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

Apnea hypopnea index Multiple sclerosis Oximetry Screening Sleep apnea

Journal

Multiple sclerosis and related disorders
ISSN: 2211-0356
Titre abrégé: Mult Scler Relat Disord
Pays: Netherlands
ID NLM: 101580247

Informations de publication

Date de publication:
09 Apr 2024
Historique:
received: 23 01 2023
revised: 22 03 2024
accepted: 07 04 2024
medline: 14 4 2024
pubmed: 14 4 2024
entrez: 13 4 2024
Statut: aheadofprint

Résumé

Obstructive sleep apnea (OSA) screening questionnaires have been evaluated in Multiple Sclerosis (MS) but not yet validated in patients with advanced disease. The aim of this study is to identify OSA predictive factors in advanced MS and to discuss screening strategies. Oximetry data from 125 patients were retrospectively derived from polysomnographic reports. Univariate and multivariate analysis were used to determine predictive factors for OSA. A two-level screening model was assessed combining the oxygen desaturation index (ODI) and a method of visual analysis. multivariate analysis showed that among the clinical factors only age and snoring were associated with OSA. Usual predictive factors such as sleepiness, Body mass index (BMI) or sex were not significantly associated with increased Apnea Hypopnea Index (AHI). The ODI was highly predictive (p < 0.0001) and correctly identified 84.1 % of patients with moderate OSA and 93.8 % with severe OSA. The visual analysis model combined with the ODI did not outperform the properties of ODI used alone. As the usual clinical predictors are not associated with OSA in patients with advanced MS, questionnaires developed for the general population are not appropriate in these patients. Nocturnal oximetry seems a pertinent, ambulatory and accessible method for OSA screening in this population.

Sections du résumé

BACKGROUND BACKGROUND
Obstructive sleep apnea (OSA) screening questionnaires have been evaluated in Multiple Sclerosis (MS) but not yet validated in patients with advanced disease. The aim of this study is to identify OSA predictive factors in advanced MS and to discuss screening strategies.
METHODS METHODS
Oximetry data from 125 patients were retrospectively derived from polysomnographic reports. Univariate and multivariate analysis were used to determine predictive factors for OSA. A two-level screening model was assessed combining the oxygen desaturation index (ODI) and a method of visual analysis.
RESULTS RESULTS
multivariate analysis showed that among the clinical factors only age and snoring were associated with OSA. Usual predictive factors such as sleepiness, Body mass index (BMI) or sex were not significantly associated with increased Apnea Hypopnea Index (AHI). The ODI was highly predictive (p < 0.0001) and correctly identified 84.1 % of patients with moderate OSA and 93.8 % with severe OSA. The visual analysis model combined with the ODI did not outperform the properties of ODI used alone.
CONCLUSION CONCLUSIONS
As the usual clinical predictors are not associated with OSA in patients with advanced MS, questionnaires developed for the general population are not appropriate in these patients. Nocturnal oximetry seems a pertinent, ambulatory and accessible method for OSA screening in this population.

Identifiants

pubmed: 38614056
pii: S2211-0348(24)00187-1
doi: 10.1016/j.msard.2024.105608
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

105608

Informations de copyright

Copyright © 2024 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The Authors declares that there is no conflict of interest in relation to this work.

Auteurs

C Cousin (C)

Service de Physiologie et d'Explorations Fonctionnelles, AP-HP, Hôpital Raymond Poincaré, Garches, France; Unité de recherche clinique Paris Saclay Ouest, AP-HP, Hôpital Raymond Poincaré, Garches, France.

J Di Maria (J)

Service de Physiologie et d'Explorations Fonctionnelles, AP-HP, Hôpital Raymond Poincaré, Garches, France; « End:icap » U1179 Inserm, UVSQ-Université Paris-Saclay, 78000, Versailles, France.

S Hartley (S)

Service de Physiologie et d'Explorations Fonctionnelles, AP-HP, Hôpital Raymond Poincaré, Garches, France.

I Vaugier (I)

Centre d'investigation clinique 1429, AP-HP, Hôpital Raymond Poincaré, Garches, France.

V Delord (V)

SOS Oxygène Nice, France.

D Bensmail (D)

« End:icap » U1179 Inserm, UVSQ-Université Paris-Saclay, 78000, Versailles, France; Service de médecine physique et de réadaptation, AP-HP, Hôpital Raymond Poincaré, Garches, France.

H Prigent (H)

Service de Physiologie et d'Explorations Fonctionnelles, AP-HP, Hôpital Raymond Poincaré, Garches, France; « End:icap » U1179 Inserm, UVSQ-Université Paris-Saclay, 78000, Versailles, France.

A Léotard (A)

Service de Physiologie et d'Explorations Fonctionnelles, AP-HP, Hôpital Raymond Poincaré, Garches, France; « End:icap » U1179 Inserm, UVSQ-Université Paris-Saclay, 78000, Versailles, France; Sleep Lab Initiative In PMR group (SLIIP), France. Electronic address: antoine.leotard@aphp.fr.

Classifications MeSH