Sleep apnea is a common sleep disorder. The availability of an easy-to-use sleep apnea predictor would provide a public health benefit by promoting early diagnosis and treatment. Our goal was to develop a prediction tool that used commonly available variables and was accessible to the public through a web site. Using data from polysomnography (PSG) studies that measured the apnea-hypopnea index (AHI), we built a machine learning tool to predict the presence of moderate to severe obstructive sleep apnea (OSA) (defined as AHI ≥15). Our tool employs only seven widely available predictor variables: age, sex, weight, height, pulse oxygen saturation, heart rate and respiratory rate. As a preliminary step, we used 16,958 PSG studies to examine eight machine learning algorithms via five-fold cross validation and determined that XGBoost exhibited superior predictive performance. We then refined the XGBoost predictor by randomly partitioning the data into a training and a test set (13,566 and 3392 PSGs, respectively) and repeatedly subsampling from the training set to construct 1000 training subsets. We evaluated each of the resulting 1000 XGBoost models on the single set-aside test set. The resulting classification tool correctly identified 72.5 % of those with moderate to severe OSA as having the condition (sensitivity) and 62.8 % of those without moderate to-severe OSA as not having it (specificity); overall accuracy was 66 %. We developed a user-friendly publicly available website (https://manticore.niehs.nih.gov/OSApredictor). We hope that our easy-to-use tool will serve as a screening vehicle that enables more patients to be clinically diagnosed and treated for OSA.

UNASSIGNED: The aim of this study was to evaluate the safety and efficacy of microdebrider adenoidectomy on sleep-disordered breathing among pediatric patients with OSA.
UNASSIGNED: In the microdebrider group (Group I), there were 30 Japanese OSA patients consisting of 26 boys and 4 girls. For comparison, we had 15 children (13 boys and 2 girls) who underwent classical adenoidectomy (Group II). Patients in Group I were selected from a pool of 95 pediatric Japanese OSA patients and were matched by age, preoperative AHI, and Kaup index with those in Group II.Parameters such as the amount of residual adenoid tissue, bleeding, duration of the procedure, and sleep-related metrics were compared between the two groups.
UNASSIGNED: A significant improvement in postoperative AHI was observed in Group I (p<0.05). The prevalence of AHI <1 was significantly higher in Group I compared with Group II (p<0.05). Additionally, the amount of postoperative residual adenoid was significantly less in Group I (3/30 of Grade 3 and 4 adenoid size) than in Group II (7/15, p<0.05). Furthermore, a reduction in postoperative AHI was proportionally associated with a decrease in residual adenoid.
UNASSIGNED: The newly developed microdebrider adenoidectomy technique for pediatric OSA patients with adenotonsillar hypertrophy demonstrated greater accuracy and efficacy in ameliorating sleep apnea symptoms compared with the standard adenoidectomy approach.

OBJECTIVE: The objective of this study was to assess the accuracy of automatic diagnosis of obstructive sleep apnea (OSA) with a new, small, acoustic-based, wearable technology (AcuPebble SA100), by comparing it with standard type 1 polysomnography (PSG) diagnosis.
METHODS: This observational, prospective study was carried out in a Spanish hospital sleep apnea center. Consecutive subjects who had been referred to the hospital following primary care suspicion of OSA were recruited and underwent in-laboratory attended PSG, together with the AcuPebble SA100 device simultaneously overnight from January to December 2022.
RESULTS: A total of 80 patients were recruited for the trial. The patients had a median Epworth scoring of 10, a mean of 10.4, and a range of 0-24. The mean AHI obtained with PSG plus sleep clinician marking was 23.2, median 14.3 and range 0-108. The study demonstrated a diagnostic accuracy (based on AHI) of 95.24%, sensitivity of 92.86%, specificity of 97.14%, positive predictive value of 96.30%, negative predictive value of 94.44%, positive likelihood ratio of 32.50 and negative likelihood ratio of 0.07.
CONCLUSIONS: The AcuPebble SA100 (EU) device has demonstrated an accurate automated diagnosis of OSA in patients undergoing in-clinic sleep testing when compared against the gold-standard reference of in-clinic PSG.

Background and Objectives: The Baveno classification represents a new approach to the assessment of the severity of OSA (Obstructive sleep apnea), which takes significant comorbidities into account: atrial fibrillation, arterial hypertension, heart failure, stroke, diabetes mellitus, and OSA symptoms expressed through the Epworth sleepiness scale (ESS). The authors believe that the Baveno classification facilitates a better stratification of patients with OSA and can be a good guide for deciding on the therapeutic approach and clinical monitoring of patients with OSA, compared to the AHI (apnea-hypopnea index) itself. The aim of this paper is to confirm the advantage of applying the Baveno classification to the evaluation of symptoms of anxiety and depression in the OSA patients compared to the application of the AHI as a single parameter. Materials and Methods: This research represents an observational retrospective study that was performed at the Pulmonology Clinic of the University Clinical Center in Kragujevac, Serbia. The study sample included 104 patients with diagnosed OSA. Patients were divided into four categories retrogradely according to the Baveno classification (A, B, C, and D). Statistical data processing was performed using the IBM SPSS Statistics version 25.0 program. Results: In our study, we proved that the Baveno classification is better at predicting the depressive disorder in OSA patients compared to the AHI itself, according to abnormal BDI-II (Beck Depression Inventory) score (value greater than ten) and HADS-D (Hospital anxiety and depression) scale (value greater than eight). The average AHI in the entire group of examined patients was 44.3 ± 19.8, while in category A the average AHI was 25.2 ± 10, in category B, 53.4 ± 20.6; in category C, 38.2 ± 18.5; and in category D, 48.1 ± 19.2. In the total sample, AHI did not correlate with the depressive episodes, but individually, the highest frequency of the depressive symptoms was precisely in the categories with the highest AHI (group D and B), where more than half of the subjects had an abnormal score. The frequency of the anxiety disorder (HADS-A) between the analyzed groups did not differ significantly, although the largest number of patients with significant anxiety were in category B, according to the Baveno classification. Conclusions: We proved that the Baveno classification is applicable in real life, and it is better at evaluating anxiety and depression using questionnaires and can identify new patients who need CPAP therapy, independently of other OSAS symptoms, primarily daytime sleepiness.

BACKGROUND: As the prevalence of OSA increases in older patients, the proportion of elderly patients treated with CPAP is expected to become even higher. We studied CPAP-adherence in a real-life cohort involving a large population of elderly patients with OSA.
METHODS: eQUALISAS is a cross-sectional study of CPAP treated OSA patients for at least 4 months who received remote monitoring during 2021. CPAP adherence, device-reported residual AHI (AHICPAP) and 95th percentile non-intentional leaks were software collected during January 2021. Age and sex were also collected.
RESULTS: Data from 26,343 patients including 1656 patients aged [80-85] years and 639 patients aged ≥ 85 years were analysed. Median CPAP adherence increases from 6.3 h (< 50 years) to 7 h (75-80 years) and decreases after this age (p < 0.001). The decrease in CPAP adherence observed after the age of 80 was associated with an increase in the proportion of patients with a CPAP-adherence < 4 h/day (p < 0.001). Proportion of CPAP treated female, patients having AHICPAP mean ≥ 10 events per hour or 95th percentile non-intentional leaks mean over 24 l/min increase with aging of the population. However, for patients over 80 years old, Chi square test showed no association of adherence with gender, AHICPAP and leaks (p > 0.05).
CONCLUSIONS: This study demonstrates that adherence gradually increases with age until 80 years. The proportion of non-adherent patients grows with age after 80 years old. No significative relationship was found between adherence after 80 years old and gender, leaks and AHICPAP.
BACKGROUND: The study is registered on Health Data Hub platform (No. F20220715144543).

COVID-19 caused by SARS-CoV2 has reached pandemic proportions. The fear of Covid-19 has deterred many to abandon efforts for seeking timely medical help. In this setting, Obstructive sleep apnea (OSA)-like covid/non-covid cohorts have presented. Atypical pathologies can present like OSA and take the clinician unawares. With this series of misfits suffering silently, it would be unwise to underestimate its impact on quality-of-life (QOL). To determine the effect on quality-of-life by pathologies mimicking OSA and assess Covid-19 as a cause for delayed presentation. This was a prospective cross-sectional study. 127(N). Recent onset of symptoms of OSA. Study duration March 2020 to September 2021. Pittsburgh Sleep Quality Index (PSQI) screening done. Study criteria defined. Sleep parameters calculated. Primary surgical intervention given. Non-responders were put on CPAP therapy. QOL assessment done with sf-36 and SAQLI. Fear of Covid-19 scale (FCV-19S) quantified to study cause for temporal delay. Correlations computed. Level of Evidence-Level 3. 97 candidates completed study. Demographic and anthropometric details noted. Mean range was 43.85 ± 11.39 years. Male predominance. Overall AHI-19.73 ± 8.72. Moderate impact on QOL by sf-36/SAQLI. 78n Primary surgical candidates fared well. Polysomnography (PSG) and Continuous positive airway pressure (CPAP) titration/trial characteristics for 19n available. Statistically significant improvement in QOL after treatment completion. Correlations were meaningful. Body Mass Index (BMI) as a single factor was not influential on OSA-mimickers. Fear of Covid-19 significantly impacted emergency medical aid acquisition. OSA mimicking atypical airway pathologies may need emergent treatment not only from a surgical point-of-view but also from the QOL of the patient. On the contrary, these also unmask sub-clinical OSA, especially in patients with low/normal BMI. This category of recent onset OSA, if fortunately picked up at the earliest possible presentation, may hopefully not go through the significant QOL impact suffered by chronic OSA candidates.

The use of positive airway pressure (PAP) in children is a complex process determined by multiple factors. There are limited data on the response of the pediatric population to PAP therapy at home. The goal of the study was to examine real-world responses using PAP home monitoring in children with obstructive sleep apnea.
The study included PAP therapy data for 195 children aged between 1 month and 18 years with obstructive sleep apnea and polysomnogram baseline study. We collected demographics, clinical variables, and polysomnogram parameters in all study participants. The individual response to PAP therapy was calculated comparing the apnea-hypopnea index (AHI) in the initial polysomnogram with the mean AHI provided by the download of PAP devices. Multivariate models (logistic regression) were used to examine the predictors of positive PAP response defined as a reduction in AHI ≥ 75%.
We found excellent responses to PAP therapy in children (median 85% AHI reduction). However, there was substantial heterogeneity in AHI reductions while on PAP therapy. The best PAP responses were linked to more severe obstructive sleep apnea and higher PAP levels. We also identified that the response to PAP was higher in obese children and lower in males. The best predictive model for individual PAP response was biological sex, obesity, and obstructive AHI ≥ 20 events/h (area under the receiver operating characteristic curve of 0.791).
Real-world data show that PAP is overall an effective therapy in children but the response is heterogeneous. Obstructive sleep apnea parameters and individual factors can be used to predict individual AHI reductions while on PAP and optimize PAP responses at home.
Aguilar H, Kahanowitch R, Weiss M, et al. Real-world data evaluation of PAP responsiveness in pediatric obstructive sleep apnea. J Clin Sleep Med. 2023;19(7):1313-1319.

UNASSIGNED: Asthma and obstructive sleep apnea (OSA) are common chronic respiratory disorders in children. The relationship between asthma and OSA is bidirectional; these conditions share multiple epidemiological risk factors. Untreated OSA may cause attention deficit hyperactivity disorder (ADHD) symptoms. This study aimed to assess the prevalence of ADHD in asthmatic children with OSA and the link between asthma control and lung function of children with asthma and OSA.
UNASSIGNED: A total of 96 children aged 6-15 years diagnosed with asthma, according to the Global Initiative for Asthma (GINA) 2020, were enrolled in this study. All demographic data, including age, gender, body mass index, asthma control status, therapy, the Vanderbilt ADHD Diagnostic Parent Rating Scale, lung function, and exhaled nitric oxide, were collected. In addition, home respiratory polygraphy was used to identify OSA in study subjects.
UNASSIGNED: A total of 96 patients (8.4 ± 2.4 years) were included in the present study. OSA was identified in 60.4% of asthmatic children with a mean apnea-hypopnea index (AHI) of 3.5 ± 3.0 event/h. The inattentive ADHD subtype was significantly lower in the non-OSA asthmatic group than in the OSA asthmatic group (7.9 vs. 34.5%, p < 0.05). ADHD had a higher probability of presence (OR: 3.355; 95% CI: 1.271-8.859; p < 0.05) in the OSA group (AHI >1 event/h). Children with poorly controlled asthma had a significantly high risk of OSA (83.0 vs. 17.0%, p < 0.001) than children with well-controlled asthma. Allergic rhinitis increased the odds of having OSA in patients with asthma [OR: 8.217 (95% CI: 3.216-20.996); p < 0.05].
UNASSIGNED: The prevalence of OSA is increased among poorly controlled asthma. ADHD may have a higher prevalence in children with OSA. Therefore, prompt diagnosis of OSA will lead to an accurate asthma control strategy in patients with asthma.

Monitored polysomnography (PSG) is considered the gold standard technique to diagnose obstructive sleep apnea (OSA) and titrate continuous positive airway pressure (CPAP), the accepted primary treatment method. Currently, the American Academy of Sleep Medicine (AASM) considers automatic PAP therapy initiation at home comparable to laboratory titration and recommends telemonitoring-guided interventions. Advanced CPAP devices evaluate and report the residual apnea-hypopnea index (AHI). However, in order to control the effectiveness of the prescribed therapy outside of a PSG setting, the automatic event detection must provide reliable data.
A CPAP titration was performed in the sleep laboratory by PSG in patients with OSA. The residual event indices detected by the tested device (prismaLine, Loewenstein Medical Technology) were compared to the manually scored PSG indices. Results of the device (AHIFLOW) were compared according to the AASM scoring criteria 1A (AHI1A, hypopneas with a flow signal reduction of ≥ 30% with ≥ 3% oxygen reduction and/or an arousal) and 1B (AHI1B, hypopneas with a flow signal decrease by ≥ 30% with a ≥ 4% oxygen desaturation).
In 50 patients with OSA, the mean PSG AHI1A was 10.5 ± 13.8/h and the PSG AHI1B was 7.4 ± 12.6/h compared to a mean device AHIFlow of 8.4 ± 10.0/h. The correlation coefficient regarding PSG AHI1A and AHIFlow was 0.968. The correlation regarding central hypopneas on the other hand was 0.153. There were few central events to be compared in this patient group.
The device-based analysis showed a high correlation in the determination of residual obstructive AHI under therapy. The recorded residual respiratory event indices in combination with the data about leakage and adherence of the studied device provide reliable information for the implementation and follow-up of CPAP therapy in a typical group of patients with OSA.
ClinicalTrials.gov Identifier: NCT04407949, May 29, 2020, retrospectively registered.

Limited data exist concerning the indications, parameters, utility of daytime polysomnography, and treatment of infants with suspected sleep-related breathing disorders.
We retrospectively reviewed all polysomnography undertaken in a quaternary pediatric hospital for term infants up to 6 months of age between January 2017 and December 2019. Outcomes were evaluated, including a comparison among diagnostic groups.
Of 161 infants (58% male), 77 (48%) were ≤ 2 months old, and 103 (61%) were referred for either craniofacial abnormalities or an airway malformation. Daytime (n = 100) vs nighttime (n = 61) studies showed no differences in sleep architecture or treatment rates. Apnea-hypopnea index was > 10 events/h in 137 (85%) and was similar across different diagnostic groups, and 97 (78%) were prescribed noninvasive ventilation, with a mean treatment duration of 13.4 ± 9 months. Of the infants who were commenced on noninvasive ventilation 75% did not require it beyond 24 months.
Polysomnographic sleep parameters and the number of treatments prescribed were equivalent whether the polysomnography was performed during daytime or nighttime. Treatment with noninvasive ventilation was required in the short term for most infants with sleep-related breathing disorders, regardless of the indication for referral.
Singh J, Yeoh E, Castro C, Uy C, Waters K. Polysomnography in infants with clinical suspicion of sleep-related breathing disorders. J Clin Sleep Med. 2022;18(12):2803-2812.