Effective compliance (time spent at the effective pressure) with nasal CPAP in obstructive sleep apnea has been reported to be poor. The aim of our study was to evaluate effective compliance in a large European multicenter study. One hundred twenty-one consecutive newly treated patients (initial apnea-hypopnea index [AHI] = 62.0 +/- 29. 5/h, AHI under CPAP = 6.4 +/- 8.1/h, CPAP pressure = 8.7 +/- 2.6 cm H(2)O, BMI = 33.1 +/- 6.8 kg/m(2)) were randomly allocated to a group with (MC(+)) (n = 58) or without (MC(-)) (n = 63) a control unit measuring effective compliance at 1, 2, and 3 mo, which was compared with the built-in time counter data. MC(+) data were 94 +/- 10, 98 +/- 5, and 96 +/- 9% of counter data at 1, 2, and 3 mo, respectively. Using criteria of regular use already reported in the literature (at least 4 h of nCPAP per day of use and nCPAP administered more than 70% of the days) we found 77, 82, and 79% compliant patients at 1, 2, and 3 mo, respectively, 79% of the patients meeting these criteria each month. Although there were no pulmonary functions or polysomnographic differences between the two subgroups, the compliant patients did report a greater improvement in minor symptoms. We found a close correlation between effective use of CPAP and the machine run time. The main result of our study was a higher effective compliance than previously reported, approximately 80% of the patients being regular users versus 46% in a previously published study. This may result from different technical and medical follow-up.

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ResMed Autoset (AS) is a simplified diagnosis system for obstructive sleep apnoea/hypopnoea syndrome (OSAS) based on the respiratory flow/time relationship by pressure variation measured through simple nasal prongs. A multicentre prospective trial was used to compare AS and polysomnography (PSG) for diagnosing 95 patients, with suspected OSAS. Physicians gave a pretest probability of the patient having OSAS. The apnoea/hypopnoea index (AHI) was compared between the two methods of diagnosis for the whole population and for subgroups according to the pretest probability. Twenty-four patients had AHI < 15 events x h(-1) on PSG and 19 AHI 15-30, and 52 patients had AHI > or = 30. Correlation between AHI assessed by AS and PSG was r=0.87 for total sleep time (TST), p<0.0001. A Bland and Altman plot gave an agreement between the two methods of +/-40%. For a threshold of AHI > or = 15 events x h(-1) to diagnose OSAS, AS has a sensitivity of 92%, specificity of 79%, positive predictive value of 93% and negative predictive value of 76%. With a pretest probability > or = 80%, sensitivity and positive predictive value were 98 and 100% respectively. Of six false negative, four had a high pretest probability (> 80%) or Epworth score > or = 10. Using these parameters as a criterion for proceeding to PSG after a negative AS study would mean that two apnoeic patients (AHI 20 and 17 events x h(-1) by PSG) would escape detection. The Autoset is useful for the detection of obstructive sleep apnoea but with high pretest probability and a negative Autoset result polysomnography should be performed.

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The prevalence of arterial hypertension (HT), diabetes, obesity, active smoking, hyperlipidemia and family history of coronary heart disease was 54.1%, 22.8%, 65.8%, 18.3%, 33.8% and 20%, respectively. Women had significantly more HT (62.1 vs 51.4%), diabetes (29.9 vs 20.4%), obesity (77 vs 62%) and family history of coronary disease (25.1 vs 18.2%). The prevalence of active smoking was significantly higher in men (20.4 vs 12%). The prevalence of hyperlipidemia was not different between men and women (34.5 vs 31.8%). Stepwise logistic regression showed that HT and diabetes were both independently associated with BMI and age, while diabetes and not HT was independently associated with female gender. The prevalence of classical CV risk factors was very high in this population with OSAS requiring CPAP, especially in women. There is thus a very elevated CV risk level independent of that directly related to OSAS. It is important to screen for and treat classical CV risk factors in this population.

Background: No data on snoring prevalence obtained with a standard questionnaire exist for France. Major nose-throat abnormalities have been demonstrated in cases with obstructive sleep apnea; evidence of ‘minor’ abnormalities in community studies is scarce. Objectives: The fist objective of our study was to estimate the prevalence of habitual snoring in a sample of middle-aged active males in France. The second objective was to test the hypothesis that ‘minor’ nose-throat abnormalities could be associated with habitual snoring in a field survey. Methods: Three hundred thirty-four male employees of a local university volunteered for the study (93.6% of those contacted by mail); 300 returned a sleep questionnaire. The protocol also included anthropometry and a noninvasive nose-throat examination. Results: Complete data were obtained in 299 subjects aged 23–63 years. When ‘habitual snorers’ (= 32%) were compared with never-snorers, significant differences were found for all anthropometric variables, except height. In univariate analysis, habitual snoring was associated with a large number of variables, including a large soft palate, a large uvula, and altered nose patency. A logistic regression model retained 8 factors independently associated with snoring: age, neck circumference, tobacco consumption, breathing pauses during sleep, not feeling rested during the day, need for coffee to stay awake, blocked or running nose at night and a large soft palate. Conclusions: The prevalence of habitual snoring in this sample of middle-aged French males was 32%. We confirmed the significant association of habitual snoring with age, weight excess, and tobacco smoking, and identified two further factors: blocked nose at night and a large soft palate.

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