Center of Pressure- and Machine Learning-based Gait Score and Clinical Risk Factors for Predicting Functional Outcome in Acute Ischemic Stroke.

OBJECTIVES

To investigate whether machine learning (ML)-based center of pressure (COP) analysis for gait assessment, when used in conjunction with clinical information, offers additive benefits in predicting functional outcomes in patients with acute ischemic stroke.

DESIGN

A prospective, single-center cohort study.

SETTING

A tertiary hospital setting.

PARTICIPANTS

A total of 185 patients with acute ischemic stroke, capable of walking 10 m with or without a gait aid by day 7 postadmission. From these patients, 10,804 pairs of consecutive footfalls were included for analysis.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

The dependent variable was a 3-month poor functional outcome, defined as modified Rankin scale score ≥2. For independent variables, 65 clinical variables including demographics, anthropometrics, comorbidities, laboratory data, questionnaires, and drug history were included. Gait function was evaluated using a pressure-sensitive mat. Time-series COP data were parameterized into spatial and temporal variables and analyzed with logistic regression and 2 ML models (light gradient-boosting machine and multilayer perceptron [MLP]). We derived GAIT-AI output scores from the best-performing model analyzed COP data and constructed multivariable logistic regression models using clinical variables and the GAIT scores.

RESULTS

Among the included patients, 70 (37.8%) experienced unfavorable outcomes. The MLP model demonstrated the highest predictive performance with an area under the receiver operating characteristic curve (AUROC) of 0.799. Multivariable logistic regression identified age, initial National Institutes of Health Stroke Scale, and initial Fall Efficacy Scale-International as associated factors with unfavorable outcomes. The combined multivariable logistic regression incorporating COP-derived output scores improved the AUROC to 0.812.

CONCLUSIONS

Gait function, assessed through COP analysis, serves as a significant predictor of functional outcome in patients with acute ischemic stroke. ML-based COP analysis, when combined with clinical data, enhances the prediction of poor functional outcomes.