Literature DB >> 35305537

Developing a Clinical Prediction Rule for Gait Independence at Discharge in Patients with Stroke: A Decision-Tree Algorithm Analysis.

Yu Inoue1, Takeshi Imura2, Ryo Tanaka3, Junji Matsuba4, Kazuhiro Harada5.   

Abstract

OBJECTIVES: To develop a clinical prediction rule (CPR) for gait independence at discharge in patients with stroke, using the decision-tree algorithm and to investigate the usefulness of CPR at admission to the rehabilitation ward.
MATERIALS AND METHODS: We included 181 subjects with stroke during the postacute phase. The Chi-squared automatic interaction detection analysis method with 10-fold cross-validation was used to develop two CPRs; CPR 1 using easily obtainable data available at admission; CPR 2 using easily obtainable data available 1 month after admission, for prediction of gait independence at discharge.
RESULTS: The degree of independence of toileting was extracted as a first node in the development of two CPRs to predict gait independence at discharge. CPR 1 included the presence of delirium. CPR 2 included problem-solving abilities. The accuracy and area under the curve of CPR 1 were 84.5% and 0.911, respectively; those of CPR 2 were 89.0% and 0.958, respectively.
CONCLUSIONS: Toileting independence is a key factor in predicting the gait independence for the discharge of patients with stroke during the postacute phase. Early intervention, during the acute phase, for delirium and cognitive decline, as well as for toileting, increases the possibility of gait independence at discharge.
Copyright © 2022 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Clinical prediction rule; Decision-tree algorithm analysis; Gait; Independence; Stroke; Toileting

Mesh:

Year:  2022        PMID: 35305537     DOI: 10.1016/j.jstrokecerebrovasdis.2022.106441

Source DB:  PubMed          Journal:  J Stroke Cerebrovasc Dis        ISSN: 1052-3057            Impact factor:   2.136


  1 in total

1.  Decision effect of a deep-learning model to assist a head computed tomography order for pediatric traumatic brain injury.

Authors:  Sejin Heo; Juhyung Ha; Weon Jung; Suyoung Yoo; Yeejun Song; Taerim Kim; Won Chul Cha
Journal:  Sci Rep       Date:  2022-07-21       Impact factor: 4.996

  1 in total

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