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Drug-Induced Sleep Endoscopy (DISE) with Target Controlled Infusion (TCI) and Bispectral Analysis in Obstructive Sleep Apnea
07:54

Drug-Induced Sleep Endoscopy (DISE) with Target Controlled Infusion (TCI) and Bispectral Analysis in Obstructive Sleep Apnea

Published on: December 6, 2016

Pediatric OSA-Spectrum of the Disease and Opportunities for Personalized Interventions

Hui-Leng Tan1, Athanasios Kaditis2, David Gozal3

  • 1Department of Pediatric Respiratory Medicine, Royal Brompton Hospital, London SW3 6NP, UK.

Journal of Personalized Medicine
|May 26, 2026
PubMed

Insights

Pediatric obstructive sleep-disordered breathing presents a wide spectrum of phenotypes. Current diagnostic criteria and polysomnography indices inadequately predict associated morbidities, necessitating novel approaches for individualized treatment.

Area of Science:

  • Pediatric Pulmonology
  • Sleep Medicine
  • Genetics

Background:

  • Pediatric obstructive sleep-disordered breathing (oSDB) encompasses a broad range of conditions, from primary snoring to severe obstructive sleep apnea.
  • Significant phenotypic variation exists within oSDB classifications, complicating diagnosis and treatment.
  • Current polysomnography (PSG) indices show poor correlation with clinical morbidities.

Purpose of the Study:

  • To summarize current diagnostic criteria for pediatric oSDB.
  • To explore the phenotypic spectrum of pediatric oSDB.
  • To highlight the relevance of phenotypic variation to treatment decisions and outcomes.

Main Methods:

  • Review of current diagnostic criteria for pediatric obstructive sleep-disordered breathing.
  • Exploration of polysomnographic (PSG) data and its limitations.
  • Discussion of novel approaches including biomarker analysis and data-mining PSG.

Main Results:

  • Polysomnographic indices do not reliably correlate with associated morbidities in pediatric obstructive sleep-disordered breathing.
  • Phenotypic variation within oSDB classifications is substantial.
  • Current diagnostic standards may not fully capture the complexity of the disease spectrum.

Conclusions:

  • Individualized treatment plans for pediatric obstructive sleep-disordered breathing require integrating clinical data with advanced phenotypic information.
  • Novel diagnostic and data-mining approaches are needed to better understand and manage the phenotypic spectrum of pediatric oSDB.
  • Optimizing therapeutic decisions and patient outcomes necessitates a move beyond traditional PSG indices.

Keywords:
DISEartificial intelligencebiomarkersmachine learningobstructive hypoventilationobstructive sleep apneaprimary snoringupper airway resistance syndrome

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