A 5-year longitudinal, retrospective, cohort study.
Develop a prediction model based on electronic health record (EHR) data to identify veterans with spinal cord injury/diseases (SCI/D) at highest risk for new pressure injuries (PIs).
Structured (coded) and text EHR data, for veterans with SCI/D treated in a VHA SCI/D Center between October 1, 2008, and September 30, 2013.
A total of 4709 veterans were available for analysis after randomly selecting 175 to act as a validation (gold standard) sample. Machine learning models were created using ten-fold cross validation and three techniques: (1) two-step logistic regression; (2) regression model employing adaptive LASSO; (3) and gradient boosting. Models based on each method were compared using area under the receiver-operating curve (AUC) analysis.
The AUC value for the gradient boosting model was 0.62 (95% CI = 0.54–0.70), for the logistic regression model it was 0.67 (95% CI = 0.59–0.75), and for the adaptive LASSO model it was 0.72 (95% CI = 0.65–80). Based on these results, the adaptive LASSO model was chosen for interpretation. The strongest predictors of new PI cases were having fewer total days in the hospital in the year before the annual exam, higher vs. lower weight and most severe vs. less severe grade of injury based on the American Spinal Cord Injury Association (ASIA) Impairment Scale.
While the analyses resulted in a potentially useful predictive model, clinical implications were limited because modifiable risk factors were absent in the models.
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The datasets generated during and/or analyzed during the current study are not publicly available due the fact that they included Individually Identifiable Data which can only be shared pursuant to a written request and IRB approved waiver of HIPAA authorization, with the approval of the Under Secretary for Health, in accordance with VHA Handbook 1605.1 §13.b(1)(b) or §13.b(1)(c) or superseding versions of that Handbook.
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This work was supported by a grant awarded from the Health Services Research and Development Service, Veterans Health Administration (IIR 12-064). The views expressed here are those of the authors and do not represent the official policy or position of the Department of Veterans Affairs or the United States government. The authors have no conflict of interests to declare.
The authors declare no competing interests.
The study was approved by the James A. Haley Veterans Hospital Research and Development Committee and VA Central Institutional Review Board.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Luther, S.L., Thomason, S.S., Sabharwal, S. et al. Machine learning to develop a predictive model of pressure injury in persons with spinal cord injury. Spinal Cord 61, 513–520 (2023). https://doi.org/10.1038/s41393-023-00924-z