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Polysomnographic features in three insomnia subtypes
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Abstracts / Sleep Medicine 14S (2013) e239–e317
and some of these emerged as the main predictors. In T1, the first model included age, apnea-hypopnea index (AHI), outcome expectations and coping spiritual support, as main predictors to distinguish adherence patterns. In T2 and T3, two models emerged adjusted for the variables of model 1, that included leakage, self- efficacy, family coping (mobilizing family acquire/accept support and reframing) in model 2 and self-efficacy in model 3. Generally, the areas under the ROC curve, presented a good discrimination. Conclusion: Findings revealed an integrative heuristic model that accounted for the joint influence of demographic, clinical, and psychological factors on poor, moderate and optimal adherence patterns. Acknowledgements: This work was supported by a grant (SFRH/BD/ 38388/2007) from the Portuguese Foundation of Science and Technology. We thank all patients that agreed to participate in this study. http://dx.doi.org/10.1016/j.sleep.2013.11.620
Polysomnographic features in three insomnia subtypes F. Sánchez-Narváez, D. De La Orta, A. Labra, R. Haro Sleep Disorders Clinic, National University of Mexico, México D.F, Mexico
Introduction: Insomnia is a prevalent condition worldwide. Anxiety is a frequently observed symptom in insomnia and benzodiazepines (BZD) are one of the most common prescribed drugs not only for anxiety treatment but also for sleep difficulties assessment. It has been described that a class effect of BZDs is to cause negative changes on sleep pattern, so it could be possible that sleep features worsen in insomniacs treated with BZD. So our objective was to compare PSG values in insomniac patients with anxiety (AI), insomniacs with benzodiazepine use (BZDI), and primary insomnia (PI) to a group of good sleepers (GS). Materials and methods: The study included 148 insomniacs and 50 GS. Subjects underwent a nocturnal PSG study under standard procedures and they were divided in the 3 mentioned conditions according to their etiology. Results were analyzed by ANOVA. Results: TST (min.)*: GS (8.0 + 0.002) vs. PI (7.89 + 0.1), AI (8.0 + 0.02) and BZDI (7.9 + 0.05). Sleep efficiency index (%)*: GS (91.5 + 0.3) vs. PI (78.9 + 1.4), AI (76.8 + 1.09) and BZDI (79.5 + 1.39). Sleep onset latency (min)*: GS (9.5 + 0.7) vs. PI (38.4 + 4.1), BZI (20.2 + 2.4) and AI (45.8 + 4.5); BZDI (20.2 + 2.4) vs. PI (38.4 + 4.1) and AI (45.8 + 4.5). Sleep onset REM (min.)*: GS (98.1 + 1.7) vs. PI (112.8 + 6.4), AI (52.8 + 2.8) and BDZI (119.05 + 7.6); AI (52 + 2.8) vs. PI (112.8 + 6.4) and BDZI (119.05 + 7.6). N1 (%)*: GS (10.3 + 0.2) vs. AI (14.3 + 0.5) and BDZI (15.2 + 0.6); PI (12.2 + 0.7) vs. BDZI (15.21 + 0.6). N2 (%)*: GS (51.2 + 0.4) vs. AI (59.2 + 0.5) and BDZI (63.8 + 1.2). AI (59.2 + 0.6) vs. PI (50.4 + 1.2) and BDZI (63 + 1.2); PI vs. BDZI. N3 (%)*: BDZI (5.9 + 0.7) vs. AI (9.5 + 0.6), PI (19 + 1.2) and GS (18.3 + 0.2); PQI (9.5 + 0.6) vs. GS and PI. REM (%)*: GS (20.1 + 0.3) vs. AI (17.04 + 0.6) and BZDI (15.02 + 0.7); PI (18.03 + 0.7) vs. BDZI. *p < 0.05. Conclusion: Our data indicate that there are several significant changes in PSG parameters between the 3 groups of insomnia: PI, insomnia secondary to anxiety and in patients with benzodiazepines use. Changes are also present when compared to GS. These changes must be considered at the time of choosing the therapeutic tool to manage insomnia. http://dx.doi.org/10.1016/j.sleep.2013.11.621
Nicturia and morbid obesity: predictors for severe AHÍ and SaO2 decrease A. Palacios, F. Sánchez-Narváez, A. Labra, R. Haro Sleep Disorders Clinic, National University of Mexico, Mexico
Introduction: Nicturia is defined by the International Continence Society as the complaint of getting up more than twice a night to urinate. Nicturia is a frequently found symptom in patients with obstructive sleep apnea syndrome (OSAS). Its prevalence has been found from 41% to 80%. Despite the description of the relationship between nicturia and sleep apnea; the risk factors are not yet clear. The aim of this study is to describe the risk association of OSAS, nicturia and SaO2. Materials and methods: We included 349 patients with sleep apnea and no other morbility. They were divided into 2 groups, 191were patients without, and 158 with nicturia. Nicturias was defined as getting up to urinate at least twice during the night. They underwent supervised cardiorespiratory polygraphy. The results were analyzed with a Students t test for not related samples. Results: Nicturia vs No nicturia: AHÍ* (37.8 + 2.4 vs 61.79 + 3.01), mean Sa02* (90.8 + 0.25 vs 86.4 + 0.58), minimal SaO2* (77.2 + 0.87 vs 70.1 + 1.32), BMI* (27.8 + 0.31 vs 31.21 + 0.53), Epworth* (9.4 + 0.45 vs 11.8 + 0.55) *P < 0.01. Odds ratio controlled by age and gender for AHÍ higher tan 30 with confidence interval. 2.1* (1.3–3.5). Morbid Obesity 3.5* (2.1–6.0). Somnolence. 2.03*(1.2– 3.3). Odds ratio controlled by age and gender for mean Sa02 with confidence interval 4.49* (2.7–7.4), Morbid obesity 3.09* (1.8–5.1). Somnolence 1.7* (1.02–2.85). *P < 0.05. Conclusion: Our results suggest that patients with Nicturia higher than 2, show a higher AHÍ, lower mean SaO2 and an increase in the somnolence scale and BMI. The significant risk factors for AHI higher than 30 and SaO2 lower than 89% were the number of urinating events, morbid obesity and somnolence. http://dx.doi.org/10.1016/j.sleep.2013.11.622
The effect of two nights of partial sleep restriction on objective and subjective pain measurements S. Ødegård, P. Omland, K. Nilsen, G. Gravdahl, M. Stjern, T. Sand Department of Neuroscience, Faculty of Medicine, NTNU, Norway
Introduction: The exact nature of the relationship between sleep reduction and pain is still not known and further clinical studies are needed to elucidate the relationship between sleep and pain further. Materials and methods: Students were recruited through intranet advertisement at our University and Hospital. Of the 80 students who responded to the invitation, 34 (mean age 22.9) were included in the present paper and randomly assigned to either sleep deprivation (SD) or habitual sleep (HS) subgroups. They had repeated neurophysiological examinations with two nights of sleep deprivation or habitual sleep in between. Pain responses were measured with laser evoked potentials (LEP), thermal threshold and suprathreshold test. Repeated measures ANOVA was used to evaluate the possible interaction between sleep deprivation and pain measures. The effect of sleep deprivation was also investigated using two additional categorizations; as actual PSG-recorded REM sleep and SWS (median splits). The difference between N2P2 amplitude, thermal pain threshold and pain ratings were compared between sleep categorization subgroups with Mann-Whitney U-test. The difference between baseline and follow-up values within the sleep groups where investigated with the Paired T-test.
Abstracts / Sleep Medicine 14S (2013) e239–e317
and some of these emerged as the main predictors. In T1, the first model included age, apnea-hypopnea index (AHI), outcome expectations and coping spiritual support, as main predictors to distinguish adherence patterns. In T2 and T3, two models emerged adjusted for the variables of model 1, that included leakage, self- efficacy, family coping (mobilizing family acquire/accept support and reframing) in model 2 and self-efficacy in model 3. Generally, the areas under the ROC curve, presented a good discrimination. Conclusion: Findings revealed an integrative heuristic model that accounted for the joint influence of demographic, clinical, and psychological factors on poor, moderate and optimal adherence patterns. Acknowledgements: This work was supported by a grant (SFRH/BD/ 38388/2007) from the Portuguese Foundation of Science and Technology. We thank all patients that agreed to participate in this study. http://dx.doi.org/10.1016/j.sleep.2013.11.620
Polysomnographic features in three insomnia subtypes F. Sánchez-Narváez, D. De La Orta, A. Labra, R. Haro Sleep Disorders Clinic, National University of Mexico, México D.F, Mexico
Introduction: Insomnia is a prevalent condition worldwide. Anxiety is a frequently observed symptom in insomnia and benzodiazepines (BZD) are one of the most common prescribed drugs not only for anxiety treatment but also for sleep difficulties assessment. It has been described that a class effect of BZDs is to cause negative changes on sleep pattern, so it could be possible that sleep features worsen in insomniacs treated with BZD. So our objective was to compare PSG values in insomniac patients with anxiety (AI), insomniacs with benzodiazepine use (BZDI), and primary insomnia (PI) to a group of good sleepers (GS). Materials and methods: The study included 148 insomniacs and 50 GS. Subjects underwent a nocturnal PSG study under standard procedures and they were divided in the 3 mentioned conditions according to their etiology. Results were analyzed by ANOVA. Results: TST (min.)*: GS (8.0 + 0.002) vs. PI (7.89 + 0.1), AI (8.0 + 0.02) and BZDI (7.9 + 0.05). Sleep efficiency index (%)*: GS (91.5 + 0.3) vs. PI (78.9 + 1.4), AI (76.8 + 1.09) and BZDI (79.5 + 1.39). Sleep onset latency (min)*: GS (9.5 + 0.7) vs. PI (38.4 + 4.1), BZI (20.2 + 2.4) and AI (45.8 + 4.5); BZDI (20.2 + 2.4) vs. PI (38.4 + 4.1) and AI (45.8 + 4.5). Sleep onset REM (min.)*: GS (98.1 + 1.7) vs. PI (112.8 + 6.4), AI (52.8 + 2.8) and BDZI (119.05 + 7.6); AI (52 + 2.8) vs. PI (112.8 + 6.4) and BDZI (119.05 + 7.6). N1 (%)*: GS (10.3 + 0.2) vs. AI (14.3 + 0.5) and BDZI (15.2 + 0.6); PI (12.2 + 0.7) vs. BDZI (15.21 + 0.6). N2 (%)*: GS (51.2 + 0.4) vs. AI (59.2 + 0.5) and BDZI (63.8 + 1.2). AI (59.2 + 0.6) vs. PI (50.4 + 1.2) and BDZI (63 + 1.2); PI vs. BDZI. N3 (%)*: BDZI (5.9 + 0.7) vs. AI (9.5 + 0.6), PI (19 + 1.2) and GS (18.3 + 0.2); PQI (9.5 + 0.6) vs. GS and PI. REM (%)*: GS (20.1 + 0.3) vs. AI (17.04 + 0.6) and BZDI (15.02 + 0.7); PI (18.03 + 0.7) vs. BDZI. *p < 0.05. Conclusion: Our data indicate that there are several significant changes in PSG parameters between the 3 groups of insomnia: PI, insomnia secondary to anxiety and in patients with benzodiazepines use. Changes are also present when compared to GS. These changes must be considered at the time of choosing the therapeutic tool to manage insomnia. http://dx.doi.org/10.1016/j.sleep.2013.11.621
Nicturia and morbid obesity: predictors for severe AHÍ and SaO2 decrease A. Palacios, F. Sánchez-Narváez, A. Labra, R. Haro Sleep Disorders Clinic, National University of Mexico, Mexico
Introduction: Nicturia is defined by the International Continence Society as the complaint of getting up more than twice a night to urinate. Nicturia is a frequently found symptom in patients with obstructive sleep apnea syndrome (OSAS). Its prevalence has been found from 41% to 80%. Despite the description of the relationship between nicturia and sleep apnea; the risk factors are not yet clear. The aim of this study is to describe the risk association of OSAS, nicturia and SaO2. Materials and methods: We included 349 patients with sleep apnea and no other morbility. They were divided into 2 groups, 191were patients without, and 158 with nicturia. Nicturias was defined as getting up to urinate at least twice during the night. They underwent supervised cardiorespiratory polygraphy. The results were analyzed with a Students t test for not related samples. Results: Nicturia vs No nicturia: AHÍ* (37.8 + 2.4 vs 61.79 + 3.01), mean Sa02* (90.8 + 0.25 vs 86.4 + 0.58), minimal SaO2* (77.2 + 0.87 vs 70.1 + 1.32), BMI* (27.8 + 0.31 vs 31.21 + 0.53), Epworth* (9.4 + 0.45 vs 11.8 + 0.55) *P < 0.01. Odds ratio controlled by age and gender for AHÍ higher tan 30 with confidence interval. 2.1* (1.3–3.5). Morbid Obesity 3.5* (2.1–6.0). Somnolence. 2.03*(1.2– 3.3). Odds ratio controlled by age and gender for mean Sa02 with confidence interval 4.49* (2.7–7.4), Morbid obesity 3.09* (1.8–5.1). Somnolence 1.7* (1.02–2.85). *P < 0.05. Conclusion: Our results suggest that patients with Nicturia higher than 2, show a higher AHÍ, lower mean SaO2 and an increase in the somnolence scale and BMI. The significant risk factors for AHI higher than 30 and SaO2 lower than 89% were the number of urinating events, morbid obesity and somnolence. http://dx.doi.org/10.1016/j.sleep.2013.11.622
The effect of two nights of partial sleep restriction on objective and subjective pain measurements S. Ødegård, P. Omland, K. Nilsen, G. Gravdahl, M. Stjern, T. Sand Department of Neuroscience, Faculty of Medicine, NTNU, Norway
Introduction: The exact nature of the relationship between sleep reduction and pain is still not known and further clinical studies are needed to elucidate the relationship between sleep and pain further. Materials and methods: Students were recruited through intranet advertisement at our University and Hospital. Of the 80 students who responded to the invitation, 34 (mean age 22.9) were included in the present paper and randomly assigned to either sleep deprivation (SD) or habitual sleep (HS) subgroups. They had repeated neurophysiological examinations with two nights of sleep deprivation or habitual sleep in between. Pain responses were measured with laser evoked potentials (LEP), thermal threshold and suprathreshold test. Repeated measures ANOVA was used to evaluate the possible interaction between sleep deprivation and pain measures. The effect of sleep deprivation was also investigated using two additional categorizations; as actual PSG-recorded REM sleep and SWS (median splits). The difference between N2P2 amplitude, thermal pain threshold and pain ratings were compared between sleep categorization subgroups with Mann-Whitney U-test. The difference between baseline and follow-up values within the sleep groups where investigated with the Paired T-test.