Machine Learning Predicts Improvement of Functional Outcomes in Spinal Cord Injury Patients After Inpatient Rehabilitation

Mohammad Rasoolinejad¹Irene Say¹Peter B. Wu¹Xinran Liu²Yan Zhou¹Nathan Zhang³Emily R. Rosario⁴Daniel C. Lu^1,5,6*

• ¹Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, United States
• ²Department of Molecular, Cell, and Developmental Biology, College of Life Sciences, University of California, Los Angeles, Los Angeles, California, United States
• ³Department of Computer Science, Samueli School of Engineering, University of California, Los Angeles, Los Angeles, California, United States
• ⁴Casa Colina Hospital and Centers for Healthcare, Pomona, California, United States
• ⁵Neuromotor Recovery and Rehabilitation Center, David Geffen School of Medicine, University of California, Los Angeles, United States
• ⁶Brain Research Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, United States

Introduction: Spinal cord injury (SCI) presents a significant burden to patients, families, and the healthcare system. The ability to accurately predict functional outcomes for SCI patients is essential for optimizing rehabilitation strategies, guiding patient and family decision making, and improving patient care. Methods: We conducted a retrospective analysis of 589 SCI patients admitted to a single acute rehabilitation facility and used the dataset to train advanced machine learning algorithms to predict patients’ rehabilitation outcomes. The primary outcome was the Functional Independence Measure (FIM) score at discharge, reflecting the level of independence achieved by patients after comprehensive inpatient rehabilitation. Results: Tree-based algorithms, particularly Random Forest (RF) and XGBoost, significantly outperformed traditional statistical models and Generalized Linear Models (GLMs) in predicting discharge FIM scores. The RF model exhibited the highest predictive accuracy, with an R-squared value of 0.90 and a Mean Squared Error (MSE) of 0.29 on the training dataset, while achieving 0.52 R-squared and 1.37 MSE on the test dataset. The XGBoost model also demonstrated strong performance, with an R-squared value of 0.74 and an MSE of 0.75 on the training dataset, and 0.51 R-squared with 1.39 MSE on the test dataset. Our analysis identified key predictors of rehabilitation outcomes, including the initial FIM scores and specific demographic factors such as level of injury and prehospital living settings. The study also highlighted the superior ability of tree-based models to capture the complex, non-linear relationships between variables that impact recovery in SCI patients. Discussion: This research underscores the potential of machine learning models to enhance the accuracy of outcome predictions in SCI rehabilitation. The findings support the integration of these advanced predictive tools in clinical settings to better guide decision making for patients and families, tailor rehabilitation plans, allocate resources efficiently, and ultimately improve patient outcomes.