Characterization of obstructive sleep apnea-hypopnea syndrome (OSA) population by means of cluster analysis

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.

Obstructive sleep apnea heterogeneity and autonomic function: a role for heart rate variability in therapy selection and efficacy monitoring

Obstructive sleep apnea is a highly prevalent sleep-related breathing disorder, resulting in a disturbed breathing pattern, changes in blood gases, abnormal autonomic regulation, metabolic fluctuation, poor neurocognitive performance, and increased cardiovascular risk. With broad inter-individual differences recognised in risk factors, clinical symptoms, gene expression, physiological characteristics, and health outcomes, various obstructive sleep apnea subtypes have been identified. Therapeutic efficacy and its impact on outcomes, particularly for cardiovascular consequences, may also vary depending on these features in obstructive sleep apnea. A number of interventions such as positive airway pressure therapies, oral appliance, surgical treatment, and pharmaceutical options are available in clinical practice. Selecting an effective obstructive sleep apnea treatment and therapy is a challenging medical decision due to obstructive sleep apnea heterogeneity and numerous treatment modalities. Thus, an objective marker for clinical evaluation is warranted to estimate the treatment response in patients with obstructive sleep apnea. Currently, while the Apnea-Hypopnea Index is used for severity assessment of obstructive sleep apnea and still considered a major guide to diagnosis and managements of obstructive sleep apnea, the Apnea-Hypopnea Index is not a robust marker of symptoms, function, or outcome improvement. Abnormal cardiac autonomic modulation can provide additional insight to better understand obstructive sleep apnea phenotyping. Heart rate variability is a reliable neurocardiac tool to assess altered autonomic function and can also provide cardiovascular information in obstructive sleep apnea. Beyond the Apnea-Hypopnea Index, this review aims to discuss the role of heart rate variability as an indicator and predictor of therapeutic efficacy to different modalities in order to optimise tailored treatment for obstructive sleep apnea.

Polysomnographic Phenotypes of Obstructive Sleep Apnea in a Real-Life Cohort: A Pathophysiological Approach

INTRODUCTION

Obstructive sleep apnea (OSA) is heterogeneous and complex, but its severity is still based on the apnea-hypoapnea index (AHI). The present study explores using cluster analysis (CA), the additional information provided from routine polysomnography (PSG) to optimize OSA categorization.

METHODS

Cross-sectional study of OSA subjects diagnosed by PSG in a tertiary hospital sleep unit during 2016-2020. PSG, demographical, clinical variables, and comorbidities were recorded. Phenotypes were constructed from PSG variables using CA. Results are shown as median (interquartile range).

RESULTS

981 subjects were studied: 41% females, age 56 years (45-66), overall AHI 23events/h (13-42) and body mass index (BMI) 30kg/m2 (27-34). Three PSG clusters were identified: Cluster 1: "Supine and obstructive apnea predominance" (433 patients, 44%). Cluster 2: "Central, REM and shorter-hypopnea predominance" (374 patients, 38%). Cluster 3: "Severe hypoxemic burden and higher wake after sleep onset" (174 patients, 18%). Based on classical OSA severity classification, subjects are distributed among the PSG clusters as severe OSA patients (AHI≥30events/h): 46% in cluster 1, 17% in cluster 2 and 36% in cluster 3; moderate OSA (15≤AHI<30events/h): 57% in cluster 1, 34% in cluster 2 and 9% in cluster 3; mild OSA (5≤AHI<15events/h): 28% in cluster 1, 68% in cluster 2 and 4% in cluster 3.

CONCLUSIONS

The CA identifies three specific PSG phenotypes that do not completely agree with classical OSA severity classification. This emphasized that using a simplistic AHI approach, the OSA severity is assessed by an incorrect or incomplete analysis of the heterogeneity of the disorder.

Relationship between Symptom Profiles and Endotypes among Patients with Obstructive Sleep Apnea: A Latent Class Analysis

Rationale: Obstructive sleep apnea (OSA) is a heterogeneous syndrome with various endotypic traits and symptoms. A link among symptoms, endotypes, and disease prognosis has been proposed but remains unsupported by empirical data. Objectives: To link symptom profiles and endotypes by clustering endotypic traits estimated using polysomnographic signals. Methods: We recruited 509 patients with moderate to severe OSA from a single sleep center. Polysomnographic data were collected between May 2020 and January 2022. Endotypic traits, namely arousal threshold, upper airway collapsibility, loop gain, and upper airway muscle compensation, were retrieved using polysomnographic signals during non-rapid eye movement periods. We used latent class analysis to group participants into endotype clusters. Demographic and polysomnographic parameter differences were compared between clusters, and associations between endotype clusters and symptom profiles were examined using logistic regression analyses. Results: Three endotype clusters were identified, characterized by high collapsibility/loop gain, low arousal threshold, and low compensation, respectively. Patients in each cluster exhibited similar demographic characteristics, but those in the high collapsibility/loop gain cluster had the highest proportion of obesity and severe oxygen desaturation observed in polysomnographic studies. The low compensation cluster was characterized by fewer sleepy symptoms and exhibited a lower rate of diabetes mellitus. Compared with the excessively sleepy group, disturbed sleep symptoms were associated with the low arousal threshold cluster (odds ratio, 1.89; 95% confidence interval, 1.16-3.10). Excessively sleepy symptoms were associated with the high collapsibility/loop gain cluster (odds ratio, 2.16; 95% confidence interval, 1.39-3.37) compared with the minimally symptomatic group. Conclusions: Three pathological endotype clusters were identified among patients with moderate to severe OSA, each exhibiting distinct polysomnographic characteristics and clinical symptom profiles.

The efficacy of cryogenic plasma tonsillectomy in the treatment of obstructive sleep apnea-hypopnea syndrome in children: A meta-analysis

BACKGROUND

Obstructive sleep apnea hypopnea syndrome (OSAHS) refers to the apnea and hypopnea caused by partial or complete obstruction of the upper airway collapse during sleep. The cryogenic plasma tonsillectomy is mostly used for the clinical treatment of children with OSAHS.

AIMS

The objective of this meta-analysis is to investigate the clinical efficacy of cryogenic plasma tonsillectomy for OSAHS in children.

MATERIALS & METHODS

The literature search was conducted through China National Knowledge Infrastructure (CNKI), Wanfang Database, Embase, PubMed, and Web of Science databases. The search was from the establishment of each database to June 2022. Randomized controlled trials (RCTs) meeting the criteria for partial/total cryo-plasma tonsillectomy for treating patients with obstructive sleep apnea-hypopnea syndrome in children were included, with data extracted. The meta-analysis was performed using the Stata 16.0 and Review Manager 5.4. Seven RCTs were included in this study.

RESULTS

The results showed that the partial/complete cryo-plasma tonsillectomy in the experimental group had a better therapeutical effective rate than the control group of patients treated with conventional surgery [Odds ratio (OR) = 2.181, 95% CI: 1.306-3.645, P < 0.05]. Also, in terms of postoperative adverse reactions, the number of adverse reactions in the experimental group was significantly lower than in the control group (OR = 0.445, 95% CI: 0.287-0.689, P = 0.001). The analysis of surgical efficacy showed that the operative time, intraoperative blood loss, and postoperative pain score were all significantly lower than those of the control group. Furthermore, further analysis of the apnea-hypopnea index (AHI) and the lowest oxygen saturation (LSaO2) of the two groups showed that the cryo-plasma tonsillectomy treatment had higher LSaO2 levels than conventional treatment [Standardized mean difference (SMD) = 0.380, 95% CI: 0.094-0.667, P = 0.009].

CONCLUSION

The application of cryo-plasma tonsillectomy can significantly improve the treatment effect of OSAHS, reducing adverse reactions.

Cluster analysis of clinical phenotypic heterogeneity in obstructive sleep apnea assessed using photoplethysmography

BACKGROUND

We evaluated heterogeneity in clinical phenotypes among patients with obstructive sleep apnea syndrome (OSAHS) using photoplethysmography (PPG) in cluster analysis.

METHODS

All enrolled patients underwent polysomnography (PSG) monitoring while wearing a PPG device. Pulse wave signals were recorded with a modified pulse oximetry probe in the PPG device. The pulse wave-derived cardiac risk composite parameter (CRI) and eight derived signal parameters were used to assess OSAHS phenotype. We defined a high cardiovascular risk OSAHS group (CRI ≥0.5) and low cardiovascular risk OSAHS group (CRI <0.5). K-means clustering was performed for analysis of clinical phenotype heterogeneity in OSAHS by combining the CRI and its derived signals.

RESULTS

The OSAHS group had high cardiovascular risk for sex, age, body mass index, systolic and diastolic blood pressure, apnea hypopnea index, and obstructive arousal index and higher risk of developing hypertension, diabetes, and cerebrovascular comorbidities. The low cardiovascular risk OSAHS group had higher blood oxygen levels. Three clinical phenotypes were identified in CRI clustering: 1) typical OSAHS with high risk of hypertension (characterized by middle age, obesity, hypertension with severe OSAHS); 2) older women and mild OSAHS; 3) older men and mild OSAHS. Three subtypes were obtained based on the eight cardiac risk-derived parameters: 1) hypoxia combined with decreased pulse wave amplitude variation; 2) decreased vascular pulse wave amplitude combined with decreased pulse frequency; 3) arrhythmia combined with hypoxia.

CONCLUSIONS

Establishing OSAHS clinical phenotypes with the CRI and derived parameters using PPG may help in establishing multi-dimensional assessment of cardiovascular risk in OSAHS.

Cancer sleep symptom-related phenotypic clustering differs across three cancer specific patient cohorts

Specific sleep disorders have been linked to disease progression in different cancers. We hypothesised sleep symptom clusters would differ between cancer types. The aim of this study was to compare sleep symptom clusters in post-treatment melanoma, breast and endometrial cancer patients. Data were collected from 124 breast cancer patients (1 male, 60 ± 15 years, 28.1 ± 6.6 kg/m² ), 82 endometrial cancer patients (64.0 ± 12.5 years, 33.5 ± 10.4 kg/m² ) and 112 melanoma patients (59 male, 65.0 ± 18.0 years, 29.1 ± 6.6 kg/m² ). All patients completed validated questionnaires to assess sleep symptoms, including the Epworth Sleepiness Scale (ESS), Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), and Functional Outcomes of Sleep Questionnaire-10 (FOSQ-10). Snoring, tiredness, observed apneas, age, BMI, and gender data were also collected. Binary values (PSQI, ISI, FOSQ), or continuous variables for sleepiness (ESS) and perceived sleep quality (PSQI), were created and sleep symptom clusters were identified and compared across cancer cohorts. Four distinct sleep symptom clusters were identified: minimally symptomatic (n = 152, 47.7%); insomnia-predominant (n = 87, 24.9%); very sleepy with upper airway symptoms (n = 51, 16.3%), and severely symptomatic with severe dysfunction (n = 34, 11.1%). Breast cancer patients were significantly more likely to be in the insomnia predominant or severely symptomatic with severe dysfunction clusters, whereas melanoma patients were more likely to be minimally symptomatic or sleepy with upper airway symptoms (p <0.0001). Endometrial cancer patients were equally distributed across symptom clusters. Sleep symptom clusters vary across cancer patients. A more personalised approach to the management of sleep-related symptoms in these patients may improve the long term quality of life and survival.

Respiratory Arousals in Patients with Very Severe Obstructive Sleep Apnea and How They Change after a Non-Framework Surgery

Respiratory arousal is the change from a state of sleep to a state of wakefulness following an apnea or hypopnea. In patients with obstructive sleep apnea (OSA), it could have a helpful role to activate upper airway muscles and the resumption of airflow and an opposing role to contribute to greater ventilatory instability, continue cycling, and likely exacerbate OSA. Patients with very severe OSA (apnea-hypopnea index (AHI) ≥ 60 events/h) may have specific chemical (e.g., possible awake hypercapnic hypoxemia) and mechanical (e.g., restricted dilator muscles) stimuli to initiate a respiratory arousal. Little was reported about how respiratory arousal presents in this distinct subgroup, how it relates to AHI, Epworth Sleepiness Scale (ESS), body mass index (BMI), and oxygen saturation, and how a non-framework surgery may change it. Here, in 27 patients with very severe OSA, we show respiratory arousal index was correlated with each of AHI, mean oxyhemoglobin saturation of pulse oximetry (SpO2), mean desaturation, and desaturation index, but not in BMI or ESS. The mean (53.5 events/h) was higher than other reports with less severe OSAs in the literature. The respiratory arousal index can be reduced by about half (45.3%) after a non-framework multilevel surgery in these patients.

Multidimensional assessment and cluster analysis for OSA phenotyping

STUDY OBJECTIVES

Obstructive sleep apnea (OSA) is a heterogeneous disease with varying phenotype. A cluster analysis based on multidimensional disease characteristics, including symptom, anthropometry, polysomnography (PSG), and craniofacial morphology, in combination with auto-continuous positive airway pressure (CPAP) titration response and comorbidity profiles was performed within a well-characterized cohort of patients with OSA, with the aim to refine current phenotypic expressions of OSA with clinical implications.

METHODS

Two hundred and ninety-one subjects with a new diagnosis of moderate to severe OSA, referred for auto-CPAP titration to the sleep center were included for analysis. In-laboratory PSG and craniofacial computed tomography (CT) scanning was performed, followed by an auto-CPAP titration. The symptom of excessive daytime sleepiness (EDS) was assessed by Epworth sleepiness scale (ESS).

RESULTS

Three patient phenotypes, corresponding to the "normal weight, non-sleepy and moderate OSA", the "obese, non-sleepy and severe OSA" and "obese, sleepy, very severe OSA with craniofacial limitation" were identified. Among the PSG parameters, only N3% and mean pulse oxygen saturation (SPO₂) were found to be associated with ESS, and they only explain small fraction of the variation (R²=0.136). Neck circumference and craniofacial limitation were associated the more severe phenotype, which had higher prevalence of hypertension, metabolic syndrome, greater diurnal blood gas abnormalities and worse PAP titration response.

CONCLUSIONS

Three OSA phenotypes were identified according to multiple aspect of clinical features in patients with moderate to severe OSA, which differed in prevalence of hypertension, metabolic syndrome, diurnal blood gas parameters and CPAP titration response. Self-reported EDS was not related with the severity of sleep breathing disturbance, and craniofacial limitation was associated the more severe phenotype. These findings highlight the necessity of integrate multiple disease characters into phenotyping to achieve better understanding of the clinical pictures of OSA.

Obstructive Sleep Apnea Syndrome Comorbidity Phenotypes in Primary Health Care Patients in Northern Greece

Background: Obstructive sleep apnea syndrome (OSAS) is a significant public health issue. In the general population, the prevalence varies from 10% to 50%. We aimed to phenotype comorbidities in OSAS patients referred to the primary health care (PHC) system. Methods: We enrolled 1496 patients referred to the PHC system for any respiratory- or sleep-related issue from November 2015 to September 2017. Some patients underwent polysomnography (PSG) evaluation in order to establish OSAS diagnosis. The final study population comprised 136 patients, and the Charlson comorbidity index was assessed. Categorical principal component analysis and TwoStep clustering was used to identify distinct clusters in the study population. Results: The analysis revealed three clusters: the first with moderate OSAS, obesity and a high ESS score without significant comorbidities; the second with severe OSAS, severe obesity with comorbidities and the highest ESS score; and the third with severe OSAS and obesity without comorbidities but with a high ESS score. The clusters differed in age (p < 0.005), apnea–hypopnea index, oxygen desaturation index, arousal index and respiratory and desaturation arousal index (p < 0.001). Conclusions: Predictive comorbidity models may aid the early diagnosis of patients at risk in the context of PHC and pave the way for personalized treatment.

Time Under 90% Oxygen Saturation and Systemic Hypertension in Patients with Obstructive Sleep Apnea Syndrome

PURPOSE

The diagnosis and severity of obstructive sleep apnea (OSA) are commonly based on the apnea hypopnea index (AHI). However, patients with similar severity AHIs may show widely varying comorbidities and risks for cardiovascular disease, which may be associated with different severities of nocturnal hypoxia. The percentage of cumulative time with oxygen saturation below 90% in total sleep time (T90) is receiving increasing attention in OSA research because it describes the duration and degree of hypoxia during the whole sleep. This study aimed to explore the distribution of T90 in OSA patients with similar severity and to evaluate the relationship between T90 and hypertension.

PATIENTS AND METHODS

A total of 775 patients with OSA were enrolled in this study, all participants were divided into groups according to the T90 value: light hypoxia (T90≤5%), mild hypoxia (T90 accounted for 5-10%), moderate hypoxia (T90 accounted for 10-25%), and severe hypoxia (T90>25%). Multivariate logistic regression analysis was performed to assess the association between T90 and hypertension.

RESULTS

Of the patients with mild OSA, 94.33% had light hypoxia, and 88.64% of moderate OSA patients had light hypoxia. The proportions of light, mild, moderate, and severe hypoxia among patients with severe OSA were 28.60%, 17.69%, 21.40%, and 32.31%, respectively. After adjustment for potential confounders, the risk of hypertension in patients with severe OSA increased according to the severity categories of T90. The odds ratio for T90 accounting for 10-25% relative to T90≤5% was 2.544 (95%confidence interval, 1.254-5.164; P=0.010) and as high as 2.692 (95%confidence interval, 1.403-5.166; P=0.003) in patients with T90>25%.

CONCLUSION

OSA patients with similar degree of AHI may have different T90 values, especially in severe OSA. A higher T90 was independently associated with the risk of hypertension after adjustment for traditional risk factors in patients with severe OSA. Our findings highlight the potential role for T90 in predicting hypertension in patients severe OSA.

Association between apnea-hypopnea index and coronary artery calcification: a systematic review and meta-analysis

BACKGROUND

The present study aimed to evaluate the association between presence and severity of obstructive sleep apnoea (OSA) and the presence of subclinical coronary artery disease (CAD) as assessed by coronary calcium score.

METHODS

Medline, Cochrane, and Google Scholar databases were searched. The presence of coronary artery calcification (CAC) and CAC score were assessed.

RESULTS

Irrespective of the cut-off value of apnoea-hypopnea index (AHI) (5 or 15 events/h), patients in the OSA group had higher rate of CAC presence and mean CAC score than those in the control group. Subgroup analyses of patients monitored with home sleep apnoea testing (HSAT) or in-hospital/laboratory polysomnography showed that the OSA group had higher rate of CAC presence and mean CAC score than the control group, except in the comparison of mean CAC score between AHI ≥5 vs. <5 events/h for patients using HSAT, which was not significant. Pair-wise comparison showed that CAC score may increase with increased OSA severity.

CONCLUSIONS

In participants without symptomatic coronary disease, the presence of OSA was associated with the presence and extent of CAC. However, potential confounders such as age, gender, and BMI and the diversity of CAC scores may affect the association.

Association between Interleukin-6 and vitamin D serum levels in patients with obstructive sleep apnea syndrome and impact of long-term continuous positive airway pressure therapy on biomarker levels

OBJECTIVE

Hypoxia induces interleukin-6 (IL-6) production in obstructive sleep apnea syndrome (OSAS). Low serum 25 hydroxyvitamin D (25(OH)D) levels have been linked to OSAS susceptibility. Serum 25(OH)D levels have been negatively correlated with serum IL-6 levels in patients with chronic inflammation. No data exist to assess whether there is a correlation between 25(OH)D and IL-6 serum levels in OSAS, while the impact of continuous positive airway pressure (CPAP) therapy on IL-6 or 25(OH)D levels needs further investigation. We aimed to compare the serum 25(OH)D and IL-6 levels between OSAS patients and controls, examine a possible correlation between 25(OH)D and IL-6 levels and the changes of their concentrations after twelve months of CPAP therapy in OSAS patients.

METHODS

15 newly-diagnosed OSAS patients and 15 non-apneic controls were recruited. Serum IL-6 and 25(OH)D levels were measured in the study population at baseline and twelve months after CPAP initiation in OSAS patients.

RESULTS

IL-6 levels were elevated in OSAS patients than controls and were positively and negatively correlated with body mass index (BMI) and minimum oxyhemoglobin saturation (minSpO₂), respectively. Diabetes mellitus, BMI and minSpO₂ independently predicted IL-6 levels. No difference was found in 25(OH)D levels between groups. No correlation between IL-6 and 25(OH)D levels was detected. Effective CPAP therapy did not impact IL-6 or 25(OH)D levels after one year in OSAS patients.

CONCLUSIONS

No correlation between IL-6 and 25(OH)D levels was found. IL-6 levels were significantly elevated in OSAS patients than the controls and positively correlated with BMI, diabetes mellitus, and nocturnal hypoxemia.

Efficacy of Low-Temperature Plasma-Assisted Unilateral/Bilateral Tonsillectomy and Adenoidectomy in Children with Obstructive Sleep Apnea Hypopnea Syndrome

Background

The aim of this study was to investigate the efficacy of low-temperature plasma-assisted bilateral or unilateral tonsillectomy (LTPABT or LTPAUT) and adenoidectomy in treatment of children with obstructive sleep apnea hypopnea syndrome (OSAHS).

Material/Methods

The present observational cohort study included a total of 244 children with OSAHS during December 2017 to December 2018. The patients were divided into the LTPABT group, the LTPAUT group, and the control group that received traditional bilateral tonsillectomy and adenoidectomy. The apnea hypopnea index (AHI) and the lowest oxygen saturation (LSaO₂) were measured. The patients’ pain condition was determined using the Visual Analog Score (VAS) and intraoperative indices were collected. Patients’ quality of life was assessed by OSA-18 scale and patients’ immune function was determined.

Results

The postoperative VAS scores were remarkably lower in the LTPABT and LTPAUT groups. At 6 months after surgery, the AHI significantly had decreased and LSaO₂ levels were significantly enhanced for all groups, and the AHI was significantly lower and LSaO₂ was markedly higher in the LTPABT group. The ratio of patients with efficacy of cured and remarkably effective was remarkably higher in the LTPABT group. The rates of postoperative hemorrhage and infection were significantly lower in the LTPABT and LTPAUT groups, and 4 cases showed tonsillar hyperplasia. OSA-18 scores were lowest in the LTPABT group. No significant difference was found in levels of IgM, IgA and IgG, and T lymphocyte subtypes.

Conclusions

The low-temperature plasma-assisted bilateral tonsillectomy combined with adenoidectomy had the best efficacy. None of the surgery methods influenced the patients’ immune function.

Experience of low-temperature plasma radiofrequency treatment of 53 patients with tongue hemangioma

BACKGROUND

The main treatments of hemangioma of the tongueare are injection of sclerotherapy, freezing, surgical resection or combined application of the above methods. It is extremely difficult to remove it completely. Even if combined with multiple methods, it is often unable to control its continued growth, and many important physiological functions of the tongue will be affected, resulting in poor quality of life for patients.

OBJECTIVE

To probe into the therapeutic effect of low-temperature plasma on hemangioma of the tongue.

METHODS

The clinical data of 53 patients with hemangioma of the tongue admitted to our department from July 2013 to July 2019 were retrospectively analyzed and studied.

RESULTS

Low-temperature plasma radiofrequency to treat hemangioma of the tongue might have advantages of simple operation, wide vision, fewer postoperative complications and quick recovery.

SIGNIFICANCE

The low-temperature plasma system possesses advantages of precise location, light collateral damage, non‑carbonization of the wound surface, light postoperative response, fewer complications and faster recovery. Combined with the endoscopic system with different angles, it can fully expose the surgical field and is especially suitable for the surgical treatment of patients with tumor of the tongue, which is worth popularizing application in clinic.

Enhancing Obstructive Sleep Apnea Diagnosis With Screening Through Disease Phenotypes: Algorithm Development and Validation

BACKGROUND

The American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used in patients with obstructive sleep apnea (OSA) without replacing polysomnography, which is the gold standard.

OBJECTIVE

This study aims to develop a clinical decision support system for OSA diagnosis according to its standard definition (apnea-hypopnea index plus symptoms), identifying individuals with high pretest probability based on risk and diagnostic factors.

METHODS

A total of 47 predictive variables were extracted from a cohort of patients who underwent polysomnography. A total of 14 variables that were univariately significant were then used to compute the distance between patients with OSA, defining a hierarchical clustering structure from which patient phenotypes were derived and described. Affinity from individuals at risk of OSA phenotypes was later computed, and cluster membership was used as an additional predictor in a Bayesian network classifier (model B).

RESULTS

A total of 318 patients at risk were included, of whom 207 (65.1%) individuals were diagnosed with OSA (111, 53.6% with mild; 50, 24.2% with moderate; and 46, 22.2% with severe). On the basis of predictive variables, 3 phenotypes were defined (74/207, 35.7% low; 104/207, 50.2% medium; and 29/207, 14.1% high), with an increasing prevalence of symptoms and comorbidities, the latter describing older and obese patients, and a substantial increase in some comorbidities, suggesting their beneficial use as combined predictors (median apnea-hypopnea indices of 10, 14, and 31, respectively). Cross-validation results demonstrated that the inclusion of OSA phenotypes as an adjusting predictor in a Bayesian classifier improved screening specificity (26%, 95% CI 24-29, to 38%, 95% CI 35-40) while maintaining a high sensitivity (93%, 95% CI 91-95), with model B doubling the diagnostic model effectiveness (diagnostic odds ratio of 8.14).

CONCLUSIONS

Defined OSA phenotypes are a sensitive tool that enhances our understanding of the disease and allows the derivation of a predictive algorithm that can clearly outperform symptom-based guideline recommendations as a rule-out approach for screening.

Comorbidity clusters in patients with moderate-to-severe OSA

PURPOSE

Obstructive sleep apnea (OSA) is a prevalent and multifaceted disease. To date, the presence and severity of objectively identified comorbidities and their association with specific OSA phenotypes, CPAP adherence, and survival remain to be elucidated. The aim of this study is to cluster patients with OSA based on 10 clinically important objectively identified comorbidities, and to characterize the comorbidity clusters in terms of clinical and polysomnographic characteristics, CPAP adherence, and survival.

STUDY DESIGN AND METHODS

Seven hundred ten consecutive patients starting CPAP for moderate-to-severe OSA were included. Comorbidities were based on generally accepted cutoffs identified in the peer-reviewed literature. Self-organizing maps were used to order patients based on presence and severity of their comorbidities and to generate clusters.

RESULTS

The majority of patients were men (80%). They were generally middle-aged (52 years) and obese (BMI: 31.5 kg/m²). Mean apnea-hypopnea index (AHI) was 41 ± 20 per h of sleep. More than 94% of the patients had one or more comorbidities with arterial hypertension, dyslipidemia, and obesity being the most prevalent. Nine comorbidity clusters were identified. The clinical relevance of these comorbidity clusters was highlighted by the difference in symptoms, PSG parameters, and cardiovascular risk. Also, differences in CPAP adherence, improvements in ESS, and long-term survival were present between the clusters.

CONCLUSION

Comorbidity prevalence in patients with OSA is high, and different comorbidity clusters, demonstrating differences in cardiovascular risk, CPAP adherence, and survival, can be identified. These results further substantiate the need for a comprehensive assessment of patients with OSA beyond the AHI.

Shifting and reducing breathing disturbance in patients with very severe obstructive sleep apnea by modified Z-palatoplasty with one-layer closure in one-stage multilevel surgery

Very severe obstructive sleep apnea (OSA) with apnea-hypopnea index (AHI) ≥ 60 events/h differs in several areas from OSA with other severities, including having a low-level daytime partial pressure of oxygen and residual on-CPAP (continuous positive airway pressure) AHIs greater than 20/h. Patients with very severe OSA show narrow retroglossal space and confined framework, which is difficult to be enlarged via conventional Uvulopalatopharyngoplasty (UPPP) surgery, resulting in poor response to non-framework surgeries. Our latest report showed efficacy and efficiency for subjects undergoing modified Z-palatoplasty (ZPP) with one-layer closure in a one-stage multilevel surgery. It is unclear whether and how this procedure could help patients with very severe OSA characterized with confined framework. From Mar. 2015 to May 2018, we enrolled 12 patients with very severe OSA receiving one-stage multi-level surgery with modified ZPP with one-layer closure, CO2 laser partial tongue-base glossectomy, and bilateral septomeatoplasty. Our results show that the surgery reduced AHI from 73.8 ± 10.7 to 30.8 ± 23.2 events/h and achieved a mean AHI reduction of 58.3% (p < 0.001 against 0 reduction or no surgery). The surgery shifted components of the breathing disturbances. It reduced more apnea than hypopnea and might convert some apnea to hypopnea.

Defining the Heterogeneity of Sleep Apnea Syndrome: A Cluster Analysis With Implications for Patient Management

INTRODUCTION

Obstructive sleep apnea (OSA) is a complex pathology with heterogeneity that has not been fully characterized to date. Our objective is to identify groups of patients with common clinical characteristics through cluster analysis that could predict patient prognosis, the impact of comorbidities and/or the response to a common treatment.

METHODS

Cluster analysis was performed using the hierarchical cluster method in 2025 patients in the apnea-HUGU cohort. The variables used for building the clusters included general data, comorbidity, sleep symptoms, anthropometric data, physical exam and sleep study results.

RESULTS

Four clusters were identified: (1) young male without comorbidity with moderate apnea and otorhinolaryngological malformations; (2) middle-aged male with very severe OSA with comorbidity without cardiovascular disease; (3) female with mood disorder; and (4) symptomatic male with established cardiovascular disease and severe OSA.

CONCLUSIONS

The characterization of these four clusters in OSA can be decisive when identifying groups of patients who share a special risk or common therapeutic strategies, orienting us toward personalized medicine and facilitating the design of future clinical trials.

Combined unsupervised-supervised machine learning for phenotyping complex diseases with its application to obstructive sleep apnea

Unsupervised clustering models have been widely used for multimetric phenotyping of complex and heterogeneous diseases such as diabetes and obstructive sleep apnea (OSA) to more precisely characterize the disease beyond simplistic conventional diagnosis standards. However, the number of clusters and key phenotypic features have been subjectively selected, reducing the reliability of the phenotyping results. Here, to minimize such subjective decisions for highly confident phenotyping, we develop a multimetric phenotyping framework by combining supervised and unsupervised machine learning. This clusters 2277 OSA patients to six phenotypes based on their multidimensional polysomnography (PSG) data. Importantly, these new phenotypes show statistically different comorbidity development for OSA-related cardio-neuro-metabolic diseases, unlike the conventional single-metric apnea–hypopnea index-based phenotypes. Furthermore, the key features of highly comorbid phenotypes were identified through supervised learning rather than subjective choice. These results can also be used to automatically phenotype new patients and predict their comorbidity risks solely based on their PSG data. The phenotyping framework based on the combination of unsupervised and supervised machine learning methods can also be applied to other complex, heterogeneous diseases for phenotyping patients and identifying important features for high-risk phenotypes.

Gender and Polysomnographic Profiles Findings in Obstructive Sleep Apnea Syndrome Patients Living in High Altitude

PURPOSE

Obstructive sleep apnea (OSA) is a common sleep disorder which prevalence is 22% in men and 17% in women. It is well described that females presented different clinical and polysomnographic characteristics compared with men. Those studies were performed in plain areas. We described the analysis by gender and clinical profiles of a sample of patients with diagnostic of OSA and living at high altitude.

PATIENTS AND METHODS

It is an observational study that describes differences between clinical and polysomnographic characteristics by gender in patients with OSA. Additionally, an unsupervised cluster algorithm was used to find groups of patients with similar clinical and polysomnographic characteristics.

RESULTS

We included 709 patients, 51.6% were females and 48.3% were males with mean age of 64 and 62 years old, respectively, in which 90.97% presented OSA. Men presented a higher apnea and hypopnea index than women (p=0.002), besides presented more sleep polysomnographic alterations. Meanwhile, women evidenced better sleep quality based on parameters. Additionally, in the sample of patients, we found four separated clinical profiles characterized mainly by differences in the severity of polysomnographic parameters.

CONCLUSION

The patients were more obese, older, and had lower SpO₂ values than most of those previously reported. Men had greater severity in most of the parameters measured by polysomnography. Polysomnographic variables were different both in the OSA patient profiles and in the gender comparison. However, the REM sleep apnea hypopnea index did not differ between sexes, indicating the importance of this variable in the evaluation of OSA severity in women. In contrast to previous reports, clinical and demographic characteristics showed few differences in both analyses. This result suggests that the behavior of OSA at high altitudes may have particularities with respect to low altitudes.

The Complex Interaction Between the Major Sleep Symptoms, the Severity of Obstructive Sleep Apnea, and Sleep Quality

Introduction: Little information exists in the general population whether clinical presentation phenotypes of obstructive sleep apnea (OSA) differ in terms of sleep quality and comorbidities. Aim: The purpose of our study was to assess possible differences between symptomatic and asymptomatic OSA patients concerning syndrome's severity, patients' sleep quality, and comorbidities. Subjects and methods: First, in a nationwide, stratified, epidemiological survey, 4,118 Cypriot adult participants were interviewed about sleep habits and complaints. In the second stage of the survey, 264 randomly selected adults underwent a type III sleep study for possible OSA. Additionally, they completed the Greek version of Pittsburgh Sleep Quality Index (Gr-PSQI), Epworth Sleepiness Scale (ESS), Athens Insomnia Scale (AIS), and Hospital Anxiety and Depression Scale (HADS). Results: From 264 enrolled participants, 155 individuals (40 females and 115 males) were first diagnosed with OSA. Among these 155 patients, 34% had ESS ≥ 10 and 49% AIS ≥ 6. One or both symptoms present categorized the individual as symptomatic (60%) and neither major symptom as asymptomatic (40%). There were no significant statistical differences (SSDs) between the two groups (symptomatic-asymptomatic) with regard to anthropometrics [age or gender; neck, abdomen, and hip circumferences; and body mass index (BMI)]. The two groups had no differences in OSA severity-as expressed by apnea-hypopnea index (AHI), oxygen desaturation index (ODI), and mean oxyhemoglobin saturation (SaO₂)-and in cardiometabolic comorbidities. Symptomatic patients expressed anxiety and depression more often than asymptomatics (p < 0.001) and had poorer subjective sleep quality (Gr-PSQI, p < 0.001). According to PSQI questionnaire, there were no SSDs regarding hours in bed and the use of sleep medications, but there were significant differences in the subjective perception of sleep quality (p < 0.001), sleep efficiency (p < 0.001), duration of sleep (p = 0.001), sleep latency (p = 0.007), daytime dysfunction (p < 0.001), and finally sleep disturbances (p < 0.001). Conclusion: According to our data, OSA patients reporting insomnia-like symptoms and/or sleepiness do not represent a more severe phenotype, by the classic definition of OSA, but their subjective sleep quality is compromised, causing a vicious cycle of anxiety or depression.

Obstructive sleep apnoea phenotypes: The many faces of a public health monolith

See related article

A Non-Framework Multilevel Surgery May Reduce Mean Heart Rate in Patients with Very Severe Obstructive Apnea Having Confined Retroglossal Space and Framework

An elevated mean heart rate in untreated patients of obstructive sleep apnea (OSA) may lead to a higher risk of mortality and the development of various cardiovascular diseases. The elevation may positively relate to the severity of OSA and present in both wakefulness and sleep. A reduction in heart rate has been presented in reports of treating OSA patients with continuous positive airway pressure (CPAP). However, patients with very severe OSA may refuse use of CPAP devices and advocated surgeries, such as direct skeletal surgery or tracheostomy. It is unclear whether the non-framework multilevel surgery we reported previously can overcome the unfavorable anatomy and reduce mean heart rate, which serves as a risk factor of mortality. Here, we show that multilevel surgery reduced the mean heart rate from 68.6 to 62.7 with a mean reduction of 5.9 beats/min. The results suggest that the surgery may reduce the risk of consequences and mortality associated with an elevated mean heart rate, such as various cardiovascular diseases. We disclose these findings, along with the variations and possible risks to our future patients with very severe OSA who refuse or cannot use a CPAP device or reject direct skeletal surgery.

Clusters of sleep apnoea phenotypes: A large pan-European study from the European Sleep Apnoea Database (ESADA)

BACKGROUND AND OBJECTIVE

To personalize OSA management, several studies have attempted to better capture disease heterogeneity by clustering methods. The aim of this study was to conduct a cluster analysis of 23 000 OSA patients at diagnosis using the multinational ESADA.

METHODS

Data from 34 centres contributing to ESADA were used. An LCA was applied to identify OSA phenotypes in this European population representing broad geographical variations. Many variables, including symptoms, comorbidities and polysomnographic data, were included. Prescribed medications were classified according to the ATC classification and this information was used for comorbidity confirmation.

RESULTS

Eight clusters were identified. Four clusters were gender-based corresponding to 54% of patients, with two clusters consisting only of men and two clusters only of women. The remaining four clusters were mainly men with various combinations of age range, BMI, AHI and comorbidities. The preferred type of OSA treatment (PAP or mandibular advancement) varied between clusters.

CONCLUSION

Eight distinct clinical OSA phenotypes were identified in a large pan-European database highlighting the importance of gender-based phenotypes and the impact of these subtypes on treatment prescription. The impact of cluster on long-term treatment adherence and prognosis remains to be studied using the ESADA follow-up data set.

Increasing Hypopnea in Sleep Breathing Disturbance Improves Postoperative Oxygen Saturation in Patients with Very Severe Obstructive Sleep Apnea

In patients of very severe obstructive sleep apnea (OSA) with confined framework, reducing volume is difficult to achieve a postoperative apnea-hypopnea index (AHI) qualifying the classical surgical success. However, a higher AHI with a larger part of hypopneas may have similar or even less severity of oxygen (O2) desaturation, compared to a lower index mostly made of apneas. Here, in 27 consecutive enrolled patients, we show that besides the improvement of mean AHI, the multilevel surgery increased hypopnea in AHI from 29.1% to 77.3%, and improves postoperative O2 saturation by reducing desaturation frequency (mean desaturation index decreased from 62.5 to 24.4 events/h) and level (mean oxyhemoglobin saturation of pulse oximetry (SpO2) desaturation cut down from 10.0 to 5.8%). The mean SpO2 improved from 92.3% to 94.7%, and the improvement was positively related to the proportion increase of hypopnea/AHI. The results suggest that the non-framework surgery could help patients with very severe OSA whose AHIs are ≥60 events/h in terms of improving postoperative O2 saturation. Due to the improvement also presented in those not qualified as classical surgical success, further studies are needed to clarify the connection between O2 desaturation and various consequences to reconsider defining a surgical success.

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

Conventionally, apnea–hypopnea index (AHI) is used to define and categorize the severity of obstructive sleep apnea. However, routine polysomnography (PSG) includes multiple parameters for assessing the severity of obstructive sleep apnea. The goal of this study is to identify and categorize obstructive sleep apnea phenotypes using unsupervised learning methods from routine PSG data. We identified four clusters from 4,603 patients by using 29 PSG variable and arranged according to their mean AHI. Cluster 1, spontaneous arousal (mean AHI = 8.52/h); cluster 2, poor sleep and periodic limb movements (mean AHI = 12.16/h); cluster 3, hypopnea (mean AHI = 38.60/h); and cluster 4, hypoxia (mean AHI = 69.66/h). Conventional obstructive sleep apnea classification based on apnea–hypopnea index severity showed no significant difference in cardiovascular or cerebrovascular mortality (Log rank P = 0.331), while 4 clusters showed an overall significant difference (Log rank P = 0.009). The risk of cardiovascular or cerebrovascular mortality was significantly increased in cluster 2 (hazard ratio = 6.460, 95% confidence interval 1.734–24.073) and cluster 4 (hazard ratio = 4.844, 95% confidence interval 1.300–18.047) compared to cluster 1, which demonstrated the lowest mortality. After adjustment for age, sex, body mass index, and underlying medical condition, only cluster 4 showed significantly increased risk of mortality compared to cluster 1 (hazard ratio = 7.580, 95% confidence interval 2.104–34.620). Phenotyping based on numerous PSG parameters gives additional information on patients’ risk evaluation. Physicians should be aware of PSG features for further understanding the pathophysiology and personalized treatment.

Clinical presentation and comorbidities of obstructive sleep apnea-COPD overlap syndrome

Background

More advanced knowledge is needed on how COPD alters the clinical presentation of obstructive sleep apnea (OSA) and how the association of both diseases, known as ‘overlap syndrome’ (OVS), impacts on cardiovascular health.

Objective

To investigate differences between patients with OVS and those with moderate-to-severe OSA alone.

Methods

A cross-sectional study conducted in the French National Sleep Apnea Registry between January 1997 and January 2017. Univariable and multivariable logistic regression models were used to compare OVS versus OSA alone on symptoms and cardiovascular health.

Results

46,786 patients had moderate-to-severe OSA. Valid spirometry was available for 16,466 patients: 14,368 (87%) had moderate-to-severe OSA alone and 2098 (13%) had OVS. A lower proportion of OVS patients complained of snoring, morning headaches and excessive daytime sleepiness compared to OSA alone (median Epworth Sleepiness Scale score: 9 [interquartile range (IQR) 6–13] versus 10 (IQR 6–13), respectively; P <0.02). Similarly, a lower proportion of OVS patients (35.6% versus 39.4%, respectively; P <0.01) experienced sleepiness while driving. In contrast, 63.5% of the OVS population experienced nocturia compared to 58.0% of the OSA population (P<0.01). Apnea hypopnea index (36 [25; 52] vs 33.1 [23.3; 50]), oxygen desaturation index (28 [15; 48] vs 25.2 [14; 45]) and mean nocturnal SaO2 (92 [90; 93.8] vs 93 [91.3; 94]) were significantly more altered in the OVS group. Associated COPD had no effect on the prevalence of hypertension and stroke. After controlling for main confounders, COPD severity was associated in a dose-response relationship with a higher prevalence of coronary heart disease, heart failure and peripheral arteriopathy.

Conclusions

In adults with moderate-to-severe OSA, OVS was minimally symptomatic, but exhibited higher odds for prevalent coronary heart disease, heart failure and peripheral arteriopathy.

Phenotypic Subtypes of OSA: A Challenge and Opportunity for Precision Medicine

Current strategies for the management of OSA reflect a one-size-fits-all approach. Diagnosis and severity of OSA are based on the apnea-hypopnea index and treatment initiated with CPAP, followed by trials of alternatives (eg, oral appliances) if CPAP "fails." This approach does not consider the heterogeneity of individuals with OSA, reflected by varying risk factors, pathophysiological causes, clinical manifestations, and consequences. Recently, studies using analytic approaches such as cluster analysis have taken advantage of this heterogeneity to identify OSA phenotypes, or subtypes of patients with unique characteristics, that may enable more personalized approaches to prognostication and treatment. Examples include symptom-based subtypes such as "excessively sleepy" and "disturbed sleep" with differing impact of CPAP on symptoms and health-related quality of life. Polysomnographic subtypes, distinguished by respiratory event association with hypoxemia, arousals, or both, exhibit varying risks of cardiovascular disease and response to therapy. This review summarizes the findings from recent cluster analysis studies in sleep apnea and synthesizes common themes to describe the potential role (and limitations) of phenotypic subtypes in precision medicine for OSA. It also highlights future directions, including linking of phenotypes to clinically relevant outcomes, rigorous and transparent assessment of phenotype reproducibility, and need for tools that categorize patients into subtypes, to prospectively validate phenotype-based prognostication and treatment approaches. Finally, we highlight the critical need to include women and more racially/ethnically diverse populations in this area of research if we are to leverage the heterogeneity of OSA to improve patient lives.

The use of visceral adiposity variables in the prediction of obstructive sleep apnea: evidence from a large cross-sectional study

PURPOSE

The purposes of this study were to evaluate the ability of visceral adiposity variables [the lipid accumulation product (LAP), the visceral adiposity index (VAI), and the triglyceride-glucose index (TyG)] in predicting obstructive sleep apnea hypopnea syndrome (OSAHS) and to determine the effect of sex on the prediction.

METHODS

A total of 5539 subjects admitted to the sleep center for suspected OSAHS were consecutively recruited from 2007 to 2016. Anthropometric measurements, biological indicators, Epworth sleepiness scale score, and polysomnographic variables were collected. Prediction models for diagnosing OSAHS were established in the test group by logistic regression and verified in the validation group by receiver operating characteristic (ROC) curves.

RESULTS

A total of 4703 patients were included in total. LAP and TyG were of moderate diagnostic accuracy for OSAHS, with the diagnostic efficiency differing between men and women. A prediction model was developed that combined visceral adiposity indicators with waist circumstance and the lowest SpO₂. The sensitivity of those indicators were both 84% in men and women, respectively, and their specificity were both 90%. In addition, the model was confirmed in the validation group with a sensitivity and specificity of 83% and 85% in men and 85% and 84% in women.

CONCLUSIONS

LAP and TyG were of moderate efficiency in screening for OSAHS. The prediction model provides a simple and practical screening tool for OSAHS.

Patterns of multimorbid health conditions: a systematic review of analytical methods and comparison analysis

Background

The latest review of studies on multimorbidity patterns showed high heterogeneity in the methodology for identifying groups of multimorbid conditions. However, it is unclear how analytical methods used influence the identified multimorbidity patterns.

Methods

We undertook a systematic review of analytical methods used to identify multimorbidity patterns in PubMed and EMBASE from their inception to January 2017. We conducted a comparison analysis to assess the effect the analytical methods had on the multimorbidity patterns identified, using the Australian National Health Survey (NHS) 2007-08 data.

Results

We identified 13 194 studies and excluded 13 091 based on titles/abstracts. From the full-text reviews of the 103 remaining publications, we identified 41 studies that used five different analytical methods to identify multimorbid conditions in the studies. Thirty-seven studies (90%) adopted either the factor-analysis or hierarchical-clustering methods, but heterogeneity arises for the use of different proximity measures within each method to form clusters. Our comparison analysis showed the variation in identified groups of multimorbid conditions when applying the methods to the same NHS data. We extracted main similarities among the groupings obtained by the five methods: (i) cardiovascular and metabolic diseases, (ii) mental health problems and (iii) allergic diseases.

Conclusion

We showed the extent of effects for heterogeneous analytical methods on identification of multimorbidity patterns. However, more work is needed to guide investigators for choosing the best analytical method to improve the validity and generalizability of findings. Investigators should also attempt to compare results obtained by various methods for a consensus grouping of multimorbid conditions.

Application of machine learning to predict obstructive sleep apnea syndrome severity

Introduction:

Obstructive sleep apnea syndrome has become an important public health concern. Polysomnography is traditionally considered an established and effective diagnostic tool providing information on the severity of obstructive sleep apnea syndrome and the degree of sleep fragmentation. However, the numerous steps in the polysomnography test to diagnose obstructive sleep apnea syndrome are costly and time consuming. This study aimed to test the efficacy and clinical applicability of different machine learning methods based on demographic information and questionnaire data to predict obstructive sleep apnea syndrome severity.

Materials and methods:

We collected data about demographic characteristics, spirometry values, gas exchange (PaO2, PaCO2) and symptoms (Epworth Sleepiness Scale, snoring, etc.) of 313 patients with previous diagnosis of obstructive sleep apnea syndrome. After principal component analysis, we selected 19 variables which were used for further preprocessing and to eventually train seven types of classification models and five types of regression models to evaluate the prediction ability of obstructive sleep apnea syndrome severity, represented either by class or by apnea–hypopnea index. All models are trained with an increasing number of features and the results are validated through stratified 10-fold cross validation.

Results:

Comparative results show the superiority of support vector machine and random forest models for classification, while support vector machine and linear regression are better suited to predict apnea–hypopnea index. Also, a limited number of features are enough to achieve the maximum predictive accuracy. The best average classification accuracy on test sets is 44.7 percent, with the same average sensitivity (recall). In only 5.7 percent of cases, a severe obstructive sleep apnea syndrome (class 4) is misclassified as mild (class 2). Regression results show a minimum achieved root mean squared error of 22.17.

Conclusion:

The problem of predicting apnea–hypopnea index or severity classes for obstructive sleep apnea syndrome is very difficult when using only data collected prior to polysomnography test. The results achieved with the available data suggest the use of machine learning methods as tools for providing patients with a priority level for polysomnography test, but they still cannot be used for automated diagnosis.

Recognizable clinical subtypes of obstructive sleep apnea across international sleep centers: a cluster analysis

Study Objectives

A recent study of patients with moderate-severe obstructive sleep apnea (OSA) in Iceland identified three clinical clusters based on symptoms and comorbidities. We sought to verify this finding in a new cohort in Iceland and examine the generalizability of OSA clusters in an international ethnically diverse cohort.

Methods

Using data on 972 patients with moderate-severe OSA (apnea-hypopnea index [AHI] ≥ 15 events per hour) recruited from the Sleep Apnea Global Interdisciplinary Consortium (SAGIC), we performed a latent class analysis of 18 self-reported symptom variables, hypertension, cardiovascular disease, and diabetes.

Results

The original OSA clusters of disturbed sleep, minimally symptomatic, and excessively sleepy replicated among 215 SAGIC patients from Iceland. These clusters also generalized to 757 patients from five other countries. The three clusters had similar average AHI values in both Iceland and the international samples, suggesting clusters are not driven by OSA severity; differences in age, gender, and body mass index were also generally small. Within the international sample, the three original clusters were expanded to five optimal clusters: three were similar to those in Iceland (labeled disturbed sleep, minimal symptoms, and upper airway symptoms with sleepiness) and two were new, less symptomatic clusters (labeled upper airway symptoms dominant and sleepiness dominant). The five clusters showed differences in demographics and AHI, although all were middle-aged (44.6-54.5 years), obese (30.6-35.9 kg/m2), and had severe OSA (42.0-51.4 events per hour) on average.

Conclusions

Results confirm and extend previously identified clinical clusters in OSA. These clusters provide an opportunity for a more personalized approach to the management of OSA.

Changing Faces of Obstructive Sleep Apnea: Treatment Effects by Cluster Designation in the Icelandic Sleep Apnea Cohort

Study Objectives

Distinct clinical phenotypes of obstructive sleep apnea (OSA) have been identified: Disturbed Sleep, Minimally Symptomatic, and Sleepy. Determining whether these phenotypes respond differently to standard treatment helps us to create a foundation for personalized therapies. We compared responses to positive airway pressure (PAP) therapy in these clinical OSA phenotypes.

Methods

The study sample included 706 patients from the Icelandic Sleep Apnea Cohort with moderate-to-severe OSA who were prescribed PAP. Linear and logistic mixed models were used to compare 2-year changes in demographics, comorbid diseases, and sleep-related health issues within and across OSA clinical phenotypes. Relationships between changes in symptoms and PAP adherence were also examined.

Results

Overall, effect sizes were moderate to large when comparing sleepiness, insomnia-related, and apneic symptom changes in the Sleepy group with changes in other two groups, especially those in the Minimally Symptomatic group. Within the Disturbed Sleep group, PAP users and nonusers demonstrated similar changes in insomnia-related symptoms. The Minimally Symptomatic group remained relatively asymptomatic, but reported significant decreases in daytime sleepiness and physical fatigue; PAP users generally had larger improvements. The Sleepy group had reductions in nearly all measured symptoms, including large reductions in drowsy driving; almost all of these improvements were greater among PAP users than nonusers.

Conclusions

OSA treatment response patterns differed by initial clinical phenotype and PAP adherence. Individuals with insomnia-related symptoms may require additional targeted therapy for these complaints. These findings underscore the need for a personalized approach to management that recognizes patients with a range of OSA presentations.

Prevalence and association analysis of obstructive sleep apnea with gender and age differences - Results of SHIP-Trend

Identification of obstructive sleep apnea and risk factors is important for reduction in symptoms and cardiovascular risk, and for improvement of quality of life. The population-based Study of Health in Pomerania investigated risk factors and clinical diseases in a general population of northeast Germany. Additional polysomnography was applied to measure sleep and respiration with the objective of assessing prevalence and risk factors of obstructive sleep apnea in a German cohort. One-thousand, two-hundred and eight people between 20 and 81 years old (54% men, median age 54 years) underwent overnight polysomnography. The estimated obstructive sleep apnea prevalence was 46% (59% men, 33% women) for an apnea-hypopnea index ≥5%, and 21% (30% men, 13% women) for an apnea-hypopnea index ≥ 15. The estimated obstructive sleep apnea syndrome prevalence (apnea-hypopnea index ≥5; Epworth Sleepiness Scale >10) was 6%. The prevalence of obstructive sleep apnea continuously increased with age for men and women with, however, later onset for women. Gender, age, body mass index, waist-to-hip ratio, snoring, alcohol consumption (for women only) and self-reported cardiovascular diseases were significantly positively associated with obstructive sleep apnea, whereas daytime sleepiness was not. Diabetes, hypertension and metabolic syndrome were positively associated with severe obstructive sleep apnea. The associations became non-significant after adjustment for body mass. Women exhibited stronger associations than men. The prevalence of obstructive sleep apnea was high, with almost half the population presenting some kind of obstructive sleep apnea. The continuous increase of obstructive sleep apnea with age challenges the current theory that mortality due to obstructive sleep apnea and cardiovascular co-morbidities affect obstructive sleep apnea prevalence at an advanced age. Also, gender differences regarding obstructive sleep apnea and associations are significant for recognizing obstructive sleep apnea mechanisms and therapy responsiveness.

Challenges and perspectives in obstructive sleep apnoea

Obstructive sleep apnoea (OSA) is a major challenge for physicians and healthcare systems throughout the world. The high prevalence and the impact on daily life of OSA oblige clinicians to offer effective and acceptable treatment options. However, recent evidence has raised questions about the benefits of positive airway pressure therapy in ameliorating comorbidities.

An international expert group considered the current state of knowledge based on the most relevant publications in the previous 5 years, discussed the current challenges in the field, and proposed topics for future research on epidemiology, phenotyping, underlying mechanisms, prognostic implications and optimal treatment of patients with OSA.

The group concluded that a revision to the diagnostic criteria for OSA is required to include factors that reflect different clinical and pathophysiological phenotypes and relevant comorbidities (e.g.nondipping nocturnal blood pressure). Furthermore, current severity thresholds require revision to reflect factors such as the disparity in the apnoea–hypopnoea index (AHI) between polysomnography and sleep studies that do not include sleep stage measurements, in addition to the poor correlation between AHI and daytime symptoms such as sleepiness. Management decisions should be linked to the underlying phenotype and consider outcomes beyond AHI.

Evaluating the clinical value of the hypoxia burden index in patients with obstructive sleep apnea

PURPOSE

We aimed to develop a novel index, the hypoxia burden index (HBI), that reflects the degree and duration of oxygen desaturation, and to determine whether this index is better than the apnea hypopnea-index (AHI) for evaluating obstructive sleep apnea (OSA) and cardiovascular diseases morbidities.

METHODS

We retrospectively analyzed 459 adult subjects who underwent overnight polysomnography (PSG) in this study. The HBI was calculated by dividing the total desaturation area of SpO₂ below 90% by the TST measured in seconds. Correlations between the HBI and PSG parameters and the cardiovascular diseases (CVDs) of inpatients were explored.

RESULTS

For the 459 participants, the HBI was highly correlated with the AHI (r = 0.690), lowest oxygen saturation (SpO₂min) (r = -0.733), and proportion of cumulative sleep time with SpO₂ below 90% in total sleep time (TST) (CT90%) (r = 0.801). The HBI was mildly correlated with Epworth sleepiness scale score, morning systolic blood pressure, diastolic blood pressure, and mean arterial pressure. Among the significantly different parameters in the CVD and non-CVD groups in 202 inpatients (age, sex, the longest time of apnea, AHI, and HBI), only age, sex, and HBI [OR 1.006 (1.001-1.011), p = 0.021] were independently correlated with CVDs.

CONCLUSIONS

The HBI considers both hypoxia duration and severity during sleep. It may have potential value in predicting the clinical outcomes of OSA based on its correlation with ESS scores and morning blood pressure as well as its independent association with the prevalence of CVDs, whereas the AHI is not significantly associated with CVD prevalence.

Symptom-Based Subgroups of Koreans With Obstructive Sleep Apnea

STUDY OBJECTIVES

Recently, the Icelandic Sleep Apnea Cohort (ISAC) identified three subgroups in patients with obstructive sleep apnea (OSA) recruited from the sleep clinic based on clinical symptoms and comorbidities: excessively sleepy, minimally symptomatic, and disturbed sleep. This study sought to determine whether the three OSA subgroups are applicable to a population-based cohort in Korea.

METHODS

Study subjects are participants of an ongoing cohort study in Korea. Of the 2,918 participants, 422 new moderate to severe OSA cases (apneahypopnea index [AHI] ≥ 15 events/h) were diagnosed by home sleep studies. All participants completed a detailed questionnaire on sleep-related symptoms, comorbidities, medication, and other information. A latent class analysis was performed.

RESULTS

When examining solutions for up to 10 clusters, the a priori three-cluster solution was the optimal clustering solution. The three-cluster solution demonstrated a subgroup presentation similar to the clusters identified in the ISAC. The minimally symptomatic subgroup was most prevalent (55.7%) in the Korean cohort. Among the three subgroups, there were no differences in mean AHI and body mass index; however, the disturbed sleep subgroup was older and had more females.

CONCLUSIONS

Combined with the ISAC data, we suggest that the three-symptom cluster solution for patients with OSA may be more widely applicable, irrespective of ethnicity and study population.

Heritability of Heart Rate Response to Arousals in Twins

Objectives

To determine if the large and highly reproducible interindividual differences in arousal intensity and heart rate response to arousal (ΔHR) during non-REM sleep are heritable.

Methods

Polysomnograms of 55 monozygotic (14 male and 41 female pairs) and 36 dizygotic (15 male and 21 female pairs) same-sex twin pairs were analyzed. Arousals were scored using the 2012 American Academy of Sleep Medicine criteria. Arousal intensity was scaled (between 0 and 9) using an automatic algorithm based on the change in electroencephalogram time and frequency characteristics. The ΔHR was determined at each arousal. We calculated average arousal duration, average arousal intensity, average overall ΔHR, average ΔHR at a given arousal intensity, slope of ΔHR per arousal intensity, and arousal intensity threshold of ΔHR.

Results

The intraclass correlations among monozygotic and dizygotic twin pairs were 0.663 and 0.146, respectively, for average arousal intensity, and 0.449 and 0, respectively, for arousal intensity threshold of ΔHR controlling for age, sex, and race. These values imply large broad sense heritability (H2) for these traits. This evidence was confirmed by a robust maximum likelihood-based variance components estimation approach, with an additive genetic heritability of 0.64 (95% confidence interval: 0.48 to 0.80) for average arousal intensity and a combined additive and dominance genetic heritability and of 0.46 (0.25 to 0.68) for arousal intensity threshold of ΔHR. Results also suggested significant additive genetic effects for average arousal duration, ΔHR at arousal intensity scale 4 and the overall average ΔHR.

Conclusion

Genetic factors explain a significant fraction of the phenotypic variability for average arousal intensity and arousal intensity threshold of ΔHR. Results suggest that the duration of arousals and specific average ΔHR values may also be heritable traits.

Clinical trial registration

NCT02827461.

Which place of pharmacological approaches beyond continuous positive airway pressure to treat vascular disease related to obstructive sleep apnea?

Obstructive sleep apnea (OSA) is characterized by recurrent episodes of partial or complete upper airway obstruction, occurring during sleep, leading to chronic intermittent hypoxia (IH), which harms the cardiovascular system. OSA is associated with both functional and structural vascular alterations that contribute to an increased prevalence of fatal and non-fatal cardiovascular events. OSA is a heterogeneous disease with respect to the severity of hypoxia, the presence of daytime symptoms, obesity, and cardiovascular comorbidities. Various clusters of OSA phenotypes have been described leading to more highly personalized treatment. The aim of this review is to describe the various therapeutic strategies including continuous positive airway pressure (CPAP), oral appliances, surgery, weight loss, and especially pharmacological interventions that have been evaluated to reduce vascular alterations in both OSA patients and preclinical animal models. Conventional therapies, predominantly CPAP, have a limited impact on vascular alterations in the presence of co-morbidities. A better knowledge of pharmacological therapies targeting IH-induced vascular alterations will facilitate the use of combined therapies and is crucial for designing clinical trials in well-defined OSA phenotypes.

Personalised medicine in sleep respiratory disorders: focus on obstructive sleep apnoea diagnosis and treatment

In all fields of medicine, major efforts are currently dedicated to improve the clinical, physiological and therapeutic understanding of disease, and obstructive sleep apnoea (OSA) is no exception. The personalised medicine approach is relevant for OSA, given its complex pathophysiology and variable clinical presentation, the interactions with comorbid conditions and its possible contribution to poor outcomes. Treatment with continuous positive airway pressure (CPAP) is effective, but CPAP is poorly tolerated or not accepted in a considerable proportion of OSA patients. This review summarises the available studies on the physiological phenotypes of upper airway response to obstruction during sleep, and the clinical presentations of OSA (phenotypes and clusters) with a special focus on our changing attitudes towards approaches to treatment. Such major efforts are likely to change and expand treatment options for OSA beyond the most common current choices (i.e CPAP, mandibular advancement devices, positional treatment, lifestyle changes or upper airway surgery). More importantly, treatment for OSA may become more effective, being tailored to each patient's need.

Polysomnographic phenotypes and their cardiovascular implications in obstructive sleep apnoea

BACKGROUND

Obstructive sleep apnoea (OSA) is a heterogeneous disorder, and improved understanding of physiologic phenotypes and their clinical implications is needed. We aimed to determine whether routine polysomnographic data can be used to identify OSA phenotypes (clusters) and to assess the associations between the phenotypes and cardiovascular outcomes.

METHODS

Cross-sectional and longitudinal analyses of a multisite, observational US Veteran (n=1247) cohort were performed. Principal components-based clustering was used to identify polysomnographic features in OSA's four pathophysiological domains (sleep architecture disturbance, autonomic dysregulation, breathing disturbance and hypoxia). Using these features, OSA phenotypes were identified by cluster analysis (K-means). Cox survival analysis was used to evaluate longitudinal relationships between clusters and the combined outcome of incident transient ischaemic attack, stroke, acute coronary syndrome or death.

RESULTS

Seven patient clusters were identified based on distinguishing polysomnographic features: 'mild', 'periodic limb movements of sleep (PLMS)', 'NREM and arousal', 'REM and hypoxia', 'hypopnoea and hypoxia', 'arousal and poor sleep' and 'combined severe'. In adjusted analyses, the risk (compared with 'mild') of the combined outcome (HR (95% CI)) was significantly increased for 'PLMS', (2.02 (1.32 to 3.08)), 'hypopnoea and hypoxia' (1.74 (1.02 to 2.99)) and 'combined severe' (1.69 (1.09 to 2.62)). Conventional apnoea-hypopnoea index (AHI) severity categories of moderate (15≤AHI<30) and severe (AHI ≥30), compared with mild/none category (AHI <15), were not associated with increased risk.

CONCLUSIONS

Among patients referred for OSA evaluation, routine polysomnographic data can identify physiological phenotypes that capture risk of adverse cardiovascular outcomes otherwise missed by conventional OSA severity classification.

Characterization of the CPAP-treated patient population in Catalonia

There are different phenotypes of obstructive sleep apnoea (OSA), many of which have not been characterised. Identification of these different phenotypes is important in defining prognosis and guiding the therapeutic strategy. The aim of this study was to characterise the entire population of continuous positive airway pressure (CPAP)-treated patients in Catalonia and identify specific patient profiles using cluster analysis.

A total of 72,217 CPAP-treated patients who contacted the Catalan Health System (CatSalut) during the years 2012 and 2013 were included. Six clusters were identified, classified as “Neoplastic patients” (Cluster 1, 10.4%), “Metabolic syndrome patients” (Cluster 2, 27.7%), “Asthmatic patients” (Cluster 3, 5.8%), “Musculoskeletal and joint disorder patients” (Cluster 4, 10.3%), “Patients with few comorbidities” (Cluster 5, 35.6%) and “Oldest and cardiac disease patients” (Cluster 6, 10.2%). Healthcare facility use and mortality were highest in patients from Cluster 1 and 6. Conversely, patients in Clusters 2 and 4 had low morbidity, mortality and healthcare resource use.

Our findings highlight the heterogeneity of CPAP-treated patients, and suggest that OSA is associated with a different prognosis in the clusters identified. These results suggest the need for a comprehensive and individualised approach to CPAP treatment of OSA.