Demographic factors associated with postoperative polysomnography for pediatric patients with persistent obstructive sleep apnea

Fujita, Kevin; Huang, Thomas; Rohrbaugh, Tagan; Trott, Kiley

doi:10.1016/j.ijporl.2022.111428

Cited by 2 publications

(3 citation statements)

References 24 publications

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“…In keeping with the literature, Patients with trisomy 21 had higher rates of persistent T&A, likely secondary to underlying anatomic airway abnormalities, as these children often demonstrate during DISE obstruction at the base of the tongue or the arytenoids when compared to children without trisomy 21, explaining the lack of significance of BMI in this group 59 . In addition, patients undergoing a repeat PSG were more likely to undergo another sleep‐related intervention, including DISE, lingual tonsillectomy, and inferior turbinate reduction 58 . Quan et al 60 found that pediatric PSG was underused nationally, with only 5% of children having preoperative PSG.…”

Section: Treatment Of Sdbmentioning

confidence: 96%

“…While the AAP recommends only postoperative PSG for high‐risk patients, 54 the American Academy of Otolaryngology‐Head and Neck Surgery (AAO‐HNS) neither recommend nor oppose a PSG posttreatment 54 . Fujita et al 58 found that in a population of 1034 patients undergoing adenotonsillectomy <25 years at Yale, 101 had postsurgical PSG. Patients with a higher BMI (26.64 vs. 21.84, p = <0.01) and trisomy 21 (9.9% vs. 1.5%, p < 0.01) had a higher likelihood of undergoing repeat PSG.…”

Section: Treatment Of Sdbmentioning

confidence: 99%

“…59 In addition, patients undergoing a repeat PSG were more likely to undergo another sleep-related intervention, including DISE, lingual tonsillectomy, and inferior turbinate reduction. 58 Quan et al 60 found that pediatric PSG was underused nationally, with only 5% of children having preoperative PSG. Factors such as male sex, obesity, medical comorbidities, non-White race/ethnicity, and higher parental education were associated with receiving PSG.…”

Section: Treatment Of Sdbmentioning

confidence: 99%

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Pediatric pulmonology year in review 2022: Sleep medicine

Cabrera,

Tapia

2023

Pediatric Pulmonology

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Pediatric pulmonology publishes original research, review articles, and case reports on a wide variety of pediatric respiratory disorders. In this article, we summarized the past year's publications in sleep medicine and reviewed selected literature from other journals in this field. We focused on original research articles exploring aspects of sleep‐disordered breathing in patients with underlying conditions such as asthma, neuromuscular disorders, and Down syndrome. We also explored sleep‐disordered breathing risk factors, monitoring, diagnosis, and treatment; and included recent recommendations for drug‐induced sleep endoscopy and ways to monitor and improve PAP adherence remotely.

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Section: Treatment Of Sdbmentioning

confidence: 96%

Section: Treatment Of Sdbmentioning

confidence: 99%

Section: Treatment Of Sdbmentioning

confidence: 99%

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Pediatric pulmonology year in review 2022: Sleep medicine

Cabrera,

Tapia

2023

Pediatric Pulmonology

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Predictive Power of XGBoost_BiLSTM Model: A Machine-Learning Approach for Accurate Sleep Apnea Detection Using Electronic Health Data

Javeed,

Berglund,

Dallora

et al. 2023

Int J Comput Intell Syst

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Sleep apnea is a common disorder that can cause pauses in breathing and can last from a few seconds to several minutes, as well as shallow breathing or complete cessation of breathing. Obstructive sleep apnea is strongly associated with the risk of developing several heart diseases, including coronary heart disease, heart attack, heart failure, and stroke. In addition, obstructive sleep apnea increases the risk of developing irregular heartbeats (arrhythmias), which can lead to low blood pressure. To prevent these conditions, this study presents a novel machine-learning (ML) model for predicting sleep apnea based on electronic health data that provides accurate predictions and helps in identifying the risk factors that contribute to the development of sleep apnea. The dataset used in the study includes 75 features and 10,765 samples from the Swedish National Study on Aging and Care (SNAC). The proposed model is based on two modules: the XGBoost module assesses the most important features from feature space, while the Bidirectional Long Short-Term Memory Networks (BiLSTM) module classifies the probability of sleep apnea. Using a cross-validation scheme, the proposed XGBoost_BiLSTM algorithm achieves an accuracy of 97% while using only the six most significant features from the dataset. The model’s performance is also compared with conventional long-short-term memory networks (LSTM) and other state-of-the-art ML models. The results of the study suggest that the proposed model improved the diagnosis and treatment of sleep apnea by identifying the risk factors.

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Demographic factors associated with postoperative polysomnography for pediatric patients with persistent obstructive sleep apnea

Cited by 2 publications

References 24 publications

Pediatric pulmonology year in review 2022: Sleep medicine

Pediatric pulmonology year in review 2022: Sleep medicine

Predictive Power of XGBoost_BiLSTM Model: A Machine-Learning Approach for Accurate Sleep Apnea Detection Using Electronic Health Data

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