Evidence map›Paper›PMID 33538691›Full record

ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2021

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

Open access · bronzeAbstract read
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In one paragraph

Article in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.7field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 14 citations in OpenAlex.

  1. Pleiotropic effects of a high confidence Autism Spectrum Disorder gene,Neurobiology of sleep and circadian rhythms · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 4 institutions in 1 country.

Miriam R WeissDivision of Pediatric Pulmonary and Sleep Medicine, Children's National Hospital, George Washington University, Washington, DC.
Michelle L AllenDivision of Pediatric Pulmonary and Sleep Medicine, Children's National Hospital, George Washington University, Washington, DC.
Jeremy S Landeo-GutierrezDivision of Pediatric Pulmonary and Sleep Medicine, Children's National Hospital, George Washington University, Washington, DC.
Jenny P LewDivision of Pediatric Pulmonary and Sleep Medicine, Children's National Hospital, George Washington University, Washington, DC.
Julia K AzizDivision of Pediatric Pulmonary and Sleep Medicine, Children's National Hospital, George Washington University, Washington, DC.
Sylvan S MintzDepartment of Pediatrics, George Washington University, Washington, DC.
Claire M LawlorDepartment of Pediatrics, George Washington University, Washington, DC.
Benjamin J BecerraDepartment of Information and Decision Sciences, California State University, San Bernardino, California.
Diego A PreciadoDepartment of Pediatrics, George Washington University, Washington, DC.
Gustavo NinoDivision of Pediatric Pulmonary and Sleep Medicine, Children's National Hospital, George Washington University, Washington, DC.
George Washington University · USGeorge Washington University Hospital · USChildren's National · USDecision Sciences (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

STUDY

objectivesThe 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.

methodsPAP 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.

resultsWe 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.

conclusionsA 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.

Indexed as

PediatricsSleep Apnea, ObstructiveChildCluster AnalysisContinuous Positive Airway PressureHumansPatient ComplianceRetrospective StudiesadherencechildrenclustersCPAPPAPpediatric obstructive sleep apnea

Identifiers

PMID33538691
PMCPMC8320501
OpenAlexW3128508789

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.