Cluster Analysis of Home Polygraphic Recordings in Symptomatic Habitually-Snoring Children: A Precision Medicine

Marco Zaffanello1, Angelo Pietrobelli1, David Gozal2

  • 1Department of Surgical Sciences, Dentistry, Gynecology and Pediatrics, University of Verona, 37129 Verona, Italy.

Insights

This study identified three distinct groups of children with sleep-disordered breathing (SDB) based on age, respiratory disturbance index (RDI), and weight. Children with higher RDI showed lower body weight, suggesting a link between SDB severity and nutritional status in pediatric populations.

Area of Science:

  • Pediatric Pulmonology
  • Sleep Medicine
  • Biostatistics

Background:

  • Sleep-disordered breathing (SDB) is a common condition in children.
  • Cluster analysis can identify homogeneous patient groups within clinical data.
  • Phenotyping SDB in children using anthropometric and polysomnographic data is valuable.

Purpose of the Study:

  • To identify distinct phenotypes of SDB in habitually snoring children using cluster analysis.
  • To analyze anthropometric and polysomnographic measures to define these phenotypes.
  • To explore the clinical relevance of identified clusters for SDB management.

Main Methods:

  • Retrospective analysis of home-based cardiorespiratory polygraphic recordings and anthropometric data from 326 snoring children.
  • K-medoids clustering applied to standardized respiratory and anthropometric measures.
  • Silhouette statistics and indices like RDI and oxygen desaturation were used to determine optimal clusters.

Main Results:

  • Three distinct clusters of SDB phenotypes were identified.
  • Cluster 3, representing 12% of the cohort, showed the highest mean RDI (25.5 e/ehSleep) and significantly lower weight and BMI z-scores compared to other clusters.
  • Clusters differed significantly in age and RDI, but not in height z-score.

Conclusions:

  • Cluster analysis successfully delineated three clinically relevant phenotypes of SDB in children.
  • A subset of children with severe SDB (high RDI) exhibited reduced body weight, highlighting a potential link between SDB severity and nutritional status.
  • These findings support the use of cluster analysis on multicenter data for objective SDB severity categorization, aiding clinical guidelines and management.