Diagnosis Of Obstructive Sleep Apnea Research Articles

Enhanced machine learning approaches for OSA patient screening: model development and validation study

Age, gender, body mass index (BMI), and mean heart rate during sleep were found to be risk factors for obstructive sleep apnea (OSA), and a variety of methods have been applied to predict the occurrence of OSA. This study aimed to develop and evaluate OSA prediction models using simple and accessible parameters, combined with multiple machine learning algorithms, and integrate them into a cloud-based mobile sleep medicine management platform for clinical use. The study data were obtained from the clinical records of 610 patients who underwent polysomnography (PSG) at the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University between January 2021 and December 2022. The participants were randomly divided into a training-test group (80%) and an independent validation group (20%). The logistic regression, artificial neural network, naïve Bayes, support vector machine, random forest, and decision tree algorithms were used with age, gender, BMI, and mean heart rate during sleep as predictors to build a risk prediction model for moderate-to-severe OSA. To evaluate the performance of the models, we calculated the area under the receiver operating curve (AUROC), accuracy, recall, specificity, precision, and F1-score for the independent validation set. In addition, the calibration curve, decision curve, and clinical impact curve were generated to determine clinical usefulness. Age, gender, BMI, and mean heart rate during sleep were significantly associated with OSA. The artificial neural network model had the best efficacy compared with the other prediction algorithms. The AUROC, accuracy, recall, specificity, precision, F1-score, and Brier score were 80.4% (95% CI 76.7–84.1%), 69.9% (95% CI 69.8–69.9%), 86.5% (95% CI 81.6–91.3%), 61.5% (95% CI 56.6–66.4%), 53.2% (95% CI 47.7–58.7%), 65.9% (95% CI 60.2–71.5%), and 0.165, respectively, for the artificial neural network model. The AUROCs for the LR, NB, SVM, RF, and DT models were 80.2%, 79.7%, 79.2%, 78.4%, and 70.4%, respectively. The six models based on four simple and easily accessible parameters effectively predicted moderate-to-severe OSA in patients with PSG screening, with the artificial neural network model having the best performance. These models can provide a reliable tool for early OSA diagnosis, and their integration into a cloud-based mobile sleep medicine management platform could improve clinical decision making.

Obstructive Sleep Apnea Is Associated with Worsened Hospital Outcomes in Children Hospitalized with Asthma.

Studies have shown a bidirectional relationship between asthma and obstructive sleep apnea (OSA). However, there is a paucity of national-level data evaluating the impact of OSA on hospital outcomes in pediatric hospitalizations for asthma. We analyzed the National Inpatient Sample and Kids Inpatient Database to include all pediatric hospitalizations with a primary diagnosis of asthma between 2003-2016. Using ICD codes, the pediatric asthma cohort was divided into two groups: those with and those without a concomitant diagnosis of OSA. The primary outcomes were in-hospital mortality and the need for mechanical ventilation. The secondary outcomes were the lengths of each hospital stay and total hospitalization charges. We analyzed 1,606,248 hospitalizations during the 14-year study period. The overall prevalence rate of OSA was 0.7%. Patients with asthma and OSA were significantly older (8.2 versus 5.9 years) and were more often male, p < 0.001. The OSA group had several increased comorbidities. The overall mortality rate was 0.03%, and multivariate regression analysis showed that OSA was associated with 4.3 times higher odds of in-hospital mortality (95% CI: 2.4 to 7.6, p < 0.001). Furthermore, OSA was associated with a 5.2 times greater need for mechanical ventilation (95% CI: 4.8 to 5.5, p < 0.001). Linear regression analyses demonstrated that OSA independently contributed an additional 0.82 days to the hospital stay length (95% CI: 0.79 to 0.86, p < 0.001) and an extra 10,479 USD (95% CI: 10,110 to 10,848, p < 0.001) in hospitalization charges. OSA in children admitted with asthma is associated with poor hospital outcomes such as increased mortality risk, the need for mechanical ventilation, and increased healthcare utilization.

Early detection and treatment of obstructive sleep apnoea in infants with Down syndrome: a prospective, non-randomised, controlled, interventional study

Infants with Down syndrome (DS) are at high risk of obstructive sleep apnoea (OSA) which is associated with neurocognitive dysfunction and behaviour problems. The aim of our study was to evaluate the effect of early OSA treatment in infants with DS on neurocognitive development and behaviour. In this prospective, interventional, non-randomised study, 40 infants with DS underwent polysomnography (PSG) every 6 months in room air between 6 and 36 months of age (Screened Group) and were compared to a control group of 40 infants with DS receiving standard of care and a single, systematic PSG in room air at 36 months of age (Standard Care Group). When present, OSA was treated. The primary endpoint was the total score of the Griffiths Scales of Child Development, Third Edition (Griffiths III) and its subscores at 36 months. Secondary endpoints included a battery of neurocognitive and behaviour questionnaires, and PSG outcomes. On the Griffiths III, the total score was significantly higher in the Screened Group compared to the Standard Care Group (difference: 4.1; 95%CI: 1.3; 7.6; p=0.009). Results in Griffiths III subscores and secondary endpoints were in support of better neurocognitive outcomes in the Screened Group compared with the Standard Care Group. At 36 months, median (Q1; Q3) apnoea-hypopnea index was higher in the Standard Care Group (4.0 [1.5; 9.0] events/hour) compared to the Screened Group (1.0 [1.0; 3.0] events/hour, p=0.006). Moderate and severe OSA were more frequent in the Standard Care Group as compared to the Screened Group (18.9% versus 3.7% for moderate OSA and 27.0% versus 7.4% for severe OSA). Early diagnosis and treatment of OSA in infants with DS may contribute to a significantly better neurocognitive outcome and behaviour at the age of 36 months. The study was funded by the Jérôme Lejeune Foundation.

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.

A Survey Study of the 3D Facial Landmark Detection Techniques Used as a Screening Tool for Diagnosis of the Obstructive Sleep Apnea Syndrome.

Obstructive Sleep Apnea (OSA) is a common disorder affecting both adults and children. It is characterized by repeated episodes of apnea (stopped breathing) and hypopnea (reduced breathing), which result in intermittent hypoxia. We recognize pediatric and adult OSA, and this paper focuses on pediatric OSA. While adults often suffer from daytime sleepiness, children are more likely to develop behavioral abnormalities. Early diagnosis and treatment are important to prevent negative effects on children's development. Without the treatment, children may be at increased risk of developing high blood pressure or other heart problems. The gold standard for OSA diagnosis is the polysomnography (sleep study) PSG performed at a sleep center. Not only is it an expensive procedure, but it can also be very stressful, especially for children. Patients have to stay at the sleep center during the night. Therefore, screening tools are very important. Multiple studies have shown that OSA screening tools can be based on facial anatomical landmarks. Anatomical landmarks are landmarks located at specific anatomical locations. For the purpose of the screening tool, a specific list of anatomical locations needs to be identified. We are presenting a survey study of the automatic identification of these landmarks on 3D scans of the patient's head. We are considering and comparing both knowledge-based and AI-based identification techniques, with a focus on the development of the automatic OSA screening tool.

Association between adherence to life’s simple 7 metrics and risk of obstructive sleep apnea among adults in the United States

BackgroundWe aimed to explore the impact of adherence to Life’s Simple 7 (LS7) metrics on risk of obstructive sleep apnea (OSA), and the impact of inflammation on the association, in adults in the United States.MethodsData from 13,825 community-dwelling adults aged ≥ 20 years recruited in the National Health and Nutrition Examination Surveys (NHANES) 2005–2008, 2015–2018 was analyzed. The LS7 score was calculated based on the AHA definition of LS7 metrics. The diagnosis of OSA was based on self-reported symptoms of sleep disturbance using a standard questionnaire. The Multivariable Apnea Prediction (MAP) Index score was also calculated to assess the risk of OSA. Log-binominal regression and negative binomial regression were performed to estimate the associations between LS7 and OSA and MAP index, with odds ratios (ORs) and prevalence ratios (PRs) and their 95% confidence intervals (CIs) calculated. Mediation analysis was performed to estimate the mediating effects of inflammatory indicators on the associations.ResultsA total of 4473 participants (32.4%) had OSA, and the mean MAP index was 0.39. In fully adjusted log-binominal regression models, with total score < 6 as the reference, the ORs (95% CIs) for risk of OSA were 0.90 (0.73, 1.10), 0.76 (0.65, 0.89), 0.78 (0.64, 0.95), and 0.45 (0.38, 0.54) for total score = 6, total score = 7, total score = 8, and total score > 8, respectively (P for trend < 0.001). When LS7 score was analyzed as a continuous variable, each 1-point increase in LS7 score was associated with a 15% decrease in OSA risk (P < 0.001). In negative binominal regression models, the adjusted PRs (95% CIs) for the MAP index were 0.93 (0.90, 0.97), 0.87 (0.84, 0.91), 0.80 (0.77, 0.84), and 0.55 (0.53, 0.57) for total score = 6, total score = 7, total score = 8, and total score > 8, respectively (P for trend < 0.001). For each 1-point increase in LS7 score, the risk of OSA decreased by 13% (P < 0.001). Consistent results were observed in subgroup analysis. Mediation analysis indicated that inflammatory factors, including blood cell count, neutrophil count, and C-reactive protein, positively mediated the association of LS7 with OSA, with a mediation proportion of 0.022 (P = 0.04), 0.02 (P = 0.04), and 0.02 (P = 0.02), respectively.ConclusionsIn a nationally representative sample of US adults, adherence to LS7 metrics was independently associated with reduced OSA risk. Inflammation plays a mediating role in the association between LS7 and OSA.

OSA diagnosis goes wearable: are the latest devices ready to shine?

Since 2019, the FDA has cleared nine novel obstructive sleep apnea (OSA)-detecting wearables for home sleep apnea testing, with many now commercially available for sleep clinicians to integrate into their clinical practices. To help clinicians comprehend these devices and their functionalities, we meticulously reviewed their operating mechanisms, sensors, algorithms, data output, and related performance evaluation literature. We collected information from PubMed, FDA clearance documents, ClinicalTrial.gov, and web sources, with direct industry input whenever feasible. In this "device-centered" review, we broadly categorized these wearables into two main groups: those that primarily harness Photoplethysmography (PPG) data and those that do not. The former include the peripheral arterial tonometry (PAT)-based devices. The latter was further broken down into two key subgroups: acoustic-based and respiratory effort-based devices. We provided a performance evaluation literature review and objectively compared device-derived metrics and specifications pertinent to sleep clinicians. Detailed demographics of study populations, exclusion criteria, and pivotal statistical analyses of the key validation studies are summarized. In the foreseeable future, these novel OSA-detecting wearables may emerge as primary diagnostic tools for patients at risk for moderate-to-severe OSA without significant comorbidities. While more devices are anticipated to join this category, there remains a critical need for cross-device comparison studies as well as independent performance evaluation and outcome research in diverse populations. Now is the moment for sleep clinicians to immerse themselves in understanding these emerging tools to ensure our patient-centered care is improved through the appropriate implementation and utilization of these novel sleep technologies.

Accuracy of respiratory event indices downloaded from positive airway pressure devices: can they be relied upon when making treatment decisions?

The gold standard for diagnosis and treatment of obstructive sleep apnea (OSA) is attended overnight polysomnography (PSG) in a sleep laboratory. Alternative diagnostic techniques are urgently needed for both diagnosis and treatment control when initiating positive airway pressure (PAP) in the home setting. Current PAP devices compute data such as residual AHI (apnea-hypopnea index) using their proprietary algorithms to provide an automatic event detection (AED). It was suggested that these should be labeled AHIFLOW. The current study situation shows an acceptable agreement with the tendency of mildly overestimating the AHI by the PAP devices. But, the literature is still limited in terms of accuracy, especially when considering the lack of standardization and different detection algorithms. A validation of PAP devices regarding the agreement of AHIFLOW compared to AHIPSG is extremely important, given that the device analysis is exclusively based on the measured flow. There are considerable concerns about assessing the PAP effectiveness in higher risk groups based on the AED function alone. Given these reservations, the use of PAP event detection can be an important addition to the diagnostic spectrum and, in combination with telemonitoring, offers promising potential.

A novel deep learning model for obstructive sleep apnea diagnosis: hybrid CNN-Transformer approach for radar-based detection of apnea-hypopnea events.

The demand for cost-effective and accessible alternatives to polysomnography (PSG), the conventional diagnostic method for obstructive sleep apnea (OSA), has surged. In this study, we have developed and validated a deep learning model for detecting apnea-hypopnea events using radar data. We conducted a single-center prospective cohort study, dividing participants with suspected sleep-disordered breathing into development and temporally independent test sets. Utilizing a hybrid CNN-Transformer architecture, we performed 5-fold cross-validation on the development set to develop and subsequently validate the model. Evaluation metrics included sensitivity for event detection, mean absolute error (MAE), intraclass correlation coefficient (ICC), and Pearson correlation coefficient (r) for apnea-hypopnea index (AHI) estimation. Linearly weighted kappa statistics (κ) assessed OSA severity. The development set comprised 54 participants (July 2021-May 2022), while the test set included 35 participants (June 2022-June 2023). In the test set, our model achieved an event detection sensitivity of 67.2% (95% CI: 65.8%, 68.5%) and demonstrated a MAE of 7.54 (95% CI: 5.36, 9.72), indicating good agreement (ICC = 0.889 [95% CI: 0.792, 0.942]) and a strong correlation (r = 0.892 [95% CI: 0.795, 0.945]) with the ground truth for AHI estimation. Furthermore, OSA severity estimation showed substantial agreement (κ = 0.780 [95% CI: 0.658, 0.903]). Our study highlights radar sensors and advanced AI models' potential to improve OSA diagnosis, paving the path for future radar-based diagnostic models in sleep medicine research.

Is there an association between serum 25-hydroxyvitamin D concentrations and obstructive sleep apnoea? A cross-sectional analysis of NHANES 2007–2008 data

ObjectivesThe study aimed to investigate the relationship between serum 25-hydroxyvitamin D (25(OH)D) concentrations and obstructive sleep apnoea (OSA) and to assess the confounding effect of body mass index (BMI) on...

Obstructive Sleep Apnea, Platelet Aggregation, and Cardiovascular Risk.

Although related, the precise mechanisms linking obstructive sleep apnea (OSA) and cardiovascular disease (CVD) are unclear. Platelets are mediators of CVD risk and thrombosis and prior studies suggested associations of OSA and platelet activity. The aim of this study is to assess the link between OSA, platelet activity, and CVD-related risk factors. We studied the association of OSA-measures and platelet aggregation in participants dually enrolled in the SHHS (Sleep Heart and Health Study) and FHS (Framingham Heart Study). We applied linear regression models with adjustment for demographic and clinical covariates and explored interactions with OSA and CVD-related factors, including age, sex, body mass index, hypertension, OSA diagnosis (apnea-hypopnea index 4%≥5), and aspirin use. Our final sample was of 482 participants (60 years [14.00], 50.4% female). No associations were observed between apnea-hypopnea index 4% and platelet aggregation in the main sample. Stratified analysis revealed an association in aspirin users (n=65) for our primary exposure (apnea-hypopnea index 4%, β=0.523; P<0.001; n=65), and secondary exposures: hypoxic burden (β=0.358; P<0.001), minimum saturation (β=-0.519; P=0.026), and oxygen desaturation index 3% (β=74.672; P=0.002). No associations were detected in nonaspirin users (n=417). No associations were detected between OSA and platelet aggregation in a community sample. Our finding that OSA associates with increased platelet aggregation in the aspirin group, most of whom use it for primary prevention of CVD, suggests that platelet aggregation may mediate the adverse impact of OSA on vascular health in individuals with existing CVD risk, supporting further investigation.

Are Depressive Symptoms in Obstructive Sleep Apnea Attributable to a Syndrome of Dysregulation of Rhythms and Hyperactivity (DYMERS)?

Background: Obstructive sleep apnea (OSA) is characterized by repeated airway obstructions during sleep, causing hypopnea, apnea, intermittent hypoxia, and sleep fragmentation. The severity of OSA is measured using the apnea-hypopnea index (AHI), with AHI ≥ 5 indicating OSA. This study aims to assess the frequency and type of depressive disorder characteristics of OSA patients and to evaluate the impact on quality of life, also considering the presence of hyperactivity. Methods: A case-control study using OSA patients referred to Cagliari's sleep disorder center. Controls were matched by age and sex from community databases. OSA diagnoses were made with AHI > 15. Depressive episodes were identified using BDI-SF, and H-QoL (Health related Quality of Life) was measured with the SF-12, focusing on item 10 for hyper-energy. Results: The clinical sample (n = 25) had a higher frequency of depressive episodes (36%) compared to controls (7% and 4%). Depressed OSA patients had worse H-QoL and higher hyper-energy scores, but the additional burden from depression was relatively low. Conclusions: The OSA sample has a higher frequency of depressive episodes compared to the general population. Depressive episodes in OSA patients are linked to higher scores on item 10 of the SF-12, indicating hyper-energy despite lower overall quality of life scores. While OSA significantly impacts quality of life, the additional burden from depression is less severe than in other chronic diseases. These findings suggest that depressive episodes in OSA may be related to rhythm dysregulation and hyperactivity (DYMERS).

Screening of preoperative obstructive sleep apnea by cardiopulmonary coupling and its risk factors in patients with plans to receive surgery under general anesthesia: a cross-sectional study.

Preoperative obstructive sleep apnea (OSA) is supposed to be the abnormally high occurrence of OSA the night before surgery under general anesthesia. This study aimed to evaluate the prevalence preoperative OSA using cardiopulmonary coupling (CPC) and its correlation with imbalance of sympathetic/parasympathetic nervous system. A total of 550 patients with plans to receive surgery under general anesthesia were enrolled. All patients were assigned to wear CPC on the night before surgery until the next day. Sleep quality characteristics, heart rate variation parameters, and apnea-hypopnea index were acquired. The diagnosis of pre-existing OSA was not considered in the current study. According to apnea-hypopnea index, 28.4%, 32.2%, 26.2%, and 13.3% patients were assessed as no, mild, moderate, and severe operative OSA, respectively. Multivariate logistic regression model revealed that higher age [p < 0.001, odds ratio (OR) = 1.043] was independently and positively associated with preoperative OSA; heart rate variation parameters representing the imbalance of sympathetic/parasympathetic nervous system, such as higher low-frequency (p < 0.001, OR = 1.004), higher low-frequency/high-frequency ratio (p = 0.028, OR = 1.738), lower NN20 count divided by the total number of all NN intervals (pNN20; p < 0.001, OR = 0.950), and lower high-frequency (p < 0.001, OR = 0.998), showed independent relationships with a higher probability of preoperative OSA. Higher age (p = 0.005, OR = 1.024), higher very-low-frequency (p < 0.001, OR = 1.001), and higher low-frequency/high-frequency ratio (p = 0.003, OR = 1.655) were associated with a higher probability of moderate-to-severe preoperative OSA, but higher pNN10 (p < 0.001, OR = 0.951) was associated with a lower probability of moderate-to-severe preoperative OSA. Preoperative OSA is prevalent. Higher age and imbalance of sympathetic/parasympathetic nervous system are independently and positively associated with a higher occurrence of preoperative OSA. CPC screening may promote the management of preoperative OSA.

Prevalence and regional distribution of obstructive sleep apnea in Canada: Analysis from the Canadian Longitudinal Study on Aging.

Obstructive sleep apnea (OSA) is a common chronic condition that is often undiagnosed or diagnosed after many years of symptoms and has an impact on quality of life and several health factors. We estimated the Canadian national prevalence of OSA using a validated questionnaire and physical measurements in participants in the Canadian Longitudinal Study on Aging (CLSA). The method used individual risk estimation based upon the validated STOP-BANG scale developed for OSA. This stratified population sample spans Canada to provide regional estimates. In this sample of adults aged 45 to 85years old, the overall prevalence in 2015 of combined moderate and severe OSA in the 51,337 participants was 28.1% (95% confidence intervals, 27.8‒28.4). The regional prevalence varied statistically between Atlantic Canada and Western Canada (p < 0.001), although clinically the variations were limited. The provincial prevalence for moderate and severe OSA ranged from 27.5% (New Brunswick and British Columbia) to 29.1% (Manitoba). Body mass index (BMI) was the dominant determinant of the variance between provinces (β = 0.33, p < 0.001). Only 1.2% of participants had a clinical diagnosis of OSA. The great majority (92.9%) of the participants at high risk of OSA were unrecognized and had no clinical diagnosis of OSA.

Is video demonstration for type III sleep studies performance worse than technical face-to-face teaching?

Obstructive sleep apnea (OSA) is the most common sleep-disordered breathing. The high prevalence makes its diagnosis a priority. To perform type III sleep studies, patients usually receive instructions from a technician. The switch to instructions through a video could save professionals time and make OSA diagnosis more accessible. This study aimed to compare the technical quality of type III sleep studies when instructions are provided by face-to-face technical teaching or via video. One hundred consecutive patients aged ≥ 18 years with suspected OSA were randomly assigned to receive device placement instructions in person by a technician or through video (50 in each group). The overall quality of the sleep studies was analyzed by determining the number of technically invalid studies. The recording quality of four sensors (pulse oximeter, nasal flow cannula, chest, and abdominal bands) was evaluated by checking for signal artifacts. The majority (86%) of the studies were valid. 20% of the studies in the face-to-face group and 8% of the studies in the video-instruction group were technically invalid, but no statistically significant difference was found (p = 0.148). The quality of the oximetry signal was better in those who received video instructions (p = 0.05). Regarding the recording quality of the remaining sensors, no significant differences were found. Type III sleep studies with previous explanation through a video are as effective as those with an explanation performed by a technician, with associated advantages, without increased errors. The quality of the oximetry signal was better in the video group, a critical signal for OSA diagnosis.

Obstructive Sleep Apnea and Short-Term Outcomes After Pediatric Cardiac Surgery.

Obstructive sleep apnea (OSA) has been associated with negative cardiovascular effects and negative outcomes following cardiovascular surgery in the adult population. Our objective was to evaluate if there is a similar association in children. Single center retrospective matched cohort study, we collected data on patients aged 1-18 who were admitted to the cardiac intensive care unit (CICU) between Jan 2012 and Dec 2021. We used a 3:1 propensity score matching for cases not diagnosed with OSA. Primary outcome was a composite variable: "adverse outcome," consisting of prolonged hospital and CICU stay, prolonged duration of mechanical ventilation, need for extracorporeal membrane oxygenation, and death. The study comprised 80 patients diagnosed with OSA and 240 patients without a diagnosis of OSA before cardiac surgery. The median age was 5.3years (IQR 2.7-11.2). There were 184 (57.5%) males, and 102 (31.9%) had chromosomal and genetic abnormalities. There was a difference in "adverse outcome" between the OSA and non-OSA groups [34 (42.50%) vs 68 (28.33%), p = 0.027]. Moreover, bivariate analysis revealed that CICU length of stay, and chromosomal anomalies were statistically different between the groups. By logistic regression the composite variable "adverse outcome" remained associated to the OSA group (p = 0.009) with an adjusted odds ratio (OR) of 4.09 (1.83-9.18), p < 0.001. Children diagnosed with OSA had a higher risk of "adverse outcome" following cardiac surgery. The risk disappeared if the patient had Tonsillectomy and Adenoidectomy before cardiac surgery. Further studies should explore a proactive treatment for OSA in pediatric patients who need cardiac surgery.

MFS-DBF: A trustworthy multichannel feature sieve and decision boundary formulation system for Obstructive Sleep Apnea detection

The fine identification of sleep apnea events is instrumental in Obstructive Sleep Apnea (OSA) diagnosis. The development of sleep apnea event detection algorithms based on polysomnography is becoming a research hotspot in medical signal processing. In this paper, we propose an Inverse-Projection based Visualization System (IPVS) for sleep apnea event detection algorithms. The IPVS consists of a feature dimensionality reduction module and a feature reconstruction module. First, features of blood oxygen saturation and nasal airflow are extracted and used as input data for event analysis. Then, visual analysis is conducted on the feature distribution for apnea events. Next, dimensionality reduction and reconstruction methods are combined to achieve the dynamic visualization of sleep apnea event feature sets and the visual analysis of classifier decision boundaries. Moreover, the decision-making consistency is explored for various sleep apnea event detection classifiers, which provides researchers and users with an intuitive understanding of the detection algorithm. We applied the IPVS to an OSA detection algorithm with an accuracy of 84% and a diagnostic accuracy of 92% on a publicly available dataset. The experimental results show that the consistency between our visualization results and prior medical knowledge provides strong evidence for the practicality of the proposed system. For clinical practice, the IPVS can guide users to focus on samples with higher uncertainty presented by the OSA detection algorithm, reducing the workload and improving the efficiency of clinical diagnosis, which in turn increases the value of trust.

Evaluation of serum salusin-α and β levels in patients with obstructive sleep apnea.

Aim: Salusin-α and salusin-β peptides are crucial in the development of cardiovascular diseases like coronary artery disease (CAD). This study compared serum levels of these peptides in patients with obstructive sleep apnea (OSA), those with both OSA and CAD.Materials & methods: Patients without OSA were included in Group 1, those with OSA alone comprised Group 2, and those with OSA and CAD were in Group 3.Results: Salusin-α level was significantly higher in controls than in Groups 2 and 3, while salusin-β levels were significantly higher in Groups 2 and 3 compared with the control group.Conclusion: Salusin-α and -β levels may be parameters that can guide the diagnosis of OSA in patients with a consistent clinical history.

Demographics of Adults With Obstructive Sleep Apnea Who Undergo Nasal Surgery.

To assess the demographic characteristics between adult obstructive sleep apnea (OSA) patients who did and did not undergo nasal surgery (NS). Retrospective cohort study. Kaiser Permanente Northern California clinical database. Retrospective study of adult patients with ≥1 OSA diagnoses linked to clinical encounters from 2009 to 2016. Qualifying NS procedures performed on or after cohort entry through 2017 were ascertained. Demographic and clinical characteristics were compared; multivariable logistic regression examined associations of these characteristics with undergoing NS. A total of 174,821 patients had an OSA diagnosis. Among these, 3518 (2.0%) underwent NS, including septoplasty (61.9%), sinus-related (12.9%), turbinate (14.2%), and rhinoplasty (11.1%) procedures. Compared to the nonsurgery group, NS patients were more likely to be male (75.5% vs 62.1%), younger (48.2 ± 13.0 vs 54.7 ± 14.1), have lower body mass index (31.8 ± 6.4 vs 34.3 ± 8.1), and no comorbid conditions (63.1% vs 53.5%), P < .001. After adjusting for sex, age, body mass index (BMI), neighborhood deprivation, and comorbidities, black and Asian/Pacific Islander adults with OSA had 42% and 46% decreased odds of undergoing NS compared with non-Hispanic white patients (odds ratio, OR [95% confidence interval, CI]: 0.58 [0.50-0.67] and 0.54 [0.49-0.61]), while Hispanic patients had similar odds (OR [95% CI]: 1.02 [0.93-1.12]). Patients living in neighborhoods of highest deprivation had 18% lower odds of undergoing NS, compared with patients from neighborhoods corresponding to areas of lowest deprivation (adjusted odds ratio [95% CI]: 0.82 [0.75-0.91]). These findings suggest that younger age, male sex, lower BMI, and higher SES may be associated with a higher likelihood of undergoing NS in OSA patients.

Persistent Obstructive Sleep Apnea Post-adenotonsillectomy in Children.

Background Childhood obstructive sleep apnea (OSA) is a common disorder in children mostly due to adenotonsillar hypertrophy. Therefore, adenotonsillectomy is the mainstay of treatment. However, the outcome of adenotonsillectomy is limited in some patients who develop persistent OSA (POSA). We aim to evaluate the prevalence, risk factors, and treatments of POSA in the pediatric population in Qatar. Methodology This is a retrospective review of medical electronic records of patients aged 1-18 years, who underwent adenoidectomy and/or tonsillectomy at Sidra Medicine (Doha, Qatar) between June 2017 and September 2022. Demographic, clinical, and polysomnography (PSG) data were collected. POSA was defined as the persistence of at least one of the following OSA symptoms: snoring, gasping, mouth breathing or witnessed sleep apnea during post-surgery clinic visits, and/or post-surgical diagnosis of OSA by PSG. The prevalence of POSA was defined as the number of patients who had persistent symptoms divided by patients who were followed at outpatient clinics (ENT/pulmonology) post-surgery. Risk factors for POSA were evaluated using multivariate regression analysis. Results A total of 410 patients (259 males and 151 females) underwent adenotonsillectomy during the study period. The average age at surgery was 3.6 ± 2.5 years. The majority of patients (85.9%) had no history of underlying medical conditions. The rest of the patients (14.1%) were diagnosed with chromosomal abnormalities or neuromuscular disorders. All patients (100%) had a history of snoring before surgery, and 32.4% of patients had a history of witnessed sleep apnea. A total of 52 patients had persistent symptoms four months post-surgery. POSA prevalence was estimated at 15.4%. Univariate analysis showed young age at the time of surgery (p = 0.015), history of asthma (23%, 12/52) (p = 0.002), allergic rhinitis (13%, 7/52) (p = 0.001), gastroesophageal reflux disease ((11%, 6/52) (p < 0.001), and genetic syndromes (17%, 9/52) (p < 0.005) as significant risk factors for POSA. Multiple regression analysis showed that syndromic disorders and allergic rhinitis were significantly correlated with persistent OSA (p = 0.021 and p = 0.000, respectively). Conclusions POSA is prevalent in children post-tonsillectomy and adenoidectomy, especially in patients with genetic syndromes and those with symptoms of allergic rhinitis. Future studies are needed to better define the condition and provide evidence-based diagnostic and therapeutic approaches.