Marta Kaminska

Obstructive sleep apnea is associated with fatigue in multiple sclerosis.

Background: Multiple sclerosis (MS) patients often suffer from fatigue. Objective: We evaluated the relationship of obstructive sleep apnea (OSA) to fatigue and sleepiness in MS patients. Methods: Ambulatory MS patients without known sleep disorders and healthy controls underwent diagnostic polysomnography and a multiple sleep latency test (objective sleepiness measure). Fatigue was measured with the Fatigue Severity Scale (FSS) and the Multidimensional Fatigue Inventory (MFI), and subjective sleepiness by Epworth Sleepiness Scale. Covariates included age, sex, body mass index, Expanded Disability Status Scale (EDSS), depression, pain, nocturia, restless legs syndrome, and medication. Results: OSA (apnea–hypopnea index ≥15) was found in 36 of 62 MS subjects and 15 of 32 controls. After adjusting for confounders, severe fatigue (FSS ≥5) and MFI-mental fatigue (>group median) were associated with OSA and respiratory-related arousals in MS, but not control subjects. Subjective and objective sleepiness were not related to OSA in either group. In a multivariate model, variables independently associated with severe fatigue in MS were severe OSA [OR 17.33, 95% CI 2.53–199.84], EDSS [OR 1.88, 95% CI 1.21–3.25], and immunomodulating treatment [OR 0.14, 95% CI 0.023–0.65]. Conclusions: OSA was frequent in MS and was associated with fatigue but not sleepiness, independent of MS-related disability and other covariates.

Impact of sleep disorder treatment on fatigue in multiple sclerosis

Background: We recently reported that sleep disorders are significantly associated with fatigue in multiple sclerosis (MS). Objective: The objective of this paper is to assess the effects of sleep disorder treatment on fatigue and related clinical outcomes in MS. Methods: This was a controlled, non-randomized clinical treatment study. Sixty-two MS patients completed standardized questionnaires including the Fatigue Severity Scale (FSS), Multidimensional Fatigue Inventory (MFI), Epworth Sleepiness scale (ESS) and Pittsburgh Sleep Quality Index (PSQI), and underwent polysomnography (PSG). Patients with sleep disorders were offered standard treatment. Fifty-six subjects repeated the questionnaires after ≥ three months, and were assigned to one of three groups: sleep disorders that were treated (SD-Tx, n=21), sleep disorders remaining untreated (SD-NonTx, n=18) and no sleep disorder (NoSD, n=17). Results: FSS and MFI general and mental fatigue scores improved significantly from baseline to follow-up in SD-Tx (p <0.03), but not SD-NonTx or NoSD subjects. ESS and PSQI scores also improved significantly in SD-Tx subjects (p <0.001). Adjusted multivariate analyses confirmed significant effects of sleep disorder treatment on FSS (-0.87, p = 0.005), MFI general fatigue score (p = 0.034), ESS (p = 0.042) and PSQI (p = 0.023). Conclusion: Treatment of sleep disorders can improve fatigue and other clinical outcomes in MS.

Polysomnographic measures of disturbed sleep are associated with reduced quality of life in multiple sclerosis

BACKGROUND The relationship of objective sleep parameters with health-related quality of life (HRQoL) in multiple sclerosis (MS) has not been studied. OBJECTIVE To evaluate the relationship between polysomnographic (PSG) parameters and HRQoL in MS. METHODS Ambulatory MS patients without a known sleep disorder completed the Short Form (36) Health Survey (SF-36), pain visual analog scale, and two consecutive overnight PSGs. HRQoL was assessed using SF-36 Physical and Mental Component Summary (PCS, MCS) scores. Standard objective PSG measures of sleep quality were determined. The relationship between objective sleep parameters and HRQoL was evaluated with multivariate linear regression, adjusting for age, sex, body mass index, disability, and pain. RESULTS 62 MS patients were included. PSG measures of sleep disruption including stage changes, awakenings, time in N1 sleep, and apnea-hypopnea and total arousal indices were negatively associated (p<0.05) with MCS scores (lower scores indicating poorer HRQoL). PSG parameters reflective of better sleep quality including total sleep time, sleep efficiency, and time in REM sleep were positively associated with MCS scores. PSG parameters were not significantly associated with PCS scores. CONCLUSIONS PSG-documented sleep disruption negatively impacts, while better objective sleep quality positively impacts on the mental domain of HRQoL in MS.

Validation of respiratory signal derived from suprasternal notch acceleration for sleep apnea detection

This study evaluates the respiration signal derived from an accelerometer mounted on the suprasternal notch in three body positions and three respiration types simulating normal sleep conditions. The Acceleration Derived Respiratory signal (ADR) is compared with single strain gauge belt and a standard spirometry signal taken as reference. The results demonstrate the potential of ADR as a simple, low cost and unintrusive method of screening breath disorders such as obstructive sleep apnea/hypopnea.

Assessment of respiratory flow and efforts using upper-body acceleration

AbstractIn this paper, an innovative method for estimating the respiratory flow and efforts is proposed and evaluated in various postures and flow rates. Three micro electro-mechanical system accelerometers were mounted on the suprasternal notch, thorax and abdomen of subjects in supine, prone and lateral positions to record the upper airway acceleration and the movements of the chest and abdomen wall. The respiratory flow and efforts were estimated from the recorded acceleration signals by applying machine learning methods. To assess the agreement of the estimated signals with the well-established measurement methods, standard error of measurement (SEM) was calculated and

Optimal method for assessment of respiratory muscle strength in neuromuscular disorders using sniff nasal inspiratory pressure (SNIP)

\rho = 1-{\rm SEM}

High false-positive rate of questionnaire-based restless legs syndrome diagnosis in multiple sclerosis

ρ=1-SEM was estimated for every condition. A significant agreement between the estimated and reference signals was found (