Prospectively Classifying Community Walkers After Stroke: Who Are They?

Area of Science:

• Neurology
• Rehabilitation Medicine
• Biostatistics

Background:

• Stroke rehabilitation aims to restore functional independence.
• Predicting long-term outcomes like community ambulation is crucial for discharge planning.
• Subgrouping patients aids in tailoring interventions and resource allocation.

Purpose of the Study:

• To classify stroke patients into subgroups at discharge.
• To predict community ambulation outcomes 6 months post-discharge.
• To identify key predictors for community ambulation.

Main Methods:

• Prospective cohort study design.
• Classification And Regression Tree (CART) analysis for model development.
• Community ambulation questionnaire for outcome assessment.
• Baseline predictors included demographics, stroke details, gait speed, balance, and psychological factors.

Main Results:

• A CART model accurately predicted community ambulation.
• Comfortable gait speed of ≥0.5 m/s at discharge predicted independent community ambulation.
• Gait speeds <0.5 m/s correctly predicted non-community walkers.

Conclusions:

• Comfortable gait speed is a critical prognostic factor for community ambulation post-stroke.
• The CART model can assist clinicians in organizing community services.
• Early prediction facilitates timely and appropriate patient support.