1. Practical tool to identify Spasticity-Plus Syndrome amongst patients with multiple sclerosis. Algorithm development based on a conjoint analysis
- Author
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Óscar Fernández Fernández, Lucienne Costa-Frossard, Maria Luisa Martínez Ginés, Paloma Montero Escribano, José María Prieto González, Lluís Ramió-Torrentà, Yolanda Aladro, Ana Alonso Torres, Elena Álvarez Rodríguez, Andrés Labiano-Fontcuberta, Lamberto Landete Pascual, Ambrosio Miralles Martínez, Ester Moral Torres, and Pedro Oliva-Nacarino
- Subjects
multiple sclerosis ,spasticity ,Spasticity-Plus Syndrome ,conjoint analysis ,nabiximols ,bladder dysfunction ,Neurology. Diseases of the nervous system ,RC346-429 - Abstract
IntroductionThe 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.MethodsA 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.Results72 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 (r = 0.936) and Spearman’s (r = 0.893) correlation coefficients. The application of the algorithm provides with a probability of showing SPS and the following ranges are proposed to interpret the results: high (> 60%), moderate (30–60%), or low (< 30%) probability of SPS.DiscussionThis 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.
- Published
- 2024
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