ArticleFrontiers in neurology2024
Practical tool to identify Spasticity-Plus Syndrome amongst patients with multiple sclerosis. Algorithm development based on a conjoint analysis.
Article in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A proof-of-concept study on the effectiveness of botulinum toxin on spasticity plus syndrome in multiple sclerosis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Article
- Peripheral neuromodulation in spasticity-plus syndrome: effects of pulsed radiofrequency on tonic-painful disorders in multiple sclerosis.Frontiers in neurology · 2025Article
- Enhancing the endocannabinoid system to treat residual disease in relapse-free multiple sclerosis.Frontiers in neurologyReview
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Authors and funding
14 authors.
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Abstract
Introduction: The Spasticity-Plus Syndrome (SPS) in multiple sclerosis (MS) refers to a combination of spasticity and other signs/symptoms such as spasms, cramps, bladder dysfunction, tremor, sleep disorder, pain, and fatigue. The main purpose is to develop a user-friendly tool that could help neurologists to detect SPS in MS patients as soon as possible. Methods: A survey research based on a conjoint analysis approach was used. An orthogonal factorial design was employed to form 12 patient profiles combining, at random, the eight principal SPS signs/symptoms. Expert neurologists evaluated in a survey and a logistic regression model determined the weight of each SPS sign/symptom, classifying profiles as SPS or not. Results: 72 neurologists participated in the survey answering the conjoint exercise. Logistic regression results of the survey showed the relative contribution of each sign/symptom to the classification as SPS. Spasticity was the most influential sign, followed by spasms, tremor, cramps, and bladder dysfunction. The goodness of fit of the model was appropriate (AUC = 0.816). Concordance between the experts' evaluation vs. model estimation showed strong Pearson's ( Discussion: This study offers an algorithmic tool to help healthcare professionals to identify SPS in MS patients. The use of this tool could simplify the management of SPS, reducing side effects related with polypharmacotherapy.
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