Electromyographic Prediction of Walking Independence in Patients With Incomplete Spinal Cord Injury

Area of Science:

• Neurology
• Rehabilitation Medicine
• Biomedical Engineering

Background:

• Incomplete cervical cord injury (ICCI) presents challenges in predicting functional recovery, particularly walking independence.
• Accurate prediction of walking ability is crucial for effective rehabilitation planning and patient management.

Purpose of the Study:

• To develop and validate a predictive model for walking independence in ICCI patients upon hospital discharge.
• To identify key electromyography (EMG) and clinical parameters that predict ambulation status.

Main Methods:

• A prospective cohort study involving 40 ICCI patients was conducted.
• Electromyography (EMG) of the trunk and lower extremity, along with clinical data, were collected during straight-leg raising (SLR).
• Logistic regression was used to build a prediction model for walking independence.

Main Results:

• The predictive model incorporating lower extremity motor score (LEMS) and root mean square (RMS) of the contralateral external oblique (EO) demonstrated high accuracy (0.875).
• Both LEMS and contralateral EO RMS during SLR were significant predictors (P=.019 and P=.034, respectively).
• The model achieved excellent sensitivity (0.850), specificity (0.900), and AUC (0.975).

Conclusions:

• Higher LEMS and lower contralateral EO muscle activity during SLR are significant predictors of walking independence in ICCI patients.
• These findings offer a valuable tool for early prognostication and tailored rehabilitation strategies.
• The study highlights the utility of EMG during functional tasks for predicting recovery in ICCI.