2021
DOI: 10.5664/jcsm.9100
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Defining the patterns of PAP adherence in pediatric obstructive sleep apnea: a clustering analysis using real-world data

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Cited by 14 publications
(11 citation statements)
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References 34 publications
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“…In this case, although CPAP therapy was not well tolerated initially, tolerance of CPAP therapy can be influenced by a variety of underlying factors. 25 Cheng et al reported six cases with RS where CPAP treatment failed due to severe airway obstruction, and highlighted the need to evaluate for additional airway problems that require more complex surgery in these cases. 4 In our third case, with maturation and in the presence of documented improvement in the severity of airway obstruction, the infant was able to tolerate a later period of home CPAP therapy.…”
Section: Discussionmentioning
confidence: 99%
“…In this case, although CPAP therapy was not well tolerated initially, tolerance of CPAP therapy can be influenced by a variety of underlying factors. 25 Cheng et al reported six cases with RS where CPAP treatment failed due to severe airway obstruction, and highlighted the need to evaluate for additional airway problems that require more complex surgery in these cases. 4 In our third case, with maturation and in the presence of documented improvement in the severity of airway obstruction, the infant was able to tolerate a later period of home CPAP therapy.…”
Section: Discussionmentioning
confidence: 99%
“…This personalized cluster approach may be possible to target PAP therapy optimization at an individualized level. While this study shows promise, additional validation with more extensive longitudinal studies is needed 66 …”
Section: Remote Monitoring and Utility Of Big Data In Monitoring Pap Usementioning
confidence: 94%
“…Since acceptance, response, and adherence to PAP therapy can be heterogeneous in the pediatric population; tailored interventions may be required to optimize adherence using these remote monitoring capabilities. Weiss et al 66 retrospectively examined cloud‐based PAP therapy data in 250 children, and by using a novel method of cluster analysis, they created subgroups based on PAP tolerance (average hours of PAP use on days used) and consistency of PAP use (percentage of days used during the recording period). The cohort was classified into five clusters (A, B, C, D, and E), representing best PAP tolerance and consistent use (cluster A), acceptable PAP tolerance and consistent use (cluster B), poor tolerance to PAP therapy but relatively consistent use (cluster C), acceptable PAP tolerance and inconsistent use (cluster D) and poor PAP tolerance and inconsistent use (cluster E).…”
Section: Remote Monitoring and Utility Of Big Data In Monitoring Pap Usementioning
confidence: 99%
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“…Acceptance of this form of therapy can be a significant challenge, with recent evidence confirming that adherence is generally poor. WEISS et al [50] used statistical cluster analysis to examine clinical characteristics and adherence measures for paediatric CPAP patients. Fewer than half of the children were consistently using CPAP for >4 h a night (the level identified in adult literature as bringing tangible gains).…”
Section: Adenotonsillectomymentioning
confidence: 99%