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Identification of obstructive sleep apnea in children with obesity: A cluster analysis approach
Dvir Gatt1,2, Mojtaba Ahmadiankalati3, Giorge Voutsas4
1Division of Respiratory Medicine, Hospital for Sick Children, Toronto, Ontario, Canada.
Insights
Obesity in children increases obstructive sleep apnea (OSA) risk. Easily measured factors like BMI, age, and neck-height ratio can help identify high-risk youth for early OSA detection.
Area of Science:
- Pediatric Pulmonology
- Sleep Medicine
- Obesity Research
Background:
- Obstructive sleep apnea (OSA) affects 25%-60% of children with obesity.
- Current diagnostic tools lack the ability to effectively identify children at high risk for OSA.
Purpose of the Study:
- To identify clinical variables that can help identify obesity-related OSA in children.
- To stratify risk for OSA in pediatric obesity using cluster analysis.
Main Methods:
- A prospective cohort study enrolled 118 children (aged 8-19) with obesity.
- K-means cluster analysis was performed using age, BMI z-score, and neck-height ratio (NHR).
- Hierarchical clustering was used to confirm the stability of identified clusters.
Main Results:
- Two distinct clusters were identified, with OSA prevalence of 22.4% and 58.3%.
- Cluster 2, with higher OSA prevalence, exhibited significantly higher BMI z-scores, NHR, and age compared to Cluster 1.
- No significant differences in sex or OSA symptoms were observed between clusters.
Conclusions:
- Body Mass Index (BMI), NHR, and age are readily available clinical measures.
- These easily obtainable variables can effectively identify children with obesity at high risk for OSA.
Background:
Obstructive sleep apnea (OSA) is a heterogeneous disorder with a prevalence of 25%-60% in children with obesity. There is a lack of diagnostic tools to identify those at high risk for OSA.
Method:
Children with obesity, aged 8-19 years old, were enrolled into an ongoing multicenter, prospective cohort study related to OSA. We performed k-means cluster analysis to identify clinical variables which could help identify obesity related OSA.
Results:
In this study, 118 participants were included in the analysis; 40.7% were diagnosed with OSA, 46.6% were female and the mean (SD) body mass index (BMI) and age were 39.7 (9.6) Kg/m², and 14.4 (2.6) years, respectively. The mean (SD) obstructive apnea-hypopnea index (OAHI) was 11.0 (21.1) events/h. We identified two distinct clusters based on three clustering variables (age, BMI z-score, and neck-height ratio [NHR]). The prevalence of OSA in clusters 1 and 2, were 22.4% and 58.3% (p = 0.001), respectively. Children in cluster 2, in comparison to cluster 1, had higher BMI z-score (4.7 (1.1) versus 3.2 (0.7), p < 0.001), higher NHR (0.3 (0.02) versus 0.2 (0.01), p < 0.001) and were older (15.0 (2.2) versus 13.7 (2.9) years, p = 0.09), respectively. However, there were no significant differences in sex and OSA symptoms between the clusters. The results from hierarchical clustering were similar to k-means analysis suggesting that the resulting OSA clusters were stable to different analysis approaches.
Interpretation:
BMI, NHR, and age are easily obtained in a clinical setting and can be utilized to identify children at high risk for OSA.
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