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Related Concept Videos

Survival Tree01:19

Survival Tree

128
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
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Related Experiment Video

Updated: Aug 8, 2025

Motor Dual-Tasks for Gait Analysis and Evaluation in Post-Stroke Patients
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Predictive factors for walking in acute stroke patients: a multicenter study using classification and regression tree

Kohei Shida1,2, Kazuhiro Fukata1, Yuji Fujino3

  • 1Department of Rehabilitation Center, Saitama Medical University International Medical Center: 1397-1 Yamane, Hidaka-shi, Saitama 350-1298, Japan.

Journal of Physical Therapy Science
|March 3, 2023
PubMed
Summary

Early prediction of walking ability in acute stroke patients is crucial. A new model using bedside assessments accurately predicts independent walking, aiding rehabilitation planning.

Keywords:
Acute strokeClassification and regression treeMulticenter study

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Area of Science:

  • Neurology
  • Rehabilitation Medicine
  • Clinical Prediction Modeling

Background:

  • Predicting walking ability in acute stroke is essential for timely rehabilitation.
  • Current bedside assessments lack a comprehensive predictive model for independent ambulation.

Purpose of the Study:

  • To develop and validate a prediction model for independent walking in acute stroke patients.
  • Utilize classification and regression tree analysis for model construction.

Main Methods:

  • Multicenter case-control study with 240 acute stroke patients.
  • Data collected: NIH Stroke Scale, Brunnstrom Recovery Stage (lower extremities), ability to turn over from supine, and higher brain dysfunction.
  • Functional Ambulation Categories used to define independent (FAC ≥ 4) and dependent (FAC ≤ 3) walkers.

Main Results:

  • A prediction model was developed using Brunnstrom Recovery Stage, ability to turn over, and higher brain dysfunction.
  • Four patient categories identified with distinct independent walking probabilities (0% to 82.5%).
  • Model effectively stratified patients based on predicted walking outcomes.

Conclusions:

  • A validated bedside prediction model for independent walking in acute stroke patients was established.
  • The model, based on three key criteria, offers a practical tool for early clinical decision-making.
  • Facilitates personalized and efficient rehabilitation strategies for stroke survivors.