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    Development of restless legs syndrome severity prediction models for people with multiple sclerosis using machine learning
    (Galenos Publ House, 2025) Kaya, Ergi; Emec, Murat; Ozdogar, Asiye Tuba; Zengin, Eta Simay; Karakas, Hitat; Dastan, Seda; Ozakbas, Serkan
    Objectives: This study aimed to develop an artificial intelligence-supported restless legs syndrome (RLS) severity prediction model for people with multiple sclerosis using machine learning methods. Patients and methods: Twenty-three individuals (14 females, 7 males; mean age: 40.6 +/- 10.9 years; range, 33 to 44 years) with multiple sclerosis with RLS were included in this observational study between March 2022 and March 2023. The International Restless Legs Syndrome Study Group Rating Scale was used to determine the RLS severity of the participants. The age, sex, body mass index, regular exercise habits, disease duration, Expanded Disability Status Scale (EDSS), estimated maximal aerobic capacity (VO2max), Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale, Multiple Sclerosis International Quality of Life Questionnaire, Multiple Sclerosis Walking Scale-12 (MSWS-12), and timed 25-foot walk test were determined as predictive variables. A correlation matrix was created. DecisionTree, RandomForest, and XGBoost machine learning methods were used to develop a model for predicting the RLS severity. Results: According to the obtained correlation matrix, PSQI scores strongly correlated with RLS severity (Pearson r=0.76). Meanwhile, EDSS scores (0.49), MSWS-12 scores (0.45), and disease duration (0.45) showed moderate correlations with RLS. Among the three different meachine learning methods, XGBoost demonstrated the best performance in predicting the severity of RLS, with a mean absolute error of 1.94, mean squared error of 4.58, mean absolute percentage error of 0.0735, and a test accuracy of 92.65%. The results showed that the severity of RLS could be estimated with 92.65% accuracy. Conclusion: This study showed a strong correlation between PSQI scores and RLS severity and that RLS severity could be predicted using machine learning methods.
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    Factors related to restless leg syndrome in neuromyelitis optica spectrum disorder
    (Elsevier Sci Ltd, 2025) Ozdogar, Asiye Tuba; Karakas, Hilal; Dastan, Seda; Kaya, Ergi; Sagici, Ozge; Ozcelik, Sinem; Ozakbas, Serkan
    Objective: Although the feeling of unrest in the legs is frequently reported as a sensory symptom by people with Neuromyelitis Optica Spectrum Disorder (NMOSD, pwNMOSD), there are limited studies to investigate the relationship between Restless Legs Syndrome (RLS) and NMOSD. The study's primary aim is to determine the frequency and severity of RLS in pwNMOSD. The other aim is to compare the sleep quality, daytime sleepiness level, quality of life, fatigue, magnetic resonance imaging results, and cognitive functions in RLS-positive and negative pwNMOSD. Methods: The RLS diagnosis was performed with RLS-Diagnostic Index criteria. The patient-reported outcomes were RLS Severity Rating Score, The Preference-Based Multiple Sclerosis Index (PBMSI), the Modified Fatigue Impact Scale (MFIS), Pittsburgh Sleep Quality Index (PSQI), and Epworth Sleepiness Scale (ESS). Cognitive function was assessed with The Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) battery. The neurologist recorded the demographic and clinical characteristics of the participants. Results: The RLS was detected in 17 (21.5 %) of the 79 pwNMOSD participants. Fifty-six pwNMOSD were reached to assess cognitive functions and patient-reported outcomes. The rate of RLS was 60.71 % in this group. The PBMSI, PSQI, MFIS, and ESS scores were significantly different in RLS-positive participants than in RLS-negative (p < 0.05). Moreover, while participants' visuospatial and verbal learning was similar, the processing speed was slow in the RLS-positive group (p > 0.05). Conclusions: Our preliminary results have shown that the RLS frequency is high in pwNMOSD. This study suggests a connection between the presence of RLS and worse sleep quality, fatigue level, processing speed, and quality of life in the NMOSD population. However, our results should be considered with the fact that the study has a small sample size and needs future studies to confirm our results for solid evidence.