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Author Spotlight: Enhancing Upper Limb Rehabilitation in Stroke Patients Through Advanced Robotic and Neuromodulation Technologies
Published on: October 11, 2024
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Simplified assessment of upper limb dysfunction after stroke: decision tree analysis based on the International
Xiaobing Chen1,2, Kai L Catherine Chan3,4, Xinyue Wang3,4
1Department of Rehabilitation Medicine, Zhongda Hospital Southeast University, Nanjing, China.
European Journal of Physical and Rehabilitation Medicine
|December 1, 2025
Summary
This study developed a decision tree model using International Classification of Functioning, Disability and Health (ICF) items to assess upper limb dysfunction after stroke. The model simplifies assessment, reducing evaluation time and enhancing clinical application for stroke rehabilitation.
Area of Science:
- Rehabilitation Medicine
- Clinical Assessment
- Health Informatics
Background:
- The International Classification of Functioning, Disability and Health (ICF) offers a comprehensive stroke patient evaluation framework.
- However, its extensive items and complexity make practical application time-consuming and training-intensive.
Purpose of the Study:
- To create and validate a decision tree model using ICF items for assessing upper limb dysfunction post-stroke.
- To develop a more efficient and practical assessment tool for clinicians.
Main Methods:
- A cross-sectional study involving 464 stroke patients across acute, subacute, and chronic phases.
- Utilized the 56-item comprehensive ICF Core Set for stroke.
- Constructed a decision tree model using R package rpart, focusing on ICF items correlated with the Fugl-Meyer Upper Extremity Scale (FM-UE).
Main Results:
- Identified ten ICF items strongly correlated with the FM-UE (P<0.05).
- The final decision tree model incorporated ICF items: 'd4401: grasping,' 'd4553: turning or twisting the hands or arms,' and 'd4551: pushing'.
- The model achieved a statistically significant accuracy of 0.7381 (P=5.008e-13) and an AUC of 0.8406 in validation.
Conclusions:
- Successfully identified key ICF items and developed a statistically significant decision tree model for assessing upper limb dysfunction after stroke.
- The model offers a simplified, efficient, and time-saving assessment tool, enhancing the clinical utility of the ICF in stroke rehabilitation.
