Association between mean disease alleviation and Epworth Sleepiness Scale in adult obstructive sleep apnoea (OSA)

Introduction

Obstructive sleep apnoea (OSA) has an estimated global prevalence of almost 1 billion, with 425 million having moderate severity [Apnoea-Hypopnoea Index (AHI) ≥15] (1). OSA is characterised by repeated episodes of upper airway obstruction during sleep, resulting in intermittent hypoxemia, arousals from sleep, and subsequent fragmented sleep architecture (2). The condition is associated with a high economic and societal burden, with diagnoses and treatment of OSA in Australia costing approximately $18.9 billion annually (3). These pathophysiological disturbances contribute to a broad spectrum of adverse consequences, including cardiovascular morbidity, metabolic dysfunction, cognitive impairment, and reduced quality of life and all-cause mortality (4,5). Daytime somnolence is commonly experienced by patients with OSA and significantly impacts their quality of life, highlighting the need for effective, patient-centred treatment strategies.

The management of OSA aims to mitigate these pathophysiological disturbances while addressing patient-reported symptoms, such as excessive daytime sleepiness. The AHI remains one of the most commonly used measures of disease severity and a surrogate for treatment efficacy in clinical practice (6). However, the available published evidence demonstrates that a reduction in AHI poorly correlates with symptom improvement (6-8). This is a critical limitation of relying solely on AHI as a measure of treatment effectiveness, particularly in evaluating interventions with varying mechanisms of action and adherence requirements. This underscores a key distinction between efficacy and effectiveness, wherein treatment efficacy refers to outcomes under ideal, controlled conditions, while treatment effectiveness reflects outcomes in more realistic real-world scenarios.

Mean disease alleviation (MDA) is a novel metric that integrates treatment efficacy (reduction in AHI) with adherence, offering a more comprehensive assessment of the treatment effect (9,10). MDA is particularly relevant when comparing continuous positive airway pressure (CPAP), a first-line OSA therapy that is highly effective but adherence-dependent, with alternative modalities such as multilevel airway surgery, which is inherently adherence-independent (10). Despite its theoretical advantages, the utility of MDA as a predictor of patient-reported outcomes remains underexplored, particularly in the context of symptom relief and functional improvement, as measured by the Epworth Sleepiness Scale (ESS) (11). The ESS is a simple, widely utilised tool in sleep medicine, which has been validated across diverse ethnic and clinical groups (12).

This study aims to evaluate the relationship between MDA and changes in ESS among adult patients treated for OSA with either CPAP or multilevel airway surgery. The study contributes to the growing body of literature advocating for the integration of adherence and patient-reported outcomes into the assessment of treatment effectiveness, emphasising a more comprehensive approach beyond the traditional reliance on AHI alone.

Methods

The study is reported according to the STROBE reporting guidelines (available at https://www.theajo.com/article/view/10.21037/ajo-2025-1-94/rc).

Ethics approval

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Approval for this retrospective cohort study was obtained from the Illawara and Shoalhaven Local Health District Human Research Ethics Committee (No. ISLHD/LNR/2021-156), the governing ethics authority for the Wollongong Hospital. This was done prior to commencement and individual consent for this retrospective analysis was waived.

Study population

This was an analysis of adult patients with diagnosed OSA [AHI ≥5.0 on polysomnography (PSG)], who were seen (2008–2021) in a joint specialty outpatient clinic attended by a fellowship-trained sleep otolaryngologist (S.M.), a sleep respiratory physician (A.J.) and trainee medical staff in the Illawarra region, New South Wales, Australia. Inclusion criteria included PSG-diagnosed OSA, adult age (≥18 years), and treatment with CPAP (>30-day objective use report available) or multilevel upper airway surgery (postoperative PSG available). Patients using CPAP were excluded if they lacked baseline diagnostic PSG, CPAP objective use data, or never showed use (refused). Surgery patients were excluded if they lacked either baseline or postoperative PSG. Patients were excluded if they did not have a documented pre- and post-treatment ESS recorded.

Data collection

Data were extracted from electronic medical records, including demographic information, body mass index (BMI), baseline AHI, post-treatment AHI, and ESS scores. Post-treatment ESS scores were collected during follow-up visits, defined as the earliest assessment following therapy whilst on CPAP or post-surgery. CPAP adherence data were derived from device usage reports, calculating the mean nightly use over the follow-up period.

MDA calculation

MDA was calculated as the product of treatment efficacy and adherence, expressed as a percentage, per the following formulae (10,13):

MDA=Efficacy×Adherence[1]

Efficacy=BaselineAHI−On-treatmentAHIBaselineAHI[2]

Adherence=TimeontreatmentTotalSleepTime[3]

Treatment efficacy was defined as the percentage reduction in AHI from baseline to follow-up. For CPAP patients, adherence was the average nightly use (in hours) divided by 8 hours (maximum assumed duration of nightly use). For surgical patients, adherence was considered 100% (9,14).

Statistical analysis

Descriptive statistics were used to summarise baseline characteristics and outcomes. Paired t-tests were performed to assess changes in AHI and ESS pre- and post-treatment. Pearson correlation coefficients were calculated to evaluate the relationships between:

• MDA and the difference in pre- and post-treatment ESS;
• Change in AHI (i.e., pre-treatment minus post-treatment AHI) and difference in pre- and post-treatment ESS.

Subgroup analyses were conducted for CPAP and surgical patients separately to explore treatment-specific correlations. Statistical significance was set at P<0.05 for all analyses. Where potential sources of bias in the data were identified (e.g., outliers or differences in follow-up duration between treatment groups), sensitivity analyses were performed to assess the robustness of the primary results, including analyses restricted to comparable follow-up periods. All statistical analyses were performed using SPSS Statistics (Version 29.0).

Results

During the study period, 870 patients were diagnosed with OSA in a specialist sleep clinic. Of these, 110 met the inclusion and exclusion criteria for analysis (n=40 CPAP, and n=70 surgical patients). The cohort had a mean age of 44.3±13.4 years and a mean BMI of 30.1±4.0 kg/m². This was a predominantly male cohort (89/110, 80.9%), including 36/40 (90.0%) in the CPAP group and 53/70 (75.7%) in the surgical group (Table 1). The median follow-up for surgical patients was 5.0 months [interquartile range (IQR): 3.5–6.5 months], and for CPAP patients was 8.4 months (IQR: 2.3–50.3 months). The interventions performed in the surgical group are described in Table 2.

Outcomes as measured by PSG

The CPAP treatment group had a mean device adherence of 61%±26.7%. Both treatment groups demonstrated statistically significant reductions in AHI (Table 3). The mean pre-treatment AHI for the overall cohort was 41.1±23.7, which decreased to 8.8±9.7 post-treatment. This represents a reduction of 32.3 events/hour (95% CI: 28.4–36.9; P<0.001). CPAP patients had a reduction of 39.7±23.7 to 3.7±4.1 (mean reduction: 36.0; 95% CI: 28.5–43.5; P<0.001), while surgical patients had a reduction from 41.9±23.9 to 11.8±10.8 (mean reduction 30.1; 95% CI: 25.5–35.9; P<0.001).

The overall mean MDA for the cohort was 69.4%±27.8%. The mean MDA of the CPAP group 72.9%±23.4%, and the mean MDA of the surgical group 67.3%±29.9% (Table 4).

For the overall cohort, the mean pre-treatment ESS was 11.6±5.6, which decreased to 5.0±3.8 post-treatment. This represented a mean decrease in ESS score of 6.6 (95% CI: 5.6–7.7; P<0.001). CPAP patients had a reduction from 12.3±6.1 to 6.3±4.9 (mean reduction: 6.0; 95% CI: 4.2–7.9; P<0.001), and surgical patients had a reduction from 11.3±5.4 to 4.3±2.9 (mean reduction 7.0; 95% CI: 5.8–8.2; P<0.001; Table 5).

Correlations between objective measures (AHI & MDA) and reported symptoms (ESS)

AHI vs. ESS

Across all patients, there was no correlation between AHI and ESS (r=0.18, P=0.053). There was no statistically significant relationship between AHI and ESS in the CPAP (r=0.18, P=0.26) or surgical group (r=0.21, P=0.084; Table 6).

MDA vs. ESS

No significant correlation was found for the overall cohort (r=0.15, P=0.12) or CPAP patients (r=0.00, P=0.99). There was a small but statistically significant positive correlation observed in surgical patients (r=0.24, P=0.042; Table 6).

The modelled linear correlation between AHI vs. ESS and MDA vs. ESS is shown in Figure 1.

Figure 1 Relationship between MDA and change in ESS. Scatter plot showing the association between MDA and change in ESS. Correlation was assessed using the Pearson correlation coefficient. CPAP, continuous positive airway pressure; ESS, Epworth Sleepiness Scale; MDA, mean disease alleviation.

Discussion

This study evaluated the relationship between MDA and patient-reported outcomes of somnolence, as measured by the ESS, in adult patients treated for OSA. In the studied cohort, both CPAP and multilevel airway surgery patient groups demonstrated significant reductions in AHI and ESS, and an average MDA of 69.4%±27.8%. This shows the overall benefit of treatment in terms of objective lab-based (i.e., PSG) and subjective patient-related measures (i.e., patient symptoms as measured by ESS). There was a modest positive correlation between MDA and symptom improvement (ESS reduction) in surgical patients, and this correlation only accounted for a small amount of the variance in the data [coefficient of determination (R²) =0.06]. However, this correlation was not found in the overall cohort. Furthermore, reduction in AHI, a traditional marker of treatment efficacy (7), did not demonstrate a statistically significant correlation with symptom improvement across both treatment groups, which is consistent with previously conducted studies (8,15).

One patient in the surgical group included in our data had a negative MDA as well as a higher post-treatment ESS (Figure 1). Sensitivity analysis excluding this observation showed a loss of statistical significance of the correlation between MDA and ESS in the surgical group, suggesting that the observed association was influenced by this data point. As this outcome reflects a genuine clinical outcome and was therefore retained in the primary analysis; however, the instability of the correlation following its removal indicates that the relationship should be interpreted with caution. Accordingly, these findings should be considered exploratory and warrant validation in larger cohorts.

Median follow-up duration was longer and more variable in the CPAP group compared to the surgical group [8.4 (IQR, 2.3–50.3) vs. 5.0 (IQR, 3.5–6.5) months], reflecting differences in the routine follow-up patterns between patients receiving ongoing CPAP therapy and those undergoing surgery. A sensitivity analysis was therefore performed, restricting the CPAP cohort to patients with follow-up durations within the interquartile range of the surgical cohort’s follow-up (3.5–6.5 months), resulting in 18 CPAP patients being included. Reanalysis of the outcomes with this restricted sample produced results consistent with the primary findings.

The findings suggest that while MDA provides a more comprehensive measure of treatment success by integrating adherence and efficacy, its utility in predicting symptom improvement is limited to a small predictive value in surgical patients only, based on the findings from this cohort. The modest correlation between MDA and ESS in surgical patients may reflect the unique characteristics of surgical interventions, which are independent of device usage and adherence. By contrast, the absence of a significant correlation in CPAP-treated patients highlights the complexity of adherence-dependent therapies, where factors beyond nightly use—such as pressure intolerance and dislike for device compliance—may influence symptomatic outcomes (16,17).

The lack of correlation between reduction in AHI and ESS reduction across both treatment groups supports previous studies indicating that AHI alone is insufficient to capture the multidimensional impact of OSA and its treatment (8,15).

Clinical implications

These findings underscore the need for a multidimensional approach to treatment evaluation that incorporates both objective metrics (e.g., AHI, MDA) and subjective outcomes (e.g., ESS, quality of life). While MDA represents a step forward by accounting for adherence, its limited correlation with symptomatic improvement suggests that additional metrics may be needed to fully capture treatment success.

Furthermore, the differences between CPAP and surgical patients highlight the importance of tailoring treatment strategies to individual patient needs. For patients struggling with CPAP adherence or seeking an adherence-independent solution, surgery may offer comparable or superior outcomes in terms of MDA (10). However, the modest correlation between MDA and ESS in surgical patients suggests that even adherence-independent treatments require careful follow-up to address residual symptoms, and that patient expectations should be set accordingly.

Limitations

The retrospective design limits causal inferences, and the reliance on ESS as the sole patient-reported outcome may not capture the full spectrum of OSA-related symptom complaints, as it primarily assesses propensity to fall asleep in various situations (18). Further limitations of the usage of the ESS as a measure of sleepiness in OSA patients include inherent bias as a self-reporting tool (19). Adherence in the CPAP group was assessed via device logs, which may not reflect actual therapeutic effectiveness (e.g., mask leaks or residual apnoea). CPAP device logs and recordings are dependent on individual devices with proprietary software, which may differ between manufacturers. Future studies should consider repeated formal in-lab PSG to lessen the risk of introducing bias.

This study was conducted in a single tertiary care centre, which may limit the generalisability of the findings to other populations or care settings, albeit representative of real-life patients. Further, a linear model was used to determine the correlation between MDA and ESS, which may oversimplify the statistical correlation between these factors. There were also a larger number of patients in the surgical group, meaning the CPAP group may have been underpowered to detect a statistical difference.

Future directions

Future research should explore the integration of additional patient-reported outcomes (such as quality of life and cognitive function) into metrics like MDA could enhance our understanding of the relationship between objective measures and symptom outcomes.

Conclusions

This study highlights the potential and limitations of MDA as a metric for assessing OSA treatment. While MDA captures treatment adherence and efficacy, it has only a modest correlation with symptomatic improvement in surgical patients. These findings reinforce the importance of individualised treatment strategies that prioritise both objective and patient-reported outcomes to achieve optimal care.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://www.theajo.com/article/view/10.21037/ajo-2025-1-94/rc

Data Sharing Statement: Available at https://www.theajo.com/article/view/10.21037/ajo-2025-1-94/dss

Peer Review File: Available at https://www.theajo.com/article/view/10.21037/ajo-2025-1-94/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://www.theajo.com/article/view/10.21037/ajo-2025-1-94/coif). Prof. S.M. serves as an unpaid editorial board member of Australian Journal of Otolaryngology from January 2025 to December 2027. Prof. S.M. reports that he received research grants from the National Health and Medical Research Council (NHMRC) and the RWGPF; and he currently serves as a consultant for CNXII Medical. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Approval for this retrospective cohort study was obtained from the Illawara and Shoalhaven Local Health District Human Research Ethics Committee (No. ISLHD/LNR/2021-156), the governing ethics body attached to The Wollongong Hospital, prior to commencement. Individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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