Abstract
BackgroundThe pendulum test is commonly used to quantify knee extensor spasticity, but it is currently unknown to what extent common pendulum test metrics can detect spasticity in patients with neurological injury or disease, and if the presence of flexor spasticity influences the test outcomes.MethodsA retrospective analysis was conducted on 131 knees, from 93 patients, across four different patient cohorts. Clinical data included Modified Ashworth Scale (MAS) scores for knee extensors and flexors, and years since diagnosis. BioTone™ measures included extensor strength, passive and active range of motion, and pendulum tests of most affected or both knees. Pendulum test metrics included the relaxation index (RI), 1st flexion amplitude (F1amp) and plateau angle (Plat), where RI=F1amp/Plat. Two-way ANOVA tests were used to determine if pendulum test metrics were influenced by the degree of knee flexor spasticity graded by the MAS, and ANCOVA was used to test for confounding effects of age, years since injury, strength and range of motion (ROM). In order to identify the best pendulum test metrics, Receiver Operator Characteristic analysis and logistic regression (LR) analysis were used to classify knees by spasticity status (none or any) and severity (low/moderate or high/severe).ResultsPendulum test metrics for knee extensors were not influenced by degree of flexor spasticity, age, years since injury, strength or ROM of the limb. RI, F1amp and Plat were > 70% accurate in classifying knees by presence of clinical spasticity (from the MAS), but were less accurate (< 70%) for grading spasticity level. The best classification accuracy was obtained using F1amp and Plat independently in the model rather than using RI alone.ConclusionsWe conclude that the pendulum test has good predictive value for detecting the presence of extensor spasticity, independent of the existence of flexor spasticity. However, the ability to grade spasticity level as measured by MAS using the RI and/or F1amp may be limited. Further study is warranted to explore if the pendulum test is suitable for quantifying more severe spasticity.
Highlights
The pendulum test is commonly used to quantify knee extensor spasticity, but it is currently unknown to what extent common pendulum test metrics can detect spasticity in patients with neurological injury or disease, and if the presence of flexor spasticity influences the test outcomes
Modified Ashworth Scale (MAS) score for knee flexors and extensors for patient cohorts are shown in Table 2, and are grouped according to most affected and least affected side, with the total number of knees shown in the last row
For Model 2 (Table 8.) the highest classification accuracy value was 70.9% for the model using Plateau angle alone, followed by 68.6% for the model using F1amp and Plateau angle in combination. It is clear for Model 2 that predictively is heavily biased toward the negative condition; that is, the classifier was better at identifying negative results than positive results. These results show that F1amp and Plateau angle explained more variance in the logistic regression analysis than relaxation index (RI) alone, for detecting the presence of spasticity per a MAS or 1 or greater (Ext1)
Summary
The pendulum test is commonly used to quantify knee extensor spasticity, but it is currently unknown to what extent common pendulum test metrics can detect spasticity in patients with neurological injury or disease, and if the presence of flexor spasticity influences the test outcomes. Spasticity is typically assessed by inducing a rapid stretch of the muscle, or administering a “stretch-reflex” test. Clinical tests such as the Modified Ashworth Scale [5] and Tardieu Scale [6] apply this method to quantify spasticity subjectively, but their inter-rater reliability [7,8,9,10] and validity [11] have been questioned. Several studies have examined objective approaches to quantifying spasticity in the clinic using wearable sensor technologies during passive muscle stretches [9, 12,13,14,15], but there is not yet a clear consensus on testing protocol and how to best translate the resulting electrophysiological and biomechanical signals. Additional details of the larger study can be found elsewhere [13]
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