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PREVALENCE AND FACTORS AFFECTING REM AND SLOW WAVE SLEEP REBOUND ON CPAP TITRATION STUDY IN PATIENTS WITH OBSTRUCTIVE SLEEP APNEA

Keywords: sleep deprivation, prevalence, titrimetry (titration), sleep apnea, obstructive.

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Abstract:

background. in patients with obstructive sleep apnea syndrome (osas) treatment with cpap results in an increase of rem sleep and slow wave sleep, but there is limited information about the prevalence of rem rebound in patients with osas and possible factors related to the rebound. objective. rem rebound (rr) and slow wave sleep rebound (swsr) has been described as a frequent phenomenon that occurs during cpap titration, but the quantity that qualify for rr has not been mentioned in literature. the objective of our study was to determine the prevalence of rem rebound and slow wave sleep rebound in our sleep disorders center, to attempt to define rr and look for factors that may affect rr and swsr on the first night of cpap titration. materials and methods. we included patients who had both baseline polysomnogram (bpsg) and cpap polysomnogram (cpsg) studies done in the same laboratory. we included 179 patients>18 years with apnea hypopnea index (ahi)>10/hr on the baseline study, with an adequate cpap titration study. we compared the percentages of rem sleep and slow wave sleep during bpsg and cpsg. we analyzed the frequency of presentation and looked for the factors affecting rr and swsr. results. 179 patients were enrolled (m/f:118/61), with a mean age of 48.6±4 for men, and 51.6±12.9 for women. the mean interval between the bpsg and cpsg was 45 days. the mean rem percentage during the bpsg was 15.55 percent and during cpsg study it was 21.57 percent. we took 6 percent as our differential point as the results became statistically significant at this point (p:0001). we therefore present our data by dividing our patients population with rr<6% and rr>6%. the mean sws percentage during the bpsg was 8.11±9.68 and during the cpsg was 13.17±10, with a p:0.35 which is not statistically significant. the multiple regression model showed that the variables that contribute more to the rem change are: rem sleep during bpsg (-0.56), bahi (0.24) and the body mass index (0.081). con

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