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Published on: January 26, 2019
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.
Abstract:
(1) Background: Sleep-disordered breathing (SDB) is a frequent problem in children. Cluster analyses offer the possibility of identifying homogeneous groups within a large clinical database. The application of cluster analysis to anthropometric and polysomnographic measures in snoring children would enable the detection of distinctive clinically-relevant phenotypes; (2) Methods: We retrospectively collected the results of nocturnal home-based cardiorespiratory polygraphic recordings and anthropometric measurements in 326 habitually-snoring otherwise healthy children. K-medoids clustering was applied to standardized respiratory and anthropometric measures, followed by Silhouette-based statistics. Respiratory Disturbance Index (RDI) and oxygen desaturation index (≤3%) were included in determining the optimal number of clusters; (3) Results: Mean age of subjects was 8.1 ± 4.1 years, and 57% were males. Cluster analyses uncovered an optimal number of three clusters. Cluster 1 comprised 59.5% of the cohort (mean age 8.69 ± 4.14 years) with a mean RDI of 3.71 ± 3.23 events/hour of estimated sleep (e/ehSleep). Cluster 2 included 28.5% of the children (mean age 6.92 ± 3.43 years) with an RDI of 6.38 ± 3.92 e/ehSleep. Cluster 3 included 12% of the cohort (mean age 7.58 ± 4.73 years) with a mean RDI of 25.5 ± 19.4 e/ehSleep. Weight z-score was significantly lower in cluster 3 [-0.14 ± 1.65] than in cluster 2 [0.86 ± 1.78; p = 0.015] and cluster 1 [1.04 ± 1.78; p = 0.002]. Similar findings emerged for BMI z scores. However, the height z-score was not significantly different among the 3 clusters; (4) Conclusions: Cluster analysis of children who are symptomatic habitual snorers and are referred for clinical polygraphic evaluation identified three major clusters that differed in age, RDI, and anthropometric measures. An increased number of children in the cluster with the highest RDI had reduced body weight. We propose that the implementation of these approaches to a multicenter-derived database of home-based polygraphic recordings may enable the delineation of objective unbiased severity categories of pediatric SDB. Our findings could be useful for clinical implementation, formulation of therapeutic decision guidelines, clinical management, prevision of complications, and long-term follow-up.
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