Management of obstructive sleep apnea in Europe - A 10-year follow-up

Machine learning-Based model for prediction of Narcolepsy Type 1 in Patients with Obstructive Sleep Apnea with Excessive Daytime Sleepiness

Background

Excessive daytime sleepiness (EDS) forms a prevalent symptom of obstructive sleep apnea (OSA) and narcolepsy type 1 (NT1), while the latter might always be overlooked. Machine learning (ML) models can enable the early detection of these conditions, which has never been applied for diagnosis of NT1.

Objective

The study aimed to develop ML prediction models to help non-sleep specialist clinicians identify high probability of comorbid NT1 in patients with OSA early.

Methods

Totally, clinical features of 246 patients with OSA in three sleep centers were collected and analyzed for the development of nine ML models. LASSO regression was used for feature selection. Various metrics such as the area under the receiver operating curve (AUC), calibration curve, and decision curve analysis (DCA) were employed to evaluate and compare the performance of these ML models. Model interpretability was demonstrated by Shapley Additive explanations (SHAP).

Results

Based on the analysis of AUC, DCA, and calibration curves, the Gradient Boosting Machine (GBM) model demonstrated superior performance compared to other machine learning (ML) models. The top five features used in the GBM model, ranked by feature importance, were age of onset, total limb movements index, sleep latency, non-REM (Rapid Eye Movement) sleep stage 2 and severity of OSA.

Conclusion

The study yielded a simple and feasible screening ML-based model for the early identification of NT1 in patients with OSA, which warrants further verification in more extensive clinical practices.

Performance of the Alice PDx Device With the Somnolyzer Automated Scoring Algorithm for the Diagnosis of Obstructive Sleep Apnea

Objective Automated scoring of respiratory events could allow a swifter obstructive sleep apnea (OSA) identification. We assessed the accuracy of the Alice PDx device with the Somnolyzer automated scoring algorithm, compared to the manually reviewed scoring by a trained sleep technician, for the diagnosis of OSA. Methods A prospective study was conducted between March 2021 and March 2022 in Centro Hospitalar do Baixo Vouga, a level 2 hospital in Aveiro, Portugal. Patients with high pre-test probability for OSA performed a type III home sleep apnea testing with the Alice PDx device. Data were scored automatedly by the Sleepware G3 with the Somnolyzer digital system and manually by a trained sleep technician. Correlation and dependent t-tests were used. Sensitivity, specificity, positive predictive values (PPVs), negative predictive values (NPVs), and area under the receiver operating characteristic curve (AUROC) of automated scoring were calculated. Data were analyzed using the Stata Statistical Software (Release 17, StataCorp., 2023, College Station, TX: StataCorp LLC). Results In 150 participants (mean age 57.8 ± 13.9 years), the mean apnea-hypopnea index (AHI) was 21.9 ± 21.8 events/hour by manual scoring and 25.4 ± 21.6 events/hour by automated scoring. The mean difference was 3.4 ± 4.4 events/hour, and a strong, positive, linear correlation was found between the two scores (r = 0.98). At the altered AHI (AHI ≥ 5 events/hour), mild, moderate, and severe OSA, the automated scoring sensitivity/specificity values were 91.2%/100.0%, 80.0%/68.6%, 91.6%/41.9%, and 98.1%/80.9%, respectively. The PPVs/NPVs for the same categories were 100.0%/69.4%, 89.3%/51.1%, 79.7%/66.7%, and 91.8%/95.0%, respectively. Finally, the AUROC was 0.85, 0.70, 0.73, and 0.93, respectively. Conclusion The automated scoring obtained from the Alice PDx portable device, using Sleepware G3 with the Somnolyzer digital system, seems accurate enough to diagnose OSA and validate the initiation of PAP therapy in the correct clinical setting. Nevertheless, it does not replace manual reviewing by a trained sleep technician in the case of mild and moderate OSA, to obtain a correct severity classification. With this valuable time-saving tool, we expect to hasten OSA diagnosis and treatment and thus tackle the underdiagnosis problem.

Letter to the Editor of Journal of Otolaryngology regarding "Risk of diabetes in patients with sleep apnea: comparison of surgery versus CPAP in a long-term follow-up study"

Obstructive sleep apnea (OSA) is associated with multiple chronic comorbidities with treatments including continuous positive airway pressure (CPAP), upper airway surgery (UAS), and hypoglossal nerve stimulation (HNS). Given the complexity of the condition and multiple treatment options, there is an ongoing debate to determine the best management. O'Connor-Reina et al. recently published a paper titled "Risk of diabetes in patients with sleep apnea: comparison of surgery versus CPAP in a long-term follow-up study." In their study, the authors stated that OSA patients who received surgery had a 50% less chance of developing diabetes compared to patients who only received CPAP treatment. However, we would like to point out some limitations that warrant attention and caution interpretation of the findings by physicians and patients.

Accuracy of positive airway pressure titration through telemonitoring of auto-adjusting positive airway pressure device connected to a pulse oximetry in patients with obstructive sleep apnea

PURPOSE

In COVID-19 era, all forms of access of patients to the sleep units should be reduced as much as possible when implementing telemedicine. In the field of obstructive sleep apnea (OSA) therapy with positive airway pressure (PAP) devices, telemedicine includes the use of built-in software (BIS) and storage of PAPs and remote-controlled data (BISrc data) that are processed and transmitted daily to sleep units. We compared two methods of evaluating the final residual severity of OSA patients in home PAP titration: BISrc data versus nocturnal portable multichannel monitoring (PM) data in PAP (reference method) and to verify whether the efficacy PAP therapy guided by BISrc data was clinically adequate.

METHODS

We conducted a real-life prospective study in newly diagnosed patients with OSA. Patients used an auto-adjusting positive airway pressure (AirSense 10 ResMed) with a pulse oximeter that allows daily transfer of BISrc data (apnea hypopnea index [AHI] and SaO2 ) and remote changes in ventilator setting. Once the PAP titration was completed, the pressure value or ranges were kept constant for 3 days and home PM was repeated.

RESULTS

There were 41 patients with moderate to severe OSA who completed the study. When considering AHI only, the diagnostic accuracy of BISrc on the third day was equal to 97.5%; when considering AHI > 10/h, ODI > 10/h, and SaO2  < 90%, the diagnostic accuracy slightly decreased to 90.2%.

CONCLUSION

In clinical practice, the two measurement methods are equivalent. The use of BISrc data for home titration would reduce the access to sleep units. We urge that widespread use of BISrc be promoted in the current practice of management of OSA.

National Knowledge-Driven Management of Obstructive Sleep Apnea-The Swedish Approach

INTRODUCTION

This paper describes the development of "Swedish Guidelines for OSA treatment" and the underlying managed care process. The Apnea Hypopnea Index (AHI) is traditionally used as a single parameter for obstructive sleep apnea (OSA) severity classification, although poorly associated with symptomatology and outcome. We instead implement a novel matrix for shared treatment decisions based on available evidence.

METHODS

A national expert group including medical and dental specialists, nurses, and patient representatives developed the knowledge-driven management model. A Delphi round was performed amongst experts from all Swedish regions (N = 24). Evidence reflecting treatment effects was extracted from systematic reviews, meta-analyses, and randomized clinical trials.

RESULTS

The treatment decision in the process includes a matrix with five categories from a "very weak"" to "very strong" indication to treat, and it includes factors with potential influence on outcome, including (A) OSA-related symptoms, (B) cardiometabolic comorbidities, (C) frequency of respiratory events, and (D) age. OSA-related symptoms indicate a strong incitement to treat, whereas the absence of symptoms, age above 65 years, and no or well-controlled comorbidities indicate a weak treatment indication, irrespective of AHI.

CONCLUSIONS

The novel treatment matrix is based on the effects of treatments rather than the actual frequency of respiratory events during sleep. A nationwide implementation of this matrix is ongoing, and the outcome is monitored in a prospective evaluation by means of the Swedish Sleep Apnea Registry (SESAR).

Menopause Is Associated with Obstructive Sleep Apnea in a Population-Based Sample from Mecklenburg–Western Pomerania, Germany

Objective: Menopause is associated with multiple health risks. In several studies, a higher incidence or a higher risk for obstructive sleep apnea (OSA) in post-menopausal than pre-menopausal women is reported. This study was designed to verify such a connection between menopause and OSA in a population-based sample. Methods: For a subsample (N = 1209) of the Study of Health in Pomerania (N = 4420), complete polysomnography data was available. Of these, 559 females completed a structured interview about their menstrual cycle. Splines and ordinal regression analysis were used to analyze the resulting data. Results: In the ordinal regression analysis, a significant association between the apnea–hypopnea index (AHI) and menopause indicated that post-menopausal women had a substantially higher risk of OSA. In accordance with previous studies, risk indicators such as body mass index (BMI), age, and the influence of hysterectomies or total oophorectomies were included in the model. Conclusions: Our results clearly confirmed the assumed connection between menopause and OSA. This is important because OSA is most often associated with male patients, and it warrants further research into the underlying mechanisms.

Effect of the Implementation of a Structured Diet Management Plan on the Severity of Obstructive Sleep Apnea: A Systematic Review

PURPOSE OF REVIEW

The prevalence of obstructive sleep apnea (OSA) is increasing worldwide, in line with the increase in obesity prevalence. Taken into consideration the low compliance rates to continuous positive airway pressure (CPAP) treatment, and since obesity is a risk factor for OSA, these patients should receive additional counseling for weight loss through a diet plan. The aim of this review is to examine the role of a structured diet management plan on OSA severity, nocturnal oxygen indices, and subjective sleep parameters.

RECENT FINDINGS

Τhis systematic review of the literature resulted in four studies and demonstrated that severity of OSA, assessed by the apnea-hypopnea index, is reduced by a dietary management plan when delivered through an educational program. Moreover, nocturnal oxygenation is improved, as well as subjective sleep parameters, when initiating a diet on top of CPAP use. In summary, the present systematic review reports on the beneficial effects of a structured diet management plan in patients with OSA. Although CPAP remains the gold standard of OSA treatment, a specific dietary plan should be sought when managing patients with OSA. Nevertheless, still the evidence is low, and further research on this field is needed to reduce the burden of OSA.