Multidimensional machine learning models predicting outcomes after trauma.

Autor: Moris D; Medical Center, Duke University Durham, NC. Electronic address: dimitrios.moris@duke.edu., Henao R; School of Medicine, Duke University Durham, NC., Hensman H; DecisionQ, Arlington, VA; Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD., Stempora L; Medical Center, Duke University Durham, NC; Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD., Chasse S; Medical Center, Duke University Durham, NC; Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD., Schobel S; Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc, Bethesda, MD., Dente CJ; Department of Surgery, Emory University, Atlanta, GA., Kirk AD; Medical Center, Duke University Durham, NC; Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD., Elster E; Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD; Walter Reed National Military Medical Center, Bethesda, MD.
Jazyk: angličtina
Zdroj: Surgery [Surgery] 2022 Dec; Vol. 172 (6), pp. 1851-1859. Date of Electronic Publication: 2022 Sep 16.
DOI: 10.1016/j.surg.2022.08.007
Abstrakt: Background: An emerging body of literature supports the role of individualized prognostic tools to guide the management of patients after trauma. The aim of this study was to develop advanced modeling tools from multidimensional data sources, including immunological analytes and clinical and administrative data, to predict outcomes in trauma patients.
Methods: This was a prospective study of trauma patients at Level 1 centers from 2015 to 2019. Clinical, flow cytometry, and serum cytokine data were collected within 48 hours of admission. Sparse logistic regression models were developed, jointly selecting predictors and estimating the risk of ventilator-associated pneumonia, acute kidney injury, complicated disposition (death, rehabilitation, or nursing facility), and return to the operating room. Model parameters (regularization controlling model sparsity) and performance estimation were obtained via nested leave-one-out cross-validation.
Results: A total of 179 patients were included. The incidences of ventilator-associated pneumonia, acute kidney injury, complicated disposition, and return to the operating room were 17.7%, 28.8%, 22.5%, and 12.3%, respectively. Regarding extensive resource use, 30.7% of patients had prolonged intensive care unit stay, 73.2% had prolonged length of stay, and 23.5% had need for prolonged ventilatory support. The models were developed and cross-validated for ventilator-associated pneumonia, acute kidney injury, complicated dispositions, and return to the operating room, yielding predictive areas under the curve from 0.70 to 0.91. Each model derived its optimal predictive value by combining clinical, administrative, and immunological analyte data.
Conclusion: Clinical, immunological, and administrative data can be combined to predict post-traumatic outcomes and resource use. Multidimensional machine learning modeling can identify trauma patients with complicated clinical trajectories and high resource needs.
(Copyright © 2022 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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