Predicting spasticity after stroke in those surviving to 12 months

Area of Science:

• Neurology
• Rehabilitation Medicine
• Clinical Prediction Modeling

Background:

• Stroke is a leading cause of long-term disability, with spasticity being a common and often debilitating complication.
• Early identification of patients at risk for spasticity is crucial for timely intervention and improved patient outcomes.
• Current methods for predicting spasticity post-stroke are limited, necessitating the development of more effective tools.

Purpose of the Study:

• To assess muscle tone 12 months after stroke in a patient cohort.
• To develop and validate a predictive model for spasticity using routinely collected stroke data.
• To identify early predictors of abnormal muscle tone and severe spasticity post-stroke.

Main Methods:

• A prospective cohort study involving 106 stroke survivors assessed at 12 months post-stroke.
• Muscle tone and spasticity were measured using the Tone Assessment Scale across various joints.
• Logistic regression analysis was employed to identify significant predictors of spasticity.

Main Results:

• Spasticity was observed in 36% of patients, with 20% experiencing more severe spasticity at 12 months.
• A lower day 7 Barthel Index score and early arm/leg weakness predicted abnormal muscle tone.
• Predictors for more severe muscle tone included a lower day 7 Barthel Index score, left-sided weakness, and a history of smoking.

Conclusions:

• Preliminary models suggest that spasticity development 12 months post-stroke can be predicted using readily available clinical data.
• The identified predictors (e.g., early functional status, limb weakness, smoking history) are routinely collected, enhancing model utility.
• Prospective validation in new cohorts is essential to confirm the models' reliability, validity, and efficiency for clinical application.