Influence of Marital Status and Employment Status on Long-Term Adherence with Continuous Positive Airway Pressure in Sleep Apnea Patients

) of consecutive OSAHS patients in whom CPAP had been prescribed for at least 90 days, we studied the impact on long-term treatment adherence of socioeconomic factors, patients and disease characteristics prior to CPAP initiation. living alone; p = 0.01). Age, gender, Epworth sleepiness scale, depressive syndrome, associated cardiovascular morbidities, educational attainment and occupation category did not influence CPAP adherence.Marital status and employment status are independent factors of CPAP adherence in addition to BMI and disease severity. Patients living alone and/or working patients are at greater risk of non-adherence, whereas adherence is higher in married and retired patients. These findings suggest that the social context of daily life should be taken into account in risk screening for CPAP non-adherence. Future interventional studies targeting at-risk patients should be designed to address social motivating factors and work-related barriers to CPAP adherence

DEFINITIONS ET MESURES DANS LE SYNDROME D'APNEES DU SOMMEIL (DES PNEUMOLOGIE)

ANGERS-BU Médecine-Pharmacie (490072105) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Prise en charge thérapeutique à domicile du syndrome d'apnées obstructives du sommeil (enquête auprès des médecins généralistes du Maine et Loire)

ANGERS-BU Médecine-Pharmacie (490072105) / SudocSudocFranceF

Banking in France,

International audienc

Effects of sleep apnoea therapy on blood pressure and metabolism: a CPAP sex gap?

International audienc

Modalités thérapeutiques du syndrome d’apnées-hypopnées obstructives du sommeil de l’adulte en dehors de la pression positive continue

International audienc

Amélioration des performances diagnostiques de la polygraphie ventilatoire par l’ajout d’une voie d’EEG

National audienc

Ticagrelor-Associated Shift From Obstructive to Central Sleep Apnea: A Case Report

International audienc

Diagnosis of Sleep Apnea Without Sensors on the Patient's Face

International audienc

Towards a user-friendly sleep staging system for polysomnography part I: Automatic classification based on medical knowledge

International audienceManual sleep scoring is a time-consuming task that requires a high level of medical expertise. For this reason, a number of automatic sleep scoring algorithms have recently been implemented. However, their use by physicians remains limited for various reasons: a lack of transparency of the approach used, insufficient heterogeneity among the patients used for testing, or a lack of practicality. This paper presents a system for facilitated sleep scoring that will overcome these limitations. The proposed system, a user-friendly tool based on electrophysiological channels, was trained and tested on large datasets of 300 and 100 distinct recordings from patients with various sleep disorders. The method replicates the manual sleep scoring process, in accordance with the American Academy of Sleep Medicine (AASM) guidelines and generates patient-dependent sleep scoring (using the SATUD system). For an improved level of precision and confidence with regard to scoring, our approach also provides a table that gives indications about the confidence level of the algorithm when scoring sleep. In contrast to recent deep learning approaches, the algorithms used were chosen for their resilience and as they are easy to understand. Medical knowledge was included in the process as much as possible. Results showed that the system is consistent with manual scoring (mean Cohen's Kappa of 0.69 and accuracy rate of 77.8%). It proves that a facilitated interpretation of the model, crucial in such fields as sleep diagnosis, can be provided when using automatic tools. This new system thereby generates sleep scoring decision support tools, which should easily contribute to significant time-saving and help sleep specialists to perform sleep diagnosis