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Machine learning to develop a predictive model of pressure injury in persons with spinal cord injury


Study design

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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Fig. 1: Observation period.
Fig. 2: Cohort and Sample Selection.

Data availability

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.

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Authors and Affiliations



SL was the study principal investigator and responsible for overall study design, execution of the study, data analysis and the manuscript. ST and SS were clinical experts who assisted in conceptualizing the study, identifying, and operationalizing variables and identifying clinical implications. DF, JM, PT, LB, and WL assisted in data management and data analysis. BH was the study project manager and administered day to day aspects of project. RH assisted with analysis of laboratory data, MM was involved in data acquisition and analysis and advised SL in data analysis, particularly in model development. GP participated in conceptualization of the study and assisted SL in writing the manuscript. All authors reviewed and edited the manuscript.

Corresponding author

Correspondence to Stephen L. Luther.

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Competing interests

The authors declare no competing interests.

Ethical approval

The study was approved by the James A. Haley Veterans Hospital Research and Development Committee and VA Central Institutional Review Board.

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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).

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