A neurophysiological study of facial numbness in multiple sclerosis: Integration with clinical data and imaging findings

Evolution of tongue somatosensory evoked potentials in people with multiple sclerosis

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A significant deterioration of the trigeminal sensory pathway function occurs in pwMS.

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Cervical spinal cord lesions were negative predictor of tSSEP improvement.

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There is a moderate to high correlation between ordinal and quantitative tSSEP scores calculations.

Introduction

The aim of the present study was to investigate the long-term evolution of tongue somatosensory evoked potentials (tSSEP) in people with multiple sclerosis (pwMS).

Methods

Out of initial 121 participants, after two-year follow-up, the data were available for 74 and after four-year follow-up for 58 pwMS. In all pwMS complete neurological examination, brain MRI, cervical spinal cord MRI (if available) and tSSEP were performed at baseline visit (M0). Complete neurological examination and tSSEP were performed 2 and 4 years later (M24 and M48). tSSEP results were interpreted in the form of ordinal tSSEP score and quantitative tSSEP zscore calculated from the sum of z-transformed tSSEP latencies.

Results

Differences in tSSEP scores and tSSEP zscores in three different timepoints showed significant worsening of both scores over time. For the tSSEP score the difference was significant for M0-M24 and M0-M48 visits, but not for M24-M48 visits. For the tSSEP zscore the difference was significant for M0-M48 and M24-M48 visits, but not for M0-M24 visits. The only significant negative predictor found for the tSSEP score improvement was presence of cervical spinal cord lesions on the MRI. A moderate to high correlation was observed between both forms of tSSEP score at all three timepoints.

Conclusion

This study demonstrates a significant deterioration of trigeminal sensory pathway in MS over time, giving further insight into trigeminal system damage in pwMS.

Multi-drug use among patients with multiple sclerosis: A cross-sectional study of associations to clinicodemographic factors

Multiple sclerosis (MS) is the most prevalent immune-mediated disease affecting the central nervous system. A treatment strategy with multiple therapies is a frequent clinical scenario. Unmonitored multi-drug use can lead to adverse outcomes, higher health care costs and medication non-adherence. The primary aim of this study was to evaluate the frequency of polypharmacy and related clinicodemographic factors in a single-center MS patient cohort. Furthermore, medication aspects of therapy management were examined. After the patients agreed to participate in the study, data were collected through patient interviews, patient records and clinical investigations. Subsequently, a statistical data analysis regarding various medication subgroups and polypharmacy (use of at least five drugs) was performed. Polypharmacy was observed in 56.5% of the patients (N = 306). High degrees of disability (odds ratio [OR] = 1.385), comorbidities (OR = 4.879) and inpatient treatment (OR = 5.146) were associated with a significantly higher risk of polypharmacy (p ≤ 0.001). Among patients with polypharmacy, disease-modifying drugs, antihypertensives, gastrointestinal drugs, thrombosis prophylactics, osteoporosis medications and sedatives were frequently used. In summary, polypharmacy plays a large role in MS patients, especially in those with higher degrees of disability, those with comorbidities and those treated in an inpatient setting.

Polypharmacy in outpatients with relapsing-remitting multiple sclerosis: A single-center study

Background

Multiple sclerosis (MS) is an immune-mediated disease of the central nervous system. Given the chronic and heterogenous nature of the disease, treatment with various therapies is a frequent scenario in clinical practice. In persons with chronic morbidity such as MS patients, polypharmacy can give rise to considerable health problems.

Objectives

The aim of the present study was to examine the frequency of polypharmacy among relapsing-remitting (RR) MS patients as well as to analyse sociodemographic and clinical factors, which might be associated with polypharmacy (use of five or more medications). Differences in medication between MS patients with and without secondary illnesses (PwSI and Pw/oSI), between men and women and between patients with and without polypharmacy (PwP and Pw/oP) were examined.

Methods

For 145 RRMS outpatients, we prospectively collected data by means of anamnesis, patient records, clinical examination and a structured patient interview. This was followed by comparative analyses of various patient subgroups (PwP vs. Pw/oP, PwSI vs. Pw/oSI, men vs. women).

Results

The proportion of included MS patients with polypharmacy (use of ≥5 medications) was 30.3%. PwP were significantly older than Pw/oP (45.9 vs. 41.7 years), had a lower level of education and showed a significantly higher median EDSS score (3.0 vs. 2.0). Comorbidities (p<0.001; odds ratio [OR] = 6.293) and higher EDSS scores (p = 0.029; OR = 1.440) were associated with a higher risk of polypharmacy. The proportion of polypharmacy among PwSI was approximately four times higher than among Pw/oSI (46.8% vs. 11.8%). Particularly in the use of antihypertensives, gastrointestinal drugs and dietary supplements, there were differences between Pw/oP and PwP.

Conclusion

We found a high burden of polypharmacy in patients with RRMS. This particularly applies to more severely disabled MS patients who suffer from comorbidities.

Multiple Sclerosis Identification Based on Fractional Fourier Entropy and a Modified Jaya Algorithm

Aim: Currently, identifying multiple sclerosis (MS) by human experts may come across the problem of “normal-appearing white matter”, which causes a low sensitivity. Methods: In this study, we presented a computer vision based approached to identify MS in an automatic way. This proposed method first extracted the fractional Fourier entropy map from a specified brain image. Afterwards, it sent the features to a multilayer perceptron trained by a proposed improved parameter-free Jaya algorithm. We used cost-sensitivity learning to handle the imbalanced data problem. Results: The 10 × 10-fold cross validation showed our method yielded a sensitivity of 97.40 ± 0.60%, a specificity of 97.39 ± 0.65%, and an accuracy of 97.39 ± 0.59%. Conclusions: We validated by experiments that the proposed improved Jaya performs better than plain Jaya algorithm and other latest bioinspired algorithms in terms of classification performance and training speed. In addition, our method is superior to four state-of-the-art MS identification approaches.