A Simplified Clinical Prediction Rule for Prognosticating Dependent Daily Living in Patients with Thoracic Spinal Cord Injury: A Multicenter Nationwide Japan Registry Study.

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, CPRs 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 the 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.

The effects of nonpharmacological sleep hygiene on sleep quality in nonelderly individuals: A systematic review and network meta-analysis of randomized controlled trials.

The prevalence of locomotive syndrome naturally increases with age, but approximately half of nonelderly individuals also meet the criteria for locomotive syndrome, suggesting that even younger people need to pay attention to their own health status. Sleep is important for physical, cognitive, and psychological health. Some individuals with poor sleep quality may be at risk of developing negative health status. Although the effects of sleep hygiene strategies for elderly individuals have been well investigated, optimal nonpharmacological sleep hygiene strategies for improving sleep quality in nonelderly individuals has not been identified. We conducted a systematic review and network meta-analysis (NMA) of randomized controlled trials aimed to elucidate the effects of various nonpharmacological interventions on sleep quality in nonelderly individuals and to identify the optimal intervention. Cochrane Central Register of Controlled Trials, Medline, Cumulative Index to Nursing and Allied Health Literature, Physiotherapy Evidence Database, and Scopus were comprehensively searched. We identified 27 studies focusing on the effects of various nonpharmacological sleep hygiene strategies in nonelderly individuals, and 24 studies were applied into NMA. The present results showed that resistance training was the most effective intervention for improving sleep quality in nonelderly individuals. In addition, this study revealed the effects of nonpharmacological interventions, such as physical activity, nutritional intervention, as well as exercise interventions. This is the first report that utilized NMA to compare the effects of various nonpharmacological interventions on sleep quality in nonelderly individuals.

Predicting the prognosis of unilateral spatial neglect using magnetic resonance imaging in patients with stroke: A systematic review.

Accurate prognosis prediction of unilateral spatial neglect (USN) is clinically important for identifying patients with potentially poor recovery who require more intensive rehabilitation and early interdisciplinary support for residual disabilities. Magnetic resonance imaging (MRI)-based neuroimaging can provide clinicians with high-quality and high-resolution neuroanatomical information from the aspects of neuroanatomy, integrity of the neural tract, and neural functional connectivity. Although the application of MRI is expected beneficial for the prognosis prediction of USN, there is still no systematic review of its usefulness, and it has not been standardized in the field of stroke rehabilitation. Therefore, we conducted this systematic review to consolidate evidence on the usefulness of MRI in predicting the prognosis of USN in patients with stroke. We comprehensively searched the Medline, Scopus, and Cumulative Index to Nursing and Allied Health Literature electronic databases. We identified 6 longitudinal studies that investigated the relationship between MRI-based neuroimaging findings and subsequent recovery of USN through comprehensive database search. All included studies showed the usefulness of MRI-based findings in predicting the prognosis of USN. The findings of this systematic review highlight the importance of a detailed evaluation of affected neural tracts considering with the differences between the USN subtypes, rather than a broad/undetailed classification of the location, for accurate prognosis prediction of USN in patients with stroke. This is the first report to consolidate evidence on the usefulness of MRI in terms of intra- and interhemispheric neural connection in predicting the prognosis of USN in patients with stroke.

Development of a clinical prediction rule for patients with cervical spinal cord injury who have difficulty in obtaining independent living.

BACKGROUND CONTEXT: A simple and easy to use clinical prediction rule (CPR) to detect patients with a cervical spinal cord injury (SCI) who would have difficulty in obtaining independent living status is vital for providing the optimal rehabilitation and education in both care recipients and caregivers. A machine learning approach was recently applied to the field of rehabilitation and has the possibility to develop an accurate and useful CPR. PURPOSE: The aim of this study was to develop and assess a CPR using a decision tree algorithm for predicting which patients with a cervical SCI would have difficulty in obtaining an independent living. STUDY DESIGN: The present study was a cohort study. PATIENT SAMPLE: In the present study, the data was obtained from the nationwide Japan Rehabilitation Database (JRD). The data on the SCIs was collected from 10 hospitals and the data was collected from the registries obtained between 2005 and 2015. The severity of SCI can vary, and patient prognosis differs depending on the damage site. In this study, the patients with cervical SCI were included. OUTCOME MEASURES: In this study, the degree of the independent living at discharge was investigated. The degree of the independent living was classified and listed as below: independent in social, independent at home, need care at home, independent at facility, need care at facility. In this study, the independent in social and independent at home were defined as "independent," and the other situations were defined as "non-independent." METHODS: We performed a classification and regression tree (CART) analysis to develop the CPR to predict whether the cervical SCI patients obtain an independent living at discharge. The area under the curve, the classification accuracy, sensitivity, specificity, and positive predictive value were used for model evaluation. RESULTS: A total of 4181 patients with SCI were registered in the JRD and the CART analysis was performed for 1282 patients with the cervical SCI. The Functional Independence Measure (FIM) total score and the American Spinal Injury Association impairment scale were identified as the first and second discriminators for predicting the degree of the independence, respectively. Subsequently, the CART model identified FIM eating, the residual function level, and the FIM bed to chair transfer as next discriminators. Each parameter for evaluating the CART model were the area under the curve 0.813, the classification accuracy 78.6%, the sensitivity 80.7%, the specificity 75.1%, and the positive predictive value 84.5%. CONCLUSIONS: In this study, we developed a clinically useful CPR with moderate accuracy to predict whether the cervical SCI patients obtain independent living at the discharge.

Effects of Combining Online Anodal Transcranial Direct Current Stimulation and Gait Training in Stroke Patients: A Systematic Review and Meta-Analysis.

Objective: Combining transcranial direct current stimulation (tDCS) and repetitive gait training may be effective for gait performance recovery after stroke; however, the timing of stimulation to obtain the best outcomes remains unclear. We performed a systematic review and meta-analysis to establish evidence for changes in gait performance between online stimulation (tDCS and repetitive gait training simultaneously) and offline stimulation (gait training after tDCS). Methods: We comprehensively searched the electronic databases Medline, Cochrane Central Register of Controlled Trials, Physiotherapy Evidence Database, and Cumulative Index to Nursing and Allied Health Literature, and included studies that combined cases of anodal tDCS with motor-related areas of the lower limbs and gait training. Nine studies fulfilled the inclusion criteria and were included in the systematic review, of which six were included in the meta-analysis. Result: The pooled effect estimate showed that anodal tDCS significantly improved the 10-m walking test (p = 0.04; I 2 = 0%) and 6-min walking test (p = 0.001; I 2 = 0%) in online stimulation compared to sham tDCS. Conclusion: Our findings suggested that simultaneous interventions may effectively improve walking ability. However, we cannot draw definitive conclusions because of the small sample size. More high-quality studies are needed on the effects of online stimulation, including various stimulation parameters.