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Defining the patterns of PAP adherence in pediatric obstructive sleep apnea: a clustering analysis using real-world
Miriam R Weiss1, Michelle L Allen1, Jeremy S Landeo-Gutierrez1,2
1Division of Pediatric Pulmonary and Sleep Medicine, Children's National Hospital, George Washington University, Washington, DC.
Insights
Clustering pediatric obstructive sleep apnea patients using positive airway pressure (PAP) therapy data reveals distinct subgroups. This approach enables personalized interventions for better PAP therapy outcomes in children.
Area of Science:
- Pediatric Pulmonology
- Sleep Medicine
- Data Science in Healthcare
Background:
- Positive airway pressure (PAP) therapy for pediatric obstructive sleep apnea (OSA) presents challenges due to heterogeneous patient data.
- Current clinical management of pediatric PAP therapy lacks generalizability, necessitating tailored approaches.
Purpose of the Study:
- To subgroup pediatric patients using positive airway pressure (PAP) therapy for obstructive sleep apnea (OSA) via clustering analysis.
- To guide the development of tailored interventions for improved PAP therapy adherence and effectiveness in children.
Main Methods:
- Retrospective analysis of PAP therapy data from 250 children with OSA.
- Unsupervised hierarchical cluster analysis based on PAP tolerance and consistency of use.
- Definition of clinical features within each cluster and generation of a decision tree for clinical implementation.
Main Results:
- Five distinct clusters (A-E) were identified among the 250 pediatric OSA patients.
- Significant differences in PAP use patterns, obesity prevalence, PAP settings, developmental delay, and adenotonsillectomy were observed across clusters.
- Individual responses to PAP therapy, including mask acceptance and objective apnea-hypopnea reductions, varied significantly between clusters.
Conclusions:
- A straightforward method utilizing cloud-based PAP data can effectively subset pediatric PAP use patterns.
- This novel approach facilitates personalized optimization of PAP therapy in children based on real-world, individual-level evidence.
Study Objectives:
The implementation of positive airway pressure (PAP) therapy to treat obstructive sleep apnea in children is a complex process. PAP therapy data are highly heterogeneous in pediatrics, and the clinical management cannot be generalized. We hypothesize that pediatric PAP users can be subgrouped via clustering analysis to guide tailored interventions.
Methods:
PAP therapy data for 250 children with obstructive sleep apnea were retrospectively examined using unsupervised hierarchical cluster analysis based on (1) PAP tolerance (average hours on days used) and (2) consistency of PAP use (percentage of days used). Clinical features in each cluster were defined, and a tree decision analysis was generated for clinical implementation.
Results:
We were able to subclassify all 250 children (median age = 11.5 years) into five clusters: A (13.6%), B (29.6%), C (17.6%), D (16.4%), and E (22.8%). The clusters showed significant differences in PAP use patterns (Kruskal-Wallis P value < 1e-16). The most consistent PAP use patterns were seen in clusters A, B, and C. Major differences across clusters included the prevalence of obesity, PAP setting, developmental delay, and adenotonsillectomy. We also identified important differences in mask acceptance, OSA severity, and individual responses to PAP therapy based on objective apnea-hypopnea reductions in PAP downloads.
Conclusions:
A simple method to subset PAP use patterns in children can be implemented by analyzing cloud-based PAP therapy data. This novel approach may contribute to optimization of PAP therapy in children of all ages based on real-world evidence at the individual level.
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