Clinical Features for Identifying the Possibility of Toileting Independence after Convalescent Inpatient Rehabilitation in Severe Stroke Patients: A Decision Tree Analysis Based on a Nationwide Japan Rehabilitation Database

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.

Prognostic Factors in Neurorehabilitation of Stroke: A Comparison among Regression, Neural Network, and Cluster Analyses

There is a large body of literature reporting the prognostic factors for a positive outcome of neurorehabilitation performed in the subacute phase of stroke. Despite the recent development of algorithms based on neural networks or cluster analysis for the identification of these prognostic factors, the literature lacks a rigorous comparison among classical regression, neural network, and cluster analysis. Moreover, the three methods have rarely been tested on a sample independent from that in which prognostic factors have been identified. This study aims at providing this comparison on a wide sample of data (1522 patients) and testing the results on an independent sample (1000 patients) using 30 variables. The accuracy was similar among regression, neural network, and cluster analyses on the analyzed sample (76.6%, 74%, and 76.1%, respectively), but on the test sample, the accuracy of neural network decreased (70.1%). The three models agreed in identifying older age, severe impairment, unilateral spatial neglect, and total anterior circulation infarcts as important prognostic factors. The binary regression analysis also provided solid results in the test sample, especially in terms of specificity (81.8%). Cluster analysis also showed a high sensitivity in the test sample (82.6%) and allowed a meaningful easy-to-use classification tree to be obtained.

Comparison of Supervised Machine Learning Algorithms for Classifying of Home Discharge Possibility in Convalescent Stroke Patients: A Secondary Analysis

OBJECTIVES

Classifying the possibility of home discharge is important during stroke rehabilitation to support decision-making. There have been several studies on supervised machine learning algorithms, but only a few have compared the performance of different algorithms based on the same dataset for the classification of home discharge possibility. Therefore, we aimed to evaluate five supervised machine learning algorithms for the classification of home discharge possibility in stroke patients.

MATERIALS AND METHODS

This was a secondary analysis based on the data of 481 stroke patients from the database of our institution. Five models developed by supervised machine learning algorithms, including decision tree (DT), linear discriminant analysis (LDA), k-nearest neighbors (k-NN), support vector machine (SVM), and random forest (RF) were compared by constructing a classification system based on the same dataset. Several parameters including classification accuracy, area under the curve (AUC), and F1 score (a weighted average of precision and recall) were used for model evaluation.

RESULTS

The k-NN model had the best classification accuracy (84.0%) with a moderate AUC (0.88) and F1 score (87.8). The SVM model also showed high classification accuracy (82.6%) along with the highest AUC (0.91), sensitivity (94.4), negative predictive value (87.5), and negative likelihood ratio (0.088). The DT, LDA, and RF models had high classification accuracies (≥ 79.9%) with moderate AUCs (≥ 0.84) and F1 scores (≥ 83.8).

CONCLUSIONS

Regarding model performance, the k-NN and SVM seemed the best candidate algorithms for classifying the possibility of home discharge in stroke patients.

Machine Learning Algorithm Identifies the Importance of Environmental Factors for Hospital Discharge to Home of Stroke Patients using Wheelchair after Discharge

BACKGROUND AND PURPOSE

Physical environmental factors are generally likely to become barriers for discharge to home of wheelchair users, compared with non-wheelchair users. However, the importance of environmental factors has not been investigated adequately. Application of machine learning technology might efficiently identify the most influential factors, although it is not easy to interpret and integrate various information including individual and environmental factors in clinical stroke rehabilitation. This study aimed to identify the influential factors affecting home discharge in the stroke patients who use a wheelchair after discharge by using machine learning technology.

METHODS

This study used the rehabilitation database of our facility, which includes all stroke patients admitted into the convalescence rehabilitation ward. The chi-squared automatic interaction detection (CHAID) algorithm was used to develop a model to classify wheelchair-using stroke patients discharged to home or not-to-home.

RESULTS

Among the variables, including basic information, motor functional factor, activities of daily living ability factor, and environmental factors, the CHAID model identified house renovation and the existence of sloping roads around the house as the first and second discriminators for home discharge.

CONCLUSIONS

Our present results could scientifically clarify that the clinician need to focus on the physical environmental factors for achieving home discharge in the patients who use a wheelchair after discharge.