Beau Muñoz1, Seth A Schobel2, Felipe A Lisboa2, Vivek Khatri2, Scott F Grey2, Christopher J Dente3, Allan D Kirk4, Timothy Buchman3, Eric A Elster5. 1. Department of Surgery at the Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD; Uniformed Services University Surgical Critical Care Initiative, Bethesda, MD. 2. Department of Surgery at the Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD; Uniformed Services University Surgical Critical Care Initiative, Bethesda, MD; Henry Jackson Foundation for the Advancement of Military Medicine, Inc, Bethesda, MD. 3. Uniformed Services University Surgical Critical Care Initiative, Bethesda, MD; Emory University, Atlanta, GA. 4. Uniformed Services University Surgical Critical Care Initiative, Bethesda, MD; Duke University, Durham, NC. 5. Department of Surgery at the Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center, Bethesda, MD; Uniformed Services University Surgical Critical Care Initiative, Bethesda, MD. Electronic address: eric.elster@usuhs.edu.
Abstract
BACKGROUND: Post-traumatic acute kidney injury has occurred in every major military conflict since its initial description during World War II. To ensure the proper treatment of combat casualties, early detection is critical. This study therefore aimed to investigate combat-related post-traumatic acute kidney injury in recent military conflicts, used machine learning algorithms to identify clinical and biomarker variables associated with the development of post-traumatic acute kidney injury, and evaluated the effects of post-traumatic acute kidney injury on wound healing and nosocomial infection. METHODS: We conducted a retrospective clinical cohort review of 73 critically injured US military service members who sustained major combat-related extremity wounds and had collected injury characteristics, assayed serum and tissue biopsy samples for the expression of protein and messenger ribonucleic acid biomarkers. Bivariate analyses and random forest recursive feature elimination classification algorithms were used to identify associated injury characteristics and biomarker variables. RESULTS: The incidence of post-traumatic acute kidney injury was 20.5%. Of that, 86% recovered baseline renal function and only 2 (15%) of the acute kidney injury group required renal replacement therapy. Random forest recursive feature elimination algorithms were able to estimate post-traumatic acute kidney injury with the area under the curve of 0.93, sensitivity of 0.91, and specificity of 0.91. Post-traumatic acute kidney injury was associated with injury severity score, serum epidermal growth factor, and tissue activin A type receptor 1, matrix metallopeptidase 10, and X-C motif chemokine ligand 1 expression. Patients with post-traumatic acute kidney injury exhibited poor wound healing and increased incidence of nosocomial infections. CONCLUSION: The occurrence of acute kidney injury in combat casualties may be estimated using injury characteristics and serum and tissue biomarkers. External validations of these models are necessary to generalize for all trauma patients. Published by Elsevier Inc.
BACKGROUND: Post-traumatic acute kidney injury has occurred in every major military conflict since its initial description during World War II. To ensure the proper treatment of combat casualties, early detection is critical. This study therefore aimed to investigate combat-related post-traumatic acute kidney injury in recent military conflicts, used machine learning algorithms to identify clinical and biomarker variables associated with the development of post-traumatic acute kidney injury, and evaluated the effects of post-traumatic acute kidney injury on wound healing and nosocomial infection. METHODS: We conducted a retrospective clinical cohort review of 73 critically injured US military service members who sustained major combat-related extremity wounds and had collected injury characteristics, assayed serum and tissue biopsy samples for the expression of protein and messenger ribonucleic acid biomarkers. Bivariate analyses and random forest recursive feature elimination classification algorithms were used to identify associated injury characteristics and biomarker variables. RESULTS: The incidence of post-traumatic acute kidney injury was 20.5%. Of that, 86% recovered baseline renal function and only 2 (15%) of the acute kidney injury group required renal replacement therapy. Random forest recursive feature elimination algorithms were able to estimate post-traumatic acute kidney injury with the area under the curve of 0.93, sensitivity of 0.91, and specificity of 0.91. Post-traumatic acute kidney injury was associated with injury severity score, serum epidermal growth factor, and tissue activin A type receptor 1, matrix metallopeptidase 10, and X-C motif chemokine ligand 1 expression. Patients with post-traumatic acute kidney injury exhibited poor wound healing and increased incidence of nosocomial infections. CONCLUSION: The occurrence of acute kidney injury in combat casualties may be estimated using injury characteristics and serum and tissue biomarkers. External validations of these models are necessary to generalize for all traumapatients. Published by Elsevier Inc.
Authors: Carlos Yánez Benítez; Antonio Güemes; José Aranda; Marcelo Ribeiro; Pablo Ottolino; Salomone Di Saverio; Henrique Alexandrino; Luca Ponchietti; Juan L Blas; Juan Pablo Ramos; Elena Rangelova; Mercedes Muñoz; Carlos Yánez Journal: World J Surg Date: 2020-09 Impact factor: 3.352
Authors: Balamurugan Packialakshmi; Ian J Stewart; David M Burmeister; Yuanyi Feng; Dennis P McDaniel; Kevin K Chung; Xiaoming Zhou Journal: Physiol Rep Date: 2022-02
Authors: Aram Avila-Herrera; James B Thissen; Nisha Mulakken; Seth A Schobel; Michael D Morrison; Xiner Zhou; Scott F Grey; Felipe A Lisboa; Desiree Unselt; Shalini Mabery; Meenu M Upadhyay; Crystal J Jaing; Eric A Elster; Nicholas A Be Journal: Sci Rep Date: 2022-08-15 Impact factor: 4.996