Abstract
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.
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Article info
Publication history
Published online: September 15, 2022
Accepted:
August 4,
2022
Footnotes
Dr Elster and Dr Kirk had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Dimitrios Moris, Seth Schobel, Allan D. Kirk, Eric Elster. Acquisition, analysis, or interpretation of data: Ricardo Henao, Hannah Hensman, Linda Stempora, Scott Chasse. Drafting of the manuscript: Dimitrios Moris, Ricardo Henao, Seth Schobel. Critical revision of the manuscript for important intellectual content: Allan D. Kirk, Seth Schobel, Eric Elster. Statistical analysis: Ricardo Henao, Hannah Hensman. Obtained funding: Eric Elster. Administrative, technical, or material support: Seth Schobel, Ricardo Henao, Hannah Hensman, Scott Chasse. Supervision: Allan D. Kirk, Eric Elster.
Identification
Copyright
© 2022 Elsevier Inc. All rights reserved.
