Machine Learning Models for Predicting In-Hospital Mortality and Pressure Ulcer Development following Traumatic Spinal Cord Injury; A Cross-sectional Study.

INTRODUCTION

Traumatic spinal cord injury (TSCI) remains a significant cause of long-term disability, with mortality and pressure ulcer (PU) development being key determinants of patient outcomes. This study aimed to develop and interpret machine learning (ML) models for predicting in-hospital mortality and PU formation in TSCI patients using data from the National Spinal Cord Injury Registry of Iran (NSCIR-IR).

METHODS

Patients with TSCI admitted between 2015 and 2023 were included in the analysis. Data preprocessing included iterative imputation, feature selection with cross-validation, and class balancing using SMOTE. Nine ML algorithms were trained and validated on an 80:20 split dataset. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and other standard metrics. Feature importance and interpretability were evaluated using permutation importance and Shapley Additive Explanations (SHAP) analysis.

RESULTS

A total of 499 patients were included. The LightGBM classifier achieved the highest performance in mortality prediction (AUC = 0.85; 95% CI: 0.73-0.94), followed by KNN and Naive Bayes. For PU prediction, the Random Forest model performed best (AUC = 0.83; 95% CI: 0.72-0.93), outperforming XGBoost and LightGBM. Ventilator use, ASIA grade, and sensory and motor scores were the strongest predictors of mortality, while first aid given, ventilator use, and the number of injured vertebrae were the strongest predictors of PU risk. SHAP analysis confirmed these findings.

CONCLUSION

ML algorithms can accurately identify TSCI patients at high risk of mortality and PU development using routinely collected clinical data. Integrating such interpretable ML tools into early triage systems could enable timely preventive interventions and improve patient outcomes.