Defining the patterns of PAP adherence in pediatric obstructive sleep apnea: a clustering analysis using real-world data

Miriam R Weiss; Michelle L Allen; Jeremy S Landeo-Gutierrez; Jenny P Lew; Julia K Aziz; Sylvan S Mintz; Claire M Lawlor; Benjamin J Becerra; Diego A Preciado; Gustavo Nino

doi:10.5664/jcsm.9100

Defining the patterns of PAP adherence in pediatric obstructive sleep apnea: a clustering analysis using real-world data

J Clin Sleep Med. 2021 May 1;17(5):1005-1013. doi: 10.5664/jcsm.9100.

Authors

Miriam R Weiss¹, Michelle L Allen¹, Jeremy S Landeo-Gutierrez^{1

2}, Jenny P Lew^{1

2}, Julia K Aziz^{1

2}, Sylvan S Mintz², Claire M Lawlor^{2

3}, Benjamin J Becerra⁴, Diego A Preciado^{2

3}, Gustavo Nino^{1

2}

Affiliations

¹ Division of Pediatric Pulmonary and Sleep Medicine, Children's National Hospital, George Washington University, Washington, DC.
² Department of Pediatrics, George Washington University, Washington, DC.
³ Division of Pediatric Otorhinolaryngology, Children's National Hospital, George Washington University, Washington, DC.
⁴ Department of Information and Decision Sciences, California State University, San Bernardino, California.

Abstract

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.

Keywords: CPAP; PAP; adherence; children; clusters; pediatric obstructive sleep apnea.

MeSH terms

Child
Cluster Analysis
Continuous Positive Airway Pressure
Humans
Patient Compliance
Pediatrics*
Retrospective Studies
Sleep Apnea, Obstructive*