Obstructive Sleep Apnea Screening Research Articles

Development and application of a machine learning-based predictive model for obstructive sleep apnea screening

ObjectiveTo develop a robust machine learning prediction model for the automatic screening and diagnosis of obstructive sleep apnea (OSA) using five advanced algorithms, namely Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF) to provide substantial support for early clinical diagnosis and intervention.MethodsWe conducted a retrospective analysis of clinical data from 439 patients who underwent polysomnography at the Affiliated Hospital of Xuzhou Medical University between October 2019 and October 2022. Predictor variables such as demographic information [age, sex, height, weight, body mass index (BMI)], medical history, and Epworth Sleepiness Scale (ESS) were used. Univariate analysis was used to identify variables with significant differences, and the dataset was then divided into training and validation sets in a 4:1 ratio. The training set was established to predict OSA severity grading. The validation set was used to assess model performance using the area under the curve (AUC). Additionally, a separate analysis was conducted, categorizing the normal population as one group and patients with moderate-to-severe OSA as another. The same univariate analysis was applied, and the dataset was divided into training and validation sets in a 4:1 ratio. The training set was used to build a prediction model for screening moderate-to-severe OSA, while the validation set was used to verify the model's performance.ResultsAmong the four groups, the LightGBM model outperformed others, with the top five feature importance rankings of ESS total score, BMI, sex, hypertension, and gastroesophageal reflux (GERD), where Age, ESS total score and BMI played the most significant roles. In the dichotomous model, RF is the best performer of the five models respectively. The top five ranked feature importance of the best-performing RF models were ESS total score, BMI, GERD, age and Dry mouth, with ESS total score and BMI being particularly pivotal.ConclusionMachine learning-based prediction models for OSA disease grading and screening prove instrumental in the early identification of patients with moderate-to-severe OSA, revealing pertinent risk factors and facilitating timely interventions to counter pathological changes induced by OSA. Notably, ESS total score and BMI emerge as the most critical features for predicting OSA, emphasizing their significance in clinical assessments. The dataset will be publicly available on my Github.

Clinical characteristics of non-sleepy obstructive sleep apnea patients: a study in a tertiary care sleep clinic in India.

Obstructive sleep apnea (OSA) encompasses a diverse population, manifesting with or without symptoms of excessive daytime sleepiness. There is contention surrounding the significance of non-sleepy OSA within clinical contexts and whether routine treatment is warranted. This study aims to evaluate epidemiological and clinical distinctions between sleepy and non-sleepy OSA patients. A retrospective analysis was conducted on consecutive patients undergoing polysomnography for OSA assessment at tertiary care hospitals between 2018 and 2023. For 176 of 250 patients, complete polysomnography records with OSA diagnoses were available. Non-sleepy OSA was defined when a patient had an Epworth sleepiness scale score <10 and polysomnography demonstrated an apnea hypopnea index ≥5/hour. Non-sleepy OSA patients were matched with sleepy OSA patients in terms of age and gender distribution (mean age 51.24±13.25 years versus 50.9±10.87 years, male 70.4% versus 73.3%). The sensitivity of STOP-BANG≥3 for the non-sleepy OSA group was 87.7%, 89.3%, and 95.2% for any OSA severity, moderate to severe OSA, and severe OSA, respectively, while the corresponding sensitivity for the sleepy OSA group was 96.5%, 98.6%, and 100% for any OSA severity, moderate to severe OSA, and severe OSA, respectively. A novel symptom scoring tool, HASSUN (hypertension, nocturnal apneas, snoring, sleep disturbance, unrefreshing sleep, and nocturia), demonstrated a sensitivity of over 90% for all severity categories of OSA in both non-sleepy and sleepy OSA groups. The prevalence of cardiovascular and metabolic comorbidities did not significantly differ between non-sleepy and sleepy OSA patients. The physiological parameters, including forced vital capacity (FVC), forced expiratory volume in one second (FEV1), FEV1/FVC ratio, arterial partial pressure of oxygen, and bicarbonate at baseline, were comparable between the two groups. To conclude, non-sleepy OSA patients are less obese, exhibit fewer symptoms, and have less severe OSA in comparison to sleepy OSA. Non-sleepy OSA patients display a similar likelihood of cardiovascular and metabolic comorbidities compared to sleepy OSA patients. Further investigations are warranted to elucidate the mechanisms underlying cardiovascular metabolic comorbidities in non-sleepy OSA patients. The proposed HASSUN scoring tool for non-sleepy OSA screening necessitates validation in future studies.

Obstructive sleep apnea and acute decompensated heart failure

Abstract Funding Acknowledgements None. Background Acute decompensated heart failure is a condition with a high frequency of hospitalizations and mortality. Screening and treatment for obstructive sleep apnea can be helpful in these patients. Purpose To determine the frequency of obstructive sleep apnea in patients with acute decompensated heart failure. To add CPAP home therapy to standard medical treatment for heart failure. To follow patients for 1 year. Methods We conducted a single-centre, prospective cohort study form 150 consecutive patients hospitalized for exacerbation heart failure in the cardiology department. 81 patients meet inclusion criteria – Apnea-Hypopnea index (AHI) &amp;gt; 5, Epworth Sleepiness Scale (ESS) &amp;gt; 8, NYHA class II/III and NTproBNP&amp;gt;900 pg/ml. Patients with OSA were offered to purchase a CPAP device for the home. They were divided into two groups with CPAP and no-CPAP. All patients receiving optimal medical treatment. The follow-up period was 1 year. At the end of the period, patients underwent a physical examination, an echocardiography, and ESS. In the non-CPAP group, 29 patients were followed up and 13 patients dropped out due to mortality. In the CPAP group, 16 patients were followed up, with 1 dropped out. Sleep apnea screening was performed with ApneaLinkTM. Echocardiographic assessment of left ventricular ejection fraction (LVEF) and the E/e‘m ratio. Results From 81 with sleep apnea 72.8% (n=59) has obstructive sleep apnea (OSA) and 27.2% (n=22) has central sleep apnea (CSA). Of the 59 patients with OSA, 17 patients (28.8%) purchased a device and continued therapy at home, while 42 patients (71.2%)on optimal drug therapy without CPAP. There is a statistically significant difference in BMI in the group with CPAP compared to the no-CPAP group at the beginning and at the end of the follow-up (CPAP group at the start 36.47±7.3 vs. the end 33.3±5.7, p=0.001 and no-CPAP group at the start 36.35±7.34 vs. the end 35.74±7.46 , p=0.356). A similar relationship is found in systolic blood pressure(SBP) and diastolic blood pressure(DBP). SBP in CPAP group at start 136.2±6.2 mmHg vs. end 123.1±5.9 mmHg; р&amp;lt;0.001. relative to no-CPAP group – start 131.2±9.8 mmHg vs. end 131.3±7.7 mmHg; р=0.965. DBP in CPAP group at start 85.9±6.7 mmHg vs. end 77.3±5.3 mmHg; р&amp;lt;0.001 and no-CPAP group start 82.1±7.4 mmHg vs. end 81.7±5.7 mmHg, р=0.644. There is a significant change in LVEF between the two groups at the beginning and at the end of the follow-up (in CPAP group at start 47.5±8.7, vs. at end 50.07±7.5; р=0.005, between no-CPAP at start 52.5±6.9 vs. at end 52.2±7.1; р=0.389). The CPAP group had a median survival of 11.7 months vs. 10.1 months in no CPAP group(Log Rank (Mental-Cox) p=0.044 ). Conclusion Obstructive sleep apnea is a common comorbidity in patients with acute decompensated heart failure. The addition of CPAP therapy in these patients improved BMI, SBP, DBP and increases LVEF. All this improves the prognosis for these patients.

0522 Screening and Diagnosis of Obstructive Sleep Apnea in Middle Eastern/North African (MENA) Women: A Pilot Study

0522 Screening and Diagnosis of Obstructive Sleep Apnea in Middle Eastern/North African (MENA) Women: A Pilot Study

Sleep studies in obese children with obstructive sleep apnea: Pulse oximetry as a diagnostic tool.

Obstructive sleep apnea is associated with many co-morbidities in children and young people. Obesity has long been recognised as an important risk factor associated with obstructive sleep apnea. Currently, polysomnography is regarded as the gold-standard diagnostic tool for obstructive sleep apnea. The validity of oximetry as a screening and diagnostic tool for obstructive sleep apnea has been the subject of several studies in the literature. There is little published evidence on the use of oximetry in children with obesity. This study assesses whether oximetry is a reliable diagnostic tool for obstructive sleep apnea in obese children. We reviewed the medical records of obese children with a suspected diagnosis of obstructive sleep apnea who underwent oximetry and cardiorespiratory polygraphy or full polysomnography at Sheffield Children's Hospital between January 2010 and March 2022. We compared oximetry results with the apnea-hypopnea index from cardiorespiratory polygraphy or full polysomnography for each patient. A total of 60 patient records were included in the analysis. The sensitivity of oximetry in diagnosing obstructive sleep apnea was 70.9%, with a specificity of 65.5% and a positive predictive value of 68.75%. In the subgroup of subjects with severe obstructive sleep apnea (apnea-hypopnea index > 10), oximetry had a sensitivity of 87.5%. We concluded that oximetry could be a helpful initial diagnostic tool for obstructive sleep apnea in obese children, but is not entirely reliable. A negative oximetry result in a symptomatic individual should prompt a referral for more detailed investigations, while a positive result can help in treatment decisions without needing a polysomnography.

Nocturnal Urinary Symptoms as Screening Tools for Obstructive Sleep Apnea.

Nocturnal lower urinary tract symptoms are part of obstructive sleep apnea (OSA), and urogynecology clinics may serve as OSA screening sites. This study's aim was to determine the accuracy of nocturia and nocturnal enuresis (NE) as screening tools for OSA in new patients at a urogynecology clinic. Using a retrospective study design, we gathered information regarding diagnostic OSA testing, continuous positive airflow pressure use, and lower urinary tract symptoms improvement from women in a urogynecology clinic who were previously screened for OSA using validated questionnaires. Nocturia and NE were tested for sensitivity and specificity using positive OSA diagnosis by polysomnography as the gold standard. Nocturia with a cutoff of ≥2 episodes per night had the best test characteristics-86.4% sensitivity (95% confidence interval [CI], 65.1-97.1) and 58.5% specificity (95% CI, 44.1-71.9) for an overall accuracy of 78.4% (95% CI, 67.0-89.8). Nocturnal enuresis with a cutoff of ≥1 episode per week had the best NE characteristics with 31.8% sensitivity (95% CI, 13.9-54.9) and 79.3% specificity (95% CI, 65.9-89.2) for an overall accuracy of 56.1% (95% CI, 41.2-71). Lower urinary tract symptoms such as nocturia and NE are routinely assessed in urogynecology clinics, making them useful for OSA screening and referral. The present study found nocturia symptoms with ≥2 episodes per night to retain acceptable test characteristics in screening for OSA, whereas NE was found to have less acceptable test characteristics for OSA screening. Urogynecology clinics may utilize nocturia symptoms in clinical decision making for OSA referral.

Parental experiences in navigating health services for paediatric residual obstructive sleep apnoea: An exploratory qualitative descriptive study.

To explore parents' experiences and perceptions regarding engagement in health services for managing residual paediatric obstructive sleep apnoea (OSA) across levels of care. Qualitative description guided study design. Data were collected through semi-structured interviews with parents of children diagnosed with residual OSA at a university-based sleep clinic. The relevant literature informed the interview guide and was piloted before data collection. Inductive, manifest content analysis was used to describe parents' perceptions and experiences using data-driven categories and sub-categories. Several strategies were employed to ensure rigour in this study. Eight interviews were conducted. Parents' views were organized into five categories: awareness of the sleep issue, interaction with non-sleep specialists, interaction with sleep specialists, interaction with dental professionals, and further actions and support. Parents reported several engagement issues due to their interactions with different care providers. These issues included having to personally identify and raise the sleep problem, feeling that care providers did not take this problem seriously, waiting for an extended period to be referred for sleep services, and receiving conflicting or insufficient treatment recommendations. Overall, parents perceived that their actions and the services received across levels of care were not effective enough to address paediatric OSA. Based on this exploratory qualitative descriptive research, along with developing evidence-based clinical guidelines for paediatric OSA screening and management tailored to different levels of care, strategies intended to improve the engagement of patients and care providers in addressing paediatric OSA should be developed and empirically tested.

Early recognition and treatment of OSA in hospitalized patients and its impact on health care utilization in rural population: a real-world study.

Obstructive sleep apnea (OSA) is a highly prevalent, yet under-diagnosed condition. Due to its adverse impact on risk for cardiopulmonary disorders, there is interest in pro-active screening of OSA in hospitalized patients. We studied the long-term outcome of such screened patients who were initiated on positive airway pressure (PAP) therapy. Hospitalized patients who screened positive for OSA and were confirmed with post-discharge polysomnography (PSG) were dichotomized by PAP adherence and followed for a period of 12 months to evaluate for the composite endpoint of hospital readmissions and emergency room (ED) visits for cardiopulmonary reasons. Cost analysis between the two groups was also conducted. 2042 hospitalized patients were assessed for OSA as part of a hospital sleep medicine program from August 2019 to June 2023. Of these, 293 patients were diagnosed with OSA and prescribed PAP therapy. Of these 293 patients, 108 were adherent to therapy and 185 were non-adherent. The overall characteristics of the groups included a mean (SD) age: 58 years (12.82), mean BMI (kg/m2): 39.72 (10.71), male sex: 57%, and apnea-hypopnea index (AHI): 25.49 (26). 78%, 41% and 43% had hypertension, congestive heart failure, and diabetes mellitus, respectively. Hospitalized patients diagnosed with OSA and adherent to therapy have reduced readmissions and ED visits for cardiopulmonary reasons 12 months after discharge. Adherent patients have reduced cost of health care and length of stay during hospitalizations.

A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators.

Obstructive sleep apnea (OSA) is a common and potentially fatal sleep disorder. The purpose of this study was to construct an objective and easy-to-promote model based on common clinical biochemical indicators and demographic data for OSA screening. The study collected the clinical data of patients who were referred to the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University from December 1, 2020, to July 31, 2023, including data for demographics, polysomnography (PSG), and 30 biochemical indicators. Univariate and multivariate analyses were performed to compare the differences between groups, and the Boruta method was used to analyze the importance of the predictors. We selected and compared 10 predictors using 4 machine learning algorithms which were "Gaussian Naive Bayes (GNB)", "Support Vector Machine (SVM)", "K Neighbors Classifier (KNN)", and "Logistic Regression (LR)". Finally, the optimal algorithm was selected to construct the final prediction model. Among all the predictors of OSA, body mass index (BMI) showed the best predictive efficacy with an area under the receiver operating characteristic curve (AUC) = 0.699; among the predictors of biochemical indicators, triglyceride-glucose (TyG) index represented the best predictive performance (AUC = 0.656). The LR algorithm outperformed the 4 established machine learning (ML) algorithms, with an AUC (F1 score) of 0.794 (0.841), 0.777 (0.827), and 0.732 (0.788) in the training, validation, and testing cohorts, respectively. We have constructed an efficient OSA screening tool. The introduction of biochemical indicators in ML-based prediction models can provide a reference for clinicians in determining whether patients with suspected OSA need PSG.

Automatic detection of obstructive sleep apnea based on speech or snoring sounds: a narrative review.

Obstructive sleep apnea (OSA) is a common chronic disorder characterized by repeated breathing pauses during sleep caused by upper airway narrowing or collapse. The gold standard for OSA diagnosis is the polysomnography test, which is time consuming, expensive, and invasive. In recent years, more cost-effective approaches for OSA detection based in predictive value of speech and snoring has emerged. In this paper, we offer a comprehensive summary of current research progress on the applications of speech or snoring sounds for the automatic detection of OSA and discuss the key challenges that need to be overcome for future research into this novel approach. PubMed, IEEE Xplore, and Web of Science databases were searched with related keywords. Literature published between 1989 and 2022 examining the potential of using speech or snoring sounds for automated OSA detection was reviewed. Speech and snoring sounds contain a large amount of information about OSA, and they have been extensively studied in the automatic screening of OSA. By importing features extracted from speech and snoring sounds into artificial intelligence models, clinicians can automatically screen for OSA. Features such as formant, linear prediction cepstral coefficients, mel-frequency cepstral coefficients, and artificial intelligence algorithms including support vector machines, Gaussian mixture model, and hidden Markov models have been extensively studied for the detection of OSA. Due to the significant advantages of noninvasive, low-cost, and contactless data collection, an automatic approach based on speech or snoring sounds seems to be a promising tool for the detection of OSA.

Assessment of the Epworth Sleepiness Scale, Pediatric Sleep Questionnaire, and Mallampati Score in Children With Obstructive Sleep Apnea: An Observational Study

Background and Objective Individuals diagnosed with obstructive sleep apnea (OSA) experience recurrent episodes of breathing cessation due to blockage of the upper airway during sleep. This study aimed to assess the Pediatric Sleep Questionnaire (PSQ) scores, Epworth Sleepiness Scale (ESS) scores, and Mallampati score in pediatric patients with OSA, compared with non-OSA.Methods An observational study was conducted on 100 pediatric female patients aged 9-13 years with retrognathic mandible. The participants were subjected to PSQ, ESS, and Mallampati score to assess the size and shape of the patient’s tongue and esophageal airway. Finally, polysomnographic examination was carried out for all the participants and they were divided into two groups: Group 1 (50 patients) who were diagnosed with OSA by polysomnography and Group 2 (50 patients) consisting of non-OSA patients. Chi-square and Mann–Whitney U test were applied to compare scores between the two groups.Results The mean PSQ, ESS, and Mallampati scores of participants were 0.78 ± 0.45, 9.86 ± 2.47, and 2.27 ± 0.98, respectively in Group 1 and 0.24 ± 0.03, 7.27 ± 2.39, and 1.32 ± 0.57, respectively in Group 2, and this difference was statistically significant (p &lt; 0.05). PSQ, ESS, and Mallampati scores had specificity of 100%, 95.5%, and 72.7%, respectively, whereas the sensitivity was 45.5%, 22.7%, and 63.6%, respectively. ESS scores ≥ 10, PSQ scores ≥ 0.3, and Mallampati scores ≥ 2 were indicative of OSA.Conclusions Patients with OSA having retrognathic mandible had higher PSQ, ESS, and Mallampati scores compared to healthy individuals without OSA. Mallampati score can be satisfactorily used for screening OSA patients.

Combining wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity.

The gold standard for diagnosing obstructive sleep apnea (OSA) is polysomnography (PSG). However, PSG is a time-consuming method with clinical limitations. This study aimed to create a wireless radar framework to screen the likelihood of two levels of OSA severity (i.e., moderate-to-severe and severe OSA) in accordance with clinical practice standards. We conducted a prospective, simultaneous study using the wireless radar system and PSG in a Northern Taiwan sleep center, involving 196 patients. The wireless radar sleep monitor, incorporating hybrid models such as deep neural decision trees, estimated the respiratory disturbance index relative to the total sleep time established by PSG (RDIPSG_TST), by analyzing continuous-wave signals indicative of breathing patterns. Analyses were performed to examine the correlation and agreement between the RDIPSG_TST and apnea-hypopnea index (AHI), results obtained through PSG. Cut-off thresholds for RDIPSG_TST were determined using Youden's index, and multiclass classification was performed, after which the results were compared. A strong correlation (ρ = 0.91) and agreement (average difference of 0.59 events/h) between AHI and RDIPSG_TST were identified. In terms of the agreement between the two devices, the average difference between PSG-based AHI and radar-based RDIPSG_TST was 0.59 events/h, while 187 out of 196 cases (95.41%) fell within the 95% confidence interval of differences. A moderate-to-severe OSA model achieved an accuracy of 90.3% (cut-off threshold for RDIPSG_TST: 19.2 events/h). A severe OSA model achieved an accuracy of 92.4% (cut-off threshold for RDIPSG_TST: 28.86 events/h). The mean accuracy of multiclass classification performance using these cut-off thresholds was 83.7%. The wireless-radar-based sleep monitoring device, with cut-off thresholds, can provide rapid OSA screening with acceptable accuracy, and also alleviate the burden on PSG capacity. However, to independently apply this framework, the function of determining the radar-based total sleep time requires further optimizations and verification in future work.

A pilot observation using ultrasonography and vowel articulation to investigate the influence of suspected obstructive sleep apnea on upper airway

Failure to employ suitable measures before administering full anesthesia to patients with obstructive sleep apnea (OSA) who are undergoing surgery may lead to developing complications after surgery. Therefore, it is very important to screen OSA before performing a surgery, which is currently done by subjective questionnaires such as STOP-Bang, Berlin scores. These questionnaires have 10–36% specificity in detecting sleep apnea, along with no information given on anatomy of upper airway, which is important for intubation. To address these challenges, we performed a pilot study to understand the utility of ultrasonography and vowel articulation in screening OSA. Our objective was to investigate the influence of OSA risk factors in vowel articulation through ultrasonography and acoustic features analysis. To accomplish this, we recruited 18 individuals with no risk of OSA and 13 individuals with high risk of OSA and asked them to utter vowels, such as /a/ (as in “Sah”), /e/ (as in “See”). An expert ultra-sonographer measured the parasagittal anterior–posterior (PAP) and transverse diameter of the upper airway. From the recorded vowel sounds, we extracted 106 features, including power, pitch, formant, and Mel frequency cepstral coefficients (MFCC). We analyzed the variation of the PAP diameters and vowel features from "See: /i/" to "Sah /a/" between control and OSA groups by two-way repeated measures ANOVA. We found that, there was a variation of upper airway diameter from “See” to “Sah” was significantly smaller in OSA group than control group (OSA: ∆12.8 ± 5.3 mm vs. control: ∆22.5 ± 3.9 mm OSA, p < 0.01). Moreover, we found several vowel features showed the exact same or opposite trend as PAP diameter variation, which led us to build a machine learning model to estimate PAP diameter from vowel features. We found a correlation coefficient of 0.75 between the estimated and measured PAP diameter after applying four estimation models and combining their output with a random forest model, which showed the feasibility of using acoustic features of vowel sounds to monitor upper airway diameter. Overall, this study has proven the concept that ultrasonography and vowel sounds analysis may be useful as an easily accessible imaging tool of upper airway.

Clinical and radiographic periodontal status in hypertensive patients with or without obstructive sleep apnea 10 years after diagnosis and CPAP initiation.

Through inflammation and hyposalivation, obstructive sleep apnea (OSA) is suggested to affect periodontal status over time. Our aim was to compare the clinical and radiographic periodontal status of hypertensive patients with or without long-term presence of OSA, treated or untreated with continuous positive airway pressure treatment (CPAP). In 2007-2009, a screening for OSA was conducted among 394 hypertensive primary care patients. Polygraphy was used to create three groups: no OSA, non-CPAP, or adherent CPAP based on the apnea hypopnea index (AHI). After 10 years, a cross-sectional sleep and periodontal examination including a clinical and radiographic examination, a questionnaire, and a matrix metalloproteinase-8 (MMP-8)chair-side test was conducted. Based on levels of alveolar bone, bleeding on probing (BoP),and probing pocket depth (PPD), patients were categorized into four periodontal stages: periodontal health/gingivitis and three periodontal disease stages. Periodontal status and periodontal stages were compared between the OSA (n = 49), non-CPAP (n = 38),or adherent CPAP (n = 34) groups. The 121 patients (53% women) had a median age of 71 years. No differences were seen between the OSA groups regarding median number of teeth (p = .061), teeth/implants, (p = .107), plaque index (p = .245), BoP (p = .848), PPD ≥ 4 mm (p = .561), PPD ≥ 6 mm (p = .630), presence of MMP-8 (p = .693) except for bone loss (p = .011). Among patients with stage periodontal health/gingivitis a significant difference was seen, as 70% of those were categorized as no OSA, 20% as non-CPAP, and 10% as adherent CPAP (p = .029). Differences were not seen in periodontal disease stages. Hypertensive patients with obstructive sleep apnea(OSA) did not have an adverse clinical periodontal status compared to patients without OSA. However, when combining radiographic and clinical status into periodontal stages, patients without OSA more frequently exhibited periodontal health or gingivitis compared to patients without OSA, regardless of CPAP treatment.

Obstructive sleep apnea screening protocol and safety measures: advancing treatment quality and reducing medical emergency team activation in patients with atrial fibrillation, respiratory diseases, and frailty.

Obstructive sleep apnea screening protocol and safety measures: advancing treatment quality and reducing medical emergency team activation in patients with atrial fibrillation, respiratory diseases, and frailty.

Symptom content analysis of OSA questionnaires: time to identify and improve relevance of diversity of OSA symptoms?

Obstructive sleep apnea (OSA) is a heterogeneous condition covering many clinical phenotypes in terms of the diversity of symptoms. Patient-based OSA screening questionnaires used in routine practice contain significantly varying contents that can impact the reliability and validity of the screening. We investigated to what extent common patient-based OSA screening questionnaires differ or overlap in their item content by conducting a rigorous, methodical, and quantified content overlap analysis. We conducted an item content analysis of 11 OSA screening questionnaires validated in adult populations and characterized their overlap using a four-step approach: i) selection of OSA screening questionnaires; ii) item extraction and selection; iii) extraction of symptoms from items; iv) assessment of content overlap with the Jaccard Index (from 0: no overlap to 1: full overlap). We extracted 72 items that provided 25 distinct symptoms from 11 selected OSA questionnaires. The overlap between them was weak (mean Jaccard Index 0.224, ranging from 0.138 to 0.329). All questionnaires contained symptoms of the "OSA symptom" dimension (e.g., snoring or witnessed apneas). The STOP-BANG (0.329) and the Berlin (0.280) questionnaires exhibited the highest overlap content. Ten symptoms (40%) were investigated in only one questionnaire. The heterogeneity of content and the low overlap across these questionnaires reflect the challenges of screening OSA. The different OSA questionnaires potentially capture varying aspects of the disorder, with the risk of biased results in studies. Suggestions are made for better OSA screening and refinement of clinical OSA phenotypes.

Gas exchange parameters for the prediction of obstructive sleep apnea in infants.

Sleep laboratory polysomnography (PSG) is the gold standard for obstructive sleep apnea (OSA) diagnosis in infants, but its access remains limited. Oximetry-capnography is another simple and widely used tool that can provide information on the presence of desaturations and alveolar hypoventilation. However, its reliability is debated. This study aimed at examining its use in determining OSA severity in infants. This retrospective study was conducted in a sleep unit in a tertiary hospital, in infants < 4 months old with clinical signs of OSA or Pierre Robin Sequence (PRS) who underwent a one-night PSG coupled with oximetry-capnography. Among the 78 infants included (median [IQR] age: 61 [45-89] days at PSG), 44 presented with PRS, and 34 presented with isolated airway obstruction. The clinical, sleep and respiratory characteristics were not significantly different between the two subgroups. In the entire cohort, 63.5% had severe OSA. Median OAHI was 14.5 events/h [7.4-5.9], SpO2 was 97.4% [96.5-98.1], and PtcCO2 was 41.1 mmHg [38.3-44.9]. The optimal threshold to predict OAHI > 10 events/h was 6 events/h for ODI3% (sensitivity 95.7%, specificity 51.9%) and 2 events/h for ODI4% (sensitivity 95.7%, specificity 48.1%). Whereas transcutaneous capnography does not appear to be sufficient in predicting severe OSA in infants < 4 months old with PRS or clinical signs of OSA, oximetry may be a useful alternative for the screening of severe OSA in infants in the absence of PSG.

Screening for obstructive sleep apnea (OSA) in children and adolescents with obesity: A scoping review of national and international pediatric obesity and pediatric OSA management guidelines.

Obstructive sleep apnea (OSA) is a prevalent complication that affects up to 60% of children and adolescents with obesity. It is associated with poorer cardiometabolic outcomes and neurocognitive deficits. Appropriate screening and intervention for OSA are crucial in the management of children with obesity. We performed a scoping review of international and national pediatric obesity (n = 30) and pediatric OSA (n = 10) management guidelines to evaluate the recommendations on OSA screening in pediatric obesity. Sixteen (53%) of the pediatric obesity guidelines had incorporated OSA screening to varying extents, with no consistent recommendations on when and how to screen for OSA, and subsequent management of OSA in children with obesity. We provide our recommendations that are based on the strength and certainty of evidence presented. These include a clinical-based screening for OSA in all children with body mass index (BMI) ≥ 85th percentile or those with rapid BMI gain (upward crossing of 2 BMI percentiles) and the use of overnight polysomnography to confirm the diagnosis of OSA in those with high clinical suspicion. We discuss further management of OSA unique to children with obesity. An appropriate screening strategy for OSA would facilitate timely intervention that has been shown to improve cardiometabolic and neurocognitive outcomes.

Real-world utility of overnight oximetry for the screening of obstructive sleep apnea in children

BackgroundObstructive sleep apnea (OSA) is a common problem in children and can result in developmental and cognitive complications if untreated. The gold-standard tool for diagnosis is polysomnography (PSG); however, it is an expensive and time-consuming test to undertake. Overnight oximetry has been suggested as a faster and cheaper initial test in comparison to PSG as it can be performed at home using limited, reusable equipment. AimThis retrospective case control study aims to evaluate the effectiveness of a home oximetry service (implemented in response to extended waiting times for routine PSG) in reducing the time between patient referral and treatment. MethodsPatients undergoing diagnostic sleep evaluation for suspected OSA who utilized the Queensland Children's Hospital screening home oximetry service in the first year since its inception in 2021 (n = 163) were compared to a historical group of patients who underwent PSG in 2018 (n = 311). Parameters compared between the two groups included time from sleep physician review to sleep test, ENT review, and definitive treatment in the form of adenotonsillectomy surgery (or CPAP initiation for those who had already undergone surgery). ResultsThe time from sleep physician review and request of the sleep-related study to ENT surgical treatment was significantly reduced (187 days for the HITH oximetry group vs 359 days for the comparable PSG group; p-value <0.05), and time from sleep study request to the report of results was significantly lower for patients in the oximetry group compared to those in the PSG group (11 days vs 105 days; p-value <0.05). ConclusionThese results suggest that for children referred to a tertiary sleep center for possible obstructive sleep disordered breathing, a home oximetry service can be effective in assisting sleep evaluation and reducing the time to OSA treatment.

Positive Airway Pressure in Surgical Patients with Sleep Apnea: What is the Supporting Evidence?

Obstructive sleep apnea (OSA) is prevalent amongst surgical patients and associated with an increased incidence of perioperative complications. The gold standard treatment for moderate-to-severe OSA is positive airway pressure (PAP) therapy. Practice guidelines by the American Society of Anesthesiologists and the Society of Anesthesia and Sleep Medicine have recommended preoperative screening for OSA and consideration of initiation of PAP therapy for patients with severe OSA. These guidelines, developed mainly by the consensus of experts, highlight the adverse impact of OSA on postoperative outcomes and recommend the use of postoperative PAP in surgical patients with moderate to severe OSA. Since the development of these guidelines, there has been an increase in the number of publications regarding the efficacy of PAP therapy in surgical patients with OSA. Our review provides an update on the existing literature on the efficacy of PAP therapy in surgical patients with OSA. We focus on the postoperative complications associated with OSA, potential mechanisms leading to the increased risk of postoperative adverse events, and summarize the perioperative guidelines for the management of patients with OSA, evidence supporting perioperative PAP therapy, as well as limitations to PAP therapy and alternatives. An update on the existing literature of the efficacy of PAP therapy in surgical patients with OSA is critical to assess the impact of prior guidelines, determine when and how to effectively implement PAP therapy, and target barriers to PAP adherence in the perioperative setting.