The Somatotropic Axis in the Sleep Apnea-Obesity Comorbid Duo

International audienc

Symptomatic response to CPAP in obstructive sleep apnea versus COPD- obstructive sleep apnea overlap syndrome: Insights from a large national registry

International audienc

Application of Inverse-Probability-of-Treatment Weighting to Estimate the Effect of Daytime Sleepiness in Obstructive sleep apnea patients.

RATIONALE: Continuous positive airway pressure (CPAP), the first line therapy for obstructive sleep apnea (OSA), is considered effective in reducing daytime sleepiness. Its efficacy relies on adequate adherence, often defined as >4 hours per night. However, this binary threshold may limit our understanding of the causal effect of CPAP adherence and daytime sleepiness and multilevel approach for CPAP adherence can be more appropriate. OBJECTIVE: In this study, we show how two causal inference methods can be applied on observational data for the estimation of the effect of different ranges of CPAP adherence on daytime sleepiness measured by the Epworth sleepiness score (ESS) . METHODS: Data were collected from a large prospective observational French cohort for OSA patients. Four groups of CPAP adherence were considered (0-4; 4-6; 6-7 and 7-10 hours per night). Multivariable regression, inverse-probability-of-treatment weighting (IPTW) and IPTW with regression adjustment (IPTW-RA) were used to assess the impact of CPAP adherence level on daytime sleepiness. RESULTS: In this study, 9,244 OSA patients treated by CPAP were included. The mean initial ESS was 11 (±5.2) with a mean reduction of 4 points (±5.1). Overall, there was an evidence of the causal effect of CPAP adherence on daytime sleepiness which was mainly observed between the lower CPAP adherence group (0-4h) compared to the higher CPAP adherence group (7-10h). There are no differences by considering higher level of CPAP adherence (>4h). CONCLUSION: We showed that IPTW and IPTW-RA can be easily implemented to answer questions regarding causal effects using observational data when randomized trials cannot be conducted. Both methods give a direct causal interpretation at the population-level and allow the assessment of the appropriate consideration of measured confounders

Impact of a Multimodal Telemonitoring Intervention on CPAP Adherence in Symptomatic OSA and Low Cardiovascular Risk

International audienc

Impact of a Multimodal Telemonitoring Intervention on CPAP Adherence in Symptomatic OSA and Low Cardiovascular Risk - A Randomized Controlled Trial

International audienc

Percentage of side effects according to the type of mask.

<p>-<b><i>for ocular irritation</i></b> § = difference between nasal mask and oronasal mask with p = 0.049; @ = difference between oronasal mask and nasal pillows with p = 0.062. <b><i>-for dry mouth</i></b>: * = difference between nasal mask and oronasal mask with p<0.0001. <b><i>-for Choking sensation under CPAP</i></b>: # = difference between nasal mask and oronasal mask with p = 0.0024. - <b><i>for psychologically perceived inconvenience</i></b>: ¤ = difference between nasal mask and oronasal mask with p = 0.0001.</p

Sleep breathing disorders severity, technical aspects of CPAP treatment and side-effects according to CPAP adherence status and risks of being non-adherent (univariate analysis).

<p>Sleep breathing disorders severity, technical aspects of CPAP treatment and side-effects according to CPAP adherence status and risks of being non-adherent (univariate analysis).</p

Adherence (a) and positive airway pressure level (b) according to the type of interface.

<p>Adherence (a) and positive airway pressure level (b) according to the type of interface.</p

Comparison between patients included in the analysis and patients excluded because of missing adherence or type of mask data.

<p>Comparison between patients included in the analysis and patients excluded because of missing adherence or type of mask data.</p

Patients’ characteristics (n = 2311).

<p>Patients’ characteristics (n = 2311).</p