Long-term Effect of Nasal Continuous Positive Airway Pressure on Lipid Profile in Patients With Dyslipidaemia and Mild-moderate Obstructive Sleep Apnea
Long-term Effect of Nasal Continuous Positive Airway Pressure on Lipid Profile in Patients With Dyslipidaemia and Mild-moderate Obstructive Sleep Apnea
OBJECTIVES Main objective: To assess if six months of treatment with CPAP, associated with conventional treatment, improves the lipid profile of patients with dyslipidemia and mild-moderate apnea-hypopnea syndrome (OSA).
Secondary objectives:
DESIGN Randomized, parallel group, non-blind, controlled clinical trial with conventional treatment.
STUDY POPULATION 35-75 year old subjects, diagnosed with dyslipidemia in last six months and in stable treatment during the last month with diet, cholesterol lowering drug, and cholesterol LDL levels> 100 mg / dl in the last two successive visits clinics.
Sample size. 38 patients who completed the test in each treatment arm.
TREATMENT
Patients will be randomized to one of the following treatment arms form:
ENDPOINTS:
Efficacy endpoints.
Security endpoints.
OBJECTIVES
Main objective: To assess if six months of treatment with CPAP, associated with conventional treatment, improves the lipid profile of patients with dyslipidemia and mild-moderate apnea-hypopnea syndrome (OSA).
Secondary objectives:
DESIGN Randomized, parallel group, non-blind, controlled clinical trial with conventional treatment.
STUDY POPULATION 35-75 year old subjects, diagnosed with dyslipidemia in last six months and in stable treatment during the last month with diet, cholesterol lowering drug, and cholesterol LDL levels> 100 mg / dl in the last two successive visits clinics.
Sample size. 38 patients who completed the test in each treatment arm.
TREATMENT
Patients will be randomized to one of the following treatment arms form:
ENDPOINTS Efficacy endpoints.
Security endpoints.
STATISTICAL PROCEDURES Data will be expressed as mean ± standard deviation, median (interquartile range) or percent, depending on type and distribution. For comparison between groups, or t-Student test, or the Mann-Whitney U-test or chi-square test will be used, as appropriate. The relationships between variables will be analyzed by Pearson correlation and multiple linear regression.
The treatment effect will be evaluated by analysis of variance for repeated measures with post-hoc multiple comparisons, using the Bonferroni test. A model of multiple logistic regression will be applied to determine the variables associated with treatment response. P values will be considered statistically significant <0.05.
Inclusion Criteria:
Exclusion Criteria: