Predicting Improved Daily Use of the More Affected Arm Poststroke Following Constraint-Induced Movement Therapy

Abstract

Background: Constraint-induced movement therapy (CI therapy) produces, on average, large and clinically meaningful improvements in the daily use of a more affected upper extremity in individuals with hemiparesis. However, individual responses vary widely.

Objective: The study objective was to investigate the extent to which individual characteristics before treatment predict improved use of the more affected arm following CI therapy.

Design: This study was a retrospective analysis of 47 people who had chronic (> 6 months) mild to moderate upper extremity hemiparesis and were consecutively enrolled in 2 CI therapy randomized controlled trials.

Methods: An enhanced probabilistic neural network model predicted whether individuals showed a low, medium, or high response to CI therapy, as measured with the Motor Activity Log, on the basis of the following baseline assessments: Wolf Motor Function Test, Semmes-Weinstein Monofilament Test of touch threshold, Motor Activity Log, and Montreal Cognitive Assessment. Then, a neural dynamic classification algorithm was applied to improve prognostic accuracy using the most accurate combination obtained in the previous step.

Results: Motor ability and tactile sense predicted improvement in arm use for daily activities following intensive upper extremity rehabilitation with an accuracy of nearly 100%. Complex patterns of interaction among these predictors were observed.

Limitations: The fact that this study was a retrospective analysis with a moderate sample size was a limitation.

Conclusions: Advanced machine learning/classification algorithms produce more accurate personalized predictions of rehabilitation outcomes than commonly used general linear models.

Trial registration: ClinicalTrials.gov NCT02631850 NCT01725919.

Figure 1

Average rates of selection of 6 inputs in combinations with an accuracy of at least 75%.

Figure 2

Scatterplot of the predictors in the most accurate prediction model. Poor responders improved less than the minimal clinically important difference (1.0); moderate responders’ Motor Activity Log (MAL) change was between 1.0 and 2.0; best responders had a MAL change of > 2.0. The main trends were as follows: (1) those with the poorest sensation were moderate or best responders; (2) those with better sensation and motor ability were moderate or best responders; (3) in the presence of relatively intact sensation, those with the poorest gross motor ability were poor responders. Although the spatial clustering of data points affects the classification, machine learning discovers spatial distortions in the variable space that cause some points to be statistically “closer” or farther from each other; such distortions would not be evident from this scatterplot. This 3-dimensional Figure is accessible for viewing via MATLAB at https://drive.google.com/file/d/14-pmb4ydT6zbXVqNZ_m_-ZLe2rZpFcgf/view?usp=sharing. WMFT = Wolf Motor Function Test.