Cargando…

Application of Logistic Regression and Decision Tree Models in the Prediction of Activities of Daily Living in Patients with Stroke

An improvement in the activities of daily living (ADLs) is significantly related to the quality of life and prognoses of patients with stroke. However, the factors predicting significant improvement in ADL (SI-ADL) have not yet been clarified. Therefore, we sought to identify the key factors affecti...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Qile, Zhang, Zheyu, Huang, Xiuqing, Zhou, Chun, Xu, Jian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8816537/
https://www.ncbi.nlm.nih.gov/pubmed/35126507
http://dx.doi.org/10.1155/2022/9662630
_version_ 1784645456920313856
author Zhang, Qile
Zhang, Zheyu
Huang, Xiuqing
Zhou, Chun
Xu, Jian
author_facet Zhang, Qile
Zhang, Zheyu
Huang, Xiuqing
Zhou, Chun
Xu, Jian
author_sort Zhang, Qile
collection PubMed
description An improvement in the activities of daily living (ADLs) is significantly related to the quality of life and prognoses of patients with stroke. However, the factors predicting significant improvement in ADL (SI-ADL) have not yet been clarified. Therefore, we sought to identify the key factors affecting SI-ADL in patients with stroke after rehabilitation therapy using both logistic regression modeling and decision tree modeling. We retrospectively collected and analyzed the clinical data of 190 patients with stroke who underwent rehabilitation therapy at our hospital between January 2020 and July 2020. General and rehabilitation therapy data were extracted, and the Barthel index (BI) score was used for outcome assessment. We defined SI-ADL as an improvement in the BI score by 15 points or more during hospitalization. Logistic regression and decision tree models were established to explore the SI-ADL predictors. We then used receiver operating characteristic (ROC) curves to compare the logistic regression and decision tree models. Univariate analysis revealed that compared with the non-SI-ADL group, the SI-ADL group showed a significantly shorter course of stroke, longer hospital stay, and higher rate of receiving occupational and speech therapies (all P < 0.05). Binary logistic regression analysis revealed the course of stroke at admission (odds ratio (OR) = 0.986, 95%confidence interval (CI) = 0.979–0.993; P < 0.001) and the length of hospital stay (OR = 1.030, 95%CI = 1.013–1.047; P =0.001) as the independent predictors of SI-ADL. ROC comparisons revealed no significant differences in the areas under the curves for the logistic regression and decision tree models (0.808 vs. 0.831; z = 0.977, P = 0.329). Both models identified the course of disease at admission and the length of hospital stay as key factors affecting SI-ADL. Early initiation of rehabilitation therapy is of immense importance for improving the ADLs in patients with stroke.
format Online
Article
Text
id pubmed-8816537
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Hindawi
record_format MEDLINE/PubMed
spelling pubmed-88165372022-02-05 Application of Logistic Regression and Decision Tree Models in the Prediction of Activities of Daily Living in Patients with Stroke Zhang, Qile Zhang, Zheyu Huang, Xiuqing Zhou, Chun Xu, Jian Neural Plast Research Article An improvement in the activities of daily living (ADLs) is significantly related to the quality of life and prognoses of patients with stroke. However, the factors predicting significant improvement in ADL (SI-ADL) have not yet been clarified. Therefore, we sought to identify the key factors affecting SI-ADL in patients with stroke after rehabilitation therapy using both logistic regression modeling and decision tree modeling. We retrospectively collected and analyzed the clinical data of 190 patients with stroke who underwent rehabilitation therapy at our hospital between January 2020 and July 2020. General and rehabilitation therapy data were extracted, and the Barthel index (BI) score was used for outcome assessment. We defined SI-ADL as an improvement in the BI score by 15 points or more during hospitalization. Logistic regression and decision tree models were established to explore the SI-ADL predictors. We then used receiver operating characteristic (ROC) curves to compare the logistic regression and decision tree models. Univariate analysis revealed that compared with the non-SI-ADL group, the SI-ADL group showed a significantly shorter course of stroke, longer hospital stay, and higher rate of receiving occupational and speech therapies (all P < 0.05). Binary logistic regression analysis revealed the course of stroke at admission (odds ratio (OR) = 0.986, 95%confidence interval (CI) = 0.979–0.993; P < 0.001) and the length of hospital stay (OR = 1.030, 95%CI = 1.013–1.047; P =0.001) as the independent predictors of SI-ADL. ROC comparisons revealed no significant differences in the areas under the curves for the logistic regression and decision tree models (0.808 vs. 0.831; z = 0.977, P = 0.329). Both models identified the course of disease at admission and the length of hospital stay as key factors affecting SI-ADL. Early initiation of rehabilitation therapy is of immense importance for improving the ADLs in patients with stroke. Hindawi 2022-01-28 /pmc/articles/PMC8816537/ /pubmed/35126507 http://dx.doi.org/10.1155/2022/9662630 Text en Copyright © 2022 Qile Zhang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhang, Qile
Zhang, Zheyu
Huang, Xiuqing
Zhou, Chun
Xu, Jian
Application of Logistic Regression and Decision Tree Models in the Prediction of Activities of Daily Living in Patients with Stroke
title Application of Logistic Regression and Decision Tree Models in the Prediction of Activities of Daily Living in Patients with Stroke
title_full Application of Logistic Regression and Decision Tree Models in the Prediction of Activities of Daily Living in Patients with Stroke
title_fullStr Application of Logistic Regression and Decision Tree Models in the Prediction of Activities of Daily Living in Patients with Stroke
title_full_unstemmed Application of Logistic Regression and Decision Tree Models in the Prediction of Activities of Daily Living in Patients with Stroke
title_short Application of Logistic Regression and Decision Tree Models in the Prediction of Activities of Daily Living in Patients with Stroke
title_sort application of logistic regression and decision tree models in the prediction of activities of daily living in patients with stroke
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8816537/
https://www.ncbi.nlm.nih.gov/pubmed/35126507
http://dx.doi.org/10.1155/2022/9662630
work_keys_str_mv AT zhangqile applicationoflogisticregressionanddecisiontreemodelsinthepredictionofactivitiesofdailylivinginpatientswithstroke
AT zhangzheyu applicationoflogisticregressionanddecisiontreemodelsinthepredictionofactivitiesofdailylivinginpatientswithstroke
AT huangxiuqing applicationoflogisticregressionanddecisiontreemodelsinthepredictionofactivitiesofdailylivinginpatientswithstroke
AT zhouchun applicationoflogisticregressionanddecisiontreemodelsinthepredictionofactivitiesofdailylivinginpatientswithstroke
AT xujian applicationoflogisticregressionanddecisiontreemodelsinthepredictionofactivitiesofdailylivinginpatientswithstroke