Abstract 100: A Simple Bedside Grading Scale Can Effectively Predict Severe Post-stroke Upper-extremity Spasticity

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Introduction: Upper-limb spasticity is a very disabling complication after stroke. There has been no simple clinical standard scale to predict spasticity immediately after stroke. This study aims to develop a simple bedside grading scale with the information collected during the acute phase to predict spasticity at 3 month post-strokeMethods: This is a prospective cohort study (Prediction and Imaging Biomarker of Post-stroke Motor Recovery) of patients with first-ever acute ischemic stroke with various degrees of motor impairment. NIH stroke scale (NIHSS) was assessed 2-7 days after onset of stroke symptoms. Modified Ashworth Spasticity Scale was used as an assessment tool in biceps, wrist flexors and finger flexors at 90 days (± 15 days) and score ≥2 at any muscle was considered as severe spasticity. Infarction volume was measured based on the lesion on MRI/DWI. Independent predictors of upper-limb spasticity at 90 days were identified by multivariate logistic regression. A risk stratification scale was developed with weighting independent predictors based on beta coefficient.Results: One hundred twenty three patients were recruited for this study. Covariates associated with upper-limb spasticity are NIHSS arm score (p<0.0001), sub-cortical location (p=0.004) and lesion volume >65 cc (p=0.025). The proposed grading scale is summation of individual points as followed: NIHSS Arm Score: =4 (2 point), <4 (0 point); infarct location: sub-cortical (1 point), non sub-cortical (0 point); infarct volume: ≥65 cc (1 point), <65cc (0 point). The rates of severe upper limb spasticity for the bedside spasticity scale, in order 0-4, are 8.9%, 29.2%, 65%, 88.7%, 96.2%. In other words, the likelihood of developing severe spasticity increases steadily using the score.Conclusion: A simple bedside grading scale can effectively predict severe post-stroke upper-limb spasticity at 90 days. Validation with an independent external dataset is a planned next step.

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