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

Spinal Cord volume 61, pages 513–520 (2023)Cite this article

Subjects

Abstract

Study design

A 5-year longitudinal, retrospective, cohort study.

Objectives

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).

Setting

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.

Methods

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.

Results

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.

Conclusions

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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Funding

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.

Author information

Authors and Affiliations

  1. Research Service, James A. Haley Veterans’ Hospital, Tampa, FL, USA

    Stephen L. Luther, Dezon K. Finch, James McCart, Peter Toyinbo, William Lapcevic & Bridget Hahm

  2. College of Public Health, University of South Florida, Tampa, FL, USA

    Stephen L. Luther

  3. Tampa VA Research and Education Foundation, Inc, Tampa, FL, USA

    Susan S. Thomason

  4. VA Boston Health Care System, Spinal Cord Injury Service, Harvard Medical School, Boston, MA, USA

    Sunil Sabharwal

  5. Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA, USA

    Sunil Sabharwal

  6. College of Business, Florida International University, Miami, FL, USA

    Lina Bouayad

  7. Yale University School of Medicine, New Haven, CT, USA

    Ronald G. Hauser

  8. Geriatrics Research Education and Clinical Care, Tennessee Valley Healthcare System, Nashville, TN, USA

    Michael E. Matheny

  9. Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA

    Michael E. Matheny

  10. Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA

    Michael E. Matheny

  11. Division of General Internal Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

    Michael E. Matheny

  12. Research & Development Service, Tennessee Valley Healthcare System, Nashville, TN College of Nursing, Nashville, TN, USA

    Michael E. Matheny

  13. University of California, School of Nursing, Davis, CA, USA

    Gail Powell-Cope

Authors
  1. Stephen L. Luther
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Contributions

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). https://doi.org/10.1038/s41393-023-00924-z

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