Machine Learning to Develop a Predictive Model of Pressure Injury in Persons with Spinal Cord Injury

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 4,709 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 was 0.67 (95% CI = 0.59-0.75), and for the adaptive LASSO model was 0.72 (95% CI = 0.65-80). Based on these results, the adaptive LASSO model 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, being in the highest vs. lower weight categories 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.