Validity and reliability of the Thai version of the pediatric obstructive sleep apnea screening tool

Tanphaichitr, Archwin; Chuenchod, Pitchayanan; Ungkanont, Kitirat; Banhiran, Wish; Vathanophas, Vannipa; Gozal, David

doi:10.1002/ppul.25534

Cited by 4 publications

(1 citation statement)

References 27 publications

(82 reference statements)

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“…Using clinical features and questionnaires is a cost-effective approach for identifying children with OSA, circumventing the need for specialized laboratory and sleep monitoring equipment (19)(20)(21)(22)(23). Notably, a study has enhanced the efficacy of OSA screening by using Selected Features and optimizing existing ones, demonstrating improved performance after eliminating redundancy features (24).…”

Section: Introductionmentioning

confidence: 99%

Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis

Qin,

Zhang,

et al. 2024

Front. Pediatr.

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ObjectiveThe objective of this study was to investigate the effectiveness of a machine learning algorithm in diagnosing OSA in children based on clinical features that can be obtained in nonnocturnal and nonmedical environments.Patients and methodsThis study was conducted at Beijing Children's Hospital from April 2018 to October 2019. The participants in this study were 2464 children aged 3–18 suspected of having OSA who underwent clinical data collection and polysomnography(PSG). Participants’ data were randomly divided into a training set and a testing set at a ratio of 8:2. The elastic net algorithm was used for feature selection to simplify the model. Stratified 10-fold cross-validation was repeated five times to ensure the robustness of the results.ResultsFeature selection using Elastic Net resulted in 47 features for AHI ≥5 and 31 features for AHI ≥10 being retained. The machine learning model using these selected features achieved an average AUC of 0.73 for AHI ≥5 and 0.78 for AHI ≥10 when tested externally, outperforming models based on PSG questionnaire features. Linear Discriminant Analysis using the selected features identified OSA with a sensitivity of 44% and specificity of 90%, providing a feasible clinical alternative to PSG for stratifying OSA severity.ConclusionsThis study shows that a machine learning model based on children's clinical features effectively identifies OSA in children. Establishing a machine learning screening model based on the clinical features of the target population may be a feasible clinical alternative to nocturnal OSA sleep diagnosis.

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Section: Introductionmentioning

confidence: 99%