Predictive factors for walking in acute stroke patients: a multicenter study using classification and regression tree analysis.

Abstract

[Purpose] Walking ability should be predicted as early as possible in acute stroke patients. The purpose is to construct a prediction model for independent walking from bedside assessments using classification and regression tree analysis. [Participants and Methods] We conducted a multicenter case-control study with 240 stroke patients. Survey items included age, gender, injured hemisphere, the National Institute of Health Stroke Scale, the Brunnstrom Recovery Stage for lower extremities, and "turn over from a supine position" from the Ability for Basic Movement Scale. The National Institute of Health Stroke Scale items, such as language, extinction, and inattention, were grouped under higher brain dysfunction. We used the Functional Ambulation Categories to classify patients into independent (four or more the Functional Ambulation Categories; n=120) and dependent (three or fewer the Functional Ambulation Categories; n=120) walking groups. A classification and regression tree analysis was used to create a model to predict independent walking. [Results] The Brunnstrom Recovery Stage for lower extremities, "turn over from a supine position" from the Ability for Basic Movement Scale, and higher brain dysfunction were the splitting criteria for classifying patients into four categories: Category 1 (0%), severe motor paresis; Category 2 (10.0%), mild motor paresis and could not turn over; Category 3 (52.5%), with mild motor paresis, could turn over, and had higher brain dysfunction; and Category 4 (82.5%), with mild motor paresis, could turn over, and no higher brain dysfunction. [Conclusion] We constructed a useful prediction model for independent walking based on the three criteria.