Risk assessment of obstructive sleep apnea syndrome and other sleep disorders in multiple sclerosis patients

Sünter, Gülin; Ozden, Hatice Omercikoglu; Vural, Ezgi; Günal, Dilek İnce; Ağan, Kadriye

doi:10.1016/j.clineuro.2021.106749

Cited by 7 publications

(3 citation statements)

References 34 publications

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“… 19 , 20 Nevertheless, the STOP-BANG test may not be an accurate tool to identify OSA patients in comorbidity with MS, where the high prevalence of fatigue might represent a confounding factor. 21 According to a recent review (2020), 5 there are only five instrumental case-control studies taking into account RDI or AHI, of which only one involved more than 50 patients (sample size = 62). AHI values varied considerably between studies, ranging from normal to moderately increased.…”

Section: Discussionmentioning

confidence: 99%

Sleep-Related Breathing Disorders in Multiple Sclerosis: Prevalence, Features and Associated Factors

Sparasci¹,

Fanfulla²,

Ferri³

et al. 2022

NSS

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Background Multiple sclerosis (MS) represents a risk factor for sleep disorders, but there are conflicting results about the prevalence and severity of sleep-related breathing disorders (SRBD) in MS. Most available data come from self-administered questionnaires. Objective To conduct a polysomnographic study in MS focused on SRBD, compared to a group of healthy controls (HC), also considering the neuroimaging findings. To evaluate the impact of SRBD on vigilance, fatigue and depression in MS. Methods In this cross-sectional, observational, instrumental study, 67 MS patients (men/women: 20/47; mean age: 50.6±8.2 years) underwent PSG and maintenance of wakefulness test. Findings were compared to 67 age-, sex-, BMI-matched HC, by using parametric (Student’s t -test) and nonparametric statistics (chi-squared test). A subgroup analysis was then performed, evaluating the influence of brainstem (mesencephalic, pontine and medullary) lesions at neuroimaging on instrumental and clinical data: MS patients with at least one brainstem lesion vs MS patients without vs HC. Results The frequency of SRBD was comparable in MS patients and HC. No MS patient had a central apnea index ≥2/h. The respiratory disturbance index (RDI) did not correlate to clinical parameters such as fatigue and depression. Patients with MS were drowsier than HC (47% vs 26%, p = 0.019) and showed a worse sleep pattern, in terms of duration, efficiency and architecture. Conclusion Our study does not provide evidence of an association between MS-specific symptoms such as fatigue, sleepiness, depression and central or obstructive apneas, even in the presence of brainstem lesions.

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Section: Discussionmentioning

confidence: 99%

Sleep-Related Breathing Disorders in Multiple Sclerosis: Prevalence, Features and Associated Factors

Sparasci¹,

Fanfulla²,

Ferri³

et al. 2022

NSS

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“…The treatment method is determined based on the type and degree of obstruction in the affected site. For mild cases, non-invasive treatments should be the first-line management, while invasive surgical treatments are the primary method for severe cases [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…lists five AEregions with the number of quantified colors (𝑞 = 4,6,8,10,12). As shown in Figure14, the greater the number of colors, the greater the number of 𝑹 in the AE-region, and the greater the change in the shape of the region.…”

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confidence: 99%

Development of a Deep Learning-Based Epiglottis Obstruction Ratio Calculation System

Su,

2023

Sensors

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Surgeons determine the treatment method for patients with epiglottis obstruction based on its severity, often by estimating the obstruction severity (using three obstruction degrees) from the examination of drug-induced sleep endoscopy images. However, the use of obstruction degrees is inadequate and fails to correspond to changes in respiratory airflow. Current artificial intelligence image technologies can effectively address this issue. To enhance the accuracy of epiglottis obstruction assessment and replace obstruction degrees with obstruction ratios, this study developed a computer vision system with a deep learning-based method for calculating epiglottis obstruction ratios. The system employs a convolutional neural network, the YOLOv4 model, for epiglottis cartilage localization, a color quantization method to transform pixels into regions, and a region puzzle algorithm to calculate the range of a patient’s epiglottis airway. This information is then utilized to compute the obstruction ratio of the patient’s epiglottis site. Additionally, this system integrates web-based and PC-based programming technologies to realize its functionalities. Through experimental validation, this system was found to autonomously calculate obstruction ratios with a precision of 0.1% (ranging from 0% to 100%). It presents epiglottis obstruction levels as continuous data, providing crucial diagnostic insight for surgeons to assess the severity of epiglottis obstruction in patients.

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Predictive factors and screening strategy for obstructive sleep apnea in patients with advanced multiple sclerosis

Cousin,

Di Maria,

Hartley

et al. 2024

Multiple Sclerosis and Related Disorders

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Risk assessment of obstructive sleep apnea syndrome and other sleep disorders in multiple sclerosis patients

Cited by 7 publications

References 34 publications

Sleep-Related Breathing Disorders in Multiple Sclerosis: Prevalence, Features and Associated Factors

Sleep-Related Breathing Disorders in Multiple Sclerosis: Prevalence, Features and Associated Factors

Development of a Deep Learning-Based Epiglottis Obstruction Ratio Calculation System

Predictive factors and screening strategy for obstructive sleep apnea in patients with advanced multiple sclerosis

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