Phenotypic variance in pediatric obstructive sleep apnea

Tan, Hui-Leng; Kaditis, Athanasios G.

doi:10.1002/ppul.25309

Cited by 15 publications

(13 citation statements)

References 108 publications

(193 reference statements)

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“…The OSA phenotype in children with complex diseases is determined by anatomical and functional abnormalities that are specific to each underlying disorder, such as Down syndrome and Prader Willi syndrome. It is essential that clinicians consider the symptoms, physical examinations, the presence of risk factors, and signs of end-organ morbidity to diagnose patients and develop a personalized management strategy [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Medical Treatment of Obstructive Sleep Apnea in Children

Ergenekon,

Gokdemir,

Ersu

2023

JCM

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Obstructive sleep apnea (OSA) is characterized by recurrent complete or partial obstruction of the upper airway. The prevalence is 1–4% in children aged between 2 and 8 years and rising due to the increase in obesity rates in children. Although persistent OSA following adenotonsillectomy is usually associated with obesity and underlying complex disorders, it can also affect otherwise healthy children. Medical treatment strategies are frequently required when adenotonsillectomy is not indicated in children with OSA or if OSA is persistent following adenotonsillectomy. Positive airway pressure treatment is a very effective modality for persistent OSA in childhood; however, adherence rates are low. The aim of this review article is to summarize medical treatment options for OSA in children.

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Section: Introductionmentioning

confidence: 99%

Medical Treatment of Obstructive Sleep Apnea in Children

Ergenekon,

Gokdemir,

Ersu

2023

JCM

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“…It is also worth noting the significant improvements in anthropometric characteristics, which demonstrates the negative and potentially reversible impact of OSA on child development. Our study highlights the need to distinguish between different phenotypes of childhood OSA and the importance of a multidisciplinary approach, as care providers may need to consider different treatment approaches in the initial clinical assessment [ 34 ].…”

Section: Discussionmentioning

confidence: 99%

Features of Obstructive Sleep Apnea in Children with and without Comorbidities

Chiner

Sancho-Chust

Pastor

et al. 2023

JCM

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Background: To compare the clinical and polysomnographic features of obstructive sleep apnea (OSA) in children with adenotonsillar hypertrophy (Group A) and comorbidities (Group B). Methods: A five-year prospective study using nocturnal polysomnography before and after treatment. Results: We included 168 patients: 121 in Group A and 47 in Group B, with differences in age (6.5 ± 3 vs. 8.6 ± 4 years; p < 0.001), body mass index (BMI) (18 ± 4 vs. 20 ± 7 kg/m2; p < 0.05), neck circumference (28 ± 4 vs. 30 ± 5 cm; p < 0.05), and obesity (17% vs. 30%; p < 0.05). Group B patients were more likely to have facial anomalies (p < 0.001), macroglossia (p < 0.01), dolichocephaly (p < 0.01), micrognathia (p < 0.001), and prognathism (p < 0.05). Adenotonsillectomy was performed in 103 Group A patients (85%) and 28 Group B patients (60%) (p < 0.001). In B, 13 children (28%) received treatment with continuous positive airway pressure (CPAP) and 2 (4%) with bilevel positive airway pressure (BiPAP), compared with 7 in Group A (6%) (p < 0.001). Maxillofacial surgery was more common in Group B (p < 0.01). Clinical and polysomnography parameters improved significantly in both groups after therapeutic intervention, though Group A showed better results. Conclusions: Obesity and facial anomalies are more frequent in childhood OSA patients with comorbidities, who often require combination therapy, such as ventilation and surgery.

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“…Additionally, the number of subjects involved should be large enough to let researchers use the latest data‐demanding machine‐learning approaches, as well as properly evaluate them. Larger databases would also help cope with different phenotypes in pediatric OSA 53 …”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Reliability of machine learning to diagnose pediatric obstructive sleep apnea: Systematic review and meta‐analysis

Gutiérrez‐Tobal

Álvarez

Kheirandish‐Gozal

et al. 2021

Pediatric Pulmonology

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Background Machine‐learning approaches have enabled promising results in efforts to simplify the diagnosis of pediatric obstructive sleep apnea (OSA). A comprehensive review and analysis of such studies increase the confidence level of practitioners and healthcare providers in the implementation of these methodologies in clinical practice. Objective To assess the reliability of machine‐learning‐based methods to detect pediatric OSA. Data Sources Two researchers conducted an electronic search on the Web of Science and Scopus using term, and studies were reviewed along with their bibliographic references. Eligibility Criteria Articles or reviews (Year 2000 onwards) that applied machine learning to detect pediatric OSA; reported data included information enabling derivation of true positive, false negative, true negative, and false positive cases; polysomnography served as diagnostic standard. Appraisal and Synthesis Methods Pooled sensitivities and specificities were computed for three apnea‐hypopnea index (AHI) thresholds: 1 event/hour (e/h), 5 e/h, and 10 e/h. Random‐effect models were assumed. Summary receiver‐operating characteristics (SROC) analyses were also conducted. Heterogeneity (I 2) was evaluated, and publication bias was corrected (trim and fill). Results Nineteen studies were finally retained, involving 4767 different pediatric sleep studies. Machine learning improved diagnostic performance as OSA severity criteria increased reaching optimal values for AHI = 10 e/h (0.652 sensitivity; 0.931 specificity; and 0.940 area under the SROC curve). Publication bias correction had minor effect on summary statistics, but high heterogeneity was observed among the studies.

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Phenotypic variance in pediatric obstructive sleep apnea

Cited by 15 publications

References 108 publications

Medical Treatment of Obstructive Sleep Apnea in Children

Medical Treatment of Obstructive Sleep Apnea in Children

Features of Obstructive Sleep Apnea in Children with and without Comorbidities

Reliability of machine learning to diagnose pediatric obstructive sleep apnea: Systematic review and meta‐analysis

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