matthew.j.bradley22.mil{at}mail.mil

Abstract

Introduction Venous thromboembolism (VTE) is a frequent complication of trauma associated with high mortality and morbidity. Clinicians lack appropriate tools for stratifying trauma patients for VTE, thus have yet to be able to predict when to intervene. We aimed to compare random forest (RF) and logistic regression (LR) predictive modelling for VTE using (1) clinical measures alone, (2) serum biomarkers alone and (3) clinical measures plus serum biomarkers.

Methods Data were collected from 73 military casualties with at least one extremity wound and prospectively enrolled in an observational study between 2007 and 2012. Clinical and serum cytokine data were collected. Modelling was performed with RF and LR based on the presence or absence of deep vein thrombosis (DVT) and/or pulmonary embolism (PE). For comparison, LR was also performed on the final variables from the RF model. Sensitivity/specificity and area under the curve (AUC) were reported.

Results Of the 73 patients (median Injury Severity Score=16), nine (12.3%) developed VTE, four (5.5%) with DVT, four (5.5%) with PE, and one (1.4%) with both DVT and PE. In all sets of predictive models, RF outperformed LR. The best RF model generated with clinical and serum biomarkers included five variables (interleukin-15, monokine induced by gamma, vascular endothelial growth factor, total blood products at resuscitation and presence of soft tissue injury) and had an AUC of 0.946, sensitivity of 0.992 and specificity of 0.838.

Conclusions VTE may be predicted by clinical and molecular biomarkers in trauma patients. This will allow the development of clinical decision support tools which can help inform the management of high-risk patients for VTE.

  • trauma management
  • intensive & critical care
  • surgery
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Footnotes

  • Contributors All authors have made contributions to the study design, data acquisition, or data analysis and interpretation. All authors contributed to drafting or revising the article, and contributed to the final approval of the version submitted.

  • Funding Research activities leading to the development of this manuscript were funded by the Department of Defense’s Defense Health Program - Joint Program Committee 6/Combat Casualty Care (USUHS HT9404-13-1-0032 and HU0001-15-2-0001).

  • Disclaimer The opinions or assertions contained herein are the private ones of the author/speaker and are not to be construed as official or reflecting the views of the Department of Defense, the Uniformed Services University of the Health Sciences or any other agency of the US Government.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Ethics approval The study was approved by the Walter Reed National Military Medical Center’s Institutional Review Board (IRB# 352329).

  • Provenance and peer review Not commissioned; internally peer reviewed.

  • Data availability statement Data are available upon reasonable request from the corresponding author.

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