Evaluation of Adherence and Therapeutic Effectiveness of Bi-Flex Versus CPAP in Children With OSA

Overview

Context: The obstructive sleep apnea syndrome (OSAS), i.e., snoring with difficulty breathing during sleep, is common in children. Continuous positive airway pressure (CPAP) therapy is the usual treatment for children who do not improve following surgery. However, CPAP is uncomfortable and is often not tolerated. We therefore plan to study a modification of bilevel positive airway pressure therapy, BiPAP with Bi-Flex that may be more comfortable. Objectives: The primary objective of this study is to determine whether BiPAP with Bi-Flex results in improvement in adherence as compared to CPAP. The secondary objective is to determine whether Bi-Flex has similar therapeutic efficacy compared to CPAP, as determined by sleep study. Additional objectives include comparing CPAP and Bi-Flex effects on comfort and determining which parameters predict adherence. Study Design/Setting/Participants: A single center, randomized controlled double-blind study of Bi-Flex vs CPAP use in children with OSAS over a 3 month period. Intervention: Bi-Flex vs CPAP Study Measures: Objective compliance recordings, sleep study results, subjective questionnaire results. .

STUDY RATIONALE The obstructive sleep apnea syndrome (OSAS) affects approximately 2% of children and results in significant morbidity. Continuous positive airway pressure (CPAP) is the standard therapy for children who fail tonsillectomy and adenoidectomy, or for those in whom surgery is not indicated. However, CPAP is uncomfortable and is often not tolerated by children. Thus, a more comfortable technology that would enhance adherence would be highly desirable. This proposal aims to compare a new technology, Bi-Flex, to standard CPAP therapy. Bilevel positive airway pressure (BiPAP): Whereas conventional CPAP therapy delivers a constant, steady pressure during inspiration and expiration, BiPAP therapy is intended to respond to both inspiration and expiration by the patient and to deliver a set amount of pressure when the patient begins spontaneous inhalation, and decreasing pressure when exhalation begins. Exhaling against lower pressure is thought to be more comfortable for most patients than the continuous pressure delivered by conventional CPAP therapy, although it has not been shown objectively to improve adherence. The BiPAP waveform is fixed in that it provides for set inspiratory and expiratory pressures. For example, the patient's clinically prescribed BiPAP pressures may be set at an Inspiratory Positive Airway Pressure (IPAP) =12 cm water (H2O), and Expiratory Positive Airway Pressure (EPAP) = 8 cm H2O. The Bi-Flex® feature provides a variation of conventional BiPAP that provides a reduction in the inspiratory positive airway pressure (IPAP) provided toward the end of the inspiratory portion of the breathing cycle when the patient's inspiratory airflow normally diminishes, compared with the level of pressure provided during the majority of inspiration, and also allows reduction in the expiratory positive airway pressure (EPAP) during the initial portion of exhalation compared with that provided during the latter portion of expiration. The purpose of this modification is to provide pressure relief for the patient to smooth the transition period between the end of IPAP and the beginning of EPAP to allow for a more comfortable delivery of PAP therapy. Because Bi-Flex provides both a lower expiratory pressure and pressure relief, it has the potential to be the most comfortable mode of positive pressure therapy delivery available, and therefore to improve adherence. However, there have been no studies of adherence to therapy using Bi-Flex in either children or adults. STUDY OBJECTIVES 1. To determine whether BiPAP with Bi-Flex as compared to CPAP results in improvement in adherence of \> 30 minutes a night averaged over 3 months of home use. 2. To determine whether BiPAP with Bi-Flex as compared to CPAP results in a lower study dropout rate. 3. To determine whether Bi-Flex has similar therapeutic efficacy compared to CPAP, as determined by polysomnographic measurements of OSAS. 4. To determine whether Bi-Flex, compared to CPAP, results in improved subjective comfort and improved quality of life. 5. To determine whether objective parameters (including demographic and polysomnographic variables) or subjective parameters predict adherence. STUDY DESIGN This will be a 3-month, double-blinded, randomized trial of BiPAP with Bi-Flex vs CPAP in children with OSAS in whom CPAP is medically indicated Subjects will undergo a baseline diagnostic sleep study as part of their routine clinical care, prior to study enrollment. Those who agree to participate will then be consented and assented to participate in this study. They will be randomized to either a Bi-Flex or CPAP treatment arm, in a three to one fashion. In each arm, each subject will undergo a clinical Bi-Flex or CPAP titration sleep study. Following these studies, the subjects in both arms will be asked to use their respective machines, in either the BiPAP with Bi-Flex or CPAP mode, at home for 3 months. During that time, they will have monthly visits to assess adherence as well as subjective measures of comfort and quality of life. A repeat sleep study will be performed at the end of 3 months on the mode assigned (Bi-Flex or CPAP). Subjects will complete standardized questionnaires regarding symptoms and quality of life throughout the study. EFFICACY EVALUATIONS Adherence will be assessed by the mean nightly usage as determined by the equipment compliance recording. Drop-out will be assessed by determining which subjects are using the device \< 2 hours a night. Efficacy of treatment will be assessed using polysomnographic parameters such as the apnea hypopnea index. SAFETY EVALUATIONS Polysomnographic parameters such as the apnea hypopnea index, arterial oxygen saturation and end-tidal PCO2 will be monitored. STATISTICAL AND ANALYTIC PLAN The primary effectiveness endpoint is the hours of device use per night over a three month period, as determined from the equipment software. Analysis Populations Two analysis populations will be evaluated. An intent-to-treat (ITT) analysis will include all patients in the trial by their assigned treatment and will be used for effectiveness and safety. For patients with missing data, values will be imputed as described below. A second analysis will be done on evaluable patients, i.e., those who have complete data and these patients will be evaluated for effectiveness. Primary Safety Analysis The rate of adverse events will be compared between the two groups by assessing the proportion of subjects who experience at least one adverse event by Fisher's exact test. If the test is statistically significant, then this univariate analysis will be followed by a multivariate logistic regression analysis. Screening of possible covariates will be done by a method similar to that described in Hosmer and Lemeshow 25 for logistic regression models. A complete list of clinically relevant variables to be screened will be provided in a detailed statistical analysis plan formulated prior to database lock but will include age, gender, height, weight, and others. The variables on this list will be augmented with variables found to be out of balance between the treatment groups from the comparability analysis described above. Models will be fit for each possible covariate to include treatment, the covariate, and the interaction between the covariate and treatment will be used. Any covariate or interaction significant at a P-value of 0.15 or lower from the screening analysis will allow the variable to enter the backward elimination procedure for the final model. If the interaction is significant, then both the covariate and its interaction need to be included in the model. The backward elimination will be done manually starting with the interaction with the highest P-value. Interaction terms will be removed from the model first and then non-significant main effects. Variables will be retained in the model if the P-value for the variable or its interaction is 0.05 or less. This analysis is intended to demonstrate that the treatment affect adjusted for possible covariates is still statistically significant. Secondary Safety Analyses The secondary safety analysis will include a descriptive presentation of individual adverse events each tested with Fisher's exact test. Serious adverse events will be presented separately with narratives of the events. Primary Effectiveness Analysis The primary effectiveness variable is the hours of use per night over three months. This variable will be summarized with mean, median, standard deviation, minimum and maximum. The one-sided lower 95% confidence interval will be computed for the Bi-Flex treated subjects and the upper one-sided 95% confidence interval will be computed for the CPAP treated subjects. These two intervals will be visually compared to determine if either interval excludes the mean of the other treatment. Such exclusion will provide suggestive evidence that the Bi-Flex treatment leads to higher compliance than the CPAP treatment. Further, a mixed models analysis of hours of use will be done through three months. This multivariate analysis incorporating clinically important covariates will screen variables for inclusion in a manner similar to that described above. Screening of possible covariates will be done by a method similar to that described in Hosmer and Lemeshow 25 for logistic regression models as discussed above. The same set of potential covariates as described in the safety analysis above will be screened. Models will be fit for each possible covariate to include treatment, the covariate, and the interaction between the covariate and treatment will be used. Any covariate or interaction significant at a P-value of 0.15 or lower from the screening analysis will allow the variable to enter the backward elimination procedure for the final model. If the interaction is significant, then both the covariate and its interaction need to be included in the model. The backward elimination will be done manually starting with the interaction with the highest P-value. Interaction terms will be removed from the model first and then non-significant main effects. Variables will be retained in the model if the P-value for the variable or its interaction is 0.05 or less. This analysis is intended to demonstrate that the treatment affect adjusted for possible covariates is still statistically significant. Secondary Effectiveness Analyses The drop out rate will be compared between the two groups will be estimated. The rate for each group will be tabulated and computed with 95% exact binomial confidence intervals. The objective sleep parameters of Apnea Hypopnea Index (AHI), arterial oxygen saturation nadir, time with elevated end-tidal partial pressure of Carbon Dioxide (PCO2) and the arousal index will be presented descriptively by treatment group. The tables will include the mean, standard deviation, median, minimum and maximum. The subjective sleep measures of OSA 18 score, modified Epworth Sleepiness Scale, the NOSE, and pediatric quality of life index (PedQL) will be presented descriptively by treatment group. The tables will include the mean, standard deviation, median, minimum and maximum. Additional Analysis Exploratory analyses of the relationships of study variables to outcomes will be done to determine if there is the treatment difference is particularly strong is specific sub-groups. Patient Accountability and Missing Data The number and proportion of patients eligible for and compliant with each follow-up examination will be presented. Patients who withdraw from the study will be tabulated with the reasons for the withdrawal. If the proportion of patients withdrawn is larger than the 15% from either arm, an analysis of the demographic and prognostic characteristics will be made between patients who withdraw and those who remain in the study. For continuous variables, parametric or non-parametric analysis of variance will be used. For categorical variables, Chi-square or Fisher's exact test will be applied. The evaluation of withdrawn patients presents a special concern. All clinical studies analyze the results based on the evaluable patients, i.e., those who complete the study. Because withdrawn patients do not have final data, they present a problem. The statistical community 26-29 recommends that multiple sensitivity analyses be conducted to determine the robustness of the result in patients who complete the study. The intention of these analyses is to demonstrate that the results obtained from the evaluable patients are not biased. As a result, sensitivity analyses using multiple imputation analyses will be conducted to evaluate the robustness of the study result accounting for missing observations. The imputation will be a non-parametric multiple imputation in which patients withdrawn from the study will be randomly assigned outcomes by grouping on demographic and prognostic characteristics including treatment assignment maintaining masking, matching the characteristics to the withdrawn patients, and randomly selecting the result for the missed observation from the results for patients with similar characteristics by method such as "hot deck" imputation or imputation by regression 30. All imputations will be stochastic imputations to preserve the variability of the imputed value. Also, imputations will be done in a manner that is consistent with the assumptions of multiple imputation theory including missing at random to the extent possible. If the missing at random assumption is clearly violated by the data, other procedures including selection modeling and pattern mixture modeling will be considered. Detailed Analysis Plan Prior to database lock a detailed statistical analysis plan will be written to provide a detailed description of the statistical analysis to be used in the final analysis. This plan will incorporate lists of relevant clinical variables to be tested as possible covariates and will incorporate any protocol changes that would affect the analysis. Statistical Software The statistical analyses will be done using SAS version 9.1 or later, statistical software (StatXact Version 7 or later, and Systat 10 or later). Each of these software packages provides special features that will be exploited to provide a comprehensive analysis with excellent graphics support.