Utilisation of machine learning to predict surgical candidates for the treatment of childhood upper airway obstruction

Xiao Liu, Yvonne Pamula, Sarah Immanuel, Declan Kennedy, James Martin, Mathias Baumert

Research output: Contribution to journalArticlepeer-review

Abstract

Objective: To investigate the effect of adenotonsillectomy on OSAS symptoms based on a data-driven approach and thereby identify criteria that may help avoid unnecessary surgery in children with OSAS. Methods: In 323 children enrolled in the Childhood Adenotonsillectomy Trial, randomised to undergo either early adenotonsillectomy (eAT; N = 165) or a strategy of watchful waiting with supportive care (WWSC; N = 158), the apnea-hypopnea index, heart period pattern dynamics, and thoraco-abdominal asynchrony measurements from overnight polysomnography (PSG) were measured. Using machine learning, all children were classified into one of two different clusters based on those features. The cluster transitions between follow-up and baseline PSG were investigated for each to predict those children who recovered spontaneously, following surgery and those who did not benefit from surgery. Results: The two clusters showed significant differences in OSAS symptoms, where children assigned in cluster A had fewer physiological and neurophysiological symptoms than cluster B. Whilst the majority of children were assigned to cluster A, those children who underwent surgery were more likely to stay in cluster A after seven months. Those children who were in cluster B at baseline PSG were more likely to have their symptoms reversed via surgery. Children who were assigned to cluster B at both baseline and 7 months after surgery had significantly higher end-tidal carbon dioxide at baseline. Children who spontaneously changed from cluster B to A presented highly problematic ratings in behaviour and emotional regulation at baseline. Conclusions: Data-driven analysis demonstrated that AT helps to reverse and to prevent the worsening of the pathophysiological symptoms in children with OSAS. Multiple pathophysiological markers used with machine learning can capture more comprehensive information on childhood OSAS. Children with mild physiological and neurophysiological symptoms could avoid AT, and children who have UAO symptoms post AT may have sleep-related hypoventilation disease which requires further investigation. Furthermore, the findings may help surgeons more accurately predict children on whom they should perform AT.

Original languageEnglish
Number of pages13
JournalSleep and Breathing
Early online date17 Jul 2021
DOIs
Publication statusE-pub ahead of print - 17 Jul 2021

Keywords

  • Adenotonsillectomy
  • Children
  • Data-driven
  • Machine learning
  • Sleep apnea

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