(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.

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

Zaffanello, M
Membro del Collaboration Group
;
Pietrobelli, A
Membro del Collaboration Group
;
Ferrante, G
Membro del Collaboration Group
;
Piazza, M
Membro del Collaboration Group
;
Piacentini, G
Membro del Collaboration Group
2022

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.
children
cluster analysis
obstructive sleep apnea
polygraphy
sleep-disordered breathing
sleep apnea
snoring
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1077227
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