Diagnostic accuracy of level IV portable sleep monitors versus polysomnography for pediatric obstructive sleep apnea: a systematic review and meta-analysis

Development and Validation of a Nomogram for Predicting Obstructive Sleep Apnea Severity in Children

Purpose

The clinical presentation of Obstructive Sleep Apnea (OSA) in children is insidious and harmful. Early identification of children with OSA, particularly those at a higher risk for severe symptoms, is essential for making informed clinical decisions and improving long-term outcomes. Therefore, we developed and validated a risk prediction model for severity in Chinese children with OSA to effectively identify children with moderate-to-severe OSA in a clinical setting.

Patients and Methods

From June 2023 to September 2023, we retrospectively analyzed the medical records of 367 Children diagnosed with OSA through portable bedside polysomnography (PSG). Predictor variables were screened using the least absolute shrinkage and selection operator (LASSO) and logistic regression techniques to construct nomogram to predict the severity of OSA. Receiver operating characteristic curve (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to determine the discrimination, calibration, and clinical usefulness of the nomogram.

Results

A total of 367 children with a median age of 84 months were included in this study. Neck circumference, ANB, gender, learning problem, and level of obstruction were identified as independent risk factors for moderate-severe OSA. The consistency indices of the nomogram in the training and validation cohorts were 0.841 and 0.75, respectively. The nomogram demonstrated a strong concordance between the predicted probabilities and the observed probabilities for children diagnosed with moderate-severe OSA. With threshold probabilities ranging from 0.1 to 1.0, the predictive model demonstrated strong predictive efficacy and yielded improved net benefit for clinical decision-making. ROC analysis was employed to classify the children into high and low-risk groups, utilizing the Optimal Cutoff value of 0.39.

Conclusion

A predictive model using LASSO regression was developed and validated for children with varying levels of OSA. This model identifies children at risk of developing OSA at an early stage.

Advancements in Home-Based Devices for Detecting Obstructive Sleep Apnea: A Comprehensive Study

Obstructive Sleep Apnea (OSA) is a respiratory disorder characterized by frequent breathing pauses during sleep. The apnea-hypopnea index is a measure used to assess the severity of sleep apnea and the hourly rate of respiratory events. Despite numerous commercial devices available for apnea diagnosis and early detection, accessibility remains challenging for the general population, leading to lengthy wait times in sleep clinics. Consequently, research on monitoring and predicting OSA has surged. This comprehensive paper reviews devices, emphasizing distinctions among representative apnea devices and technologies for home detection of OSA. The collected articles are analyzed to present a clear discussion. Each article is evaluated according to diagnostic elements, the implemented automation level, and the derived level of evidence and quality rating. The findings indicate that the critical variables for monitoring sleep behavior include oxygen saturation (oximetry), body position, respiratory effort, and respiratory flow. Also, the prevalent trend is the development of level IV devices, measuring one or two signals and supported by prediction software. Noteworthy methods showcasing optimal results involve neural networks, deep learning, and regression modeling, achieving an accuracy of approximately 99%.

AHI estimation of OSAHS patients based on snoring classification and fusion model

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a chronic and common sleep-breathing disease that could negatively influence lives of patients and cause serious concomitant diseases. Polysomnography(PSG) is the gold standard for diagnosing OSAHS, but it is expensive and requires overnight hospitalization. Snoring is a typical symptom of OSAHS. This study proposes an effective OSAHS screening method based on snoring sound analysis. Snores were labeled as OSAHS related snoring sounds and simple snoring sounds according to real-time PSG records. Three models were used, including acoustic features combined with XGBoost, Mel-spectrum combined with convolution neural network (CNN), and Mel-spectrum combined with residual neural network (ResNet). Further, the three models were fused by soft voting to detect these two types of snoring sounds. The subject's apnea-hypopnea index (AHI) was estimated according to these recognized snoring sounds. The accuracy and recall of the proposed fusion model achieved 83.44% and 85.27% respectively, and the predicted AHI has a Pearson correlation coefficient of 0.913 (R2 = 0.834, p < 0.001) with PSG. The results demonstrate the validity of predicting AHI based on analysis of snoring sound and show great potential for monitoring OSAHS at home.

Diagnosis of Paediatric Obstructive Sleep-Disordered Breathing beyond Polysomnography

Obstructive sleep-disordered breathing (SDB) has significant impacts on health, and therefore, a timely and accurate diagnosis is crucial for effective management and intervention. This narrative review provides an overview of the current approaches utilised in the diagnosis of SDB in children. Diagnostic methods for SDB in children involve a combination of clinical assessment, medical history evaluation, questionnaires, and objective measurements. Polysomnography (PSG) is the diagnostic gold standard. It records activity of brain and tibial and submental muscles, heart rhythm, eye movements, oximetry, oronasal airflow, abdominal and chest movements, body position. Despite its accuracy, it is a time-consuming and expensive tool. Respiratory polygraphy instead monitors cardiorespiratory function without simultaneously assessing sleep and wakefulness; it is more affordable than PSG, but few paediatric studies compare these techniques and there is optional recommendation in children. Nocturnal oximetry is a simple and accessible exam that has high predictive value only for children at high risk. The daytime nap PSG, despite the advantage of shorter duration and lower costs, is not accurate for predicting SDB. Few paediatric data support the use of home testing during sleep. Finally, laboratory biomarkers and radiological findings are potentially useful hallmarks of SDB, but further investigations are needed to standardise their use in clinical practice.

Alternatives to Polysomnography for the Diagnosis of Pediatric Obstructive Sleep Apnea

Diagnosis of obstructive sleep apnea (OSA) in children with sleep-disordered breathing (SDB) requires hospital-based, overnight level I polysomnography (PSG). Obtaining a level I PSG can be challenging for children and their caregivers due to the costs, barriers to access, and associated discomfort. Less burdensome methods that approximate pediatric PSG data are needed. The goal of this review is to evaluate and discuss alternatives for evaluating pediatric SDB. To date, wearable devices, single-channel recordings, and home-based PSG have not been validated as suitable replacements for PSG. However, they may play a role in risk stratification or as screening tools for pediatric OSA. Further studies are needed to determine if the combined use of these metrics could predict OSA.

Development and validation of moderate to severe obstructive sleep apnea screening test (ColTon) in a pediatric population

OBJECTIVE

Development and validation of a machine learning algorithm to predict moderate to severe obstructive sleep apnea syndrome (OSAS) in otherwise healthy children.

DESIGN

Multivariable logistic regression and cforest algorithm of a large cross-sectional data set of children with sleep-disordered breathing.

SETTING

An university pediatric sleep centre.

PARTICIPANTS

Children underwent clinical examination, acoustic rhinometry and pharyngometry, and surveying through parental sleep questionnaires, allowing the recording of 14 predictors that have been associated with OSAS. The dataset was nonrandomly split into a training (development) versus test (external validation) set (2:1 ratio) based on the time of the polysomnography. We followed the TRIPOD checklist.

RESULTS

We included 336 children in the analysis: 220 in the training set (median age [25th-75th percentile]: 10.6 years [7.4; 13.5], z-score of BMI: 1.96 [0.73; 2.50], 89 girls) and 116 in the test set (10.3 years [7.8; 13.0], z-score of BMI: 1.89 [0.61; 2.46], 51 girls). The prevalence of moderate to severe OSAS was 106/336 (32%). A machine learning algorithm using the cforest with pharyngeal collapsibility (pharyngeal volume reduction from sitting to supine position measured by pharyngometry) and tonsillar hypertrophy (Brodsky scale), constituting the ColTon index, as predictors yielded an area under the curve of 0.89, 95% confidence interval [0.85-0.93]. The ColTon index had an accuracy of 76%, sensitivity of 63%, specificity of 81%, negative predictive value of 84%, and positive predictive value of 59% on the validation set.

CONCLUSION

A cforest classifier allows valid predictions for moderate to severe OSAS in mostly obese, otherwise healthy children.

Effect of orthopedic and functional orthodontic treatment in children with obstructive sleep apnea: A systematic review and meta-analysis

Orthodontic treatment is suggested in growing individuals to correct transverse maxillary deficiency and mandibular retrusion. Since, as a secondary effect, these orthodontic procedures may improve pediatric obstructive sleep apnea (OSA), this systematic review assessed their effects on apnea-hypopnea index (AHI) and oxygen saturation (SaO2). Twenty-five (25) manuscripts were included for qualitative synthesis, 19 were selected for quantitative synthesis. Five interventions were analyzed: rapid maxillary expansion (RME, 15 studies), mandibular advancement (MAA, five studies), myofunctional therapy (MT, four studies), and RME combined with MAA (one study). RME produced a significant AHI reduction and minimum SaO2 increase immediately after active treatment, at six and 12 months from baseline. A significant AHI reduction was also observed six and 12 months after the beginning of MAA treatment. MT showed positive effects, with different protocols. In this systematic review and meta-analysis of data from mainly uncontrolled studies, interceptive orthodontic treatments showed overall favorable effects on respiratory outcomes in pediatric OSA. However, due to the low to very low level of the body evidence, this treatment cannot be suggested as elective for OSA treatment. An orthodontic indication is needed to support this therapy and a careful monitoring is required to ensure positive improvement in OSA parameters.

Nocturnal oximetry parameters as predictors of sleep apnea severity in resource-limited settings

Nocturnal oximetry is an alternative modality for evaluating obstructive sleep apnea syndrome (OSAS) severity when polysomnography is not available. The Oxygen Desaturation (≥3%) Index (ODI3) and McGill Oximetry Score (MOS) are used as predictors of moderate-to-severe OSAS (apnea-hypopnea index-AHI >5 episodes/h), an indication for adenotonsillectomy. We hypothesised that ODI3 is a better predictive parameter for AHI >5 episodes/h than the MOS. All polysomnograms performed in otherwise healthy, snoring children with tonsillar hypertrophy in a tertiary hospital (November 2014 to May 2019) were analysed. The ODI3 and MOS were derived from the oximetry channel of each polysomnogram. Logistic regression was applied to assess associations of ODI3 or MOS (predictors) with an AHI >5 episodes/h (primary outcome). Receiver operating characteristic (ROC) curves and areas under ROC curves were used to compare the ODI3 and MOS as predictors of moderate-to-severe OSAS. The optimal cut-off value for each oximetry parameter was determined using Youden's index. Polysomnograms of 112 children (median [interquartile range] age 6.1 [3.9-9.1] years; 35.7% overweight) were analysed. Moderate-to-severe OSAS prevalence was 49.1%. The ODI3 and MOS were significant predictors of moderate-to-severe OSAS after adjustment for overweight, sex, and age (odds ratio [OR] 1.34, 95% confidence interval [CI] 1.19-1.51); and OR 4.10, 95% CI 2.06-8.15, respectively; p < 0.001 for both). Area under the ROC curve was higher for the ODI3 than for MOS (0.903 [95% CI 0.842-0.964] versus 0.745 [95% CI 0.668-0.821]; p < 0.001). Optimal cut-off values for the ODI3 and MOS were ≥4.3 episodes/h and ≥2, respectively. The ODI3 emerges as preferable or at least a complementary oximetry parameter to MOS for detecting moderate-to-severe OSAS in snoring children when polysomnography is not available.

An accessible and versatile deep learning-based sleep stage classifier

Manual sleep scoring for research purposes and for the diagnosis of sleep disorders is labor-intensive and often varies significantly between scorers, which has motivated many attempts to design automatic sleep stage classifiers. With the recent introduction of large, publicly available hand-scored polysomnographic data, and concomitant advances in machine learning methods to solve complex classification problems with supervised learning, the problem has received new attention, and a number of new classifiers that provide excellent accuracy. Most of these however have non-trivial barriers to use. We introduce the Greifswald Sleep Stage Classifier (GSSC), which is free, open source, and can be relatively easily installed and used on any moderately powered computer. In addition, the GSSC has been trained to perform well on a large variety of electrode set-ups, allowing high performance sleep staging with portable systems. The GSSC can also be readily integrated into brain-computer interfaces for real-time inference. These innovations were achieved while simultaneously reaching a level of accuracy equal to, or exceeding, recent state of the art classifiers and human experts, making the GSSC an excellent choice for researchers in need of reliable, automatic sleep staging.

Sleep characterization with smart wearable devices: a call for standardization and consensus recommendations

The general public increasingly adopts smart wearable devices to quantify sleep characteristics and dedicated devices for sleep assessment. The rapid evolution of technology has outpaced the ability to implement validation approaches and demonstrate relevant clinical applicability. There are untapped opportunities to validate and refine consumer devices in partnership with scientists in academic institutions, patients, and the private sector to allow effective integration into clinical management pathways and facilitate trust in adoption once reliability and validity have been demonstrated. We call for the formation of a working group involving stakeholders from academia, clinical care and industry to develop clear professional recommendations to facilitate appropriate and optimized clinical utilization of such technologies.

Prevalence of sleep apnea in children and adolescents in Colombia according to the national health registry 2017–2021

Objective

To describe the sociodemographic and epidemiological characteristics of diagnosis and treatment of pediatric patients with sleep apnea, both central and obstructive, in Colombia between 2017 and 2021.

Methods

Observational, descriptive, cross-sectional, epidemiological study using the International Classification of Diseases and Related Health Problems as search terms for sleep apnea, based on SISPRO, the Colombian national health registry. Stratification by gender and age groups was performed. We also generated data of the amount of diagnostic and therapeutic procedures performed. A map of prevalence by place of residency was performed.

Results

National records report 15200 cases of SA between 2017 and 2021, for an estimated prevalence of 21.1 cases by 100000 inhabitants in 2019 the year with the most cases (4769), being more frequent and in the 6 to 11 age group and in males, with a male to female ratio of 1.54:1. The number of cases declined in 2020 and 2021. The map showed a concentration of cases in the more developed departments of the country.

Discussion

This is the first approximation to a nation-wide prevalence of sleep apnea in Colombia which is lower to what is found in the literature worldwide, including studies performed in Latin America and in Colombia, this could reflect sub diagnosis and sub report. The fact that the highest prevalence was found in males and in the 6–11 age group is consistent with reports in literature. The decrease in cases in 2020 and 2021 could be related to the COVID-19 pandemic impact in sleep medicine services.

Trends in Diagnosing Obstructive Sleep Apnea in Pediatrics

Obstructive sleep apnea in children has been linked with behavioral and neurocognitive problems, impaired growth, cardiovascular morbidity, and metabolic consequences. Diagnosing children at a young age can potentially prevent significant morbidity associated with OSA. Despite the importance of taking a comprehensive sleep history and performing thorough physical examination to screen for signs and symptoms of OSA, these findings alone are inadequate for definitively diagnosing OSA. In-laboratory polysomnography (PSG) remains the gold standard of diagnosing pediatric OSA. However, there are limitations related to the attended in-lab polysomnography, such as limited access to a sleep center, the specialized training involved in studying children, the laborious nature of the test and social/economic barriers, which can delay diagnosis and treatment. There has been increasing research about utilizing alternative methods of diagnosis of OSA in children including home sleep testing, especially with the emergence of wearable technology. In this article, we aim to look at the presentation, physical exam, screening questionnaires and current different modalities used to aid in the diagnosis of OSA in children.

Acupuncture for hypertension with insomnia: Study protocol for a randomized, sham-controlled, subject-and-assessor-blinded trial

BACKGROUND

Previous studies show that insomnia and hypertension are closely related. Currently, intervention for hypertension with insomnia has become a research hotspot. Acupuncture, as a representative non-pharmaceutical therapy of traditional Chinese medicine (TCM), has been widely used in improving insomnia and hypertension. However, there are few clinical studies on acupuncture for hypertension with insomnia.

METHODS

A single-center, subject-and-assessor-blind, randomized, sham-controlled trial has been designed for a study to be conducted in Jiangsu Province Hospital of Chinese Medicine. Sixty eligible patients will be randomly assigned to the treatment group and the control group in a 1:1 ratio. The treatment group will receive acupuncture treatment, while the control group will receive sham acupuncture treatment. Both groups will be treated three times per week for 4 weeks. Data will be collected at baseline and after 4 weeks of treatment and analyzed by using SPSS 25.0. The primary outcome measures are sleep parameters of portable polysomnography before and after treatment. Secondary outcomes are Pittsburgh Sleep Quality Index, Insomnia Severity Index, home blood pressure, and heart rate variability.

DISCUSSION

This study aims to evaluate the efficacy of acupuncture using the portable polysomnography combined with sleep scales, and analyze heart rate variability to preliminarily explore the underlying mechanism of acupuncture on hypertension with insomnia. The trail, if proven to be effective, will provide strong scientific evidence to support acupuncture is effective to manage patients for hypertension with insomnia.

CLINICAL TRIAL REGISTRATION

ChiCTR2200059161.