Causal associations between obstructive sleep apnea and COVID-19: A bidirectional Mendelian randomization study

BACKGROUNDS

The COVID-19 pandemic has caused significant impact on human health. Whether obstructive sleep apnea (OSA) increases the risk of COVID-19 remains unclear. We sought to clarify this issue using two-sample Mendelian randomization (TSMR) analysis in large cohorts.

METHODS

Bidirectional two-sample Mendelian randomization (MR) was used to evaluate the potential causality between OSA and COVID-19 by selecting single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) from genome-wide association studies (GWAS). The inverse-variance weighted (IVW) method was selected as the main approach for data analysis to estimate the possible causal effects. Alternative methods such as MR-Egger, the MR pleiotropy residual sum and outlier (MR-PRESSO), and leave-one-out analysis methods were implemented as sensitivity analysis approaches to ensure the robustness of the results.

RESULTS

All forward MR analyses consistently indicated the absence of a causal relationship between OSA and any COVID-19 phenotype. In the reverse MR analysis, the IVW mode demonstrated that severe respiratory confirmed COVID-19 was correlated with a 4.9% higher risk of OSA (OR, 1.049; 95%CI, 1.018-1.081; P = 0.002), consistent in MR-PRESSO (OR = 1.049, 95%CI 1.018-1.081, P = 0.004), weighted median (OR = 1.048, 95%CI 1.003-1.095, P = 0.035), and MR-Egger (OR = 1.083, 95%CI 1.012-1.190, P = 0.041) methods.

CONCLUSIONS

There is no significant evidence supporting a causal association between OSA and any COVID phenotype, while we identified potential evidence for a causal effect of severe COVID-19 on an increased risk of OSA.

Clinical Characteristics Combined with Craniofacial Photographic Analysis in Children with Obstructive Sleep Apnea

Purpose

Distinguishing obstructive sleep apnea (OSA) in a high-risk population remains challenging. This study aimed to investigate clinical features to identify children with OSA combined with craniofacial photographic analysis.

Methods

One hundred and forty-five children (30 controls, 62 with primary snoring, and 53 with OSA) were included. Differences in general demographic characteristics and surface facial morphology among the groups were compared. Risk factors and prediction models for determining the presence of OSA (obstructive sleep apnea-hypopnea index>1) were developed using logistic regression analysis.

Results

The BMI (z-score), tonsil hypertrophy, and lower face width (adjusted age, gender, and BMI z-score) were showed significantly different in children with OSA compared with primary snoring and controls (adjusted p<0.05). The screening model based on clinical features and photography measurements correctly classified 79.3% of the children with 64.2% sensitivity and 89.1% specificity. The area under the curve of the model was 81.0 (95% CI, 73.5-98.4%).

Conclusion

A screening model based on clinical features and photography measurements would be helpful in clinical decision-making for children with highly suspected OSA if polysomnography remains inaccessible in resource-stretched healthcare systems.

Selection of OSA-specific pronunciations and assessment of disease severity assisted by machine learning

STUDY OBJECTIVES

To screen all of the obstructive sleep apnea (OSA)-characteristic pronunciations, explore the pronunciations which provide a better OSA classification effect than those used previously, and further clarify the correlation between speech signals and OSA.

METHODS

A total of 158 adult male Mandarin native speakers who completed polysomnography at the Sleep Medicine Center of Beijing Tongren Hospital from November 15, 2019, to January 19, 2020, were enrolled in this study. All Chinese syllables were collected from each participant in the sitting position. The syllables, vowels, consonants, and tones were screened to identify the pronunciations that were most effective for OSA classification.

RESULTS

The linear prediction coefficients of Chinese syllables were extracted as features and mathematically modeled using a decision tree model to dichotomize participants with apnea-hypopnea index thresholds of 10 and 30 events/h, and the leave-one-out method was used to verify the classification performance of Chinese syllables for OSA. Chinese syllables such as [leng] and [jue], consonant pronunciations such as [zh] and [f], and vowel pronunciations such as [ing] and [ai] were the most suitable pronunciations for classification of OSA. An OSA classification model consisting of several syllable combinations was constructed, with areas under curve of 0.83 (threshold of apnea-hypopnea index = 10) and 0.87 (threshold of apnea-hypopnea index = 30), respectively.

CONCLUSIONS

This study is the first comprehensive screening of OSA-characteristic pronunciations and can act as a guideline for the construction of OSA speech corpora in other languages.

CITATION

Ding Y, Sun Y, Li Y, et al. Selection of OSA-specific pronunciations and assessment of disease severity assisted by machine learning. J Clin Sleep Med. 2022;18(11):2663-2672.

[Sero-epidemiological characteristics of the hepatitis D virus infection among hepatitis B virus infected-patients at a single center in Xinjiang region]

Objective: To investigate the sero-epidemiological characteristics of the hepatitis D virus (HDV) infection among hepatitis B virus (HBV)-infected patients in Xinjiang region. Methods: A single-center cross-sectional analysis method was used to select 264 cases of hepatitis B virus infection who were hospitalized in the Center for Infectious Diseases and Liver Diseases of the First Affiliated Hospital of Xinjiang Medical University from August 2021 to January 2022. All patients were tested for HDV Ag, HDV IgM, HDV IgG, and HDV RNA. The infection status of hepatitis D virus was analyzed by grouping according to their clinical type, HBV viral load, and HBsAg level. A paired t-test was used for data with measurement data conforming to normal distribution. A paired rank sum test was used for data that did not conform to normal distribution before and after treatment. Results: A total of 36 cases (13.64%) and 26 cases (9.85%) were positive for HDV serological markers and HDV RNA. According to clinical type grouping, the positive rates of HDV serum markers in patients with chronic hepatitis B, hepatitis B-related cirrhosis, liver cancer, and liver failure were 13.46%, 12.43%, and 20.83%, respectively, and there was no statistically significant difference among the three groups (χ²=0.86, P=0.649). The positive rates of HDV RNA were 11.54%, 8.11%, and 20.83%, respectively, and there was no statistically significant difference among the three groups (χ²=4.015, P=0.134). According to HBV viral load grouping, the positive rates of HDV serum markers among patients with viral loads <20, 20-2 000, and >2 000 IU/ml were 17.15%, 7.81%, and 6.67%, respectively, and the difference was not statistically significant among the three groups (χ²=4.846, P=0.089). The positive rates of HDV RNA were 9.47%, 10.94%, and 10%, respectively, and the difference was not statistically significant among the three groups (χ²=0.113, P=0.945). According to HBsAg level grouping, the positive rates of HDV serum markers in HBsAg<0.05, 0.05~250, and >250 IU/ml were 14.29%, 16.67%, and 10.85%, respectively, and there was no statistically significance between the three groups (χ²=1.745, P=0.418). The positive rates of HDV RNA were 4.76%, 8.77%, and 11.63%, respectively, and there was no statistically significant difference among the three groups (χ²=1.221, P=0.543). Clinical outcome, disease course, HBV DNA, serological markers of viral hepatitis, routine blood test, biochemical indicators, coagulation function, and other laboratory indicators were compared between HDV serum marker and/or nucleic acid positive and negative patients, and there was no statistically significant difference (P>0.05). Conclusion: The positive rate of HDV serological markers and HDV RNA is 13.64% and 9.85%, respectively, at a single center in the Xinjiang region, and there is still a high HDV infection rate among the HBV-infected patients with low levels of viral load and HBsAg.

Classification of facial phenotypes in Asian patients with obstructive sleep apnea

BACKGROUD

The facial phenotypes of Asian obstructive sleep apnea (OSA) patients remain unclear.

OBJECTIVES

(1) To describe the facial features of OSA patients. (2) To develop a model based on facial contour indicators to predict OSA. (3) To classify the facial phenotypes of Asian OSA patients.

MATERIALS AND METHODS

110 patients with OSA (apnea-hypopnea index [AHI] ≥ 10/h) and 50 controls (AHI< 10/h) were selected to measure facial contour indicators. Indicators were compared between OSA patients and the control group. We used multivariable linear regression analysis to predict OSA severity and K-means cluster analysis to classify OSA patients into different phenotypes.

RESULTS

We built a model to predict OSA which explained 49.1% of its variance and classified OSA patients into four categories. Cluster 1 (Skeletal type) had the narrowest facial width indicators with narrowing of the retroglossal airway. Cluster 2 (Obese type) had the widest face, and narrowest hard palate, retropalatal, and hypopharyngeal airways. Cluster 3 (Nose type) had the narrowest nasal cavity. Cluster 4 (Long type) had the longest airway length.

CONCLUSIONS AND SIGNIFICANCE

Patients with OSA were classified into four categories, each of which identified different anatomic risk factors that can be used to select the treatment.

MRI-based radiomics analysis in differentiating solid non-small-cell from small-cell lung carcinoma: a pilot study

AIM

To investigate the ability of a T2-weighted (W) magnetic resonance imaging (MRI)-based radiomics signature to differentiate solid non-small-cell lung carcinoma (NSCLC) from small-cell lung carcinoma (SCLC).

MATERIALS AND METHODS

The present retrospective study enrolled 152 eligible patients (NSCLC = 125, SCLC = 27). All patients underwent MRI using a 3 T scanner and radiomics features were extracted from T2W MRI. The least absolute shrinkage and selection operator (LASSO) logistic regression model was used to identify the optimal radiomics features for the construction of a radiomics model to differentiate solid NSCLC from SCLC. Threefold cross validation repeated 10 times was used for model training and evaluation. The conventional MRI morphology features of the lesions were also evaluated. The performance of the conventional MRI morphological features, and the radiomics signature model and nomogram model (combining radiomics signature with conventional MRI morphological features) was evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

Five optimal features were chosen to build a radiomics signature. There was no significant difference in age, gender, and the largest diameter. The radiomics signature and conventional MRI morphological features (only pleural indentation and lymph node enlargement) were independent predictive factors for differentiating solid NSCLC from SCLC. The area under the ROC curves (AUCs) for MRI morphological features, and the radiomics model, and nomogram model was 0.69, 0.85, and 0.90 (ROC), respectively.

CONCLUSIONS

The T2W MRI-based radiomics signature is a potential non-invasive approach for distinguishing solid NSCLC from SCLC.

User acceptance of wearable intelligent medical devices through a modified unified theory of acceptance and use of technology

Background

The acceptance of wearable intelligent medical devices and the factors influencing behavioral intention to use them have been scarcely studied. This study aimed to increase the current understanding of wearable intelligent medical devices and investigate the factors influencing their acceptance.

Methods

Integrating the unified theory of acceptance and use of technology and the theory of perceived risk, and based on the features of wearable intelligent medical devices, we proposed a modified unified theory of acceptance and use of technology model to identify factors influencing the acceptance of these devices. Using data collected from 2,192 respondents in China from an online survey, we used structural equation modeling to test the measurement and structural models.

Results

The findings suggested that facilitating conditions (path coefficient =0.942, P<0.001) were critical to the use of wearable intelligent medical devices. Behavioral intention significantly mediated the effects of perceived risk, perceived cost, health expectation, perceived ease of use, and social influence on user behavior (path coefficient =0.210, P<0.001). Health expectation (path coefficient =0.860, P<0.001), perceived ease of use (path coefficient =0.289, P<0.001), and social influence (path coefficient =0.153, P<0.001) were found to play essential roles in predicting behavioral intention. Perceived cost (path coefficient =0.034, P<0.05) and perceived risk (path coefficient =−0.031, P<0.05) had no significant effect on behavioral intention. People with underlying diseases had lower health expectations and perceived costs.

Conclusions

The modified unified theory of acceptance and use of technology model in our research is a reliable model to evaluate the user acceptance of wearable intelligent medical devices.

Differential expression profile of plasma exosomal microRNAs in chronic rhinosinusitis with nasal polyps

Exosomes mediate inflammation and immune responses. The aim of the study was to examine the expression profiles of plasma exosomal microRNAs (miRNAs) and analyze their target gene functions in participants with chronic rhinosinusitis with nasal polyps (CRSwNP). We measured plasma exosomal miRNAs in five patients with CRSwNP and five controls. Transcripts per million (TPM) was used to assess miRNAs expression and the Benjamini-Hochberg procedure was employed for multiple comparisons correction. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene ontology (GO) analyses revealed biological annotation and functional prediction of target genes. Compared with controls, we found that 159 exosomal miRNAs were differentially expressed by miRNA sequencing in CRSwNP. The top three upregulated miRNAs were novel_miR_677, novel_miR_1037, and novel_miR_79, while the top three downregulated miRNAs were novel_miR_192, novel_miR_1022, and novel_miR_4. The target functions in the GO and KEGG analyses included axon guidance, extracellular matrix (ECM)-receptor interaction, protein digestion and absorption, the calcium, the Hippo, the Notch, the ErbB, the cAMP signaling pathway, and focal adhesion. This study describes the dissection of plasma exosomal miRNA profiling in CRSwNP. Our findings may provide a certain basis for further mechanism research and exploration of diagnostic values.

Using clinical data to predict obstructive sleep apnea

Background

Methods

Results

Conclusions

Evaluation of the clinical efficacy of nasal surgery in the treatment of obstructive sleep apnoea

STUDY OBJECTIVES

The aim of the study was to evaluate the clinical efficacy of nasal surgery in the treatment of obstructive sleep apnoea (OSA) by comparing the improvement of subjective symptoms and objective metrics before surgery and after 6 months of surgery.

METHODS

Patients with the main complaint of nasal congestion combined with habitual snoring who were hospitalized and treated were selected. Patients underwent subjective symptom tests and objective indicator monitoring both before surgery and 6 months after surgery. Comparisons between groups were performed using the independent samples t-test.

RESULTS

Subjective scale evaluations demonstrated that nasal congestion, daytime sleepiness, snoring, nose-related symptoms, and sleep symptoms in patients with simple snoring or with OSA were improved after nasal surgery. Additionally, vitality was improved in all groups except for the patients with simple snoring and emotional consequence was improved in patients with simple snoring and mild OSA. Objective evaluations indicated the apnoea-hypopnoea index (AHI), the thickness of the soft palate, and the maximum cross-sectional area of the sagittal plane of the soft palate decreased after surgery in patients with mild OSA. The lowest blood oxygen concentration (LSaO₂) and anteroposterior diameter of the soft palate increased after surgery in patients with mild OSA. The arousal index also significantly decreased in patients with mild and moderate OSA. The nasal cavity volumes (NCVs) and the nasal minimal cross-sectional areas (NMCAs) of all groups showed significant differences after surgery.

CONCLUSIONS

Nasal surgery can effectively improve nose and sleep symptoms in patients with simple snoring or with OSA. It can significantly reduce the nasal resistance and increase the ventilation volume.

STATEMENT OF SIGNIFICANCE

Obstructive sleep apnoea (OSA) is becoming a global health problem. OSA is associated with several coexisting conditions, reduced health-related quality of life, and impaired work productivity. This study performed nasal surgery on OSA patients with the main complaint of nasal congestion combined with snoring and patients with simple snoring to compare the improvement of subjective symptoms and objective metrics before and after surgery. We found that: (1) symptoms such as nasal congestion, daytime sleepiness or snoring were improved after nasal surgery; (2) the apnoea-hypopnoea index (AHI) and arousal index decreased after surgery in patients with OSA; (3) the nasal and oropharyngeal cavity volumes increased after surgery. These findings suggest that patients with OSA or with simple snoring could benefit from nasal surgery.

Obstructive Sleep Apnea Detection Based on Sleep Sounds via Deep Learning

PURPOSE

This study aimed to propose a novel deep-learning method for automatic sleep apneic event detection and thus to estimate the apnea hypopnea index (AHI) and identify obstructive sleep apnea (OSA) in an event-by-event manner solely based on sleep sounds obtained by a noncontact audio recorder.

METHODS

We conducted a cross-sectional study of participants with habitual snoring or heavy breathing sounds during sleep to train and test a deep convolutional neural network named OSAnet for the detection of OSA based on sleep sounds. Polysomnography (PSG) was conducted, and sleep sounds were recorded simultaneously in a regular room without noise attenuation. The study was conducted in two phases. In phase one, eligible participants were enrolled and randomly allocated into training and validation groups for deep learning algorithm development. In phase two, eligible patients were enrolled in a test group for algorithm assessment. Sensitivity, specificity, accuracy, unweighted Cohen kappa coefficient (κ) and the area under the curve (AUC) were calculated using PSG as the reference standard.

RESULTS

A total of 135 participants were randomly divided into a training group (n, 116) and a validation group (n, 19). An independent test group of 59 participants was subsequently enrolled. Our algorithm achieved a precision of 0.81 and sensitivity of 0.78 in the test group for overall sleep event detection. The algorithm exhibited robust diagnostic performance to identify severe cases with a sensitivity of 95.6% and specificity of 91.6%.

CONCLUSION

Our results showed that a deep learning algorithm based on sleep sounds recorded by a noncontact voice recorder served as a feasible tool for apneic event detection and OSA identification. This technique may hold promise for OSA assessment in the community in a relatively comfortable and low-cost manner. Further studies to develop a tool based on a home-based setting are warranted.

Clinical outcomes of keratinized mucosa augmentation in jaws reconstructed with fibula or iliac bone flaps

This prospective study was undertaken to evaluate the treatment outcomes of keratinized mucosa augmentation (KMA) on the buccal and palatal/lingual sides of implants in jaws reconstructed after oncological surgery. Forty-two implants in 12 patients whose jaws had been reconstructed with a fibula or iliac bone flap were included. KMA was performed at 3 months after implant placement; this included an apically displaced partial-thickness flap and a free gingival graft (FGG) around the implants to increase the keratinized mucosa width (KMW). Patients were followed up for at least 6 months post-surgery. KMW, shrinkage, and patient pain and discomfort measured on a visual analogue scale were analysed. A histological analysis was performed of tissue epithelium from two patients. The results showed that KMW was >2 mm on both the buccal and palatal/lingual sides during follow-up. Before surgery, histological analysis showed epithelium with no epithelial spikes; normal keratinized epithelial spikes were observed at 8 weeks after KMA. Greater KMW was observed around implants in reconstructed maxillae than around those in reconstructed mandibles (P < 0.001). Patients felt more pain at the donor site than at the recipient site during the first 3 days post-surgery. KMA with FGG was predictable in reconstructed jaws and may help maintain the long-term stability of implants.

LINC00174 triggers the malignant development of breast cancer by negatively regulating miR-1827 level

OBJECTIVE

Long non-coding RNAs (lncRNAs) are extensively involved in tumor development. In-depth researches on cancer-associated lncRNAs provide a theoretical basis for developing prognostic hallmarks and individualized therapeutic targets in breast cancer (BCa). This study aims to detect expression characteristics of LINC00174 in BCa and its biological role in regulating BCa cell phenotypes.

PATIENTS AND METHODS

LINC00174 levels in BCa and adjacent normal tissues were detected by quantitative real-time polymerase chain reaction (qRT-PCR). The influence of LINC00174 on pathological indicators of BCa was analyzed. In MCF-7 and MDA-MB-231 cells with LINC00174 knockdown, proliferative and migratory abilities were examined by cell counting kit-8 (CCK-8), colony formation and transwell assay, respectively. At last, molecular mechanisms of LINC00174 and its downstream gene miR-1827 in regulating BCa development were explored by Luciferase assay and rescue experiments.

RESULTS

LINC00174 was upregulated in BCa tissues than adjacent normal ones. High level of LINC00174 predicted advanced tumor staging, high metastasis rate and poor prognosis in BCa. Knockdown of LINC00174 attenuated proliferative and migratory abilities in BCa cells. MiR-1827 was the target gene binding LINC00174, showing a negative correlation between each other. Silence of miR-1827 abolished the regulatory effects of LINC00174 on proliferative and migratory abilities in BCa cells.

CONCLUSIONS

LINC00174 is upregulated in BCa samples. It is closely linked to tumor staging, metastasis and prognosis in BCa. By negatively regulating miR-1827 level, LINC00174 aggravates the malignant development of BCa.

Machine learning from quantitative coronary computed tomography angiography predicts ischemia and impaired myocardial blood flow

Background

Atherosclerotic plaque characteristics influence the hemodynamic consequences of coronary lesions. This study sought to assess the performance of a machine learning (ML) score integrating coronary computed tomography angiography (CCTA)-based quantitative plaque features for the prediction of ischemia by invasive fractional flow reserve (FFR) and impaired myocardial blood flow (MBF) by [15O]H2O positron emission tomography (PET).

Methods

This post-hoc analysis of the PACIFIC (Prospective Comparison of Cardiac PET/CT, SPECT/CT Perfusion Imaging and CT Coronary Angiography With Invasive Coronary Angiography) trial included 208 patients with suspected coronary artery disease who underwent CCTA, [15O]H2O PET, and 3-vessel invasive FFR. Plaque quantification from CCTA was performed using semiautomated software. A boosted ensemble ML algorithm (XGBoost) trained on data from the NXT (Analysis of Coronary Blood Flow using CT Angiography: Next Steps) trial was used to develop a ML score for the prediction of per-vessel ischemia (invasive FFR ≤0.80). The performance of the ML score was evaluated in 551 vessels from the PACIFIC trial for external validation. Thereafter, we assessed the discriminative ability of the ML score for per-vessel impaired hyperemic MBF (≤2.30 mL/min/g).

Results

In total, 138 (25.0%) vessels had ischemia and 195 (35.4%) vessels had impaired hyperemic MBF. CCTA-derived quantitative percent diameter stenosis and low-density noncalcified plaque (LDNCP) volume were higher in ischemic vessels compared with non-ischemic vessels (60.8% vs. 19.9%; and 42.3 mm3 vs. 9.1 mm3; both p&lt;0.001). The ML score demonstrated a significantly higher area under the receiver-operating characteristic curve (AUC) for predicting ischemia (0.92, 95% confidence interval [CI] 0.89–0.94) compared with visual stenosis grade (0.84, 95% CI 0.80–0.87; p&lt;0.001). Overall, quantitative percent diameter stenosis and LDNCP volume had greatest feature importance for ML, followed by percent area stenosis, minimum luminal diameter, and contrast density drop (Figure 1). An individualized explanation of ML ischemia prediction is shown in Figure 2. When applied for impaired MBF discrimination, the ML score exhibited an AUC of 0.82 (95% CI 0.78–0.85) and was superior to visual stenosis grade (AUC 0.76, 95% CI 0.72–0.80; p=0.03).

Conclusions

An externally validated ML score integrating CCTA-based quantitative plaque features accurately predicts FFR-defined ischemia and abnormal MBF by PET, outperforming standard visual CCTA interpretation.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Heart, Lung, and Blood Institute, United States Performance of the ML scoreIndividual explanation of ML prediction

[Trajectories of body mass index Z-score and risk of high blood pressure in late adolescence in Suzhou children]

Objective: To identify age and gender standardized body mass index among children and adolescents and explore their associations with high blood pressure (HBP) in late adolescence. Methods: The current study was based on the Health Promotion Program for Children and Adolescents, school-based surveillance successively conducted from 2012 to 2019 in Suzhou, China. A total of 11 812 children and adolescents aged 16-18 years, who had ≥4 examination records during 2012-2018 and were also involved in a surveillance program in 2019, were included. Latent class growth mixture modeling was used to identify the BMI-Z trajectories in different genders, and multivariate logistic regression was used to analyze the associations between different BMI-Z trajectories and risk of HBP in late adolescence. Results: Six distinct BMI-Z trajectories were determined for both genders:thin, slightly thin,standard, declining, overweight, and obese. Compared with the regular group, the obesity group had 94.0% (OR=1.94, 95%CI: 1.43-2.63) and 107.0% (OR=2.07, 95%CI: 1.33-3.22) increased risk of developing HBP in late adolescence in boys and girls, respectively. However, a neutral association was found between the descending group and HBP in late adolescence. Conclusions: Persistent obesity in children may increase the risk of HBP in their late adolescence. If an obese child restores normal weight before late adolescence, the risk of HBP may reduce.

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

BACKGROUND

Obstructive sleep apnea (OSA) is one of the common sleep-related breathing disorders in children. However, polysomnography (PSG) is an expensive and labor-intensive diagnostic modality that may not always be feasible, especially in low-income countries or in non-tertiary hospitals. Portable monitors (PMs), a new approach for OSA diagnosis, have become more widely used with lower intolerance and cost in recent years. We aimed to analyze the diagnostic performance of Level IV PMs compared with PSG for the diagnosis of pediatric OSA.

METHODS

PubMed and Embase databases were searched for studies published in English up to December 31, 2020 evaluating the diagnostic accuracy of Level IV PMs against the apnea-hypopnea index (AHI) measured using overnight in-laboratory polysomnography (PSG) in children and adolescents. A random-effects bivariate model was used to estimate the summary sensitivity and specificity of oximetry-based statistical classifiers. A qualitative evaluation of studies was performed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) rating.

RESULTS

In total, 20 studies involving 7062 participants were included in this systematic review. Among these articles, seven studies (oximetry based on new mathematical classifiers) involving 5098 individuals satisfied the criteria for quantitative synthesis. Compared with AHI evaluation measured by PSG, different PM systems achieved diagnostic accuracy with variable degrees of success. A meta-analysis showed a pooled sensitivity of 74% (95% confidence interval [CI]: 66-80%) and pooled specificity of 90% (95% CI: 85-94%). The area under the summary receiver operating characteristic (SROC) curve was 0.89 (95% CI: 0.86-0.92).

CONCLUSION

This study showed the potential of Level IV PMs for screening pediatric OSA patients. Oximetry based on new mathematical classifiers may provide a simple and effective alternative to PSG in the diagnosis of pediatric OSA especially in the context of appropriate clinical evaluation.