Clinical Practice Guideline for Diagnostic Testing for Adult Obstructive Sleep Apnea: An American Academy of Sleep Medicine Clinical Practice Guideline

Treatment of obstructive sleep apnea syndrome (OSAS) with mandibular advancement devices-A statement of the Portuguese Society of Pulmonology, the Portuguese Society of Stomatology and Dental Medicine, the Portuguese Dental Association, and the Portuguese Society of Temporomandibular Disorders, Orofacial Pain and Sleep

With the purpose of establishing a consensus around clinical orientations for professionals involved in managing patients with sleep breathing disorders (SBD), an interdisciplinary group of scientific societies involved in this field discussed and reviewed all the published international guidelines from the American Dental Association, American Academy of Sleep Medicine, American Academy of Dental Sleep Medicine and the European counterparts. Treatment of SBD is multidisciplinary and should be made in concert with the patient, the sleep physician, and the qualified dentist to solve the individual, social, and economic burden of the disease. This consensus document represents the current thinking of a team of Portuguese experts on managing patients with SBD based on the available evidence.

Baseline lipid profile is associated with adverse outcomes in adults with coronary artery disease and obstructive sleep apnea despite lipid-lowering drugs and CPAP treatment: A secondary analysis of the RICCADSA cohort

BACKGROUND

Reducing lipid levels is essential for preventing major adverse cardiovascular and cerebrovascular events (MACCE) in management of coronary artery disease (CAD) patients with obstructive sleep apnea (OSA). The first line treatment of OSA is continuous positive airway pressure (CPAP), however, its impact on the association between lipid profile and MACCEs is uncertain.

METHODS

This was a secondary analysis of the RICCADSA cohort. In all, 224 revascularized CAD patients with OSA (apnea-hypopnea index [AHI] ≥15 events) were allocated to CPAP, 103 to no-CPAP, and 86 patients had no OSA (AHI<5/h). All patients were on lipid-lowering medication. Circulating triglycerides (TG), high-density lipoprotein (HDL) and low-density lipoprotein (LDL) levels (all in mg/dL) were measured at baseline and after one year. The undesired TG levels were defined as circulating TG ≥ 150 mg/dL, and undesired LDL levels were defined as ≥70 mg/dL.

RESULTS

OSA patients had higher TG and lower HDL levels than no-OSA patients whereas LDL levels were similar at baseline. There were no significant within-group differences in the CPAP group, no-CPAP group and no-OSA group regarding the lipid levels after one year. Undesired LDL levels at baseline predicted MACCEs med Hazard Ratio 2.18 (%95 CI 1.03-4.60; p = 0.04.) CONCLUSIONS: Most of the RICCADSA cohort had undesired LDL levels at baseline despite statin treatment, and CPAP had no additional lipid lowering effect after one year, suggesting that a more aggressive lipid lowering therapy as well as a more effective OSA treatment in addition to lifestyle changes should be targeted in the management of CAD patients with concomitant OSA.

The effects of mandibular setback surgery on the upper airway and sleep quality in skeletal Class III patients and comparison with control subjects: a pilot study

The aim of this study was to determine the effects of mandibular setback surgery exceeding 5 mm on upper airway and sleep quality in skeletal Class III patients, with comparisons to Class I controls. Sixteen individuals per group were selected based on their ANB angle and surgical need. 2D and 3D airway analyses were conducted. Sleep quality was assessed using the Epworth Sleepiness Scale, the STOP-Bang questionnaire, and a sleep study. Following surgery, all of the skeletal Class III malocclusion patients transitioned to a Class I relationship. This transformation was accompanied by a significant reduction in pharyngeal airway space, minimum cross-sectional area (minCSA), and airway volume. Additionally, the mandibular plane to hyoid distance and airway length increased, and the total apnea-hypopnea index (AHI), obstructive AHI, and snoring levels were elevated. Compared to the control group, significant narrowing of the minCSA and reduction in airway volume, especially in the lower segment, were observed. However, no significant change in sleep quality was noted. In conclusion, the altered upper airway anatomy after mandibular setback surgery exceeding 5 mm in skeletal Class III patients was associated with an increase in AHI, but overall sleep quality was not altered compared to Class I subjects.

Individualised Therapy for Obstructive Sleep Apnoea: Predictive Models and Anatomical Phenotyping of Mandibular Advancement Devices Responses

OBJECTIVES

This non-randomised clinical study aimed to identify the phenotypic characteristics that distinguish responders from non-responders. Additionally, it sought to establish a predictive model for treatment response to obstructive sleep apnoea (OSA) using mandibular advancement devices (MAD), based on the analysed phenotypic characteristics.

MATERIAL AND METHODS

This study, registered under identifier NCT05596825, prospectively analysed MAD treatment over 6 years using two-piece adjustable appliances according to a standardised protocol. Two response definitions aligned with the latest International Consensus Statement on OSA severity were established. Logistic regression and CHAID models integrated baseline clinical, anthropometric, cephalometric anatomical, soft tissue characteristics and physiological upper airway variables.

RESULTS

A total of 112 patients completed the study: 64 responders and 48 non-responders according to response definition 1, and 81 responders and 31 non-responders according to response definition 2. Responders to MAD treatment had lower body mass index (BMI), neck and waist circumference, Epworth Sleepiness Scale scores, apnoea-hypopnea index (AHI), snoring intensity on the Visual Analog Scale, CPAP pressure, and higher T90% and minSaO2. Patients exhibiting greater anatomical imbalance, smaller airway volume, smaller minimum cross-sectional area (CSAmin) and longer airway length demonstrated a poorer response to treatment.

CONCLUSIONS

Airway length, initial T90% and anterior facial height collectively formed a highly predictive logistic regression model for response definition 1. Jarabak's ratio, gonial angle, CSAmin, airway length, initial BMI and baseline AHI constituted a highly predictive model for the second response definition. Furthermore, the CHAID regression tree established cutoff values for the variables that form the predictive models.

Lipopolysaccharide-binding protein levels, obstructive sleep apnea, and depression: A cross-sectional study of adults

Obstructive sleep apnea (OSA) and depression are highly comorbid. Increased intestinal permeability has been hypothesized to play a role in the pathogenesis of both. The current study aimed to assess the severity of OSA symptoms, comorbid depressive symptoms, and lipopolysaccharide-binding protein (LBP) levels in adult patients being diagnosed for OSA syndrome. The study population consisted of 176 subjects. An apnea-hypopnea index (AHI) ≥ 5/hour was used for the diagnosis of OSA syndrome. Depressive symptoms were assessed with the Beck Depression Inventory-2. LBP levels were measured in the blood serum by enzyme-linked immunosorbent assay (ELISA). Associations between clinical symptom profiles or severity and LBP as an intestinal permeability biomarker marker were tested. LBP levels were not different between patients with different OSA severity, as assessed with AHI or daily sleepiness. Nor were LBP levels different in subjects with different depressiveness severity. Daily sleepiness was weakly positively correlated with depression score, and LBP levels correlated positively with a neutrophils-to-lymphocytes ratio. Finally, LBP levels were not explained by multiple linear regression models, including sleep-related parameter values and depression score. Intestinal permeability, as measured with LBP level, may not explain the comorbidity of depression and daily sleepiness in the course of OSA syndrome.

Effect of Lifestyle Modifications on Polysomnography in Obstructive Sleep Apnea

Background

Lifestyle modifications (LSM) is an ignored but essential component for Obstructive sleep apnea (OSA) treatment. We aimed to assess the role of LSM in OSA with customized program and its effect on polysomnographic parameters and body mass index (BMI).

Methods

Prospective study of fifty six OSA patients randomized into 2 groups after establishing the diagnosis by polysomnography (PSG). Both the groups were advised LSM which included weight loss and incorporating physical activity as first line of treatment. Group A had 28 OSA patients who were given a customized diet chart, counselled and kept on regular follow. Group B with 28 OSA patients were advised by the treating physician to lose weight with no specific tailor-made diet plan. Patients were reassessed at the end of 6 months and underwent a repeat PSG. BMI along with PSG parameters like Apnea hypopnea index (AHI), lowest saturation (LSAT), and time of SpO2 < 90% (cT90) were analysed.

Results

We analysed 54 subjects (mean age 42.38, 92.9% men). In Group A, 89.28% of the subjects had a significant decrease in AHI (p < 0.001) and time of SpO2 < 90% (p < 0.001) with improvement in mean LSAT (p < 0.001) and % of time with saturation < 90% (p = 0.001) and but in Group B only LSAT (p = 0.008) and cT90 (p = 0.028) showed improvement.

Conclusion

Lifestyle modifications in the form of a customised diet programme with proper diet plan, emphasis on incorporation of physical activity and periodic counselling has a better outcome which can act as an adjuvant for OSA treatment.

The Obstructive Sleep Apnea Physical Exam: A Systematic Review and Meta-Analysis

Introduction

This systematic review aimed to determine the clinical utility of various physical exam findings in the diagnosis of obstructive sleep apnea (OSA).

Methods

A systematic review of English articles identified from PubMed, Embase, CENTRAL, and Web of Science databases. Search terms included "sleep apnea," "physical exam," "polysomnography," and all relevant synonyms. Two reviewers independently screened abstracts, reviewed full texts, and extracted data from all studies that presented associations between physical characteristics and apnea-hypopnea index (AHI).

Results

A total of 35 studies representing 13,854 patients were included in this review. The mean difference between high AHI and low AHI groups was 4.09 kg/m2 (95% CI: 2.78-5.39) for BMI, 7.93 cm (3.59-12.28) for waist circumference, and 3.67 cm (2.64-4.71) for neck circumference. The odds ratios for having a high AHI were 2.44 (1.07-5.55) for macroglossia, 2.23 (1.68-2.96) for Mallampati > 2, 1.88 (1.67-2.11) for tonsil grade > 2, 3.99 (1.94-8.21) for pharyngeal grade > 2, and 1.57 (1.48-1.67) for enlarged uvula. Thyromental distance, retrognathia, Friedman grade, septal deviation, and enlarged turbinates were also assessed and were not found to be statistically significant between AHI groups.

Discussion

Several physical exams have strong evidence in the literature supporting their strength at differentiating patients with and without OSA. These should be used routinely among providers who treat OSA, regardless of specialty, to help guide decisions about recommending a sleep study and selecting appropriate treatment. Other physical characteristics may be better assessed through advanced exam techniques or require more research and standardization in the way they are assessed by practitioners.

Robust performances of a nocturnal long-term ECG algorithm for the evaluation of sleep apnea syndrome: A pilot study

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is one of the most common sleep disorders affecting nearly one billion of the global adult population, making it a major public health issue. Even if in-lab polysomnography (PSG) remains the gold standard to diagnose OSAHS, there is a growing interest to develop new solutions with more convenient at home devices enhanced with AI-based algorithms for the detection of sleep apnea. This retrospective study aimed to assess the performances of a new method based on nocturnal long-term electrocardiogram signal to detect apneas and hypopneas, in patients who performed attended in-lab PSG. After assessing the quality of the ECG signal, the new method automatically detected apneas and hypopneas using dedicated machine learning algorithm. The agreement between the new ECG-based detection method and the standard interpretation of PSG by a sleep clinician was determined in a blind manner. Eighty-five exams were included into the study with a mean bias between the proposed method and the scorer of 3.5 apneas-hypopneas/hour (/h) (95% CI -48.1 to 55.1). At a threshold of 15/h, sensibility and specificity were 93.3% and 66.7% respectively, and positive and negative predictive values were 87.5% and 80%, respectively. The proposed method using nocturnal long-term electrocardiogram signals showed very high performances to detect apneas and hypopneas. Its implementation in a simple ECG-based device would offer a promising opportunity for preliminary evaluation of patients suspected or at-risk of OSAHS.

Sleep-disordered breathing profiles in patients with cardiovascular diseases: Kurume SDB-CVD study

Sleep-disordered breathing (SDB) links to various cardiovascular diseases (CVDs), but comprehensive insights and detailed profiles remain scarce. This study examined the clinical characteristics of CVD patients complicated by SDB in the enrolled all consecutive 5765 patients who underwent screening tests for SDB during hospitalization from January 2014 to December 2019, using the database of Cardiovascular Medicine, Kurume University Hospital. Desaturation during sleep in SDB was significantly worse in terms of age, sex, daytime resting oxygen saturation (SpO2), systolic blood pressure (SBP), pulse pressure, body mass index (BMI), white blood cell count, hemoglobin A1c, serum creatinine, N-terminal pro-B-type natriuretic peptide (NT-proBNP), cardiothoracic ratio on chest X-ray, and left ventricular ejection fraction on echocardiography (all p-values < 0.001, except for SBP p = 0.002, and NT-proBNP p = 0.004). Coronary artery disease and heart failure, but not pulmonary hypertension, were associated with a higher prevalence and a greater risk of moderate to severe SDB, typically in older patients. Notably, low SpO2 during daytime and elevated BMI were indicative of SDB across all age categories for both sexes. Heatmaps of daytime SpO2 and BMI can effectively predict the severe SDB in patients with CVDs, suggesting the potential for efficient therapeutic interventions for SDB.

Validation of a fingertip home sleep apnea testing system using deep learning AI and a temporal event localization analysis

STUDY OBJECTIVES

This paper validates TipTraQ, a compact home sleep apnea testing (HSAT) system. TipTraQ comprises a fingertip-worn device, a mobile application, and a cloud-based deep learning artificial intelligence (AI) system. The device utilizes photoplethysmography (red, infrared, and green channels) and accelerometer sensors to assess sleep apnea by the AI system.

METHODS

We prospectively enrolled 240 participants suspected of obstructive sleep apnea (OSA) at a tertiary medical center for internal validation and 112 participants independently at another center for external validation. All participants underwent simultaneous polysomnography and TipTraQ HSAT. We compared TipTraQ-derived total sleep time (TQ-TST) and TipTraQ-derived Respiratory Events Index (TQ-REI) with expert-determined TST and apnea-hypopnea index (AHI), based on AASM standards with the 1B hypopnea rule. Temporal event localization analysis for respiratory event prediction was conducted at both event and hourly levels.

RESULTS

In the external validation, the Spearman correlation coefficients for TQ-TST vs. TST and TQ-REI vs. AHI were 0.81 and 0.95, respectively. The root mean square error were 0.53 hours for TQ-TST vs. TST and 7.53 events/h for TQ-REI vs. AHI. For apnea/hypopnea prediction with a 10-second grace period, the true positive, false positive, and false negative rates in temporal event localization analysis were 0.76, 0.24, and 0.23, respectively. The four-way OSA severity classification achieved a Cohen's kappa of 0.7.

CONCLUSIONS

TQ-TST and TQ-REI predict TST and AHI with comparable performance to existing devices of the same type, and respiratory event prediction is validated through temporal event localization analysis.

CLINICAL TRIALS

Development of TipTraQ Home Sleep Test, https://clinicaltrials.gov/study/NCT06474351?term=NCT06474351&rank=1, NCT06474351TipTraQ Home Sleep Test Study, SHH, https://clinicaltrials.gov/study/NCT06633887?term=NCT06633887&rank=1, NCT06633887.

A randomized controlled trial of telemedicine CBTI and PUMAS for prenatal insomnia: Reducing nocturnal cognitive arousal is a treatment mechanism for alleviating insomnia and depression during pregnancy

OBJECTIVE

Insomnia is common in pregnancy and fuels perinatal depression (PND). Cognitive-behavioral therapy for insomnia (CBTI) is effective during pregnancy, but unresolved cognitive arousal limits treatment outcomes. Enhancing reduction of cognitive arousal may improve patient outcomes. This randomized controlled trial (RCT) evaluated the effectiveness of CBTI and Perinatal Understanding of Mindful Awareness for Sleep (PUMAS, which combines mindfulness with behavioral sleep strategies) on insomnia, depression, and nocturnal cognitive arousal relative to sleep hygiene education (SHE).

METHODS

A single-site, three-arm RCT of N = 64 pregnant women with clinically significant insomnia symptoms who received CBTI, PUMAS, or SHE. Active treatment was delivered via six weekly telemedicine video sessions. Outcomes included the insomnia severity index (ISI), Edinburgh postnatal depression scale (EPDS), and the pre-sleep arousal scale's cognitive factor (PSASC; nocturnal cognitive arousal).

RESULTS

Over 95 % of active therapy patients completed ≥4 sessions, indicating high engagement. CBTI (ΔISI = -11.20 ± 6.93; 65.0 % insomnia remission) and PUMAS (ΔISI = -11.05 ± 3.84; 81.8 % insomnia remission) significantly alleviated insomnia relative to SHE (ΔISI = -4.50 ± 1.71; 13.6 % remission), which was replicated in women with comorbid OSA. PUMAS produced large reductions in PND and nocturnal cognitive arousal relative to SHE, whereas CBTI did not. Mediation analyses supported reducing cognitive arousal as a key mechanism by which PUMAS alleviated insomnia and PND.

CONCLUSIONS

CBTI and PUMAS are effective for prenatal insomnia, even in women with comorbid OSA. PUMAS may be especially beneficial for pregnant women presenting with PND and/or high cognitive arousal (including perinatal rumination and worry), whereas CBTI may yield more modest benefits for these non-sleep outcomes.

Obstructive sleep apnea is associated with cardiac structural and functional alterations in patients with advanced diabetic kidney disease

AIMS

Obstructive sleep apnea (OSA) is common in type 2 diabetes mellitus (T2DM), but its association with cardiac structure and function in advanced diabetic kidney disease (DKD) remains unclear.

METHODS

T2DM patients with estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 and albuminuria were assessed for OSA using the apnea-hypopnea index (AHI). Cardiac structure and function were assessed by transthoracic echocardiography following standard guidelines. 39 patients without OSA (AHI < 5) were compared to 34 patients with moderate-severe OSA (AHI ≥ 15).

RESULTS

Mean age was 71.4 ± 9.4 years (73 % male), and eGFR was 32.1 ± 12.3 mL/min/1.73 m2. DKD patients with moderate-severe OSA had a higher left atrial volume index (LAVI: 36.6 ± 13.9 vs. 28.1 ± 10.5 mL/m2, p < 0.01) left ventricular mass index (LVMI: 48.8 ± 11.7 vs. 41.8 ± 9.7 g/m2.7, p < 0.01) and right ventricular diameter (RVD: 34.1 ± 5.8 vs. 28.4 ± 4.4 mm, p < 0.001) than DKD patients without OSA. Left ventricular ejection fraction (LVEF) did not differ, but global longitudinal strain (GLS) was reduced (-15.1 ± 3.0 vs. -16.6 ± 2.8 %, p < 0.05). In multivariable linear regression analyses, moderate-severe OSA remained significantly associated with LAVI, LVMI, RVD, and GLS but not with LVEF.

CONCLUSIONS

Moderate-severe OSA is associated with cardiac hypertrophy and chamber dilatation, potentially contributing to cardiovascular risk in advanced DKD.

Status and Opportunities of Machine Learning Applications in Obstructive Sleep Apnea: A Narrative Review

Background

Obstructive sleep apnea (OSA) is a prevalent and potentially severe sleep disorder characterized by repeated interruptions in breathing during sleep. Machine learning models have been increasingly applied in various aspects of OSA research, including diagnosis, treatment optimization, and developing biomarkers for endotypes and disease mechanisms.

Objective

This narrative review evaluates the application of machine learning in OSA research, focusing on model performance, dataset characteristics, demographic representation, and validation strategies. We aim to identify trends and gaps to guide future research and improve clinical decision-making that leverages machine learning.

Methods

This narrative review examines data extracted from 254 scientific publications published in the PubMed database between January 2018 and March 2023. Studies were categorized by machine learning applications, models, tasks, validation metrics, data sources, and demographics.

Results

Our analysis revealed that most machine learning applications focused on OSA classification and diagnosis, utilizing various data sources such as polysomnography, electrocardiogram data, and wearable devices. We also found that study cohorts were predominantly overweight males, with an underrepresentation of women, younger obese adults, individuals over 60 years old, and diverse racial groups. Many studies had small sample sizes and limited use of robust model validation.

Conclusion

Our findings highlight the need for more inclusive research approaches, starting with adequate data collection in terms of sample size and bias mitigation for better generalizability of machine learning models in OSA research. Addressing these demographic gaps and methodological opportunities is critical for ensuring more robust and equitable applications of artificial intelligence in healthcare.

Osteoarthritis and the risk of sleep apnea: a general population-based cohort study

OBJECTIVE

Osteoarthritis (OA) is the most common joint disease, leading to pain and inflammation, two risk factors for sleep apnea (SA). We aimed to examine whether OA may increase the risk of incident SA, a common source of mortality and sudden death.

METHOD

Using data from the IQVIA Medical Research Database, we conducted three cohort studies (i.e., knee, hip, and hand cohort) among individuals aged 50 years or older. For each patient with incident OA, up to five non-OA individuals were matched by age, sex, entry-time, and BMI. We calculated the incidence rate of SA for the OA and non-OA cohorts, respectively, and examined the relation of OA to the risk of incident SA using a Cox proportional hazard model adjusting for potential confounders.

RESULTS

During the follow-up, 869 SA cases occurred in 58,674 patients with knee OA and 2,186 in 235,850 non-OA individuals (2.29 vs. 1.41/1000 person-years). Compared with the non-OA cohort, the multivariable-adjusted hazard ratio (HR) of SA in the knee OA cohort was 1.45 (95% confidence interval [CI]: 1.34-1.56). A higher risk of SA was also observed among patients with incident hip OA (adjusted HR: 1.45, 95%CI: 1.28-1.66) and hand OA (adjusted HR: 1.50, 95%CI: 1.26-1.78) than the matched non-OA individuals.

CONCLUSIONS

This general population-based study provides the first evidence that patients with knee, hip, and hand OA may have an increased risk of incident SA. Strategies aimed at screening, prevention, and treatment of SA could be developed to reduce the disease burden in OA patients.

Clinical utility of limited channel sleep studies versus polysomnography for obstructive sleep apnoea

BACKGROUND

Obstructive sleep apnoea (OSA) is a common cause of sleep disturbance, characterised by the presence of repetitive upper airway obstruction during sleep. OSA is associated with sleepiness during the day, reduced quality of life and an increased risk of cardiovascular disease. OSA can be diagnosed using several different strategies. The current reference test is fully supervised polysomnography, which is expensive and time-consuming. Other diagnostic tests, referred to as limited channel sleep studies because they include fewer parameters than polysomnography, are less resource-intensive but may also have different diagnostic performances, resulting in a difference in clinical outcomes.

OBJECTIVES

To assess the clinical impact (outcome on a participant level) of a strategy where treatment follows diagnostic testing (test-treatment combination) using limited channel sleep studies compared to polysomnography in people with suspected obstructive sleep apnoea (OSA).

SEARCH METHODS

We searched two databases (CENTRAL, MEDLINE) up to 11 May 2023 using search terms related to OSA and polysomnography developed by our information specialist.

SELECTION CRITERIA

We included randomised controlled trials that compared any limited channel sleep studies with Level I fully supervised polysomnography in adults (aged 18 years and older) with suspected OSA. Our primary outcome was sleepiness, and our secondary outcomes were quality of life, all-cause mortality, cardiovascular events and correlating risk factors, continuous positive airway pressure (CPAP) usage, serious adverse events, and cost-effectiveness.

DATA COLLECTION AND ANALYSIS

Four review authors extracted data from the included trials and assessed the risk of bias. We summarised treatment effects using random-effects meta-analyses and expressed as mean difference (MD) or standardised mean difference (SMD) with corresponding 95% confidence intervals (CI) where possible. We used GRADE to assess the certainty of the evidence.

MAIN RESULTS

We included three trials with 1143 participants. One trial compared Level III sleep studies to a Level I fully supervised polysomnography, one trial compared Level IV sleep studies to Level I sleep studies, and one trial compared Level IV sleep studies versus Level III sleep studies versus Level I sleep studies. The follow-up of these trials ranged from four to six months. Level III sleep studies versus Level I sleep studies There is high-certainty evidence that Level III sleep studies result in little to no difference in sleepiness (MD 0.47, 95% CI -0.23 to 1.18; P = 0.19, I2 = 0%; 2 trials, 701 participants) or quality of life (SMD 0.01, 95% CI -0.14 to 0.16; P = 0.93, I2 = 0%; 2 trials, 701 participants) compared to Level I sleep studies. Level III sleep studies are also probably slightly more cost-effective (moderate-certainty evidence). There is low-certainty evidence that they may result in little to no difference in cardiovascular events and correlating risk factors, CPAP adherence (MD -0.18 hours per day, 95% CI -0.56 to 0.20; P = 0.36, I2 = 0%; 2 trials, 360 participants) or serious adverse events. Level IV sleep studies versus Level I sleep studies There is low-certainty evidence that Level IV sleep studies may not increase sleepiness compared to Level I sleep studies (MD 0.66, 95% CI -0.41 to 1.72; P = 0.23, I2 = 39%; 2 trials, 573 participants). Additionally, there is low-certainty evidence that they may result in little to no difference in cardiovascular events and correlating risk factors. For quality of life, CPAP adherence, serious adverse events and cost-effectiveness, the evidence is very uncertain. None of the included trials reported on all-cause mortality.

AUTHORS' CONCLUSIONS

Level III sleep studies may result in little to no difference in clinical outcomes when compared to Level 1 sleep studies in people with suspected OSA. Level IV sleep studies may not increase sleepiness and may result in little to no difference in cardiovascular events and correlating risk factors compared to Level I sleep studies; the evidence was too uncertain to make statements for other outcomes. Overall, the body of evidence was limited, therefore more trials making this comparison are necessary, as are trials with a longer follow-up duration.

Effects of Soft Cervical Collar on Apnea-Hypopnea Index in Patients with Obstructive Sleep Apnea

BACKGROUND Obstructive sleep apnea (OSA) is a sleep disorder characterized by recurrent upper airway obstruction, leading to intermittent hypoxia and sleep fragmentation. Continuous positive airway pressure (CPAP) is the criterion standard treatment, but adherence remains a significant challenge. This study aimed to evaluate whether a soft cervical collar could reduce the apnea-hypopnea index (AHI) and improve oxygen saturation by preventing excessive neck flexion and airway collapse, potentially serving as an alternative or adjunctive therapy for OSA. MATERIAL AND METHODS Ethical approval was obtained, and 34 OSA-diagnosed participants underwent polysomnography (PSG) and CPAP titration. Exclusion criteria ensured a focused cohort. Demographic data, including age, sex, and BMI, were collected. The first night, a 16-channel Embla device was used for PSG, and the apnea-hypopnea index (AHI) was calculated. The second night, patients were monitored with a neck brace for 2 hours, followed by standard PAP titration for the rest of the night during the PSG recording. RESULTS The mean AHI decreased from 44.44±26.3 to 36.69±37.48 with the cervical collar, although this difference was not statistically significant (p=0.08). However, in patients with BMI <30 kg/m², AHI significantly decreased (p=0.02). The lowest oxygen saturation improved from 76.35±10.26 to 83.74±5.02 (p=0.01), indicating better oxygenation with the cervical collar. CONCLUSIONS Although CPAP is the criterion standard for OSA treatment, this study suggests that a neck collar could be an alternative treatment, especially for patients with a BMI below 30. The findings support further exploration of neck collars as a potential intervention in cases where CPAP accessibility or adherence is challenging.

The critical role of melody and harmony in sleep induction: Direct evidence from electroencephalogram-based analysis

Insomnia, prevalent in contemporary society, is characterized by difficulties in sleep initiation and maintenance, leading to fatigue, depression, and impaired cognitive function. Although research has demonstrated the sleep induction effects of certain musical genres, the neurophysiological mechanisms through which musical constituents, namely, melody, harmony, and rhythm, induce sleep remain inconclusive. To elucidate how musical constituents influence sleep onset, we used both subjective and objective measures, including the Pittsburgh Sleep Quality Index and Karolinska Sleepiness Scale as the former and electroencephalography (EEG) analysis as the latter. The EEG data showed that melody and harmony significantly enhance sleep quality, particularly impacting the theta band and the dispersion entropy in the temporal lobes. Conversely, the effect of rhythm in sleep induction appeared less significant, with minimal activity observed in the frontal and parietal lobes. We believe the data provide a foundation for innovative approaches to music composition that emphasize melody and harmony to enhance the therapeutic potential of music in sleep management, while de-emphasizing the role of rhythm. The findings also support adapting traditional musical forms, such as classical music, for sleep induction purposes and the development of sleep aid music composition rooted in Western music theory.

The AutoStrain LV technique is a sensitive method for detecting subclinical left ventricular dysfunction in patients with obstructive sleep apnea syndrome

Obstructive sleep apnea syndrome (OSAS) is strongly associated with multiple cardiovascular diseases, however, early detection of subclinical myocardial damage is a challenge. We aimed to compare the sensitivity of AutoStrain LV technology versus conventional echocardiography for assessing left ventricular (LV) impairment in patients with subclinical OSAS and to identify sensitive echocardiographic indicators of LV injury. Classifying 126 qualified participants based on their apnea-hypopnea index (AHI), we formed control, mild, moderate, and severe OSAS categories. LV global longitudinal strain (LVGLS) was evaluated by AutoStrain LV technique. Conventional two-dimensional echocardiography was used to measure different factors including LV end-diastolic diameter, LV end-systolic diameter, interventricular septum diameter, LV posterior wall diameter, and LV functional shortening. LV ejection fraction was calculated by modified biplane Simpson method, and the Doppler ultrasound was used to measure the LV diastolic function indices E/A and E/E'. We calculated the correlations between these ultrasound parameters and the AHI. Although LV ejection fraction and LV functional shortening are normal, the LVGLS in the OSAS group decreased with the severity of the disease (P < .001). The values of E/A in the mild, moderate, and severe OSAS groups, as well as the values of E/E' in the mild and severe OSAS groups, showed significant differences compared to the control group, but no significant differences were found between different OSAS subgroups. The IVST and LVPWT values in the moderate and severe OSAS groups were higher than those in the control group and mild OSAS group, but there were no significant differences between the other groups. Conventional echocardiographic parameters did not change with the severity of the disease. Correlation analysis showed that LVGLS had the strongest correlation with AHI (r = -0.732, P < .001). Compared with conventional echocardiography, AutoStrain LV technology has a higher sensitivity for monitoring LV function impairment in patients with subclinical OSAS.

Machine learning identifies potential diagnostic biomarkers associated with ferroptosis in obstructive sleep apnea

Obstructive sleep apnea (OSA) is the most common sleep apnea-related disorder, with a high prevalence and a range of associated complications. Ferroptosis is a new mode of cell death that is involved in the development of OSA, but the mechanism has remained elusive. In the present study, ferroptosis-related genes in OSA were assessed and their potential clinical value was discussed. Data were downloaded and merged, and screened for differentially expressed genes (DEGs) through the Gene Expression Omnibus database. The OSA ferroptosis-related genes were obtained after intersecting with the downloaded ferroptosis-related genes. Subsequently, key ferroptosis-associated differential genes were obtained using two machine learning methods (the least absolute shrinkage and selection operators and random forest). The immune infiltration in the samples and the correlation between key differential genes and immune infiltrating cells were then analyzed. A competing endogenous (ce)RNA visualization network was constructed to find possible therapeutic targets. Finally, the expression levels of key DEGs were verified by reverse transcription-quantitative (RT-q)PCR. In this study, 3 key ferroptosis-related differential genes were identified: TXN, EGR1 and CDKN1A. Functional enrichment analysis showed that the three key differential genes in OSA can influence the development of OSA by affecting metabolism, immune response and other processes. RT-qPCR experiments verified the expression of these key genes, further confirming the findings. A persistent state of immune activation may promote the progression of OSA, with marked infiltration of T cells and natural killer cells in OSA tissues. Genipin is a possible targeted therapeutic agent for OSA. Meanwhile, ceRNA network analysis identified several long non-coding RNAs that can regulate OSA disease progression. A total of 3 key ferroptosis-related markers were identified (TXN, EGR1 and CDKN1A) that are closely associated with metabolic disorders and immune responses, and which may be targets for early diagnosis and treatment of OSA.

A novel machine learning model for screening the risk of obstructive sleep apnea using craniofacial photography with questionnaires

STUDY OBJECTIVES

Undiagnosed or untreated moderate-to-severe obstructive sleep apnea (OSA) increases cardiovascular risks and mortality. Early and efficient detection is critical, given its high prevalence. We aimed to develop a practical and efficient approach for OSA screening, using simple facial photography and sleep questionnaires.

METHODS

We retrospectively included 748 participants who completed polysomnography, sleep questionnaires (STOP-BANG), and facial photographs at a university hospital between 2012 and 2023. Owing to class imbalance, we randomly undersampled the participants, categorized into the moderate/severe or no/mild OSA group, based on an apnea-hypopnea index of 15 events/h. Using a validated convolutional neural network, we extracted the OSA probability scores from photographs, which were used as the input for the questionnaires. Four machine learning models were employed to classify the moderate/severe vs no/mild groups and evaluated in the test dataset.

RESULTS

We analyzed 426 participants (213 each in the moderate/severe and no/mild groups). The mean (standard deviation) age was 44.6 (14.7) years; 80.8% were men. Logistic regression achieved the highest performance: the area under the receiver operator curve was 97.2%, and accuracy was 91.9%. Adding OSA probability, retrieved from facial photographs, to the questionnaires improved performance, compared with using questionnaires or photographs alone (the area under the receiver operating characteristic curve 97.2% using both, 85.7% for photographs alone, and 64% and 79.1% for questionnaire threshold STOP-BANG scores of 3 and 4, respectively).

CONCLUSIONS

Using simple facial photographs and sleep questionnaires, a 2-stage approach (convolutional neural network + machine learning) accurately classified OSA into moderate/severe vs no/mild OSA groups. This method may facilitate optimal OSA treatment and avoid unnecessary costly evaluations.

CITATION

Park J-Y, Shin H-R, Kim MH, et al. A novel machine learning model for screening the risk of obstructive sleep apnea using craniofacial photography with questionnaires. J Clin Sleep Med. 2025;21(5):843-854.

Assessing fatigue in myalgic encephalomyelitis/chronic fatigue syndrome patients before and after treatment with bright light therapy: A prospective randomized controlled crossover study

OBJECTIVE

The aim of the current study was to test the effectiveness of treatment with bright light therapy (BLT) on fatigue and cognitive function in patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). A randomized-controlled cross-over study design was chosen in order to provide all patients access to BLT treatment and account for placebo effects.

METHODS

In this study, a total of 36 outpatients with a diagnosis of ME/CFS according to the criteria of the Institute of Medicine (2015) were randomly assigned to a cross-over design starting out either with BLT or waitlist for the course of 2 weeks with a washout phase in between. Portable light boxes emitting full-spectrum visible light with a luminance intensity of 10,000 lux were used by the participants at home. Primary outcome of the study was fatigue as assessed by Chalder Fatigue Score (CFQ) and the secondary outcome variable was cognitive function assessed per standardized test battery (Test of Attentional Performance - TAP).

RESULTS

The primary outcome variable fatigue was not significantly improved after treatment with BLT compared to wait list in the full crossover design, although fatigue scores improved immediately after two weeks of BLT. Additionally, patients showed decreased reaction time after treatment with BLT in a subtest of TAP compared to wait list. Over 45 % of patients were diagnosed with postural tachycardia syndrome.

CONCLUSION

BLT for two weeks is not effective for the treatment of fatigue in ME/CFS, but it might have beneficial effects on attention in patients with ME/CFS. The clinical trial is registered with www.

CLINICALTRIALS

gov (NCT06635928).

Ability of upper airway metrics to predict obstructive sleep apnea severity: a systematic review

OBJECTIVES

The lack of consensus regarding the association between airway narrowing and the severity of obstructive sleep apnea (OSA) presents a significant challenge in understanding and diagnosing this sleep disorder. The study aimed to systematically review the literature to investigate the relationship between upper airway measurements and the severity of OSA defined by the apnea-hypopnea index (AHI).

METHODS

PubMed, Scopus, and Web of Science were systematically searched on 21 March 2023 for articles on OSA patients as diagnosed by polysomnography, investigating the correlation between upper airway measurements and AHI using cone-beam CT (CBCT) or multidetector CT (MDCT). Quality assessment was done using the Newcastle-Ottawa Scale. The results were subsequently synthesized descriptively.

RESULTS

The database search identified 1253 results. Fourteen studies, encompassing 720 patients, met the eligibility criteria. Upper airway length showed moderate to weak positive correlation with AHI. Minimal cross-sectional area had varying correlations with AHI, ranging from strong negative to no correlation. Nasopharyngeal volumes showed moderate negative to weak correlations with AHI. Total upper airway volume ranged from strong negative to weak correlation with AHI. Other measurements exhibited weak or very weak correlations with AHI.

CONCLUSIONS

Among the variables investigated, the minimal cross-sectional area and, to a lesser extent, the volume of the upper airway in OSA patients demonstrated the most promising correlation with the AHI. However, the preponderance of evidence suggests that upper airway length, cross-sectional area and volume as measured by CBCT or MDCT are weak predictors of OSA.

Evaluation of 2 different wrist actigraphy devices in the adult population

STUDY OBJECTIVES

Actigraphy devices are used in sleep medicine. The Actiwatch 2 (Philips Respironics) was an example of a frequently used device in this field. Nevertheless, the discontinuation of this device has led to an increased necessity for the implementation of other available actigraphy methods capable of providing objective information. The objective of this study was to assess the performance of the new ActTrust 2 compared to the Actiwatch 2 model.

METHODS

This observational prospective study included 9 participants (77.760 activity logs) who were monitored for 7 days using 2 actigraphy wrist devices (ActTrust 2 and Actiwatch 2) simultaneously. The following variables were evaluated: midline estimating statistic of rhythm, amplitude, and acrophase; intradaily variability; interdaily stability; relative amplitude; and the mean of 5 consecutive hours with the lowest activity and the 10 consecutive hours with the highest activity. Furthermore, total sleep time, time in bed, sleep efficiency, sleep onset latency, wake after sleep onset, and awakenings were also included.

RESULTS

Actigraphy models indicated statistically significant differences in activity levels. Regarding the analysis of circadian rest-activity rhythms, 10 consecutive hours with the highest activity, midline estimating statistic of rhythm, and amplitude also exhibited these differences. Furthermore, the analysis of sleep-wakefulness revealed significant differences in the sleep onset latency and the number of awakenings.

CONCLUSIONS

The ActTrust 2 and Actiwatch 2 models showed equivalent results in measuring circadian rest-activity rhythm and sleep. However, caution is advised when interpreting parameters such as midline estimating statistic of rhythm, amplitude, sleep onset latency, awakenings, and 10 consecutive hours with the highest activity variables.

CITATION

Henríquez-Beltrán M, Benítez ID, Juez-Garcia I, et al. Evaluation of 2 different wrist actigraphy devices in the adult population. J Clin Sleep Med. 2025;21(5):805-812.

Robotic beds for the treatment of positional obstructive sleep apnea - A randomized cross-over pilot trial

BACKGROUND

Interventions leading to avoidance of supine position and thus reducing the likelihood of upper airway collapse during sleep are a treatment approach for positional obstructive sleep apnea (POSA). The aim of this randomized cross-over trial was to assess the effect of two actuated beds (trunk-elevation and sideward-tilting) on OSA severity and sleep fragmentation in POSA.

METHODS

After baseline polysomnography, adult patients with POSA were randomly assigned to two nights of intervention in the intelligent sleep apnea bed ISABel1 and ISABel2. In the case of obstructive apnea or hypopnea, ISABel1 elevated the upper body by 50° and ISABel2 induced a unilateral bed tilt of 40°, with both interventions lasting 10 min. Sustained trunk elevations without sliding down (ISABel1) and position change from supine to non-supine (ISABel2) were defined as successful interventions.

RESULTS

Six adult men (57 ± 11 years, BMI 28 ± 4 kg/m2, AHI 39 ± 15/h) with POSA were included. Neither trunk elevation (ISABel1) nor side tilt (ISABel2) - approximately 10 interventions per night - significantly reduced apnea-hypopnea index (AHI), whereas trunk elevation showed a tendency to reduce supine AHI. Actuated beds had no effect on sleep efficiency and arousals. Only 13 % of side tilts in ISABel2 resulted in a successful shift to a non-supine position. The time to the next respiratory event after bed movement was longer in the trunk elevating bed than in the side-tilting bed.

CONCLUSION

Trunk elevating beds decrease supine AHI and both side-tilting and trunk elevating beds increase the time to the next obstructive apnea or hypopnea.

Distinguishing severe sleep apnea from habitual snoring using a neck-wearable piezoelectric sensor and deep learning: A pilot study

This study explores the development of a deep learning model using a neck-wearable piezoelectric sensor to accurately distinguish severe sleep apnea syndrome (SAS) from habitual snoring, addressing the underdiagnosis of SAS in adults. From 2018 to 2020, 60 adult habitual snorers underwent polysomnography while wearing a neck piezoelectric sensor that recorded snoring vibrations (70-250 Hz) and carotid artery pulsations (0.01-1.5 Hz). The initial dataset comprised 1167 silence, 1304 snoring, and 399 noise samples from 20 participants. Using a hybrid deep learning model comprising a one-dimensional convolutional neural network and gated-recurrent unit, the model identified snoring and apnea/hypopnea events, with sleep phases detected via pulse wave variability criteria. The model's efficacy in predicting severe SAS was assessed in the remaining 40 participants, achieving snoring detection rates of 0.88, 0.86, and 0.92, with respective loss rates of 0.39, 0.90, and 0.23. Classification accuracy for severe SAS improved from 0.85 for total sleep time to 0.90 for partial sleep time, excluding the first sleep phase, demonstrating precision of 0.84, recall of 1.00, and an F1 score of 0.91. This innovative approach of combining a hybrid deep learning model with a neck-wearable piezoelectric sensor suggests a promising route for early and precise differentiation of severe SAS from habitual snoring, aiding guiding further standard diagnostic evaluations and timely patient management. Future studies should focus on expanding the sample size, diversifying the patient population, and external validations in real-world settings to enhance the robustness and applicability of the findings.

Estimating the costs and analysing the precision of several diagnostic and treatment approaches for obstructive sleep apnea patients in the Netherlands, using timed automata modelling

Obstructive Sleep Apnea (OSA) is a (highly) prevalent medical condition, linked to severe negative health consequences. In the Netherlands, diagnosis of OSA presently has long waiting times and both diagnosis and treatment have high costs. This article introduces a so-called timed automata model (UPPAAL tool) for analysing diagnostic and treatment approaches for OSA. This model is used for assessing the Dutch current traditional approach, as well as multiple alternative approaches for OSA diagnosis and treatment. The analysis shows that one alternative approach can lower the costs and waiting lists, while maintaining diagnostic precision. In this manuscript the best alternative approach is a combination of Oxygen Desaturation Index (ODI) measurement obtained by nocturnal pulse oximetry and a questionnaire to diagnose the patient. Of course, it is important that, although timed automata modelling is a reliable tool, these outcomes are meant to start a discussion regarding the above-mentioned problems and are not proven outcomes yet. The healthcare system in the Netherlands is in danger of becoming unaffordable. Therefore, this initial exploration has been carried out to see whether alternatives to the current OSA-care can be devised.

The Relationship Between Education Level and the Prevalence and Medical Behavior of Sleep-Disordered Breathing: A Population-Based Cross-Sectional Study

Objective

Sleep-disordered breathing (SDB) poses a substantial health burden. It is often marked by patients' low awareness and poor treatment-seeking. While age, obesity, and genetics are known risk factors, limited studies suggest that socioeconomic status, including educational level, may also affect its prevalence. However, the exact relationship remains unclear. This study aims to explore the link between educational level and SDB prevalence in Chinese community-dwelling individuals and its impact on medical behavior.

Patients and Methods

This cross - sectional study analyzed data from 3,717 residents in the Guangdong cohort. Binary logistic regression was used for multivariable analysis. The product - of - coefficients method was applied to analyze potential mediating effects of education level on SDB through lifestyle and BMI.

Results

The prevalence of SDB was 29.7%, 33%, 29.2%, 30.5% and 31.5% in the illiterate, primary school, junior high school, high school and university or above education groups, respectively, with no significant difference (P=0.580).After further adjustment for age, gender, and marital status, the results showed that education level had no significant impact on the risk of SDB in the population (P>0.05). Mediation analysis showed that education level had an indirect effect on SDB through BMI and smoking(P<0.0001). The rates of SDB consultation among the five groups with different education levels were 2.8%, 4.4%, 3.7%, 3.7%, and 3.9%, respectively, with no significant difference (P=0.981).

Conclusion

In Chinese community - dwelling individuals, SDB is present across all education levels. BMI and smoking may influence this. SDB patients overall have low consultation rates. As medical behavior for SDB does not differ much across education levels, disease education and intervention for SDB should target all education groups.

Circadian Biology in Obstructive Sleep Apnea-Associated Cardiovascular Disease

A dysregulated circadian system is independently associated with both Obstructive Sleep Apnea (OSA) and cardiovascular disease (CVD). OSA and CVD coexistence is often seen in patients with prolonged untreated OSA. However, the role of circadian dysregulation in their relationship is unclear. Half of the human genes, associated biological pathways, and physiological functions exhibit circadian rhythms, including blood pressure and heart rate regulation. Mechanisms related to circadian dysregulation and heart function are potentially involved in the coexistence of OSA and CVD. In this article, we provide a comprehensive overview of circadian dysregulation in OSA and associated CVD. We also discuss feasible animal models and new avenues for future research to understand their relationship. Oxygen-sensing pathways, inflammation, dysregulation of cardiovascular processes, oxidative stress, metabolic regulation, hormone signaling, and epigenetics are potential clock-regulated mechanisms connecting OSA and CVD.

The relationship between obesity and obstructive sleep apnea in four community-based cohorts: an individual participant data meta-analysis of 12,860 adults

Background

Obesity is a well-established risk factor for obstructive sleep apnea (OSA). We assessed the reciprocal prevalence of obesity and OSA and how it varies by age and sex.

Methods

Following a systematic review through March 27, 2025, the final sample included four community-based cohort studies in the US and Switzerland. OSA severity was quantified using the apnea-hypopnea index (AHI, all apneas plus hypopneas with ≥4% oxygen desaturation/hour). Random effects individual participant data (IPD) meta-analyses estimated prevalences. Logistic regression compared odds of OSA across weight groups.

Findings

Among 12,860 adults (mean ± SD age: 66.6 ± 7.3 years), 7222 (56.2%) had OSA (AHI ≥5 events/h) and 3309 (25.7%) had obesity (BMI ≥30 kg/m2). IPD meta-analysis showed 31.5% [95% CI: 16.8-48.5] of individuals with OSA had obesity and 44.4% [36.5-52.5] had overweight status (25 ≤ BMI < 30). Among subgroups of individuals with obesity and overweight, 74.3% [63.8-83.5] and 59.8% [46.5-75.7] had any OSA, respectively. Obesity was higher in females than males with OSA, and in younger (<65 years) vs. older individuals. Odds ratios for OSA in subgroups of individuals with overweight and obesity compared to BMI <25 kg/m2 were 2.18 [1.73-2.76] and 4.84 [3.09-6.00], respectively.

Interpretation

Our analyses show that most adults with OSA do not have obesity, with 44.4% having overweight and 23.5% having normal weight or underweight. Obesity was more prevalent among females compared to males and in younger individuals (<65 years) compared to older individuals with OSA. Recognizing OSA is not exclusive to obesity highlights the need for personalized treatment plans.

Funding

American Academy of Sleep Medicine, National Heart, Lung, and Blood Institute, and Apnimed.

Identifying comorbid obstructive sleep apnea in chronic musculoskeletal pain: A systematic review

Obstructive sleep apnea (OSA) is common in patients with chronic musculoskeletal pain (CMP). Understanding the characteristics of the CMP population with comorbid OSA is important for proper screening and management. This systematic review compiles evidence on methods used to identify OSA in patients with CMP and identifies the characteristic features of patients with CMP and comorbid OSA. A systematic search was conducted in Medline (PubMed), Embase and Web of Science. Broad search terms related to sleep apnea and CMP were used. Studies had to include individuals with CMP diagnosed with OSA or individuals with CMP in whom OSA symptoms were assessed as outcomes. The protocol was pre-registered on PROSPERO (CRD42024516140). The initial search identified 3832 records, and 30 studies (total n=7085 participants) were included. Laboratory sleep polysomnography was the most used objective assessment method, while the Berlin and STOP-BANG questionnaires were the most used self-report instruments. However, these questionnaires showed poor agreement with polysomnography. The results consistently showed that fatigue and pain distribution did not differ between CMP patients with and without OSA. Inconsistent results were found regarding higher age, higher body mass index, psychological factors, decreased sleep quality, increased sleepiness, and pain intensity as characteristic features of the CMP with OSA population compared to the CMP without OSA. No factors consistently characterized the CMP with OSA population. Further research is required to explore the factors that consistently characterize patients with CMP with comorbid OSA to improve the effectiveness of screening for OSA in daily practice. PERSPECTIVE: This work summarizes the tools used to assess obstructive sleep apnea in populations with chronic musculoskeletal pain and highlights the features of individuals with both conditions compared to those with only chronic musculoskeletal pain.

Preoperative assessment of patients at risk of postoperative respiratory depression

Respiratory depression during sleep is a major health challenge after surgery. The main cause is reduction in breathing due to opioids, which are commonly used for management of postoperative pain. The consequences are hypoxemia and hypercapnia, which may increase the risk of cardiovascular complications, mortality, and healthcare utilization. Identifying individuals who are at risk of postoperative respiratory depression prior to the surgery can help guide the perioperative care to reduce adverse outcomes. In this project, we developed a risk assessment model to identify individuals at risk of postoperative respiratory depression prior to the surgery, based on the demographics and changes in preoperative overnight oxyhemoglobin saturation (SpO2) levels. To achieve this, we retrospectively analyzed SpO2 signals of 159 patients, which were recorded continuously preoperatively and on the third night after surgery. Respiratory depression was defined as postoperative episodes where SpO2 was ≤85% for more than 3 minutes. From preoperative SpO2 signals, we extracted features to characterize overnight SpO2 and desaturation episodes. We streamlined a systematic process for feature selection and model development using a nested cross-validation pipeline. Our results indicated that random forest, XGBoost, and Naïve bayes demonstrated the highest predictive performance, consistently surpassing the recent available PRODIGY model. These findings suggest that demographics and preoperative SpO2 characteristics can preoperatively identify individuals at high-risk of postoperative respiratory depression, which offers a non-invasive and cost-effective method of monitoring respiratory health.

Oral appliance therapy for the management of obstructive sleep apnea in adults: an umbrella review

OBJECTIVE

The aim of this umbrella review was to assess the effectiveness of oral appliance therapy compared to continuous positive airway pressure (CPAP), surgery, inactive appliances/controls, exercise, or other conservative techniques in mitigating symptoms among adults diagnosed with obstructive sleep apnea.

INTRODUCTION

Sleep-disordered breathing encompasses a spectrum of recurrent episodes of upper airway narrowing during sleep, marked by symptoms such as snoring, heightened upper airway resistance, or obstructive sleep apnea. The management of obstructive sleep apnea involves a range of conservative and surgical approaches. Among conservative methods, oral appliances are the preferred treatment for primary snoring, mild to moderate cases, and severe cases in patients who are intolerant to CPAP. While several systematic reviews have explored the effectiveness of oral appliance therapy for obstructive sleep apnea, there has been no comprehensive evaluation or synthesis of these reviews.

INCLUSION CRITERIA

Systematic reviews, with or without meta-analysis, were examined to assess the effectiveness of various forms of oral appliances in treating obstructive sleep apnea. Polysomnography was employed as the method for evaluating the effectiveness of the appliance. The primary outcome of interest was the effectiveness of oral appliance therapy in reducing the apnea-hypopnea index. Secondary outcomes included the mean change in the respiratory arousal index, Epworth Sleepiness Scale scores, minimum oxygen saturation, sleep efficiency, rapid eye movement sleep, blood pressure, quality of life, patient preference, and adverse effects.

METHODS

A comprehensive search was conducted up to October 2023 in MEDLINE (Ovid), CINAHL (EBSCOhost), Scopus, Web of Science Core Collection, Epistemonikos, ProQuest Dissertations and Theses, Shodhganga, and the Cochrane Database of Systematic Reviews. Supplementary searches were manually performed using Google Scholar. The critical appraisal and data extraction processes were carried out independently by 2 reviewers. The extracted data were summarized using a tabular format accompanied by supporting text. The quality of evidence was evaluated utilizing the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach.

RESULTS

This umbrella review incorporated 27 systematic reviews published between 2004 and 2022. The primary studies in these systematic reviews were published between 1996 and 2021. Of the 68 primary studies, 50 were published between 1996 and 2014 and 18 were published between 2015 and 2021. The corrected covered area index was 15.04%, indicating very high overlap between the primary studies included in the systematic reviews. All systematic reviews that compared oral appliances with CPAP consistently reported that CPAP was more efficacious, evidenced by a decrease in the apnea-hypopnea index, respiratory arousal index, and improved minimum oxygen saturation levels. However, patient preference favored oral appliances over CPAP. Additionally, oral appliances demonstrated improvements in subjective sleepiness scores and indices including the apnea-hypopnea index and respiratory arousal index when compared with inactive appliances/controls. The overall quality of evidence using GRADE ranged from very low to moderate.

CONCLUSIONS

CPAP demonstrated greater efficacy than oral appliances in reducing the apnea-hypopnea index and respiratory arousal index while increasing minimum oxygen saturation levels, indicating significant improvements in obstructive sleep apnea and contributing to enhanced sleep quality and overall health. Despite these advantages, patient preference often leans toward oral appliances over CPAP. Compared with inactive appliances/controls, surgery, and other conservative management approaches, oral appliances have also shown efficacy in improving obstructive sleep apnea. This umbrella review reinforces CPAP as the gold standard for obstructive sleep apnea treatment, although oral appliances represent a viable alternative, particularly for patients who experience difficulties in accessing or tolerating CPAP. The majority of included systematic reviews were published over a decade ago, highlighting a research gap in this area; therefore, future studies should focus on comparing newer treatment options for obstructive sleep apnea.

Artificial Intelligence-guided Total Opacity Scores and Obstructive Sleep Apnea in Adults with COVID-19 Pneumonia

OBJECTIVE

We previously demonstrated that artificial intelligence (AI)-directed chest computed tomography (CT)-based total opacity scores (TOS) are associated with high-risk obstructive sleep apnea (OSA) based on the Berlin Questionnaire. In the current study, we examined the association between TOS severity and OSA severity based on polysomnography (PSG) recordings among participants with a history of Coronavirus disease-2019 (COVID-19) infection.

MATERIAL AND METHODS

This was a post-hoc analysis of 56 patients who underwent CT imaging after being diagnosed with COVID-19 pneumonia as well as overnight PSG for a validation study with a median of 406 days after the initial COVID-19 onset. The AI software quantified the overall opacity scores, which included consolidation and ground-glass opacity regions on CT scans. TOS was defined as the volume of high-opacity regions divided by the volume of the entire lung, and severe TOS was defined as the score ≥15. OSA was defined as an apnea-hypopnea index (AHI) of at least 15 events/h.

RESULTS

In total, 21 participants had OSA and 35 had no OSA. The median TOS was 10.5 [interquartile range (IQR) 1.6-21.2] in the OSA group and 2.8 (IQR 1.4-9.0) in the non-OSA group (P = 0.047). In a multivariate logistic regression analysis, OSA, AHI, and oxygen desaturation index were associated with severe TOS (P < 0.05 for all, respectively) adjusted for age, sex, body mass index, and hypertension.

CONCLUSION

AI-directed CT-based TOS severity in patients with COVID-19 pneumonia was associated with OSA severity based on PSG recordings. These results support our previous findings suggesting an association between questionnaire-based high-risk OSA and worse outcomes in COVID-19 pneumonia.

A prospective cohort study on effects of mandibular setback with or without maxillary advancement for skeletal class III malocclusion on sleep-related respiratory parameters

PURPOSE

This study aimed to investigate changes in sleep-related respiratory parameters before and after orthognathic surgery in patients with skeletal class III malocclusion.

METHODS

Adults with skeletal class III malocclusion and treated with isolated mandibular setback or bimaxillary surgery (maxillary advancement and mandibular setback) were recruited. Sleep-related respiratory parameters were obtained with type III sleep study. Epworth Sleepiness Scale (ESS) was also recorded. The pre- and post-operative (6 months) data were compared. Correlations between these changes and pre-operative characteristics were analyzed. Subjects were categorized into three groups based on changes in the respiratory event index (REI) and 3% oxygen desaturation index: Δ ≤ -2.5, -2.5 < Δ < 2.5, and Δ ≥ 2.5. Amounts of surgical movement and pre-surgical parameters were compared among the 3 groups.

RESULTS

Thirty patients with an average age of 25.4 ± 5.0 years were recruited. Eleven patients underwent isolated mandibular setbacks while nineteen received bimaxillary surgery. Pre- and post-operative sleep-related respiratory parameters were not significantly different in the total samples, and when analyzed separately according to surgical procedures. Pre-operative ESS were correlated with the changes in REI (p = 0.01), average blood oxygen levels (p = 0.01), and snoring percentage (p = 0.04). Additionally, this study found that patients with a significant decrease in REI (ΔREI ≤ -2.5) after surgery had significantly higher pre-operative REI (6.2 events/hour) compared to those with minor REI changes (2.6 events/hour).

CONCLUSION

There was no significant change in sleep-related respiratory parameters following mandibular setbacks with or without maxillary advancement in this study.

TRAIL REGISTERED

This study was retrospectively registered and approved on February 11, 2025, under registration number TCTR20250211002.

Sleep-Disordered Breathing and Hypertension-A Systematic Review

Background/Objectives: Sleep-disordered breathing (SDB), historically referred to as "sleep apnea syndrome", particularly obstructive sleep apnea (OSA), is an independent risk factor for hypertension (HTN), stroke, heart failure, arrhythmias, and other cardiovascular disorders. Despite the well-established link between OSA and HTN and its high occurrence in cardiovascular disorders, the focus on the complex OSA-HTN axis is often overlooked or inadequately managed, which might explain the lack of notable improvements in cardiovascular outcomes for this patient population. Understanding the complex relationship between OSA and HTN is crucial due to its significant implications for clinical practice and public health. Methods: Using an expanded list of relevant MeSH terms, including "sleep-disordered breathing" and "sleep apnea syndrome", and following the PRISMA model, peer-reviewed articles were systematically selected. Studies published from January 2000 through December 2024 were identified and screened based on predefined inclusion and exclusion criteria. Results: This review emphasizes both OSA's independent and interaction effects on cardiovascular health and outcomes across different populations. It identifies key factors mediating the association between OSA and HTN. Conclusions: Multimodal management, including continuous positive airway pressure and lifestyle modification, is essential for treating hypertension related to OSA. Effective management of the OSA-HTN relationship is vital to improving cardiovascular outcomes.

Detection of sleep apnea using smartphone-embedded inertial measurement unit

We previously demonstrated that sleep apnea (SA) can be detected using acceleration and gyroscope signals from smartwatches. This study investigated whether an inertial measurement unit (IMU) embedded in non-wristwatch devices, such as smartphones, can also detect SA when worn during sleep. During polysomnography (PSG), subjects wore an IMU-embedded GPS device (Amue Link®) and/or smartphones (Xperia® or iPhone®) on their abdomen. Triaxial acceleration and gyroscope signals were recorded overnight. Data were split into training and test groups (2:1) for each device. An algorithm was developed in the training groups to extract respiratory movements (0.13-0.70 Hz) and detect respiratory events, which were validated in the test groups. IMU-derived respiratory events showed breath-by-breath concordance with PSG apnea-hypopnea events, yielding F1 scores of 0.786, 0.821, and 0.796, respectively. Regression model derived from IMU signals correlated with PSG AHI in the test groups (r = 0.90, 0.93, and 0.96), with limits of agreement of -16.7 to 25.9, -17.4 to 22.5, and - 18.4 to 20.5. Using cutoff values from the training groups, moderate-to-severe SA (AHI ≥ 15) was identified in the test groups with AUCs of 0.95, 0.98, and 0.94 and F1 scores of 0.89, 0.96, and 0.92, respectively. IMUs embedded in non-wristwatch devices, including smartphones, can quantitatively detect SA when worn during sleep.

Exploring differences in signaling pathways of the circadian clock and neuromodulators in obstructive sleep apnea

Obstructive sleep apnea (OSA) is a prevalent disorder characterized by recurring upper airway obstructions, leading to disrupted sleep and systemic complications. The complexity of OSA suggests intricate interactions between neuromodulators and circadian clock genes. This study aimed to explore the expression levels of circadian rhythm genes and neurotrophins (NTs) in OSA patients compared to controls. Participants (166 OSA patients, 64 controls) were enrolled. Blood samples collected post-polysomnography (PSG), underwent RNA extraction from peripheral blood lymphocytes and RT-PCR was performed for gene expression analysis. In the OSA group, the expression of BMAL1, CLOCK, CRY1, PER1, NPAS2, NR1D1, and HIF-1β was positively correlated with BDNF expression (p < 0.05 for all). Conversely, in the control group, only PER1 mRNA showed a correlation with BDNF expression (R = 0.280, p = 0.017). NF-κβ expression demonstrated a positive correlation with BMAL1, CLOCK, CRY1, PER1, NPAS2, NR1D1, HIF-1α, and HIF-1β expressions in both groups (p < 0.05 for all). HIF-1α and HIF1-β mRNA expressions were positively correlated with BMAL1, CLOCK, CRY1, PER1, NPAS2, and NR1D1 expressions regardless of the presence of OSA (p < 0.05 for all). The study underscores the intricate interactions among circadian rhythm genes, NTs, and transcription factors in OSA, providing a basis for potential targeted therapeutic interventions.

The Global Burden of Obstructive Sleep Apnea

This study reviewed the global prevalence, health and socioeconomic impact, and management approaches of obstructive sleep apnea. The narrative review examined three key dimensions: (1) worldwide OSA prevalence across different regions, accounting for variations in diagnostic standards; (2) OSA's effects on health outcomes and socioeconomic conditions across diverse populations and healthcare systems; and (3) current global approaches to OSA diagnosis, treatment, and public health management. Despite advances in diagnosis and treatment, a large proportion of OSA cases remain undiagnosed or inadequately managed. The findings show that untreated OSA significantly increases public safety risks, particularly regarding motor vehicle and occupational accidents, while also creating a substantial pool of patients at high risk for systemic complications with severe impacts on overall health. There is a critical need for increased public awareness, universal screening approaches, and integrated care strategies to address this global health challenge and reduce its considerable socioeconomic burden. Our review uniquely addresses global disparities in OSA prevalence, clarifies the health and socioeconomic impacts that remain underexplored in the current literature, and suggests concrete strategies for public health and clinical management improvement worldwide.

Status and opportunities of machine learning applications in obstructive sleep apnea: A narrative review

Background

Obstructive sleep apnea (OSA) is a prevalent and potentially severe sleep disorder characterized by repeated interruptions in breathing during sleep. Machine learning models have been increasingly applied in various aspects of OSA research, including diagnosis, treatment optimization, and developing biomarkers for endotypes and disease mechanisms.

Objective

This narrative review evaluates the application of machine learning in OSA research, focusing on model performance, dataset characteristics, demographic representation, and validation strategies. We aim to identify trends and gaps to guide future research and improve clinical decision-making that leverages machine learning.

Methods

This narrative review examines data extracted from 254 scientific publications published in the PubMed database between January 2018 and March 2023. Studies were categorized by machine learning applications, models, tasks, validation metrics, data sources, and demographics.

Results

Our analysis revealed that most machine learning applications focused on OSA classification and diagnosis, utilizing various data sources such as polysomnography, electrocardiogram data, and wearable devices. We also found that study cohorts were predominantly overweight males, with an underrepresentation of women, younger obese adults, individuals over 60 years old, and diverse racial groups. Many studies had small sample sizes and limited use of robust model validation.

Conclusion

Our findings highlight the need for more inclusive research approaches, starting with adequate data collection in terms of sample size and bias mitigation for better generalizability of machine learning models in OSA research. Addressing these demographic gaps and methodological opportunities is critical for ensuring more robust and equitable applications of artificial intelligence in healthcare.

Can We Reduce the Diagnostic Burden of Sleep Disorders? A Single-Centre Study of Subjective and Objective Sleep-Related Diagnostic Parameters

Background and Objectives: Sleep disorders are highly prevalent in society and require focused attention within healthcare systems. While patient history, reported complaints, and subjective sleep questionnaires can provide initial insights into potential sleep issues, polysomnography (PSG) remains the gold standard for diagnosing various sleep disorders. However, long waiting times for PSG appointments in many healthcare facilities pose challenges for timely diagnosis and treatment. This study aimed to evaluate the diagnostic value of subjective measures, including patient-reported parameters, in comparison to the objective findings of PSG. Materials and Methods: In this study, we retrospectively analysed the data from 562 patients who underwent clinical evaluation and PSG testing at the Hospital of Lithuanian University of Health Sciences Kaunas Clinics between 2018 and 2024. We report the diagnostic accuracy of different sleep questionnaires to detect various sleep disorders in our population. Results: We report the corresponding sensitivity and specificity values: the Epworth Sleepiness Scale (ESS)-73.2% and 44.1% for detecting severe obstructive sleep apnoea and 87.1% and 76.8% for detecting hypersomnia; the Insomnia Severity Index (ISI)-77.2% and 63.3% for detecting insomnia; the Berlin Questionnaire (BQ)-67.8% and 68.8% for detecting obstructive sleep apnoea; the Ullanlina Narcolepsy Scale (UNS)-84.4% and 58.9% for detecting hypersomnia; the Innsbruck REM Sleep Behaviour Disorder Inventory (RBD-I)-93.3% and 52.5% for detecting RBD; the REM Sleep Behaviour Disorder Single-Question Screen (RBD1Q)-73.3% and 81.0% for detecting RBD; and the Paris Arousal Disorder Severity Scale (PADSS)-57.5% and 90.5% for detecting parasomnia. Conclusions: When comparing our findings with the previous literature, we found that the screening tools generally demonstrated a slightly poorer performance in our population. However, our results suggest that certain individual questions from the comprehensive questionnaires may provide comparable diagnostic values, while reducing the patient burden. We propose a targeted screening approach that integrates fundamental clinical parameters, key screening questions, and selected validated questionnaires, enabling primary care and outpatient clinicians to more efficiently identify patients who may require referral for specialised sleep evaluation and treatment.

Screening OSA in Chinese Smart Device Consumers: A Real-World Arrhythmia-Related Study

Introduction

Early detection of obstructive sleep apnea (OSA) is critical due to its link to cardiovascular diseases. Our previous study validated an algorithm-based photoplethysmography (PPG) smartwatch for OSA risk detection.

Objective

This study aimed to characterize OSA features and assess its association with arrhythmia risk among smart wearable device (SWD) consumers in China in a real-world setting.

Methods

Between December 15, 2019, and January 31, 2022, SWD consumers across China were screened for OSA risk using HUAWEI devices. OSA diagnosis was confirmed via telecare follow-ups, including clinical evaluations and sleep test records. Disease characteristics and arrhythmia risks were analyzed.

Results

In a large cohort of 1,056,494 participants, smart wearable devices (SWDs) effectively identified 19,563 individuals at high risk for OSA, with 1054 confirmed cases. OSA patients demonstrated high prevalence of obesity (46.8%), hypertension (19.8%), and arrhythmia (17.17%). SWDs detected abnormal heart rhythms or suspected arrhythmia in 95.9% of confirmed OSA cases. Age emerged as an independent predictor of arrhythmia risk, while hypertensive OSA patients were older, more obese, and experienced prolonged nocturnal hypoxia (Time length of SpO2<90%, P=0.020). These findings underscore the utility of SWDs in OSA screening and highlight the significant cardiovascular risks associated with OSA.

Conclusion

PPG-based SWD effectively screened for OSA and identified elevated arrhythmia risks. These findings support their utility for large-scale OSA screening and highlight cardiovascular risks management.

Clinical Trial Registry Name

Mobile Health (mHealth) technology for improved screening, patient involvement and optimizing integrated care in atrial fibrillation.

Registration Number

ChiCTR-OOC-17014138.

Date of Registration

2017-12-26.

Date of Last Refreshed On

2018-11-18.

Quantitative study on whole brain volume in patients with obstructive sleep apnea based on synthetic magnetic resonance imaging

OBJECTIVE

To apply SyMRI to quantify whole brain volume changes in patients with varying degrees of obstructive sleep apnea (OSA).

METHODS

A total of 49 untreated adult patients diagnosed with OSA via polysomnography (PSG) at our hospital were included in this study. Among these patients, 21 were categorized into the mild-to-moderate OSA group, and 28 into the severe OSA group. Additionally, 31 healthy adults were recruited as the healthy control (HC) group. SyMRI post-processing software was used to obtain whole brain volume segmentation values.

RESULTS

In terms of the STOP-BANG questionnaire, the score of the severe OSA group was significantly higher than that of the mild-to-moderate OSA group (P < 0.05). Compared with the HC group, the mild-to-moderate OSA group and the severe OSA group exhibited a reduction in N3-stage sleep (both P < 0.05). Post-hoc multiple comparisons showed that compared with the HC group, the severe OSA group had increased GMV, BPV, and ICV, while the mild-to-moderate OSA group showed an increase in CSFV (P < 0.05). Additionally, compared with the HC group, the mild-to-moderate OSA group exhibited a decrease and the severe OSA group showed an increase in MYV (P < 0.05). Multiple comparisons of normalized volume fractions revealed that GMF, WMF, CSFF, MYF and BPVF were significantly different between the HC group and OSA groups (all P < 0.05).

CONCLUSION

The brain volume parameters generated from SyMRI can quantify the degree of brain injury in patients with OSA.

CLINICAL TRIAL NUMBER

Not applicable.

Exploring the Relationship Between Obstructive Sleep Apnea and Olfactory Function

Obstructive sleep apnea (OSA) is increasingly recognized as a chronic condition that is closely interrelated to olfactory disorders, with a significant contribution to quality of health and overall quality of life. This narrative review aims to provide a thorough overview of the emerging evidence that now integrates these two previously considered distinct physiologic systems. Studies published recently have reported a significantly higher frequency of olfactory dysfunction among OSA patients compared to the general population, which raises the possibility of a causal relationship. We explore the postulated mechanisms behind this association, namely, the chronic intermittent hypoxia, local inflammatory effect, and neuroanatomical changes attributed to OSA. The review further explores the clinical impacts of this relationship through proposing the potential for an olfactory assessment to be used as a diagnostic modality for OSA and the effects of OSA treatment on olfactory function. Thus, we explore the difficulties in treating patients who experience both and suggest future areas for research. This review attempts to bridge the gap between the existing literature and impending investigation necessary for a better management of the interaction of sleep apnea and the human sense of smell.

Feasibility of Radar Vital Sign Monitoring Using Multiple Range Bin Selection

Radars are promising tools for contactless vital sign monitoring. As a screening device, radars could supplement polysomnography, the gold standard in sleep medicine. When the radar is placed lateral to the person, vital signs can be extracted simultaneously from multiple body parts. Here, we present a method to select every available breathing and heartbeat signal, instead of selecting only one optimal signal. Using multiple concurrent signals can enhance vital rate robustness and accuracy. We built an algorithm based on persistence diagrams, a modern tool for time series analysis from the field of topological data analysis. Multiple criteria were evaluated on the persistence diagrams to detect breathing and heartbeat signals. We tested the feasibility of the method on simultaneous overnight radar and polysomnography recordings from six healthy participants. Compared against single bin selection, multiple selection lead to improved accuracy for both breathing (mean absolute error: 0.29 vs. 0.20 breaths per minute) and heart rate (mean absolute error: 1.97 vs. 0.66 beats per minute). Additionally, fewer artifactual segments were selected. Furthermore, the distribution of chosen vital signs along the body aligned with basic physiological assumptions. In conclusion, contactless vital sign monitoring could benefit from the improved accuracy achieved by multiple selection. The distribution of vital signs along the body could provide additional information for sleep monitoring.

Upper airway and hyoid bone-related morphological parameters associated with the apnea-hypopnea index and lowest nocturnal oxygen saturation: a cephalometric analysis

BACKGROUND

Obstructive sleep apnea (OSA) is a common disorder characterized by repetitive complete or partial closure of the upper airway during sleep, resulting in sleep fragmentation and oxygen desaturation. Cephalogram is recognized as an effective diagnostic tool for predicting OSA risk in clinical practice. This study aims to assess and analyze the morphological characteristics of the upper airway and hyoid bone position associated with OSA using data from polysomnography studies and two-dimensional cephalometric analysis.

METHODS

The study included lateral cephalograms and polysomnography reports from the records of 105 adult (64 males & 41 females) patients who underwent comprehensive clinical examination. The severity of OSA was evaluated based on the apnea-hypopnea index (AHI) and lowest nocturnal oxygen saturation (LSaO2). The participants were divided into male and female groups to investigate the correlation between cephalometric parameters and OSA severity. Thirteen cephalometric parameters, including eleven linear measurements and two angular measurements, were analyzed. The significance level was set at P-value < 0.05.

RESULTS

The male group exhibited significantly higher severity of OSA compared to the female group, as indicated by higher AHI and lower LSaO2. There was an inverse association between AHI values with width of upper airway as well as distance between hyoid bone position relative to mandibular plane in both male and female groups. Additionally, only the male group showed a correlation between hyoid bone position relative to gonion/third-fourth vertebrae positions with AHI values. 4 out of 7 parameters associated with AHI in male group remained correlated with LSaO2, while in females only the distance between hyoid bone and line formed by ptergoid and pterygomaxillary fissure point showed correlation with LSaO2.

CONCLUSION

Correlation analysis revealed that a narrower upper airway was positively associated with increased AHI, while an inferiorly positioned hyoid bone in relation to mandible was negatively correlated with LSaO2. Our findings highlight the importance of several cephalometric parameters in predicting OSA severity based on AHI and LSaO2 levels; moreover, certain parameters exhibited significant gender-specific associations.

Outcomes following total shoulder arthroplasty in patients with obstructive sleep apnea

BACKGROUND

Obstructive sleep apnea (OSA) is a known risk factor for various medical complications including cardiovascular disease, cognitive dysfunction, and respiratory failure. With global prevalence of OSA on the rise, understanding its impact on common orthopedic procedures has become increasingly important. Although the impact of OSA has been studied in patients undergoing total hip arthroplasty and cervicothoracic spinal surgery, its relationship with total shoulder arthroplasty (TSA) is still unclear. Therefore, the purpose of this study was to evaluate the relationship of complications following TSA in patients with OSA.

METHODS

A retrospective cohort analysis using the TriNetX research platform was conducted on all patients with OSA undergoing an anatomic or reverse TSA. Patients were excluded if they had a history of revision TSA, hemiarthroplasty, chronic respiratory diseases, upper humerus fracture, a body mass index of less than 30, or were aged <18 years. Medical complications were assessed at 7, 30, and 90 days, whereas orthopedic complications were assessed at 2 and 5 years. Mortality was evaluated over a 5-year period using Kaplan-Meier analysis. Outcomes were compared using odds ratios, and a P value < .05 was considered significant.

RESULTS

Final analysis included 5636 patients in both the OSA and non-OSA in each cohort with 1:1 propensity score-based matching. Patients undergoing TSA with OSA were at higher risk of acute kidney injury (1.69% vs. 1.10%), pulmonary embolism (0.62% vs. 0.34%), pneumonia (0.89% vs. 0.46%), ischemic stroke (0.73% vs. 0.39%), respiratory failure (0.43% vs. 0.18%), emergency department visits (2.45% vs. 1.86%), and readmission rates (6.62% vs. 4.19%) at 7, 30, and 90 days postoperatively. Patients with OSA experienced higher rates of wound dehiscence (0.64% vs. 0.32%) and urinary tract infections (1.70% vs. 1.21%) at 30 and 90 days postoperatively. Finally, rates of deep vein thrombosis in the OSA cohort were only elevated at 90 days postoperatively (1.99% vs. 1.15%). All orthopedic outcomes and mortality rates were comparable between both cohorts throughout the 5 years.

CONCLUSION

The findings of this study suggest that patients with OSA may experience higher rates of medical complications in the short term, but no difference in orthopedic or mortality outcomes in the long term. Further research is warranted to explore how the severity of OSA influences complication rates after TSA and to identify preventative strategies that surgeons can implement to mitigate the adverse effects of OSA on postoperative outcomes.

The Association Between Craniofacial Morphological Parameters and the Severity of Obstructive Sleep Apnea: A Multivariate Analysis Using the Apnea-Hypopnea Index and Nocturnal Oxygen Desaturation

Background: Obstructive sleep apnea (OSA) is characterized by repetitive complete or partial closure of the upper airway during sleep, which is a potentially life-threatening disorder. A cephalogram is a simple and effective examination to predict the risk of OSA in orthodontic clinical practice. This study aims to analyze the relationship between craniofacial characteristics and the severity of OSA using polysomnography and cephalogram data. Gender differences in these parameters are also investigated. Methods: This study included 112 patients who underwent a complete clinical examination, standard polysomnography study, and cephalometric analysis to diagnose obstructive sleep apnea. This study divided the participants into male and female groups to study the correlation between cephalometric parameters and the severity of OSA. The analysis involved 39 cephalometric parameters. The severity of obstructive sleep apnea was evaluated by the apnea-hypopnea index (AHI) and the lowest nocturnal oxygen saturation (LSaO2). Results: The final assessment included 112 adult participants (male/female = 67:45, mean age: 28.4 ± 7.29 years, mean male age: 28.8 ± 7.62 years, mean female age: 27.8 ± 6.79 years). Multivariate analysis revealed that the mandibular position, incisor inclination, facial height, and maxillary first molar position were strongly associated with OSA severity. Gender-specific differences in cephalometric predictors were identified, with distinct parameters correlating with the AHI and LSaO2 in males and females. Notably, the LSaO2 demonstrated stronger associations with craniofacial morphology in females than males. Conclusions: Cephalometric analysis can be effective in assessing the risk and severity of OSA based on the correlation between cephalometric parameters and the AHI/LSaO2. There is a clear difference between the cephalometric parameters associated with OSA severity in male and female individuals. This gender-dependent pattern may assist the personalized diagnosis and management of OSA in clinical practice.

Artificial intelligence in the diagnosis of obstructive sleep apnea: a scoping review

PURPOSE

The gold standard diagnostic modality of Obstructive Sleep Apnea (OSA) is polysomnography (PSG), which is resource-intensive, requires specialized facilities, and may not be accessible to all patients. There is a growing body of research exploring the potential of artificial intelligence (AI) to offer more accessible, efficient, and cost-effective alternatives for the diagnosis of OSA.

METHODS

We conducted a scoping review of studies applying AI techniques to diagnose and assess OSA in adult populations. A comprehensive search was performed in the Web of Science database using terms related to "obstructive sleep apnea," "artificial intelligence," "machine learning," and related approaches.

RESULTS

A total of 344 articles met the inclusion criteria. The findings highlight various methodologies of disease evaluation, including binary classification distinguishing between OSA-positive and OSA-negative individuals in 118 articles, OSA event detection in 211 articles, severity evaluation in 38 articles, topographic diagnostic evaluation in 8 articles, and apnea-hypopnea index (AHI) estimation in 26 articles. 40 distinct types of data sources were identified. The three most prevalent data types were electrocardiography (ECG), used in 108 articles, photoplethysmography (PPG) in 62 articles, and respiratory effort and body movement in 44 articles. The AI techniques most frequently applied were convolutional neural networks (CNNs) in 104 articles, support vector machines (SVMs) in 91 articles, and K-Nearest Neighbors (KNN) in 57 articles. Of these studies, 229 used direct patient recruitment, and 115 utilized existing datasets.

CONCLUSION

While AI demonstrates substantial potential with high accuracy rates in certain studies, challenges remain such as model transparency, validation across diverse populations, and seamless integration into clinical practice. These challenges may stem from factors such as overfitting to specific datasets, limited generalizability, and the need for standardized protocols in clinical settings.

Clinical-level screening of sleep apnea syndrome with single-lead ECG alone is achievable using machine learning with appropriate time windows

PURPOSE

To establish a simple and noninvasive screening test for sleep apnea (SA) that imposes less burden on potential patients. The specific objective of this study was to verify the effectiveness of past and future single-lead electrocardiogram (ECG) data from SA occurrence sites in improving the estimation accuracy of SA and sleep apnea syndrome (SAS) using machine learning.

METHODS

The Apnea-ECG dataset comprising 70 ECG recordings was used to construct various machine-learning models. The time window size was adjusted based on the accuracy of SA detection, and the performance of SA detection and SAS diagnosis (apnea‒hypopnea index ≥ 5 was considered SAS) was compared.

RESULTS

Using ECG data from a few minutes before and after the occurrence of SAs improved the estimation accuracy of SA and SAS in all machine learning models. The optimal range of the time window and achieved accuracy for SAS varied by model; however, the sensitivity ranged from 95.7 to 100%, and the specificity ranged from 91.7 to 100%.

CONCLUSIONS

ECG data from a few minutes before and after SA occurrence were effective in SA detection and SAS diagnosis, confirming that SA is a continuous phenomenon and that SA affects heart function over a few minutes before and after SA occurrence. Screening tests for SAS, using data obtained from single-lead ECGs with appropriate past and future time windows, should be performed with clinical-level accuracy.