Category Archives: Sleep Apnea

Determinants of Daytime Sleepiness in Obstructive Sleep Apnea: Understanding of How an Apnea Ends

Determinants of Daytime Sleepiness in Obstructive Sleep Apnea: Understanding of How an Apnea EndsUnderstanding of How an Apnea Ends
Our failure to find statistically significant discriminants of EDS in OSAS is also due to lack of understanding of how an apnea ends: The recording of an “alpha EEG arousal” at apnea termination may be an important factor in the report of severe sleepiness and abnormal MSLT scores. Apneas and hypopneas may not systematically lead to an alpha EEG arousal, but may terminate while the subject is still in a light sleep and thus be less detrimental than an alpha EEG arousal. It is also important to note that when we used criteria more sophisticated than those of Orr et al, we were unable to confirm that hypersomnolent OSAS patients had lower nocturnal Sa02 or were more obese. Within the ^5 MSLT score group of patients with severe sleepiness score and subjective complaint of marked somnolence, we identified subjects with a normal BMI of <27 who never desaturated below 87 percent Sa02 but who terminated each apneic event with a clear EEG arousal response. read only Continue reading

Determinants of Daytime Sleepiness in Obstructive Sleep Apnea: Discussion

Surprisingly, in this large OSAS patient population, we were unable to correlate the repetitive Sa02 drops, obesity, and to an extent the apneas, with the different degrees of daytime sleepiness measured by our MSLT. Several reasons for these findings, which follow here, can be considered.
Sensitivity of the Test
Perhaps our test was not sensitive enough. Historically, there has been disagreement over the mean MSLT score that would indicate abnormal daytime sleepiness. While in middle-aged adults a mean MSLT score >10 min undoubtedly indicates normalcy,2 a mean MSLT score <10 min also may be normal, depending on the age of the subject. Van den Hoed et al studied 100 patients (excluding those with sleep apnea) referred to a sleep clinic for suspicion of daytime sleepiness. Continue reading

Determinants of Daytime Sleepiness in Obstructive Sleep Apnea: Stepwise Multiple Regression Analysis

Determinants of Daytime Sleepiness in Obstructive Sleep Apnea: Stepwise Multiple Regression AnalysisStepwise Multiple Regression Analysis
Clinical insight suggests that poor nocturnal sleep, repetitive apneas, and significant hypoxia during sleep, which are influenced by obesity, could be responsible for daytime sleepiness. We thus asked if the respiratory variables monitored during nocturnal sleep (apnea, drops in SaOg) could be significant determinants of mean MSLT results. We selected age, BMI, percentage of total time spent in Sl-4 and REM sleep, RDI and 02-80-I or percent TOO percent Sa02 as independent variables and the mean sleep latency score as the dependent variable. The distribution of percent TOO percent SaOz and 02-80-I was skewed with a kurtosis >2, and a logarithmic transformation was done. After covariate analysis, significant variables were selected for stepwise multiple regression analysis. We could not derive a model, as none of the selected variables was shown to be statistically significant. Continue reading

Determinants of Daytime Sleepiness in Obstructive Sleep Apnea: Determinants of EDS

Determinants of EDS
Comparison between Multiple Sleep Latency Test-Defined Groups. We first subdivided our population into three groups according to mean MSLT scores: population A (n = 24), “non-sleepy” with a mean MSLT >8 min; population C (n = 28), “mildly sleepy” with a mean MSLT <8 to >5 min; and population D (n = 48), “severely sleepy” with a mean MSLT <5 min. An analysis of variance disclosed that no variable was significantly different. We then compared population. Continue reading

Determinants of Daytime Sleepiness in Obstructive Sleep Apnea: Correlation Coefficient Analysis of Polygraphic Variables

Determinants of Daytime Sleepiness in Obstructive Sleep Apnea: Correlation Coefficient Analysis of Polygraphic VariablesCorrelation Coefficient Analysis of Polygraphic Variables
We subjected all data to Pearson correlation coefficient analysis. As this analysis has limitations, particularly when many variables are concerned, we have indicated only the correlation coefficient. We found correlations between mean Sa02 and percent TOO percent Sa02 (0.61), between percent TOO percent Sa02 and 02-90-I (0.88), and between lowest Sa02 and 02-80-I (0.58). Because 02-90-I was so closely correlated to percent TOO percent Sa02, it was discarded from further analysis. We decided, instead, to select percent TOO percent Sa02 and 02-80-I as Sa02 indices. Continue reading

Determinants of Daytime Sleepiness in Obstructive Sleep Apnea: Statistical Analysis

Statistical Analysis
Three types of analysis were used.
1. Analysis of variance and Students t tests were used to compare groups. Separate t or pooled t was used, depending on the results of the Levene test for equality of variances. The Bonferroni criteria for significance levels were used when multiple comparisons were made. Nonparametric Mann-Whitney or Kruskall-Wallis rank sum H statistics were used to analyze group differences when variables were not normally distributed. Continue reading

Determinants of Daytime Sleepiness in Obstructive Sleep Apnea: Determination of Oxygen Saturation Indices

Determinants of Daytime Sleepiness in Obstructive Sleep Apnea: Determination of Oxygen Saturation IndicesDetermination of Oxygen Saturation Indices
We calculated mean nocturnal sleep Sa02 using the formula of Bradley et al, recording the highest and lowest Sa02 of each polygraphically recorded epoch. Because the pattern of desaturation and resaturation in OSAS approximates a sine wave, the mean Sa02 of each epoch was estimated by averaging the high and low values. Mean nocturnal Sa02 for TST was then calculated, using the mean values of all epochs. To further focus on events leading to significant 02 desaturation, even if short-lived, we calculated the number of apnea/hypopnea-related Sa02 drops below 80 percent per hour of sleep to obtain the index 02-80-I. The percentage of TST spent <90 percent Sa02 is self-explanatory. Continue reading

Determinants of Daytime Sleepiness in Obstructive Sleep Apnea: Methods

Collection of Data
We reviewed drug intake and sleep/wake schedules at the clinical interview and medical evaluation that took place 15 days before the research polygraphic monitoring. Patients were asked to abstain from drugs known to affect sleep latency or rapid eye movement (REM) sleep latency until after polygraphic evaluation and MSLT and were told to observe their usual times for bedtime and morning arising for one week before the monitoring. They reported to the recording facilities by 7:30 pm on the day of polygraphic monitoring. Lights were turned off at the usual bedtime, and patients who awoke early were encouraged to sleep longer or at least to stay in bed in the dark until the time of their normal wakening. Any patient still sleeping at 8 am was to be awakened, but this proved unnecessary. After arising, patients underwent a urine drug screen to rule out the presence of any substance that could affect the MSLT. Continue reading

Determinants of Daytime Sleepiness in Obstructive Sleep Apnea

Determinants of Daytime Sleepiness in Obstructive Sleep ApneaExcessive daytime sleepiness (EDS) is a frequent complaint of patients with obstructive sleep apnea syndrome (OSAS). Although patients may complain mainly of tiredness and fatigue, their sleepiness may lead to significant socioeconomic hardship and to driving and industrial accidents. In a large, unselected population of patients presenting with OSAS, we studied the relationship of specific respiratory variables (particularly respiratory disturbance index [RDI] and several indices of oxygen saturation [Sa02]), age, body mass index (BMI), and sleep disturbances monitored during nocturnal sleep to the results of the multiple sleep latency test (MSLT) administered the following day. This objective test of daytime sleepiness was introduced by the Stanford team in the late 1970s and has subsequently been used in many studies. Continue reading