Abstract
Background: American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used to screen patients with obstructive sleep apnea (OSA) without replacing polysomnography, the gold standard.Objective: We aimed to identify, gather, and analyze existing machine learning approaches that are being used for disease screening in adult patients with suspected OSA. Methods: We searched the MEDLINE, Scopus, and ISI Web of Knowledge databases to evaluate the validity of different machine learning techniques, with polysomnography as the gold standard outcome measure and used the Prediction Model Risk of Bias Assessment Tool (Kleijnen Systematic Reviews Ltd) to assess risk of bias and applicability of each included study. Results: Our search retrieved 5479 articles, of which 63 (1.15%) articles were included. We found 23 studies performing diagnostic model development alone, 26 with added internal validation, and 14 applying the clinical prediction algorithm to an independent sample (although not all reporting the most common discrimination metrics, sensitivity or specificity). Logistic regression was applied in 35 studies, linear regression in 16, support vector machine in 9, neural networks in 8, decision trees in 6, and Bayesian networks in 4. Random forest, discriminant analysis, classification and regression tree, and nomogram were each performed in 2 studies, whereas Pearson correlation, adaptive neuro-fuzzy inference system, artificial immune recognition system, genetic algorithm, supersparse linear integer models, and k-nearest neighbors algorithm were each performed in 1 study. The best area under the receiver operating curve was 0.98 (0.96-0.99) for age, waist circumference, Epworth Somnolence Scale score, and oxygen saturation as predictors in a logistic regression. Conclusions: Although high values were obtained, they still lacked external validation results in large cohorts and a standard OSA criteria definition. Trial Registration: PROSPERO CRD42021221339; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=221339(J Med Internet Res 2022;24(9):e39452) doi: 10.2196/39452

Abstract

Abstract
Background and objectives: Timely treatment of pediatric obstructive sleep apnea (OSA) can prevent or reverse neurocognitive and cardiovascular morbidities. However, whether distinct phenotypes exist and account for divergent treatment effectiveness remains unknown. In this study, our goal is threefold: i) to define new data-driven pediatric OSA phenotypes, ii) to evaluate possible treatment effectiveness differences among them, and iii) to assess phenotypic information in predicting OSA resolution. Methods: We involved 22 sociodemographic, anthropometric, and clinical data from 464 children (5-10 years old) from the Childhood Adenotonsillectomy Trial (CHAT) database. Baseline information was used to automatically define pediatric OSA phenotypes using a new unsupervised subject-based association network. Follow-up data (7 months later) were used to evaluate the effects of the therapeutic intervention in terms of changes in the obstructive apnea-hypopnea index (OAHI) and the resolution of OSA (OAHI < 1 event per hour). An explainable artificial intelligence (XAI) approach was also developed to assess phenotypic information as OSA resolution predictor at baseline. Results: Our approach identified three OSA phenotypes (PHOSA1-PHOSA3), with PHOSA2 showing significantly lower odds of OSA recovery than PHOSA1 and PHOSA3 when treatment information was not considered (odds ratios, OR: 1.64 and 1.66, 95 % confidence intervals, CI: 1.03-2.62 and 1.01-2.69, respectively). The odds of OSA recovery were also significantly lower in PHOSA2 than in PHOSA3 when adenotonsillectomy was adopted as treatment (OR: 2.60, 95 % CI: 1.26-5.39). Our XAI approach identified 79.4 % (CI: 69.9-88.0 %) of children reaching OSA resolution after adenotonsillectomy, with a positive predictive value of 77.8 % (CI: 70.3 %-86.0 %). Conclusions: Our new subject-based association network successfully identified three clinically useful pediatric OSA phenotypes with different odds of therapeutic intervention effectiveness. Specifically, we found that children of any sex, >6 years old, overweight or obese, and with enlarged neck and waist circumference (PHOSA2) have less odds of recovering from OSA. Similarly, younger female children with no enlarged neck (PHOSA3) have higher odds of benefiting from adenotonsillectomy.

Abstract

Abstract

Abstract
Obstructive Sleep Apnea (OSA) is a prevalent sleep disorder that significantly affects public health, contributing to cardiovascular and metabolic impairments. Previous studies highlight the heterogeneity of OSA, which is manifested in different phenotypes, complicating personalized treatment strategies. Current phenotyping methods primarily rely on traditional clustering techniques, such as k-means, which may fail to capture complex relationships among features. This study introduces a novel approach based on subject-based SpO(2) weighted correlation networks and modularity analysis to identify clinically relevant subgroups within the OSA population. Using a subset of 2,641 subjects from the Sleep Heart Health Study (SHHS), we extracted 43 SpO(2) features from polysomnography to build correlation networks from them. A bootstrap procedure ensured robustness, while Blondel's modularity algorithm identified subgroups without requiring a predefined number of clusters. Comparison with k-means revealed that the correlation network method identified subgroups with more significantly different sociodemographic, clinical, and anthropometric characteristics (35 variables vs. 28 for k-means). These 35 features effectively revealed hidden SpO2 patterns, suggesting that subject-based correlation networks can identify distinct OSA phenotypes and enhance personalized treatment strategies. This approach improves clinical decisionmaking and patient care. Future research should validate these findings in longitudinal studies and explore integrating multimodal data to refine OSA phenotyping.