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Decision Tree Analysis Accurately Predicts Discharge Destination After Spinal Cord Injury Rehabilitation
Chihiro Kato1, Osamu Uemura2, Yasunori Sato3
1National Hospital Organization Murayama Medical Center, Tokyo, Japan; Department of Rehabilitation Medicine, Keio University School of Medicine, Tokyo, Japan.
Objectives:
To predict discharge destination after spinal cord injury (SCI) rehabilitation.
Study Design:
A retrospective, single-center study. We collected the following data from medical charts: age, sex, living arrangement before injury, acute length of stay (LOS), level of injury on admission, American Spinal Injury Association Impairment Scale (AIS) on admission, Upper Extremity Motor Score (UEMS) on admission, Lower Extremity Motor Score on admission (LEMS), Spinal Cord Independence Measure (SCIM) scores on admission and discharge, and discharge destination. A decision tree algorithm was used to establish prediction models in a train-test split manner using features on admission or discharge.
Setting:
A spinal center in Tokyo, Japan.
Participants:
Participants were individuals with SCI admitted to our hospital from March 2016 to October 2021 for the first rehabilitation after the injury. The study included 210 participants divided into 2 groups: training (n=140) and testing (n=70). Random sampling without replacement was used.
Interventions:
Not applicable.
Main Outcome Measures:
Prediction accuracy was evaluated with sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating curve (AUC).
Results:
AIS was significantly different between the groups. The prediction model using total SCIM scores on discharge (D-Classification and Regression Tree [CART]) revealed that a cut-off value of 40 accurately predicted the discharge destination. In contrast, the prediction model using features on admission (A-CART) revealed that subtotal SCIM mobility scores of 5, age of 74 years, and UEMS of 23 were significant predictors. Sensitivity, specificity, PPV, NPV, and AUC of D-CART and A-CART were 0.837, 0.810, 0.911, 0.680, and 0.832 and 0.857, 0.810, 0.913, 0.708, and 0.869, respectively.
Conclusions:
D-CART and A-CART showed comparable prediction accuracies. This suggests that, even during the early stages of rehabilitation, it is possible to predict the discharge destination.
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