LAUSR

Introduction 　　Clinical prediction rule (CPR) using decision tree analysis is able to show the branching of the variables under consideration in a clear, hierarchical manner, including specific reference values, which can be used as classifiers in clinical practice. However, CPR developed by decision tree analysis for predicting the degree of independent living of patients with thoracic spinal cord injury (SCI) are few. The purpose of this study was to develop a simplified CPR for prognosticating dependent daily living in patients with thoracic SCI. Methods 　　We extracted data on patients with thoracic SCI from a national multicenter registry database, the Japan Rehabilitation Database (JRD). All patients with thoracic SCI who were hospitalized within 30 days after injury onset were included. The independent living was categorized in the JRD as follows: independent socially, independent at home, needing care at home, independent at the facility, and needing care at the facility. These categories were used as the objective variables in classification and regression tree (CART) analysis. The CART algorithm was applied to develop the CPR for predicting whether patients with thoracic SCI achieve independent living at hospital discharge. Results 　　Three hundred ten patients with thoracic SCI were included in the CART analysis. The CART model identified, in a hierarchical order, patient's age, residual function level, and the bathing sub-score of Functional Independence Measure as the top three factors with moderate classification accuracy and area under the curve. Conclusions 　　We developed a simplified, moderately accurate CPR for predicting whether patients with thoracic SCI achieve independent living at hospital discharge.