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Volume 13 No. 02
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Accepted Papers





Scientific Investigations

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

Suhail Al-Saleh, MBBS, MSc1,2; Priya Sayal, HBSc2; Derek Stephens, MSc3; Joshua Florence1; Aman Sayal1; Adele Baker, RRT1; Faiza Syed, RRT1; Indra Narang, MBBCH, MD1,2; Reshma Amin, MD, MSc1,2
1Division of Respiratory Medicine, Hospital for Sick Children, Toronto, Canada; 2University of Toronto, Toronto, Canada; 3Biostatistics, Hospital for Sick Children, Toronto, Canada

ABSTRACT

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.


INTRODUCTION

Over the past few decades, the number of children requiring invasive and noninvasive positive airway pressure therapy for sleep-disordered breathing (SDB) is increasing.1,2 This is in part due to the increased awareness of sleep disorders in children as well as knowledge of the effect of treatment on end-organ function. Coincident with this, there has been a shift in patient diagnoses mandating long-term ventilation.3 Additionally, advances in medical care and respiratory technology specifically available in the home has increased the breadth of ventilatory modalities for long-term use.1,4,5 Also, there has been a deeper understanding of the family-centered benefits for transitioning out of acute care facilities.6,7 The current gold standard test for the diagnosis of SDB is a laboratory-based, technician-attended polysomnogram (PSG). PSGs are also routinely used to optimize invasive and noninvasive positive airway pressure (PAP) therapy settings for ambulatory care. Currently, there is a lack of pediatric sleep diagnostic facilities in several countries in the world, resulting in long wait lists for children.8

The optimal timing of follow-up PSGs in children prescribed invasive and noninvasive PAP therapy for SDB is an area that has not been well studied. Although there are international guidelines suggesting that PSGs should be performed every 6 to 12 mo, this is based on expert opinion rather than the existing literature.9 Currently, there has been only one previous publication that has studied this issue. Tan et al. reported on a small cohort of children using respiratory technology that underwent PSG but did not find any clinical parameters that predicted a change in technology settings.10 Our aim was to identify clinical predictors associated with changes in settings for pediatric invasive and noninvasive PAP therapy that could help inform clinical care and the subsequent allocation of limited PSG resources.

BRIEF SUMMARY

Current Knowledge/Study Rationale: The optimal timing of follow-up polysomnograms in children prescribed invasive and noninvasive positive airway pressure therapy for sleep-disordered breathing is an area that has not been well studied. Although there are international guidelines suggesting that polysomnograms should be performed every 6 to 12 mo, this is based on expert opinion rather than the existing literature.

Study Impact: Our data suggest that clinicians should pay particular attention to the type of positive airway pressure therapy, the underlying primary diagnosis, and the time from the initiation of therapy when requesting follow-up positive airway pressure therapy titration studies. Our study is the first to report clinical predictors of changes in technology settings during pediatric polysomnograms, which can begin to be translated into clinical practice and iteratively updated as new literature emerges for this rapidly growing population.

METHODS

A retrospective review was conducted in children who underwent one or more PSGs for technology titration at the Hospital for Sick Children between January 1, 2009 and December 31, 2013. 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 health care record was reviewed to collect data on patient demographics and anthropometrics (age, sex, body mass index, height, weight), primary medical diagnosis and reason for respiratory technology use (central nervous system [CNS], musculoskeletal [MSK], or Respiratory [Resp]),1 technology type (CPAP, BPAP, IPPV), obstructive apnea-hypopnea index (OAHI), central apnea-hypopnea index (CAHI), age at the time of the initiation of respiratory technology, and time from the last PSG. This study was approved (REB #1000045746) by the Research Ethics Board at the Hospital for Sick Children, University of Toronto, Canada.

All PSGs were conducted according to the The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications with a computer-based software system (XL-TEK, Oakville, Ontario, Canada).11,12 A standard overnight PSG included a six-lead electroencephalogram (C3, C4, F3, F4, O1, O2), two bilateral electrooculogram leads referenced to A1 or A2, one submental and two tibial electromyograms. Respiratory measurements included chest wall and abdominal movement using inductance pneumography; airflow using a nasal cannula connected to a nasal pressure airflow machine by Braebon (Kanata, Ontario, Canada); oxygen saturation using a Massimo pulse oximeter (Irvine, CA); and transcutaneous carbon dioxide measurement (TcCO2) using a LINDE carbon dioxide sensor (Munich, Germany). Video and audio recordings were obtained for each study. Sleep architecture was assessed by standard techniques. Information obtained from each PSG included sleep onset latency and rapid eye movement (REM) onset latency, total sleep time, sleep efficiency, time spent in each sleep stage (percentage), and number and classification of arousals and snoring. All respiratory events were also scored according to the respiratory rules for children in the AASM guidelines. Recorded respiratory data included counts and indexes of the following events: obstructive apneas, obstructive hypopneas, central apneas, and mixed apneas during sleep. The OAHI was defined as the number of obstructive apneas, hypopneas, and mixed apneas per hour. The CAHI was defined as the number of central apneas and hypopneas per hour. Hypoventilation was defined as a TcCO2 level greater than 50 mmHg for greater than or equal to 25% of the night. Obstructive sleep apnea (OSA) was defined as an OAHI ≥ 2 events/h. Moderate-severe OSA was defined as OAHI ≥ 5 events/h. Central sleep apnea was defined as a CAHI ≥ 5 events/h.

All of the titration studies were performed in accordance with our sleep center's titration protocol, which has been adapted from the AASM guideline.11 IPPV titrations were performed by a dual-trained respiratory therapist and sleep technologist. BPAP and CPAP titrations were performed by a respiratory therapist–trained sleep technologist or sleep technologist. None of our patients used autotitrating CPAP.

In general, the aim of nocturnal CPAP or BPAP for SDB was to achieve the following: (1) Baseline SpO2 > 93%; for patients with cyanotic heart disease or pulmonary hypertension, this goal was not attainable or appropriate for some patients; (2) minimal obstructive apneas; i.e., apnea-hypopnea index < 1.5 events/h and with no significant oxygen desaturations or arousals; (3) minimal central apneas and hypopneas (i.e., CAHI < 1.0 events/h); (4) almost-complete abolition of snoring, paradoxical breathing, and flow limitation; (5) TcCO2 less than 50 mmHg for at least 25% of the night; for patients with chronic lung disease and chronically elevated PaCO2, this goal may not be attainable; (6) decreased arousals and improved sleep architecture; (7) decreased work of breathing, including a decrease in the total respiratory rate to generally less than 25 breaths/min.

Patients were fit with an interface prior to the initiation of PAP therapy. The initial fit was for size and comfort. After the application of PAP pressure, the fit required adjustment and in some cases, a change to a different mask or headgear.

For patients new to CPAP, the CPAP titration was initiated at 4 cmH2O. The CPAP pressure was increased in increments of 1 to 2 cmH2O as necessary to achieve the goals of therapy as previously described. If needed, the CPAP pressures were increased to a maximum of 15 cmH2O. The patient was switched to BPAP when CPAP failed to achieve the goals of therapy.

Patients were initiated on BPAP spontaneous/timed mode. For patients new to BPAP, the titration was initiated at pressures of 8/4 cmH2O and a backup rate at 8 breaths/min. The Expiratory Positive Airway Pressure (EPAP) pressure was increased in increments of 1 to 2 cmH2O as necessary to achieve the goals of therapy as previously described. The IPAP was increased from a baseline of 8 in increments of 1 to 2 cmH2O while always maintaining a minimum pressure gradient (delta P) of 4 cmH2O. The inspiratory time max was set to exceed the patient's natural inspiratory phase (generally between 1.0–1.3 sec) but short enough to terminate positive pressure delivery in the event of a system leak (generally less than 1.5 sec). The inspiratory time min and max, rise time, and trigger and cycle sensitivity were adjusted based on bedside observation to improve patient-machine synchrony.

The primary outcome measure for the study were predictors of change in settings during IPPV, CPAP, and BPAP titration studies. A major change in settings was defined as a change in mode, the pressures and/or the rate, or mask change. A minor change in settings was defined as change in the inspiratory time, rise time, trigger, or cycle setting. Patients were excluded from the multiple regression analysis if the PSG was a CPAP or BPAP initiation study. IPPV was not initiated in our sleep laboratory.

Descriptive statistics were used to summarize the study results. Results were presented as the mean and standard deviation (SD) for normally distributed data or as the median and interquartile range. For the primary outcome measure, multivariable regression analysis for predictors of a change in settings during a technology titration study was performed. A value of p < 0.05 was considered significant. Statistical analysis was performed using SAS 9.4 (Cary, NC, USA).

RESULTS

During the study period, 274 children using CPAP, BPAP, or IPPV underwent one or more titration PSGs. See Table 1 for baseline demographics of the study population. The mean (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 had BPAP titration studies (n = 166), followed by CPAP (n = 83) and then IPPV (n = 25). Of the 166 patients who had their first study using BPAP, 137 studies (83%) were titration studies, 28 (17%) were initiation studies, and 1 patient was recommended to discontinue therapy after the study. Eighty-three patients had their first PSG using CPAP during the study period; there were 49 CPAP titration studies (59%), 31 CPAP initiation studies, (37%) 2 patients (2%) were switched to BPAP, and 1 patient (1%) was recommended to discontinue CPAP. All 25 patients who were using IPPV underwent IPPV titration studies. Respiratory disorders accounted for most of the primary diagnoses in the patients prescribed IPPV, CPAP, and BPAP, (n = 138, 50%). This was followed by MSK (n = 76, or 28%) and then CNS disorders, (n = 60, or 22%) (Table 2). Table 3 summarizes the SDB diagnoses present during the baseline PSG.

Descriptive statistics for the study population.

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Table 1

Descriptive statistics for the study population.

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Primary medical diagnoses of the study participants.

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Table 2

Primary medical diagnoses of the study participants.

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Sleep-disordered breathing diagnoses on the baseline polysomnograms by patient (n = 274).

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Table 3

Sleep-disordered breathing diagnoses on the baseline polysomnograms by patient (n = 274).

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A total of 623 technology titration PSGs were completed. Of these studies, there was a major change in settings in 388 (62.4%): 67 (10.8%) had a minor change in settings and 168 (27%) of the studies had no change in settings. There was a mask change in 26 studies (4%) and a mode change in 20 studies (3%).

Univariate analysis was performed for change in technology settings during a PSG (Table 4). Age at the time of the PSG, reason for respiratory technology, and time between ventilation initiation and the PSG were significant predictors in the univariate analysis.

Results of the univariate analysis for a change in technology settings.

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Table 4

Results of the univariate analysis for a change in technology settings.

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A multivariable regression model controlling for the variables described in the univariate analysis was generated for the predictors of change in technology settings (Table 5). Reason for respiratory technology, type of respiratory technology, and time between ventilation initiation and the PSG were significant predictors of a change in settings. If patients had a respiratory etiology to be using technology, they were then less likely to undergo a change in settings during the PSG as compared to those with a CNS- or MSK-related primary diagnosis. Patients using BPAP during the PSG had a greater chance of a change in settings as compared to those using CPAP and IPPV. A shorter period of time between ventilation initiation and the follow-up PSG was associated with a higher likelihood of a change in settings.

Repeated-measures regression model demonstrating the predictors of change in technology settings.

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Table 5

Repeated-measures regression model demonstrating the predictors of change in technology settings.

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DISCUSSION

Currently, despite the dramatic changes in airway growth and respiratory mechanics that occur throughout childhood, there is very little evidence outlining the optimal surveillance of respiratory technology settings after pediatric invasive and noninvasive PAP therapy is initiated. We have identified for the first time, to our knowledge, three clinical factors associated with a change in invasive or noninvasive positive airway therapy settings during a PAP titration PSG. Children are more likely to have a change in their technology settings if there is a shorter period of time from the original initiation, if they are using BPAP (as compared to CPAP or IPPV), and/ or if they have a primary CNS or MSK diagnosis. Clinically, these predictors seem plausible as children often do not tolerate the initial initiation of technology and significant changes in settings often occur during the subsequent titration study. In addition, children prescribed BPAP are more medically fragile than children using CPAP for SDB; changes in their underlying medical condition are likely driving the changes in settings. Similarly, primary CNS or MSK diagnosis as a predictor of major change in settings may also be due to the progression of the underlying disease.

Only one previous paper has formally evaluated clinical factors as predictors of changes in noninvasive PAP therapy. Tan et al. reviewed the PSG results of 45 children using CPAP or BPAP.10 In their program, because of resource constraints, the sleep studies are performed unattended after being set up by trained respiratory technologists and only a third were full PSG studies. The authors identified a change in respiratory technology settings in 66% of studies. Interestingly, in our study, there was a change in technology settings in 73% of the titration studies. Although only slightly higher, the greater frequency of change in settings in our study may be the result of having a sleep technologist present the entire night in our sleep center. Tan et al.10 were not able to identify any clinical predictors of change in technology settings. However, this may have been related to the relatively small cohort size.

There are several limitations to our study. First, our study was retrospective and limited to information available in the health care records. Therefore, we have PSG-based data for the titration studies, but symptom-based data relating to the use of technology or the response to changes in the technology settings was not available. Second, we did not have adherence data available on all of the patients. Therefore, we are unable to assess whether adherence or lack of adherence was associated with changes in settings during technology titrations. Third, we are reporting the experience of one tertiary sleep center, which likely reflects an inherent bias related to the medical complexity of our patients. Although it is unclear if our results are generalizable to other sleep programs around the world, it is notable that both our study as well as the one by Tan et al. from New Zealand report similar frequencies of setting changes in technology titration PSGs. Further prospective, multicentered studies are needed to determine how changes in technology settings relate to symptoms, adherence, and health care service utilization as well as patient-reported health-related quality of life.

CONCLUSIONS

In summary, 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 CNS or MSK diagnosis. Our data suggest that clinicians should pay particular attention to the type of technology as well as the underlying primary diagnosis when requesting PAP titration studies. In addition, our results suggest that the first titration study after the initiation of PAP therapy is important and should be done not more than 1 y after the initiation of therapy because it is likely to be associated with significant changes in the technology prescription. Although changes in symptoms or adherence were not available, our study was the first to report clinical predictors of changes in technology settings during pediatric PSGs, which can begin to be translated into clinical practice and iteratively updated as new literature emerges for this rapidly growing population.

DISCLOSURE STATEMENT

This was not an industry supported study. The authors have indicated no financial conflicts of interest.

ABBREVIATIONS

AASM

American Academy of Sleep Medicine

AHI

apnea-hypopnea index

BMI

body mass index

BPAP

bilevel positive airway pressure therapy

CAHI

central apnea-hypopnea index

CCHS

congenital central hypoventilation syndrome

CI

confidence interval

CNS

central nervous system

CPAP

continuous positive airway pressure

CSA

central sleep apnea

DMD

Duchenne muscular dystrophy

EEG

electroencephalogram

EMG

electromyogram

EOG

electrooculogram

IPPV

invasive positive pressure ventilation

MSK

musculoskeletal

NPAF

nasal pressure airflow

OAHI

obstructive apnea-hypopnea index

OSA

obstructive sleep apnea

PAP

positive airway pressure

PSG

polysomnogram

REM

rapid eye movement

Resp

respiratory

RT

respiratory therapist

SD

standard deviation

SDB

sleep-disordered breathing

SpO2

oxygen saturation

TcCO2

transcutaneous carbon dioxide

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