Polysomnographic phenotyping of obstructive sleep apnea and its implications in mortality in Korea

Exploring phenotypes to improve long-term mortality risk stratification in obstructive sleep apnea through a machine learning approach: an observational cohort study

BACKGROUND

Obstructive sleep apnea (OSA) is a heterogeneous sleep disorder for which the identification of phenotypes might help for risk stratification for long-term mortality. Thus, the aim of the study was to identify distinct phenotypes of OSA and to study the association of phenotypes features with long-term mortality by using machine learning.

METHODS

This retrospective study included patients diagnosed with OSA who completed a 15-year follow-up and were adherent to continuous positive airway pressure (CPAP) therapy. Multidimensional data were collected at baseline and were used to identify OSA phenotypes using the hierarchical approach. Associations between phenotypic features and long-term mortality were assessed using supervised analysis.

RESULTS

A total of 402 patients, predominantly male (70 %), were included. Clustering analysis identified three distinct phenotypes: Cluster 1 (middle-aged, severely obese, very severe OSA with nocturnal hypoxemia), Cluster 2 (young, overweight, moderate OSA with limited nocturnal hypoxemia), and Cluster 3 (elderly, obese, multimorbid, severe OSA with nocturnal hypoxemia). Mortality was significantly higher in Clusters 1 and 3 (p < 0.001). Supervised methods identified eight main features of these clusters, among which nocturnal hypoxemia was found to be the main risk factor for mortality even after confounding factors-adjustment (hazard ratio 2.63, 95 % confidence interval 1.09-6.36, p = 0.032).

CONCLUSIONS

This study demonstrated the interest of attributing OSA patients to distinct phenotypes including precise determination of nocturnal hypoxemia to improve mortality risk stratification.

Association between obstructive sleep apnea and hyperuricemia/gout in the general population: a cross-sectional study

BACKGROUND

Obstructive sleep apnea (OSA) is linked to various health conditions, including cardiovascular diseases and metabolic disorders. Hyperuricemia and gout may be associated with OSA, but large-scale studies on this are limited. This study aimed to investigate the association between hyperuricemia/gout and OSA using data from the Korea National Health and Nutrition Survey (KNHANES).

METHODS

Using the 2019-2021 KNHANES data, 11,728 participants were selected. OSA risk was assessed using the STOP-BANG questionnaire score, which is as follows: (1) high-risk (5-8), (2) intermediate-risk (3-4), and (3) low-risk (0-2). Anthropometric, socioeconomic, health-related variables, and biochemical measurements, including serum uric acid (SUA) levels, were included in the analysis. Multiple regression analyses examined the association between the STOP-BANG score and hyperuricemia/gout.

RESULTS

After assigning weights, among 25,354,276 individuals, 3,114,119 (12.2%) had a high OSA risk. The high OSA risk group exhibited higher SUA levels (5.9 mg/dL) than those of the intermediate (5.6 mg/dL) and low OSA risk groups (4.7 mg/dL) (P < 0.001). Additionally, it had a higher incidence of physician-diagnosed gout than the other groups (6.6% vs. 3.8% vs. 0.8%, respectively, P < 0.001). The STOP-BANG questionnaire scores and SUA levels were positively correlated (r = 0.383; P < 0.001). When adjusted for confounding factors, the high OSA risk group demonstrated an association with hyperuricemia (SUA ≥ 6.8 mg/d) (adjusted Odds Ratio [OR]: 1.462, 95% Confidence interval [CI]: 1.108-1.929). High and intermediate OSA risk was associated with severe hyperuricemia (SUA ≥ 9.0 mg/dL) and gout; however, the significant association between OSA and severe hyperuricemia and gout attenuated to null after adjusting for confounding factors.

CONCLUSIONS

High OSA risk was independently associated with hyperuricemia but not severe hyperuricemia or gout. Screening and management of OSA may help prevent hyperuricemia.

An expedited model for identifying potential patients with periodic leg movements

Periodic leg movements during sleep (PLMS) may have crucial consequences in adults. This study aimed to identify baseline characteristics, symptoms, or questionnaires that could help to identify sleep-disordered breathing patients with significant PLMS. Patients aged 20-80 years who underwent polysomnography for assessing sleep disturbance were included. Various factors such as sex, age, body measurements, symptoms, apnea-hypopnea index (AHI), and sleep quality scales were analysed to determine the presence of PLMS. The study included 1480 patients with a mean age of 46.4 ± 13.4 years, among whom 110 (7.4%) had significant PLMS with a PLM index of 15 or higher. There were no significant differences observed in terms of sex or BMI between patients with and without significant PLMS. However, the odds ratios (OR) for PLMS were 4.33, 4.41, and 4.23 in patients who were aged over 50 years, had insomnia, or had an ESS score of less than 10, respectively. Notably, the OR increased up to 67.89 times in patients who presented with all three risk factors. Our analysis identified significant risk factors for PLMS: age over 50, self-reported insomnia, and lower daytime sleepiness levels. These findings aid in identifying potential PLMS patients, facilitating confirmatory examinations and managing associated comorbidities.

Research progress on the clinical subtyping of obstructive sleep apnea hypopnea syndrome

Obstructive sleep apnea hypopnea syndrome (OSAHS) is a common sleep-disordered breathing condition that exhibits a notable degree of heterogeneity, a feature not fully considered in current diagnostic and therapeutic strategies. This article reviews and analyzes research progress in the subtyping of OSAHS from multiple perspectives, including clinical feature-based subtyping, comorbidity-based subtyping, polysomnography (PSG) parameter-based subtyping, and other classification approaches. Existing studies have identified common subtypes based on clinical features and clarified the characteristics of different subgroups in comorbidity-based classifications; the rich data provided by PSG have helped optimize the classification of OSAHS; and multi-dimensional clustering has provided a more precise basis for individualized treatment. Although these studies have deepened the understanding of the heterogeneity of OSAHS, challenges such as significant differences among subtypes and insufficient evidence for alternative therapies remain. Future research should focus on identifying biomarkers and elucidating the underlying pathophysiological mechanisms to advance the development of precision treatments.

A comparative analysis of unsupervised machine-learning methods in PSG-related phenotyping

Obstructive sleep apnea is a heterogeneous sleep disorder with varying phenotypes. Several studies have already performed cluster analyses to discover various obstructive sleep apnea phenotypic clusters. However, the selection of the clustering method might affect the outputs. Consequently, it is unclear whether similar obstructive sleep apnea clusters can be reproduced using different clustering methods. In this study, we applied four well-known clustering methods: Agglomerative Hierarchical Clustering; K-means; Fuzzy C-means; and Gaussian Mixture Model to a population of 865 suspected obstructive sleep apnea patients. By creating five clusters with each method, we examined the effect of clustering methods on forming obstructive sleep apnea clusters and the differences in their physiological characteristics. We utilized a visualization technique to indicate the cluster formations, Cohen's kappa statistics to find the similarity and agreement between clustering methods, and performance evaluation to compare the clustering performance. As a result, two out of five clusters were distinctly different with all four methods, while three other clusters exhibited overlapping features across all methods. In terms of agreement, Fuzzy C-means and K-means had the strongest (κ = 0.87), and Agglomerative hierarchical clustering and Gaussian Mixture Model had the weakest agreement (κ = 0.51) between each other. The K-means showed the best clustering performance, followed by the Fuzzy C-means in most evaluation criteria. Moreover, Fuzzy C-means showed the greatest potential in handling overlapping clusters compared with other methods. In conclusion, we revealed a direct impact of clustering method selection on the formation and physiological characteristics of obstructive sleep apnea clusters. In addition, we highlighted the capability of soft clustering methods, particularly Fuzzy C-means, in the application of obstructive sleep apnea phenotyping.

Relationship between chronic rhinosinusitis and risk of obstructive sleep apnea

The relationship between obstructive sleep apnea (OSA) and chronic rhinosinusitis (CRS) has not yet been fully elucidated. Therefore, the objective of this study was to evaluate the connection between OSA risk and CRS by investigating associations between the STOP-Bang questionnaire and presence of CRS in a nationwide, population-based study. This is a cross-sectional study based on the Korean National Health and Nutrition Examination Survey (KNHANES). We evaluated 10,081 subjects who completed both the STOP-Bang and CRS-related questionnaires. Among the total subjects, 390 (3.9%) were CRS patients. The median STOP-Bang score was 3.0 [2.0; 4.0] in CRS patients, compared to 2.0 [1.0; 3.0] in subjects without CRS. In a low-risk group according to the STOP-Bang questionnaire, 3.1% of subjects were CRS patients. However, a gradual increase was observed among different risk groups. In the higher risk group, CRS patients accounted for 5.3% (P < 0.001). Among the four main symptoms of CRS (nasal obstruction, nasal discharge, facial pain/pressure, and decreased sense of smell), nasal obstruction (4.1 to 7.3%) and a decreased sense of smell (1.9 to 3.3%) increased with higher STOP-Bang scores. This study found that the proportion of patients with CRS was significantly higher in the group at a higher STOP-Bang score in the general population. Among symptoms of CRS, nasal obstruction and anosmia were found to be associated with an increased STOP-Bang score.

Automatic prediction of obstructive sleep apnea in patients with temporomandibular disorder based on multidata and machine learning

Obstructive sleep apnea (OSA) is closely associated with the development and chronicity of temporomandibular disorder (TMD). Given the intricate pathophysiology of both OSA and TMD, comprehensive diagnostic approaches are crucial. This study aimed to develop an automatic prediction model utilizing multimodal data to diagnose OSA among TMD patients. We collected a range of multimodal data, including clinical characteristics, portable polysomnography, X-ray, and MRI data, from 55 TMD patients who reported sleep problems. This data was then analyzed using advanced machine learning techniques. Three-dimensional VGG16 and logistic regression models were used to identify significant predictors. Approximately 53% (29 out of 55) of TMD patients had OSA. Performance accuracy was evaluated using logistic regression, multilayer perceptron, and area under the curve (AUC) scores. OSA prediction accuracy in TMD patients was 80.00-91.43%. When MRI data were added to the algorithm, the AUC score increased to 1.00, indicating excellent capability. Only the obstructive apnea index was statistically significant in predicting OSA in TMD patients, with a threshold of 4.25 events/h. The learned features of the convolutional neural network were visualized as a heatmap using a gradient-weighted class activation mapping algorithm, revealing that it focuses on differential anatomical parameters depending on the absence or presence of OSA. In OSA-positive cases, the nasopharynx, oropharynx, uvula, larynx, epiglottis, and brain region were recognized, whereas in OSA-negative cases, the tongue, nose, nasal turbinate, and hyoid bone were recognized. Prediction accuracy and heat map analyses support the plausibility and usefulness of this artificial intelligence-based OSA diagnosis and prediction model in TMD patients, providing a deeper understanding of regions distinguishing between OSA and non-OSA.

Differences in Polysomnographic and Craniofacial Characteristics of Catathrenia Phenotypes: A Cluster Analysis

Purpose

Catathrenia is a rare sleeping disorder characterized by repetitive nocturnal groaning during prolonged expirations. Patients with catathrenia had heterogeneous polysomnographic, comorbidity, craniofacial characteristics, and responses to treatment. Identifying phenotypes of catathrenia might benefit the exploration of etiology and personalized therapy.

Patients and Methods

Sixty-six patients diagnosed with catathrenia by full-night audio/video polysomnography seeking treatment with mandibular advancement devices (MAD) or continuous positive airway pressure (CPAP) were included in the cohort. Polysomnographic characteristics including sleep architecture, respiratory, groaning, and arousal events were analyzed. Three-dimensional (3D) and 2D craniofacial hard tissue and upper airway structures were evaluated with cone-beam computed tomography and lateral cephalometry. Phenotypes of catathrenia were identified by K-mean cluster analysis, and inter-group comparisons were assessed.

Results

Two distinct clusters of catathrenia were identified: cluster 1 (n=17) was characterized to have more males (71%), a longer average duration of groaning events (18.5±4.8 and 12.8±5.7s, p=0.005), and broader upper airway (volume 41,386±10,543 and 26,661±6700 mm3, p<0.001); cluster 2 (n=49) was characterized to have more females (73%), higher respiratory disturbance index (RDI) (median 1.0 [0.3, 2.0] and 5.2 [1.2, 13.3]/h, p=0.009), more respiratory effort-related arousals (RERA)(1 [1, 109] and 32 [13, 57)], p=0.005), smaller upper airway (cross-sectional area of velopharynx 512±87 and 339±84 mm2, p<0.001) and better response to treatment (41.2% and 82.6%, p=0.004).

Conclusion

Two distinct phenotypes were identified in patients with catathrenia, primary catathrenia, and catathrenia associated with upper airway obstruction, suggesting respiratory events and upper airway structures might be related to the etiology of catathrenia, with implications for its treatment.

Characterisation of Symptom and Polysomnographic Profiles Associated with Cardiovascular Risk in a Sleep Clinic Population with Obstructive Sleep Apnoea

Aim

Recent data have identified specific symptom and polysomnographic profiles associated with cardiovascular disease (CVD) in patients with obstructive sleep apnoea (OSA). Our aim was to determine whether these profiles were present at diagnosis of OSA in patients with established CVD and in those with high cardiovascular risk. Participants in the Sydney Sleep Biobank (SSB) database, aged 30-74 years, self-reported presence of CVD (coronary artery disease, cerebrovascular disease, or heart failure). In those without established CVD, the Framingham Risk Score (FRS) estimated 10-year absolute CVD risk, categorised as "low" (<6%), "intermediate" (6-20%), or "high" (>20%). Groups were compared on symptom and polysomnographic variables.

Results

629 patients (68% male; mean age 54.3 years, SD 11.6; mean BMI 32.3 kg/m2, SD 8.2) were included. CVD was reported in 12.2%. A further 14.3% had a low risk FRS, 38.8% had an intermediate risk FRS, and 34.7% had a high risk FRS. Groups differed with respect to age, sex and BMI. OSA severity increased with established CVD and increasing FRS. The symptom of waking too early was more prevalent in the higher FRS groups (p=0.004). CVD and FRS groups differed on multiple polysomnographic variables; however, none of these differences remained significant after adjusting for age, sex, and BMI.

Conclusion

Higher CVD risk was associated with waking too early in patients with OSA. Polysomnographic variations between groups were explained by demographic differences. Further work is required to explore the influence of OSA phenotypic characteristics on susceptibility to CVD.

Clinical and Polysomnographic Characteristics of Adult Patients with Suspected Obstructive Sleep Apnea from Different Sleep Clinics at a Single Tertiary Center

INTRODUCTION

The characteristics of patients across different sleep clinics may vary because they selectively visit specific specialists on the basis of their primary symptoms. This study aimed to compare the clinical and polysomnography (PSG) features of patients with suspected obstructive sleep apnea (OSA) at three sleep specialty clinics (otolaryngology [ENT], neurology [NR], and psychiatry [PSY]).

METHODS

We retrospectively analyzed the medical records and PSG reports of adult patients who underwent full-night PSG between January 2022 and June 2023 at a tertiary medical center. The demographic, questionnaire, and PSG variables were compared.

RESULTS

Of the 407 patients, 83.0% exhibited sleep-disordered breathing (apnea-hypopnea index ≥ 5) with varying severity among the specialty pathways. Patients in the ENT group (n = 231) were the youngest and had the shortest sleep latency and most severe OSA markers with the highest positive airway pressure (PAP) acceptance, while those in the NR group (n = 79) had similar OSA-related PSG parameters to those in the ENT group but were older and had more OSA-related comorbidities, although their PAP acceptance was relatively low. The PSY group (n = 97) included a significant proportion of patients with normal or mild OSA, a female majority, high levels of depression, and subjective sleep distress.

CONCLUSION

Our results highlight the multidisciplinary aspects of sleep medicine and diverse patients, and specialist needs for diagnosing sleep disorders and PAP acceptance. Exploring the potential differences in prognosis and treatment responses across various sleep specialty clinics would facilitate the development of personalized strategies.

Identifying clusters of patient comorbidities associated with obstructive sleep apnea using electronic health records

STUDY OBJECTIVES

The objectives of this study were to understand the relative comorbidity burden of obstructive sleep apnea (OSA), determine whether these relationships were modified by sex or age, and identify patient subtypes defined by common comorbidities.

METHODS

Cases with OSA and noncases (controls) were defined using a validated electronic health record (EHR)-based phenotype and matched for age, sex, and time period of follow-up in the EHR. We compared prevalence of the 20 most common comorbidities between matched cases and controls using conditional logistic regression with and without controlling for body mass index. Latent class analysis was used to identify subtypes of OSA cases defined by combinations of these comorbidities.

RESULTS

In total, 60,586 OSA cases were matched to 60,586 controls (from 1,226,755 total controls). Patients with OSA were more likely to have each of the 20 most common comorbidities compared with controls, with odds ratios ranging from 3.1 to 30.8 in the full matched set and 1.3 to 10.2 after body mass index adjustment. Associations between OSA and these comorbidities were generally stronger in females and patients with younger age at diagnosis. We identified 5 distinct subgroups based on EHR-defined comorbidities: High Comorbidity Burden, Low Comorbidity Burden, Cardiovascular Comorbidities, Inflammatory Conditions and Less Obesity, and Inflammatory Conditions and Obesity.

CONCLUSIONS

Our study demonstrates the power of leveraging the EHR to understand the relative health burden of OSA, as well as heterogeneity in these relationships based on age and sex. In addition to enrichment for comorbidities, we identified 5 novel OSA subtypes defined by combinations of comorbidities in the EHR, which may be informative for understanding disease outcomes and improving prevention and clinical care. Overall, this study adds more evidence that OSA is heterogeneous and requires personalized management.

CITATION

Te TT, Keenan BT, Veatch OJ, Boland MR, Hubbard RA, Pack AI. Identifying clusters of patient comorbidities associated with obstructive sleep apnea using electronic health records. J Clin Sleep Med. 2024;20(4):521-533.

The relationship between periodic limb movement during sleep and dyslipidaemia in patients with obstructive sleep apnea

Periodic limb movements during sleep and obstructive sleep apnea are both associated with increased sympathetic tone, and have been proposed as risk factors for heart diseases and, in particular, cardiovascular disease. As sympathetic system activation may lead to dyslipidaemia, periodic limb movements during sleep could be an additional risk factor for cardiovascular disease in patients with obstructive sleep apnea. The aim of the study was to determine whether the presence of periodic limb movements during sleep affects serum lipid levels in obstructive sleep apnea. Total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, non- high-density lipoprotein cholesterol and triglyceride levels were investigated in 4138 patients with obstructive sleep apnea in the European Sleep Apnea Database (ESADA) cohort, divided into those with periodic limb movements during sleep index ≥ 15 per hr (n = 628) and controls (n = 3510). ANCOVA adjusted for age, sex, body mass index, apnea-hypopnea index, alcohol intake, smoking status, diabetes, insomnia and study site was used to assess differences in lipids between periodic limb movements during sleep and controls. Patients with periodic limb movements during sleep (24% female, 54.4 ± 12.1 years, body mass index 31.9 ± 5.8 kg m-2 , apnea-hypopnea index 36.7 ± 25.4 per hr) had higher triglyceride (1.81 ± 1.04 versus 1.69 ± 0.90 mmol L-1 , p = 0.002) and lower high-density lipoprotein cholesterol (1.19 ± 0.34 versus 1.24 ± 0.37 mmol L-1 , p = 0.002) levels, whilst there was no difference in either total cholesterol (4.98 ± 1.10 versus 4.94 ± 1.07 mmol L-1 ), low-density lipoprotein cholesterol (3.04 ± 0.96 versus 2.98 ± 0.98 mmol L-1 ) or non- high-density lipoprotein cholesterol (3.78 ± 1.10 versus 3.70 ± 1.05 mmol L-1 ) concentrations (all p > 0.05). The results remained unchanged after most sensitivity analyses. Patients with obstructive sleep apnea with periodic limb movements during sleep had more prevalent cardiovascular disease (11% versus 6%, p < 0.01). Periodic limb movements during sleep in obstructive sleep apnea is associated with dyslipidaemia independently of important confounders. Our results highlight periodic limb movements during sleep as an additional risk factor for cardiovascular disease in obstructive sleep apnea.

Integrating Phenotypic Information of Obstructive Sleep Apnea and Deep Representation of Sleep-Event Sequences for Cardiovascular Risk Prediction

Background

Advances in mobile, wearable and machine learning (ML) technologies for gathering and analyzing long-term health data have opened up new possibilities for predicting and preventing cardiovascular diseases (CVDs). Meanwhile, the association between obstructive sleep apnea (OSA) and CV risk has been well-recognized. This study seeks to explore effective strategies of incorporating OSA phenotypic information and overnight physiological information for precise CV risk prediction in the general population.

Methods

1,874 participants without a history of CVDs from the MESA dataset were included for the 5-year CV risk prediction. Four OSA phenotypes were first identified by the K-mean clustering based on static polysomnographic (PSG) features. Then several phenotype-agnostic and phenotype-specific ML models, along with deep learning (DL) models that integrate deep representations of overnight sleep-event feature sequences, were built for CV risk prediction. Finally, feature importance analysis was conducted by calculating SHapley Additive exPlanations (SHAP) values for all features across the four phenotypes to provide model interpretability.

Results

All ML models showed improved performance after incorporating the OSA phenotypic information. The DL model trained with the proposed phenotype-contrastive training strategy performed the best, achieving an area under the Receiver Operating Characteristic (ROC) curve of 0.877. Moreover, PSG and FOOD FREQUENCY features were recognized as significant CV risk factors across all phenotypes, with each phenotype emphasizing unique features.

Conclusion

Models that are aware of OSA phenotypes are preferred, and lifestyle factors should be a greater focus for precise CV prevention and risk management in the general population.

Application of various machine learning techniques to predict obstructive sleep apnea syndrome severity

As the incidence of obstructive sleep apnea syndrome (OSAS) increases worldwide, the need for a new screening method that can compensate for the shortcomings of the traditional diagnostic method, polysomnography (PSG), is emerging. In this study, data from 4014 patients were used, and both supervised and unsupervised learning methods were used. Clustering was conducted with hierarchical agglomerative clustering, K-means, bisecting K-means algorithm, Gaussian mixture model, and feature engineering was carried out using both medically researched methods and machine learning techniques. For classification, we used gradient boost-based models such as XGBoost, LightGBM, CatBoost, and Random Forest to predict the severity of OSAS. The developed model showed high performance with 88%, 88%, and 91% of classification accuracy for three thresholds for the severity of OSAS: Apnea-Hypopnea Index (AHI) [Formula: see text] 5, AHI [Formula: see text] 15, and AHI [Formula: see text] 30, respectively. The results of this study demonstrate significant evidence of sufficient potential to utilize machine learning in predicting OSAS severity.

Prognostic importance of obstructive sleep apnea and CPAP treatment for cardiovascular and mortality outcomes in patients with resistant hypertension: a prospective cohort study

The prognostic importance of obstructive sleep apnea (OSA) severity and other polysomnographic parameters in patients with resistant hypertension (RHT) has never been evaluated. We aimed to assess it in a prospective cohort of 422 individuals with RHT. OSA presence/severity was ascertained by complete polysomnography (PSG) at baseline. Multivariable Cox regressions assessed the risks associated with OSA severity and other PSG parameters (apnea-hypopnea index, sleep duration, nocturnal hypoxemia and periodic limb movements) for the primary (total cardiovascular events [CVEs] and all-cause mortality) and secondary outcomes (major CVEs). In the subgroup of patients with moderate/severe OSA, the risks associated with CPAP treatment were also estimated in relation to untreated individuals. One-hundred and eighty-six participants (44%) had no/mild OSA and 236 (56%) had moderate/severe OSA, and 67 of them were CPAP-treated. Over a mean follow-up of 5 years, there were 46 CVEs (37 major ones) and 44 all-cause deaths. Neither the presence of moderate/severe or severe OSA, nor being untreated during follow-up, was associated with significant excess risks for any outcome in relation to the subgroup with no/mild OSA. Similarly, no other PSG-derived parameter predicted any adverse outcome. Otherwise, CPAP treatment was associated with non-significant risk reductions of 37% for total CVEs, 49% for major CVEs and 63% for all-cause mortality in relation to those who remained untreated during follow-up. In conclusion, the presence/severity of OSA and its related PSG parameters were not associated with worse cardiovascular/mortality prognosis in patients with RHT. However, CPAP treatment might be protective in individuals with moderate/severe OSA.

Evaluation of Blood Intercellular Adhesion Molecule-1 (ICAM-1) Level in Obstructive Sleep Apnea: A Systematic Review and Meta-Analysis

Background and objective: Intercellular adhesion molecule-1 (ICAM-1) appears to be an active and important biomarker for decreasing the risk of cardiovascular issues among individuals with obstructive sleep apnea (OSA). Herein, a systematic review and meta-analysis was designed to probe whether plasma/serum ICAM-1levels are different in adults with OSA compared to adults with no OSA, as well as adults with severe OSA compared to adults with mild/moderate OSA. Materials and methods: A thorough and systematic literature search was performed in four databases (PubMed/Medline, Web of Science, Scopus, and Cochrane Library) until 17 July 2022, without any age and sample size restrictions to retrieve the relevant articles. The standardized mean difference (SMD) along with a 95% confidence interval (CI) of plasma/serum of ICAM-1 levels was reported. Analyses, including sensitivity analysis, subgroup analysis, trial sequential analysis, meta-regression, and a funnel plot analysis, were performed in the pooled analysis. Results: A total of 414 records were identified in the databases, and 17 articles including 22 studies were entered into the meta-analysis. The pooled SMD of serum/plasma ICAM-1 levels in adults with OSA compared to controls was 2.00 (95%CI: 1.41, 2.59; p < 0.00001). The pooled SMD of serum/plasma ICAM-1 levels in adults with severe compared to mild/moderate OSA was 3.62 (95%CI: 1.74, 5.51; p = 0.0002). Higher serum/plasma ICAM-1 levels were associated with a higher mean age of controls, higher scores for the apnea-hypopnea index, and with a lower mean age of adults with OSA and with smaller sample sizes. Conclusions: Th results of the present meta-analysis showed that serum/plasma ICAM-1 levels in adults with OSA was higher than serum/plasma ICAM-1 levels in controls. Similarly, serum/plasma ICAM-1 levels in adults with severe OSA were higher compared to serum/plasma ICAM-1 levels of adults with mild or moderate OSA. Therefore, ICAM-1 may be used as an additional diagnostic and therapeutic biomarker in adults with OSA.

Incidence and Risk Factors for Hypoxia in Deep Sedation of Propofol for Artificial Abortion Patients

Background

Respiratory depression is a life-threatening adverse effect of deep sedation. This study aimed to investigate the factors related to hypoxia caused by propofol during intravenous anesthesia.

Methods

Three hundred and eight patients who underwent painless artificial abortion in the outpatient department of Shanghai Tenth People’s Hospital between November 1, 2019 and June 30, 2020 were divided into two groups according to whether the patients experienced hypoxia (SpO₂ < 95%). Preoperative anxiety assessments, anesthesia process, and operation-related information of the two groups were analyzed. The univariate analysis results were further incorporated into logistic regression analysis for multivariate analysis to determine the independent risk factors affecting hypoxia.

Results

Univariate analysis revealed that body mass index (BMI) (21.80 ± 2.94 vs. 21.01 ± 2.39; P = 0.038, 95% confidence interval (CI) = [−1.54, −0.04]), propofol dose (15.83 ± 3.21 vs. 14.39 ± 3.01; P = 0.002, CI = [−2.34, −0.53]), menopausal days (49.64 ± 6.03 vs. 52.14 ± 5.73; P = 0.004, CI = [0.79, 4.21]), State Anxiety Inventory score (51.19 ± 7.55 vs. 44.49 ± 8.96; P < 0.001, CI = [−9.26, −4.15]), and Self-rating Anxiety Scale score (45.86 ± 9.48 vs. 42.45 ± 9.88; P = 0.021, CI = [−6.30, −0.53]) were statistically significant risk factors for hypoxia during the operation. Logistic regression analysis showed that propofol dosage, menopausal days, and State Anxiety Inventory score were independent risk factors for hypoxia.

Conclusion

Patient anxiety affects the incidence of hypoxia when undergoing deep intravenous anesthesia with propofol. We can further speculate that alleviating patient anxiety can reduce the incidence of hypoxia.

Clinical Trial Registration

[http://www.chictr.org.cn], identifier [ChiCTR2000032167].

Age and comorbidities are crucial predictors of mortality in severe obstructive sleep apnoea syndrome

BACKGROUND

Obstructive sleep apnoea syndrome (OSAS) is a highly prevalent disorder. The prognostic role of comorbidity in patients with OSAS and their role for risk stratification remain poorly defined.

METHODS

We studied 1,592 patients with severe OSAS diagnosed by polysomnography. The primary outcome was all-cause mortality. The standardized mortality ratio (SMR) was estimated as the ratio of observed deaths to expected number of deaths in the general population. The expected numbers of deaths were derived using mortality rates from the general Apulian population. The association of comorbidities with all-cause mortality was assessed using multivariable Cox regression analysis. Finally, recursive-partitioning analysis was applied to identify the combinations of comorbidities that were most influential for mortality and to cluster the patients into risk groups according to individual comorbidities RESULTS: During 11,721 person-years of follow-up, 390 deaths (3.33 deaths/100 person-years) occurred. The median follow-up was 7 (4-10) years. The SMR was 1.47 (95% confidence intervals 1.33-1.63). Age, sex, obesity, cardiovascular diseases (CVD), moderate-to-severe chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD) and malignancy were independently associated with mortality risk. Recursive-partitioning analysis allowed distinguishing three clinical phenotypes differentially associated with mortality risk. The combination of CKD with CVDs or with moderate-to-severe COPD conferred the highest risk.

CONCLUSIONS

Severe OSAS is associated with increased risk for all-cause death. Age and comorbidity are crucial predictors of mortality in patients with severe OSAS. Clustering patients according to comorbidities allows identifying clinically meaningful phenotypes.

Machine learning-based preoperative datamining can predict the therapeutic outcome of sleep surgery in OSA subjects

Increasing recognition of anatomical obstruction has resulted in a large variety of sleep surgeries to improve anatomic collapse of obstructive sleep apnea (OSA) and the prediction of whether sleep surgery will have successful outcome is very important. The aim of this study is to assess a machine learning-based clinical model that predict the success rate of sleep surgery in OSA subjects. The predicted success rate from machine learning and the predicted subjective surgical outcome from the physician were compared with the actual success rate in 163 male dominated-OSA subjects. Predicted success rate of sleep surgery from machine learning models based on sleep parameters and endoscopic findings of upper airway demonstrated higher accuracy than subjective predicted value of sleep surgeon. The gradient boosting model showed the best performance to predict the surgical success that is evaluated by pre- and post-operative polysomnography or home sleep apnea testing among the logistic regression and three machine learning models, and the accuracy of gradient boosting model (0.708) was significantly higher than logistic regression model (0.542). Our data demonstrate that the data mining-driven prediction such as gradient boosting exhibited higher accuracy for prediction of surgical outcome and we can provide accurate information on surgical outcomes before surgery to OSA subjects using machine learning models.

Nonalcoholic fatty liver disease is associated with the development of obstructive sleep apnea

Increasing evidence suggests that obstructive sleep apnea (OSA) is a metabolic syndrome-related disease; however, the association between nonalcoholic fatty liver disease (NAFLD) and OSA is not firmly established. In this study, we investigated the relationship between NAFLD and OSA in a general population drawn from a nationwide population-based cohort. Data from the Korean National Health Insurance System between January 2009 and December 2009 were analyzed using Cox proportional hazards model. NAFLD was defined as a fatty liver index (FLI) ≥ 60 in patients without excessive alcohol consumption (who were excluded from the study). Newly diagnosed OSA during follow-up was identified using claims data. Among the 8,116,524 participants, 22.6% had an FLI score of 30–60 and 11.5% had an FLI ≥ 60. During median follow-up of 6.3 years, 45,143 cases of incident OSA occurred. In multivariable analysis, the risk of OSA was significantly higher in the higher FLI groups (adjusted hazard ratio [aHR] 1.15, 95% confidence interval [CI] 1.12–1.18 for FLI 30–60 and aHR 1.21, 95% CI 1.17–1.26 for FLI ≥ 60). These findings were consistent regardless of body mass index and presence of abdominal obesity. In conclusion, a high FLI score may help identify individuals with a high risk of OSA. Understanding the association between NAFLD and OSA may have clinical implications for risk-stratification of individuals with NAFLD.

Evaluation of Obstructive Sleep Apnea Phenotypes Treatment Effectiveness

Background and Objective: Obstructive sleep apnea (OSA) is a heterogeneous chronic sleep associated disorder. A common apnea-hypopnea index (AHI)-focused approach to OSA severity evaluation is not sufficient enough to capture the extent of OSA related risks, it limits our understanding of disease pathogenesis and may contribute to a modest response to conventional treatment. In order to resolve the heterogeneity issue, OSA patients can be divided into more homogenous therapeutically and prognostically significant groups-phenotypes. An improved understanding of OSA phenotype relationship to treatment effectiveness is required. Thus, in this study several clinical OSA phenotypes are identified and compared by their treatment effectiveness. Methods and materials: Retrospective data analysis of 233 adult patients with OSA treated with continuous positive airway pressure (CPAP) was performed. Statistical analysis of data relating to demographic and anthropometric characteristics, symptoms, arterial blood gas test results, polysomnografic and respiratory polygraphic tests and treatment, treatment results was performed. Results: 3 phenotypes have been identified: "Position dependent (supine) OSA" (Positional OSA), "Severe OSA in obese patients" (Severe OSA) and "OSA and periodic limb movements (PLM)" (OSA and PLM). The highest count of responders to treatment with CPAP was in the OSA and PLM phenotype, followed by the Positional OSA phenotype. Treatment with CPAP, despite the highest mean pressure administered was the least effective among Severe OSA phenotype. Conclusions: Different OSA phenotypes vary significantly and lead to differences in response to treatment. Thus, treatment effectiveness depends on OSA phenotypes and treatment techniques other than CPAP may be needed. This emphasizes the importance of a more individualized approach when treating OSA.

Transforming electronic health record polysomnographic data into the Observational Medical Outcome Partnership's Common Data Model: a pilot feasibility study

Well-defined large-volume polysomnographic (PSG) data can identify subgroups and predict outcomes of obstructive sleep apnea (OSA). However, current PSG data are scattered across numerous sleep laboratories and have different formats in the electronic health record (EHR). Hence, this study aimed to convert EHR PSG into a standardized data format-the Observational Medical Outcome Partnership (OMOP) common data model (CDM). We extracted the PSG data of a university hospital for the period from 2004 to 2019. We designed and implemented an extract-transform-load (ETL) process to transform PSG data into the OMOP CDM format and verified the data quality through expert evaluation. We converted the data of 11,797 sleep studies into CDM and added 632,841 measurements and 9,535 observations to the existing CDM database. Among 86 PSG parameters, 20 were mapped to CDM standard vocabulary and 66 could not be mapped; thus, new custom standard concepts were created. We validated the conversion and usefulness of PSG data through patient-level prediction analyses for the CDM data. We believe that this study represents the first CDM conversion of PSG. In the future, CDM transformation will enable network research in sleep medicine and will contribute to presenting more relevant clinical evidence.