Does continuous positive airway pressure (CPAP) affect lipid metabolism in patients with obstructive sleep apnea (OSA)?
We will search the following electronic bibliographic databases: PubMed, Ovid MEDLINE, Clinicaltrials.gov, the Cochrane Central Register of Controlled Trials (CENTRAL, Cochrane literary latest issue), Web of Science, and Highwire. The search strategy will include only terms relating to our topics. The terms will be combined with the Cochrane MEDLINE filter for controlled trials of interventions. The search strategy is available in the published protocol. The search terms will be adapted for use with other bibliographic databases in combination with database-specific filters for controlled trials, where these are available. There will be no language restrictions. Studies published between January 1960 and the date the searches are run will be sought. The searches will be re-run just before the final analyses and further studies retrieved for inclusion.
Types of study to be included
We will include randomised controlled trials (RCTs).
Participants, aged 18 years or older of either gender, diagnosed with OSA, based on apnea-hypopnea index (AHI) 5 times per hour
The intervention of positive airway pressure comprises use of:
1) traditional CPAP design; or
2) automatic positive airway pressure (APAP).
Non-intervention reference group includes sham CPAP, oxygen supply, or observational follow-up. Active treatment for OSA, such as bariatric surgery, orthodontic procedure with oral appliance, postural therapy, or surgery, as comparators in the control group are excluded.
No limitation is set for context.
The change of lipid profile (such as total cholesterol (T-CHO), triglyceride (TG), low-density lipoprotein (LDL), or high-density lipoprotein (HDL)) from baseline to the last available follow-up after using CPAP therapy
CPAP duration (measured from randomisation).
Data extraction, (selection and coding)
For every eligible study, two reviewers independently will collect detailed information on important study characteristics and results using a data collection form. The form will be developed and piloted on three trials to refine it before use on all studies. The results of data collection will be compared and any differences will be resolved through discussion with a third reviewer. Extracted information is listed as below:
1. General information: published/unpublished, title, author, country of study, contact address, language of publication, year of publication, sponsor/funding organization, setting, study designs.
2. Methodological details: including criteria for risk of bias assessment.
3. Intervention: descriptions of positive airway pressure (duration, compliance, mode), the effect on blood pressure or glycemic control, and primary study endpoints.
4. Participants : inclusion and exclusion criteria, total number and number in comparison groups, sex, age, baseline characteristics (including index for obesity and sleepiness, such as body mass index (BMI) and Epworth sleepiness scale (ESS), baseline cormobidity, such as hypertension, diabetes mellitus, and cardiovascular disease, and OSA severity), withdrawals/losses to follow-up (reasons/description), and change of body weight during study period.
5.Outcomes: value of lipid profile (such as T-CHO, TG, LDL, and HDL).
Risk of bias (quality) assessment
We will use the risk of bias tool proposed by the Cochrane collaboration for the assessment of randomised controlled trials by two independent reviewers. This tool addresses six specific domains, namely sequence generation, allocation concealment, blinding, incomplete outcome data, selective outcome reporting and other issues. We will complete a "Risk of bias" table for each eligible study.
Strategy for data synthesis
Within each study design, we will determine whether and how the measured associations should be pooled across studies, based on the assessment of heterogeneity (I-squared >75% or <=75%) and assessment of risk of bias. For outcomes from parallel design, we use fixed-effect model with mean differences as summary statistics for continuous variables. For outcomes from crossover design, mean differences and standard errors for the mean differences will be calculated and entered into RevMan as generic inverse variance data. In case of great heterogeneity, Dersimonian and Laird random effect models will be used to pool the results. STATA 9.2 software and RevMan will be used for data analysis.
Analysis of subgroups or subsets
We will perform subgroup analysis based on important domains of sources of biases using stratified analysis. We will examine whether the summary effect size varies substantially by important study characteristics, which include: year of publication, age, gender, baseline severity of obstructive sleep apnea (such as AHI or ESS), baseline severity of obesity (BMI), baseline level of lipid profiles, underlying comorbidity in study population, different setting of continuous positive airway pressure, and effect of CPAP on other end-points (such as BP and insulin resistance). Meta-regression will be used to examine whether the observed heterogeneity between studies can be explained by these important study-level factors.
Contact details for further information
No. 15-1, Sec. 1, Nanya S. Rd., Banciao Dist, New Taipei County 220, Taiwan (R.O.C.)
Organisational affiliation of the review
Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University
Dr Ming-Tzer Lin, Department of Internal Medicine, National Taiwan University Hospital, Taipei City, Taiwan Dr Hsien-Ho Lin, Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei City, Taiwan Miss Ting-Chun Lai, Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei City, Taiwan Mr Chang-Chun Lee, Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei City, Taiwan Mr Wei Liu, Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei City, Taiwan Dr Pei-Hsuan Weng, Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei City, Taiwan Dr Peilin Lee, Department of Family Medicine, Taiwan Adventist Hospital, Taipei City, Taiwan
Details of any existing review of the same topic by the same authors
Formal screening of search results against eligibility criteria
Risk of bias (quality) assessment
PROSPERO This information has been provided by the named contact for this review. CRD has accepted this information in good faith and registered the review in PROSPERO. CRD bears no responsibility or liability for the content of this registration record, any associated files or external websites.