Abstract
Background
Positive airway pressure (PAP) including noninvasive ventilation or continuous PAP are standard of care in chronic hypercapnic respiratory failure (CHRF). PAP is applied during sleep so its impact on sleep quality and daytime sleepiness is relevant. This systematic review and meta-analysis investigated the effects of PAP for CHRF on sleep quality.
Methods
Relevant studies were identified by a PubMed/Embase search up to October 2024. Eligible studies included PAP initiation and evaluation of sleep quality/sleepiness. Evaluated outcomes were sleep efficiency, Pittsburgh Sleep Quality Index (PSQI), Severe Respiratory Insufficiency sleep subscale (SRI-AS) and Epworth Sleepiness Scale (ESS).
Results
58 studies were included (n=2511; mean age 59.1 years, 57% male) and the indication for PAP was obesity hypoventilation syndrome (n=1073), neuromuscular disease (NMD) (n=649), COPD (n=428) or other/mixed aetiologies (n=361). Overall improvements were +5.87% (95% CI 2.64–9.09) for sleep efficiency, −2.51 (95% CI −3.22–−1.80) for PSQI, +10.75 (95% CI 6.11–15.40) for SRI-AS score and −4.96 (95% CI −5.96–−3.97) for ESS score. Adherence to PAP was the only factor significantly associated with sleep efficiency improvement. ESS and PSQI improved to a greater extent in people with a higher body mass index, younger age and hypercapnia correction during PAP. ESS improvement was associated with sleep efficiency improvement. PSQI improved to a greater extent in females and those with NMD.
Conclusion
PAP initiation was associated with clinically relevant objective and subjective sleep quality improvements. Given the health benefits of good sleep, the effect of sleep quality improvements during PAP on prognosis should be investigated.
Shareable abstract
PAP initiation is associated with clinically significant improvements in objective and subjective sleep quality. Prospective well-designed studies with PAP initiation should include comprehensive assessment of subjective and objective sleep parameters. https://bit.ly/4lssI9y
Introduction
Chronic respiratory failure (CRF) ranks among the top 10 causes of death worldwide [1]. Type II respiratory failure (i.e. associated with hypercapnia) involves three mechanisms, as follows: an imbalance between increased workload of the chest/abdominal walls and insufficient respiratory muscle strength; impaired central ventilatory drive and/or peripheral motoneuron function; and direct lung dysfunction due to intrathoracic airway obstruction or parenchyma and/or vascularisation damage [2]. These mechanisms, either individually or in combination, result in alveolar hypoventilation in individuals with diseases where chronic hypercapnic respiratory failure (CHRF) is common, namely obesity hypoventilation syndrome (OHS), neuromuscular disease (NMD) and COPD.
The main consequences of CHRF include recurrent hospitalisations and increased mortality rates [3–7]. Additionally, people with CHRF have reduced quality of life due to the impact of respiratory symptoms including shortness of breath and cough, and disrupted sleep with unrefreshing sleep and daytime sleepiness [3, 8–10]. Sleep quality impairment is particularly relevant as a major determinant of somatic and mental health and quality of life in chronic conditions including CHRF [11–16].
Over the past two decades, positive airway pressure (PAP) has emerged as the standard of care in CHRF, normalising alveolar ventilation and pulmonary gas exchange. Noninvasive ventilation (NIV), initially applied in NMD and later in COPD [17, 18], and both continuous positive airway pressure (CPAP) and NIV in OHS, have significantly improved morbidity and mortality in patients with CHRF [19–23]. Beyond the prognostic benefit, large improvements in quality of life underscore the pivotal role of PAP in managing CHRF [19, 24–26].
Sleep depresses respiratory function in both physiological and pathophysiological conditions [27–29]. Moreover, CHRF is associated with impaired sleep quality and with increased wake after sleep onset, which decreases sleep efficacy [11, 12, 30, 31]. Sleep macrostructure is also disrupted with a notable reduction in slow wave sleep and rapid eye movement sleep [12, 31–33].
Due to the vulnerability of respiration during sleep and sleep disturbances in CHRF, PAP is predominantly administered during sleep. While it is intuitive to assume that PAP would enhance sleep quality by improving nocturnal alveolar ventilation and reducing respiratory muscle workload during sleep, PAP devices themselves may negatively impact sleep structure and quality [34]. Device noise and associated drawbacks, such as leaks, mask discomfort and patient ventilator asynchrony, can interfere with sleep onset and maintenance [35–40]. In addition, persistent alveolar hypoventilation or residual upper airway obstructions despite the use of PAP can prevent complete normalisation of sleep [12, 41, 42].
Sleep quality can be assessed directly by measuring sleep stability and structure from sleep recordings, such as polysomnography (PSG), and indirectly by analysing the daytime consequence of sleep, including wakefulness quality. Sleep efficiency is commonly used as an objective marker of sleep quality [43]. Subjective sleep quality is commonly assessed using sleep-specific patient-reported outcome measures (PROMs) such as the Pittsburgh Sleep Quality Index (PSQI) or specific subscales of CHRF-related quality of life indices, such as the Severe Respiratory Insufficiency questionnaire sleep and attendant symptoms subscales (SRI-AS) [44, 45]. Daytime symptoms such as sleepiness can also be evaluated using questionnaires such as the Epworth Sleepiness Scale (ESS) (table S1) [46–48].
Many studies have attempted to determine the impact of PAP on sleep quality in patients with CHRF [34, 39, 49–52]. A recent literature review concluded that improvements in both subjective and objective sleep quality are influenced by the specific parameters used for assessment [12]. Furthermore, the effects of PAP on sleep quality may vary between different CHRF aetiologies. Therefore, comprehensive analysis of existing literature is warranted to better understand if and how PAP impacts sleep quality.
The primary objective of this systematic review and meta-analysis was to evaluate changes in objective sleep efficiency measured by PSG in patients with CHRF treated with PAP. Secondary objectives were to investigate the effects of PAP on subjective sleep quality and daytime sleepiness and to determine demographic and clinical variables associated with the impact of PAP on sleep-related parameters. We hypothesised that PAP would improve sleep efficiency, subjective sleep quality and sleepiness, and that these improvements would be modulated by several factors including patient characteristics, the aetiology of CHRF, the degree of arterial carbon dioxide pressure (PaCO2) reduction and the time interval between PAP implementation and reassessment of outcomes.
Methods
Search strategy and study design
The systematic and comprehensive search for relevant studies was conducted using the bibliographic databases Medline® (PubMed) and Embase (see supplementary methods for full details of the search strategy) from database inception to 2 November 2023, with an update performed on 28 October 2024. The protocol of this systematic review and meta-analysis adheres to the current recommendations outlined in the PRISMA statement and the Cochrane handbook for meta-analysis [53, 54]. It was prospectively registered in the PROSPERO database (CRD42023495516) in December 2023.
Sleep quality and sleepiness parameters of interest
All authors participated in discussions to determine the relevant sleep parameters to include in the meta-analysis (parameters considered are detailed in table S1). After a thorough review of existing literature, there was consensus on the inclusion of four main outcomes, as follows: one objective measure (sleep efficiency) and three subjective questionnaires (PSQI, SRI-AS and ESS). These outcomes were selected based on their clinical relevance and their inclusion in studies on the topic of interest [12]. Sleep efficiency based on PSG data is defined as the total sleep time/total recording time; it can also be reliably measured with actigraphy based on multiple nights of measurement [55]. The PSQI measures sleep quality and disturbance during the last month based on seven components; a global score of ≥5 of a maximum 21 indicates poor sleep quality [56]. The SRI is a tool designed to measure quality of life in patients receiving home mechanical ventilation. The SRI-AS component measures attendant symptoms; the total possible score is 100, which indicates the best sleep quality (there is currently no consensus regarding normative values for SRI-AS) [45]. The ESS measures propensity to fall asleep in eight situations and excessive daytime sleepiness is defined as an ESS score of ≥11 out of a possible total of 24 [46].
Study inclusion criteria
Studies potentially eligible for inclusion were initially identified by two authors (P. Tankéré and L. Razakamanantsoa) and retrieved for further evaluation. The two main criteria were PAP initiation and an evaluation of sleep quality or sleepiness. A preliminary blinded selection of relevant articles based on titles and abstracts was performed by P. Tankéré and L. Razakamanantsoa using Rayyan.ai without any artificial intelligence intervention. The selection focused on identifying studies that included at least one of the predefined outcomes. Any disagreements were resolved through discussion, and when necessary, a final decision was made by a third expert (R. Tamisier). The second stage of blinded selection on full texts was performed (P. Tankéré and L. Razakamanantsoa) on the availability of at least one of the four outcomes. Moreover, this step allowed us to ensure that a same sample of population was not used in multiple manuscripts and to avoid duplicate outcomes. Any disagreements were resolved by consensus or with a third author judgment (R. Tamisier ).
Inclusion criteria were English-language articles without restriction on the year of publication, adult participants, evaluation of at least one of predefined outcomes of interest (sleep efficiency, PSQI, SRI-AS, ESS) with either evaluation before and after PAP initiation or a control group; both cohort studies and randomised trials were included. Given the primary role of CPAP in the treatment of OHS, studies involving patients treated with CPAP were included provided hypercapnia was documented using daytime arterial blood gas analysis [3]. Thus, we used the term PAP to refer to NIV or CPAP initiated for OHS, unless otherwise specified. Exclusion criteria are detailed in the supplementary methods and include invasive mechanical ventilation. Importantly COPD-OSA overlap treated with CPAP was not included since CPAP is not intended to treat alveolar hypoventilation in this context.
Data extraction
Characteristics of the studies and relevant outcome data were extracted in a specific form designed to capture the necessary parameters. Individual data extraction (P. Tankéré) was double-checked against a full-text review by a second author (L. Razakamanantsoa). In cases where additional information or clarification was required, study authors were contacted for unpublished data sets or methodological details. Data on study design, sample size, sleep efficiency, PSQI, SRI-AS, ESS, main aetiology of CHRF (COPD, OHS, neuromuscular disease or a mixture of these and other conditions (e.g. interstitial lung diseases), referred to as other/mixed indications), type of PAP (NIV or CPAP), age, sex, body mass index (BMI), and pulmonary function test results as well as time interval between the baseline and follow-up sleep outcomes measurements and mean PAP usage (hours per night) were extracted.
Risk of bias (quality) assessment
The risk of bias of included studies was independently assessed by two reviewers using the ROBINS tool (table S2) [57]. Any disagreements were resolved through discussion and, if necessary, a final decision was made by a third expert (R. Tamisier).
Statistical analysis
Population characteristics were calculated using data provided in the studies weighted by data size at the post-NIV initiation evaluation (i.e. the population finally included in the meta-analysis). Random-effect meta-analyses were performed for the four outcomes of interest using mean and standard deviation values provided in the included studies. If only median and interquartile range values were provided, these were approximated to mean±sd using standard statistical methods [58, 59]. For studies including multiple aetiologies of CHRF or treatment modalities, data were extracted for each subgroup of patients if data were reported by underlying aetiology or by the type of PAP.
Because pre- and post-treatment measures are not independent, we estimated the mean change for each outcome during PAP, assuming an a priori correlation coefficient of 0.7 between pre- and post-measurements for each sleep quality outcome. By design, in this type of meta-analysis, the unavailability of individual data prevented us from calculating the pre–post coefficient of correlation [60, 61]. Therefore, we used 0.7 for the main analysis, which is the figure most frequently cited in similar cases [62]. However, to provide a more thorough assessment of robustness, we also conducted a sensitivity analysis with two other correlation coefficients of 0.5 and 0.9 (reported as supplementary material). This methodology frames the results obtained using 0.7 with an upper and lower value.
We also calculated standardised mean changes for pre- versus post-PAP initiation values; except for ESS, which even related to sleep quality assesses rather a daily consequence of it. We used a three-level model to account for the nonindependence of the data from subgroups of the same study.
Heterogeneity between studies was evaluated using forest plots and by estimating I2 statistics. As substantial between-study heterogeneity was anticipated, a random-effects model was used to pool effect sizes. Funnel plot asymmetry was explored through visual inspection and using Egger's regression test, as recommended by the Cochrane Handbook for Systemic Reviews of Interventions [54], with p<0.05 suggesting publication bias.
We conducted a sensitivity analysis excluding CPAP-treated patients with OHS to investigate a potential differential effect of CPAP versus NIV in this aetiology of CHRF.
To explore the association between sleep quality and PAP and heterogeneity, we performed meta-regression analyses adjusted on the main confounding factors with available data, as follows: BMI, sex, age, primary indication for PAP, PaCO2 improvement after PAP initiation, time interval between the baseline and follow-up sleep outcomes measurements, sleep efficiency variation for subjective measurements, and mean PAP usage.
Statistical analysis was performed using R version 4.0.5 and R studio, as well as the meta and metafor packages.
Results
Studies included
After automatic removal of duplicate records, a total of 4820 references were screened. Of these, 97 articles met the inclusion criteria and were selected for full-text assessment (figure 1). The main reasons for exclusion in the first selection phase were publication type (e.g. case report or case series with <5 patients), studies involving paediatric populations, lack of data on the outcomes of interest or no use of NIV or CPAP. The second selection based on full-text evaluation of these 97 articles led to the exclusion of another 39 articles (due to lack of relevant outcome data, duplicate data or a study population that was included in a previously selected article). Finally, 58 articles were included in the meta-analysis (table S3).
FIGURE 1.
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 flow diagram for the studies included in the meta-analysis [53].
Risk of bias
Most of the included studies were retrospective and reported one or two of the relevant parameters selected for analysis. Evaluation using the ROBINS criteria showed heterogeneity in study quality, with a low risk of bias arising from measurement exposure, post-treatment intervention or missing data (ROBINS domains 2, 4 and 5) (table S4). Conversely, there was a high risk of confounding bias (domain 1) given the lack of randomised studies comparing NIV with a sham control or standard care. There was also a high risk of bias in the selection of reported results (domain 7) (table S4). Finally, bias related to the measurement of outcome or in the selection of participants (domains 6 and 3) (table S4) was sometimes unclear given the lack of precision in the definition of sleep efficiency (table S2).
Study population
From the 58 articles included, we extracted data from 2511 patients who were started on PAP (1073 with OHS, 649 with NMD, 428 with COPD and 361 with other/mixed indications). The overall population was middle-aged with obesity (57% male) and moderate respiratory impairment (table 1). PAP was initiated with NIV in 83.6% patients and for the OHS group, 38.4% were treated with CPAP and the remainder (61.6%) were treated with NIV. Patients with OHS had the highest mean BMI and functional vital capacity, those with COPD were older, more likely to be male and had the highest baseline PaCO2, and those with NMD patients had the lowest BMI, baseline FVC and baseline PaCO2 (table 1).
TABLE 1.
Patient characteristics and sleep-related parameters at baseline
| Characteristic | Overall (n=2511) | Missing data | OHS (n=1073) | NMD (n=649) | COPD (n=428) | Other# (n=361) |
|---|---|---|---|---|---|---|
| BMI, kg·m−2 | 36.3±9.3 | 17.1% | 43.8±5.0 | 25.4±2.2 | 28.±4.8 | 32.3±5.6 |
| Age, years | 59.1±7.3 | 9.2% | 58.3±5.9 | 58.6±7.5 | 65.1±4.0 | 50.2±11.1 |
| Male sex, n (%) | 57.0 (15.1) | 5.3% | 51.4 (12.2) | 59.5 (17.8) | 66.7 (15.4) | 57.7 (9.9) |
| FVC, % | 67.5±14.1 | 29.0% | 76.6±8.8 | 54.4±13.3 | 59.2±6.4 | 63.2±15.8 |
| PaCO2, mmHg | 51.8±5.8 | 11.2% | 51.0±3.4 | 48.6±9.2 | 54.5±5.3 | 55.7±3.5 |
| Interval, days | 162.9±238.5 | 5.4% | 288±319.9 | 41.6±53.5 | 73.8±57.7 | 111.9±75.7 |
| Adherence | 6.2±1.4 | 37.2% | 5.9±1.0 | 7.3±1.7 | 6.3±2.2 | 6.5±1.1 |
| Outcomes | ||||||
| Sleep efficiency, % | 67.1±9.6 | 62.4% | 73.7±4.1 | 63.3±11.2 | 60.8±6.4 | 66.2±10.0 |
| ESS score | 11.1±2.3 | 38.3% | 12.0±1.8 | 9.4±2.7 | 10.0±2.3 | 10.0±2.7 |
| PSQI | 8.6±1.4 | 68.3% | 9.8±1.0 | 7.5±1.2 | 9.2±1.7 | 8.6±0.8 |
| SRI-AS score | 51.1±4.2 | 89.1% | 50.6±4.7 | 50.4±1.4 | NA | NA |
Data are reported as mean±sd, unless stated otherwise. BMI: body mass index; ESS: Epworth Sleepiness Scale; FVC: functional vital capacity; Interval: time between pre- and post-pressure support therapy initiation assessment; NA: not available; NMD: neuromuscular diseases; OHS: obesity hypoventilation syndrome; PaCO2: arterial carbon dioxide tension; PSQI: Pittsburgh Sleep Quality Index; SRI-AS: Severe Respiratory Insufficiency questionnaire sleep and attendant symptoms subscales. #: Other/mixed indications for pressure support therapy, including cystic fibrosis (n=30), “predominantly restrictive” (n=60) and studies where the indication was not reported.
Sleep data
Baseline and follow-up data on sleep efficiency were available for 944 patients, PSQI for 785 patients, SRI-AS for 273 patients and ESS for 1480 patients. The mean±sd time between baseline and follow-up assessments was 5.4±7.9 months; patients with NMD or COPD had shorter interval between measurements than those with OHS.
At baseline, sleep efficiency in the overall population was low, subjective sleep quality was impaired and the ESS score indicated clinically relevant daytime sleepiness (table 1). Baseline sleep-related measures differed between groups according to CHRF aetiology, with patients who had OHS showing the highest initial PSQI and ESS scores (table 1).
Meta-analysis for primary and secondary objectives
Use of PAP significantly improved sleep efficiency (by 5.87%, 95% CI 2.64–9.09) (table 2). The largest improvement was seen in patients with COPD (+7.38%), followed by NMD (+7.36%) and OHS (+4.81%) (figure 2). PAP also significantly improved subjective sleep quality based on the PSQI, daytime sleepiness (ESS score) and the SRI-AS (table 2). The impact of PAP on sleep parameters varied by CHRF aetiology, with slight differences in the change in PSQI (figure S1) and greater between-group differences in the change in ESS score, which was greatest in the OHS subgroup (figure S2). Analysis by CHRF aetiology could not be performed for SRI-AS due to the small sample size (figure S3).
TABLE 2.
Meta analysis results: mean change from before to after initiation of positive airway pressure
| Meta-analysis reporting metric | Sleep efficiency | PSQI | SRI-AS | ESS score |
|---|---|---|---|---|
| Mean change estimate | 5.87 | −2.51 | 10.75 | −4.96 |
| p-value | <0.001 | <0.001 | <0.001 | <0.001 |
| Estimate confidence interval | 2.64–9.09 | −3.22–−1.80 | 6.11–15.40 | −5.96–−3.97 |
| Patients (n) | 944 | 785 | 273 | 1480 |
| Samples (k) | 42 | 22 | 9 | 40 |
| I2 (%) | 88.9 | 97.0 | NA | 98.3 |
| Egger (p-value) | 0.32 | 0.43 | 0.57 | 0.76 |
| Documented MCID | Not documented | 3.0 | 8.6 | 2.0 |
ESS: Epworth Sleepiness Scale, I2: I-squared statistical measure of heterogeneity; MCID: minimal clinically important difference; NA: not available; PSQI: Pittsburgh Sleep Quality Index; SRI-AS: Severe Respiratory Insufficiency questionnaire sleep and attendant symptoms subscales.
FIGURE 2.
Sleep efficiency by study and overall, by disease classification. a) Neuromuscular diseases. b) Obesity hypoventilation syndrome. c) COPD. MC: mean change. Details of the included studies are included in the supplementary material.
Although visual examination of the forest plots (figures S1, 2, S2 and S3) showed some discrepancies between studies, funnel plot analysis showed a symmetrical pattern for the estimated effect (figure S4) and Egger regressions did not support publication bias (table 2).
Meta-analysis on standardised mean change
PAP had a very close beneficial standardised effect on PSQI, SRI-AS and objective sleep efficiency, with overlapping confidence intervals (figure 3).
FIGURE 3.
Forest plot and results of the meta-analysis for standardised mean change (SMC) before and after initiation of positive airway pressure. SMC values are the measure of difference in mean change divided by standard deviation of outcome among participants. PSQI: Pittsburgh Sleep Quality Index; SRI-AS: Severe Respiratory Insufficiency questionnaire sleep and attendant symptoms subscales.
Meta regression
PAP usage was the only significant moderator of sleep efficiency on meta-regression (table 3 and figure S5). Greater improvements in the PSQI were seen in patients with NMD, higher BMI, elevated PaCO2. PSQI improvements were significantly smaller in older patients and males. No factors were found to significantly influence the SRI-AS response to PAP. Greater improvements in the ESS score were seen in patients with a higher BMI, elevated PaCO2 and those who had better sleep efficiency during PAP; improvements in the ESS score were smaller in older patients.
TABLE 3.
Meta regression result: mean change before and after positive airway pressure (PAP) initiation
| Clinical and treatment variables | Sleep efficiency | PSQI | SRI-AS | ESS |
|---|---|---|---|---|
| Disease category | NS | * | NS | NS |
| COPD | Ref. | Ref. | Ref. | Ref. |
| NMD | −0.41 | −0.67** | 3.12 | −0.39 |
| OHS | −2.64 | −0.22 | 2.92 | −2.09** |
| Other/mixed indications | −4.4 | 0.06 | 4.02 | 1.34 |
| Age, years | 0.15 | 0.06** | 0.55 | 0.10** |
| Male sex | 0.06 | 0.02*** | −0.03 | 0.01 |
| BMI, kg·m −2 | −0.02 | −0.07** | −0.03 | −0.11** |
| Use of bilevel PAP# | 1.41 | −0.09 | NA | −0.99 |
| ΔPaCO2, mmHg | 0.21 | −0.05** | −1.01 | −0.06* |
| Δ time measurement , days | 0.03 | −0.003 | −0.04 | −0.003 |
| Δ sleep efficiency, % | −0.12 | −0.68 | −0.24* | |
| PAP usage, h·day−1 | 5.80** | 0.10 | 0.003 | −0.12 |
Data are reported as mean estimated linear coefficient and statistical significance as follows: *: p<0.05, **: p<0.01, ***: p<0.0.001. BMI: body mass index; ESS: Epworth Sleepiness Scale; NA: not available; NMD: neuromuscular diseases; NS: statistically nonsignificant; OHS: obesity hypoventilation syndrome; PaCO2: arterial carbon dioxide tension; PSQI: Pittsburgh Sleep Quality Index; Ref.: reference; SRI-AS: Severe Respiratory Insufficiency questionnaire sleep and attendant symptoms subscales. #: As opposed to CPAP.
Sensitivity analysis
Findings after exclusion of patients with OHS being treated with CPAP were consistent with the overall analysis. Objective sleep efficiency improved by 5.96%, the PSQI decreased by 2.51, the SRI-AS increased by 10.75 and the ESS score decreased by 4.78. Significant moderators in the meta regression were also largely similar, except that BMI and sleep efficiency were no longer significant moderators of the change in ESS during PAP.
Sensitivity analysis with correlation coefficient of 0.5 and 0.9 between pre- and post-measurements provide very similar results (table S5).
Discussion
The results of this meta-analysis showed that treatment of CHRF with PAP was associated with an important improvement in sleep quality. Specifically, initiation of PAP significantly improved sleep efficiency, subjective sleep quality and daytime sleepiness, with the greatest standardised effect observed for sleepiness. The magnitude of sleep improvements during the use of PAP varied between CHRF aetiologies, highlighting disparities in treatment effect. Finally, in the meta regression, we identified several significant moderators of the effects of PAP on sleep. These findings suggest that the heterogeneity of sleep changes during PAP might be due to a variety of factors, including the aetiology underlying CHRF, age, sex, BMI, change in PaCO2 during PAP and adherence to therapy (device usage hours).
The changes in sleep-related parameters in our study are likely to be clinically relevant because their magnitude is comparable to those achieved with specific interventions targeting sleep quality and the values align with commonly accepted minimal clinically important differences (MCID). Sleep efficiency improved by 5.87% in our study compared to an improvement of only 1.5% with CPAP for treatment of OSA [63]. Also, for comparison after adjusting for the placebo effect, the improvement in sleep efficiency with hypnotics in patients with insomnia is 3.0% after 2 weeks [64],
A change of 2 points in the ESS score is considered the MCID [65]. The −4.96-point change after PAP initiation in our meta-analysis is greater than this MCID. For PSQI, the MCID of 3 points [65] was reached in the OHS population (figure S1) but not in the overall population. There MCID for the SRI-AS is 8.6 and the 10-point change seen in our meta-analysis represents a 20% improvement from baseline and is clinically meaningful [66].
Our pooled analysis does not allow calculation of the precise proportion of individuals who achieved normal sleep efficiency, PSQI, SRI-AS and ESS during PAP. However, the values at baseline and the mean effect derived would suggest that these often do not reach normal levels. For instance, from a baseline value of 67.1%, a 5.87% improvement in sleep efficiency would not reach the normal value of 85.7% [67]. Initial impairments are therefore often not fully resolved, potentially due to imperfect respiratory support, PAP-induced discomfort or the impact of comorbidities on sleep (comorbidities are common in patients receiving PAP) [68–72].
Improving sleep quality in CHRF may have therapeutic benefits or at least modulate the clinical response. Sleep deprivation is known to decrease not only peripheral but also respiratory muscle strength [73]. This suggests that enhanced sleep quality could contribute to the observed respiratory benefits of PAP across all aetiologies of CHRF where impairment of respiratory muscle function may play a role [30]. Moreover, interventions targeting poor sleep quality or quantity have been linked to improvement in quality of life [74, 75]. Improvement in sleep quality could be one potential contributor to these quality-of-life improvements during PAP [76]. Finally, improving sleep quality is also associated with a better cardiovascular health and sleep health is one of “life's essential 8” as defined by an advisory from the American Heart Association [77]. This is particularly relevant because CHRF is often associated with cardiovascular comorbidities [78–81].
Our meta regression analysis highlighted several determinants of improved sleep outcomes during PAP, consistent with existing literature. The parameter significantly associated with sleep efficiency was adherence to PAP, which reinforces the well-documented correlation between adherence to treatment and clinical benefits across various conditions [82–84]. The lack of statistical significance for associations between improved sleep and time since PAP initiation and disease category (global test), except for PSQI, is also in line with previous data from cross-sectional studies [37, 55]. The identification of NMD as a significant (individual) moderator for PSQI, as well as OHS as a significant moderator for ESS, reinforces the need for qualitative evaluation and suggests that personalised goals regarding sleep should be defined according to disease categories [12]. Interestingly, changes in the PSQI and ESS score were modulated by several factors including age, sex, BMI and change in PaCO2 during PAP, which argue for including this information and these measurements in CHRF clinical assessment to better predict and monitor the effect of treatments.
Key strengths of this study are that it is the first meta-analysis to evaluate sleep quality response after initiation of PAP. In addition, we evaluated several dimensions of sleep quality, determined using both objective and subjective scales, and subjective sleepiness. The findings reflect a multidimensional approach that is complementary to previous qualitative results [12]. A first limitation of this work is that, although PAP initiation was linked to meaningful improvements in subjective and objective sleep quality, meta-regression did not show a clear linear relationship between usage and perceived benefit, suggesting variability in subjective responses to PAP therapy. We also acknowledge several limitations inherent to the available literature on this topic. First, there was substantial bias in the included studies, mainly due to their retrospective design and/or the absence of randomisation and a control group (which is unlikely to be ethical for most PAP indications) [12, 85]. Thus, heterogeneity in the quality and design of included studies and design, risk of confounding bias and selective reporting limit the scope of our results. For instance, detailed data on the context of PAP initiation, including ambulatory versus hospital setting and acute versus chronic clinical status, are lacking. Additionally, unmeasured confounders such as length of hospital stay, engagement with pulmonary and sleep providers, payer status, and rural versus urban residence may also influence PAP initiation and related outcomes and should be considered as limitations. Moreover, variability in sleep efficiency assessment methods (PSG versus actigraphy) and potential circadian misalignment, especially in PSG studies, may have influenced the related results. Another limitation is the lack of systematic reporting of common sleep comorbidities such as sleep disorder breathing for COPD and NMD patients, restless leg syndrome and insomnia, but also depression or associated medications. all of which can influence sleep quality as well as PAP usage. By design, the pooled data nature of present analysis prevented us from assessing the impact of individual characteristics impact such as respiratory function parameters or NIV settings. Finally, we made the choice to focus on PROMs and applied a parsimonious approach to the number of meta-analysed criteria. A broader analysis of all PSG parameters could provide further insights.
Points for clinical practice and future research
To summarise, this meta-analysis indicates that, despite the constraints and potential discomfort associated with sleeping with an external device, PAP initiation was associated with clinically significant improvements in objective and subjective sleep quality.
The identified moderators, including patient characteristics, aetiology of CHRF and adherence to PAP are consistent with existing literature.
Given the well-established benefits of sleep improvements on global health, the beneficial effects of PAP on symptoms and prognosis in patients with CHRF are probably mediated at least in part by enhanced sleep quality.
In the future, individual data analysis and prospective, well-designed studies of PAP should include comprehensive assessment of subjective and objective sleep parameters.
Moreover, the identification of clinical phenotypes that experience the greatest sleep improvement with different PAP modes and settings could guide clinicians in optimising therapy management and informing patients about expected benefits.
Acknowledgements
We thank Nicola Ryan, independent medical writer, for providing medical writing assistance. We also thank “Agir pour les maladies chroniques” foundation as well as Linde, Asdia medical and ResMed for the travel grants allowed for this work.
Footnotes
Provenance: Submitted article, peer reviewed.
The systematic review protocol was registered with PROSPERO (https://www.crd.york.ac.uk/prospero/) with identifier: CRD42023495516.
Author contributions: Conception and design: P. Tankéré, C. Khouri, L. Peter Derex, J.L. Pépin, R. Tamisier. Data acquisition: P. Tankéré, L. Razakamanantsoa, C. Khouri, R. Tamisier. Analysis: all authors. Interpretation: all authors. Drafting the first version of the manuscript: P. Tankéré, L. Razakamanantsoa, C. Khouri, L. Peter Derex, J.L. Pépin, R. Tamisier. Review and editing of the manuscript: all authors.
Conflict of interest: P. Tankéré reports travel and congress grants from Asdia, Resmed, Linde and ALLP; and grant support through her institution from “Agir pour les maladies chroniques” foundation. M. Patout reports no support for the present manuscript; for other works he reports grants, contracts, consulting fees, honoraria for lectures, travel grants, participation on advisory board, stock and receipt of equipment from Resmed, Philips Respironics, Asten Santé, Kernel Biomedical, SOS Oxygen, Chiesi, Lowenstein, Bastide, Elivie, Antadir, Jazz Pharmaceutical, Fisher & Paykel, Orkyn Sanofi, GSK and Air Liquide Medical. E. Stauffer reports payment or honoraria for lectures, presentations, manuscript writing or educational events from Asdia Medical, and support for attending meetings from Linde homecare, Resmed SAS and Homedis santé, and participation on a data safety monitoring board or advisory board with ALLP. S. Baillieul reports payment or honoraria for lectures, presentations, manuscript writing or educational events from Pfizer and Expression santé, and support for attending meetings from Jazz pharmaceuticals, Agir a dom assistance, Vitalaire, Resmed SAS and Bioprojet Pharma, and participation on a data safety monitoring board or advisory board with ALLP. J.L. Pépin has received lecture fees or conference travel grants from ResMed, Philips, AstraZeneca, Jazz Pharmaceuticals, Agiradom and Bioprojet, and has received unrestricted research funding from ResMed, Philips, GlaxoSmithKline, Bioprojet, Fondation de la Recherche Medicale (Foundation for Medical Research), Direction de la Recherche Clinique du CHU de Grenoble (Research Branch Clinic CHU de Grenoble), and fond de dotation “Agir pour les Maladies Chroniques” (endowment fund “Acting for Chronic Diseases”). L. Peter Derex reports receiving lecture fees from Bioprojet, Eisai, Zogenix and Roche; grant support through her institution from Asten Sante, Linde and Bioprojet; and travel grants from Bioprojet and VitalAire. R. Tamisier reports receiving lecture fees from ResMed, Inspire and Bioprojet; grant support through his institution from ResMed, Inspire, Agiradom and Bioprojet; and travel grants from Agiradom. All other authors declare no competing interests related to this work.
Support statement: R. Tamisier and J.L. Pépin are supported by the French National Research Agency in the framework of the “Investissements d'avenir” program (ANR-15-IDEX-02) My Way To Health and the “e-health and integrated care and sleep health-AI international MIAI artificial intelligence” Chairs of excellence from the Grenoble Alpes University Foundation. This work was partially supported by MIAI @ Grenoble Alpes (ANR-19-P3IA-0003). P. Tankéré is partly funded by an unrestricted grant from “Agir pour les maladies chroniques” foundation and had travel grants linked to this work from Linde, Asdia medical and ResMed. Funding information for this article has been deposited with the Open Funder Registry.
Supplementary material
Please note: supplementary material is not edited by the Editorial Office, and is uploaded as it has been supplied by the author.
Supplementary material
ERR-0090-2025.SUPPLEMENT
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request: rtamisier@chu-grenoble.fr.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Please note: supplementary material is not edited by the Editorial Office, and is uploaded as it has been supplied by the author.
Supplementary material
ERR-0090-2025.SUPPLEMENT
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request: rtamisier@chu-grenoble.fr.