Factors Associated with Changes in Invasive and Noninvasive Positive Airway Pressure Therapy Settings during Pediatric Polysomnograms

Study Objectives:

Our aim was to identify clinical predictors associated with changes in settings for pediatric invasive and noninvasive positive airway pressure therapy, which could help inform the allocation of limited polysomnogram (PSG) resources.

Methods:

A retrospective review was conducted in children who underwent one or more PSGs for technology titration. Children were included if they were using continuous positive airway pressure (CPAP) therapy, bilevel positive airway pressure (BPAP) therapy, or invasive positive pressure ventilation (IPPV) the night of the PSG. The primary outcome measure for the study were predictors of change in settings during IPPV, CPAP, and BPAP titration studies.

Results:

During the study period, 274 children using CPAP, BPAP, or IPPV underwent one or more titration PSGs. The mean (standard deviation [SD]) age of the children at the time of the first titration PSG was 10.52 (5.11) y. Fifty percent (n = 136) of the study participants were male. Most patients underwent BPAP titration studies (n = 166), followed by CPAP (n = 83) and then IPPV (n = 25). A total of 623 technology titration PSGs were completed. Reason for respiratory technology, type of respiratory technology, and time between ventilation initiation and the PSG were significant predictors of a change in settings in the multivariable regression model.

Conclusions:

Children were more likely to have a change in their technology settings during a PSG if there was a shorter period of time from the original technology initiation, if they were using BPAP (as compared to CPAP or IPPV) and/or if they had a primary central nervous system or musculoskeletal diagnosis.

Citation:

Al-Saleh S, Sayal P, Stephens D, Florence J, Sayal A, Baker A, Syed F, Narang I, Amin R. Factors associated with changes in invasive and noninvasive positive airway pressure therapy settings during pediatric polysomnograms. J Clin Sleep Med. 2017;13(2):183–188.

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