Somnolencia diurna excesiva identificada con la escala de Epworth en gestantes que acuden a consulta prenatal ambulatoria / Excessive daytime sleepiness identified with the Epworth scale in pregnant women attending an outpatient prenatal consultation

INTRODUCCIÓN: Durante el embarazo se reportan a menudo trastornos del sueño, pero son poco explorados en el control prenatal. El objetivo fue estimar la frecuencia e identificar los factores asociados a somnolencia diurna excesiva (SDE) en gestantes de bajo riesgo obstétrico que acudieron a consulta prenatal. MÉTODO: Estudio transversal dentro del proyecto «Salud biopsicosocial en gestantes», aprobado por el comité de ética de la Clínica Santa Cruz de Bocagrande, Cartagena, Colombia. Se estudiaron mujeres con 12 o más semanas de gestación. Se aplicó un formulario que incluía las escalas de somnolencia diurna de Epworth (ESE), de estrés percibido de 10 ítems (EPP-10) y revisada de depresión del Centro de Estudios Epidemiológicos (CESD-R10). Se realizó regresión logística ajustada y no ajustada de SDE con las otras escalas y variables cualitativas. Se buscó correlación entre variables cuantitativas y la escala de Epworth. Se consideró significativo p < 0,05. RESULTADOS: Se estudiaron 683 mujeres de 28,3 ± 6,3 años y 31,5 ± 6,9 semanas de gestación. Puntuación en la escala de Epworth: 3,82 ± 3,45. En el 4,9% se identificó SDE: 50% leve, 32,2% moderada y 17,6% importante. No se observó en el primer trimestre de gestación y la frecuencia fue similar en los otros; la SDE importante solo se observó en el tercer trimestre. Depresión: odds ratio [OR]: 3,69, intervalo de confianza del 95% [IC95%]:1,83-7,43. Anemia: OR 3,10, IC95%:1,50-6,38. Fatiga: OR 3,22, IC95%:1,23-8,44. Nerviosismo: OR 2,49, IC95%:1,22-5,12. Estrés: OR: 2,38, IC95%:1,12-5,05. Gran paridad: OR: 2,64, IC95%: 1,01-6,89. Trabajar fuera de casa: OR: 2,33, IC95%: 1,05-5,15. Todas estas variables se asociaron con SDE. En el modelo ajustado, la anemia (OR: 3,05, IC95%: 1,44-6,45) y la depresión (OR: 2,72, IC95%: 1,26-5,85) conservaron la asociación. Se observó correlación positiva, despreciable y estadísticamente significativa, de la ESE con la CESD-R10 y con la EPP-10; y ausencia de correlación con la edad materna, la edad gestacional, el número de abortos y el número de cesáreas. CONCLUSIONES: En una de cada 20 gestantes de bajo riesgo obstétrico se identificó SDE, y varias situaciones biopsicosociales se asociaron con mayor presencia.

INTRODUCTION: Sleep disturbances are constantly reported during pregnancy, although they are not often taken care of in prenatal care. The aim was to estimate the regularity and identify factors associated with excessive daytime sleepiness (EDS) in pregnant women at low obstetric risk who attended prenatal consultation. METHOD: Cross-sectional study belonging to the project “Biopsychosocial health in pregnant women”, approved by the ethics committee of the Santa Cruz de Bocagrande Clinic, Cartagena, Colombia. Pregnant women with 12 or more weeks of gestation were studied. A form including: Epworth Daytime Sleepiness Scale, 10-item Perceived Stress and the Revised Depression Scale of the Centre for Epidemiological Studies was applied. Adjusted and unadjusted logistic regression was performed between EDS with the other scales and qualitative variables. In addition, correlation between quantitative variables and the Epworth scale. P<0.05 was significant. RESULTS: 683 pregnant women were studied, maternal age 28.3 ± 6.3 years and gestational age 31.5±6.9 weeks. Epworth Scale score: 3.82 ± 3.45. EDS was identified in 4.9%, 50% mild, 32.2% moderate and 17.6% severe. It was not observed in the first gestational trimester and the frequency was similar in the others, severe EDS only in the third trimester. Depression OR: 3.69 [95% CI: 1.83-7.43], anemia OR: 3.10 [95% CI: 1.50-6.38], fatigue OR: 3.22 [95% CI: 1.23-8.44], nervousness OR: 2.49 [95% CI: 1.22-5.12, stress OR: 2.38 [95% CI: 1.12-5.05], high parity OR: 2.64 [95% CI: 1.01-6.89] and working outside the home OR: 2.33 [95% CI: 1.05-5.15, were associated with EDS. In the adjusted model, anemia OR: 3.05 [95% CI: 1.44-6.45] and depression OR: 2.72 [95% CI: 1.26-5.85] retained the association. CONCLUSIONS: In one out of every twenty low obstetric risk pregnant women EDS was identified and several biopsychosocial situations were associated with more presence.

Diagnosis of central disorders of hypersomnolence: A reappraisal by European experts.

The aim of this European initiative is to facilitate a structured discussion to improve the next edition of the International Classification of Sleep Disorders (ICSD), particularly the chapter on central disorders of hypersomnolence. The ultimate goal for a sleep disorders classification is to be based on the underlying neurobiological causes of the disorders with clear implication for treatment or, ideally, prevention and or healing. The current ICSD classification, published in 2014, inevitably has important shortcomings, largely reflecting the lack of knowledge about the precise neurobiological mechanisms underlying the majority of sleep disorders we currently delineate. Despite a clear rationale for the present structure, there remain important limitations that make it difficult to apply in routine clinical practice. Moreover, there are indications that the current structure may even prevent us from gaining relevant new knowledge to better understand certain sleep disorders and their neurobiological causes. We suggest the creation of a new consistent, complaint driven, hierarchical classification for central disorders of hypersomnolence; containing levels of certainty, and giving diagnostic tests, particularly the MSLT, a weighting based on its specificity and sensitivity in the diagnostic context. We propose and define three diagnostic categories (with levels of certainty): 1/"Narcolepsy" 2/"Idiopathic hypersomnia", 3/"Idiopathic excessive sleepiness" (with subtypes).

Effect of mean apnea-hypopnea duration in patients with obstructive sleep apnea on clinical and polysomnography parameter.

OBJECTIVE: Apnea-hypopnea index is the number of apnea-hypopnea events observed during polysomnography within an hour. Mean apnea-hypopnea duration is the mean duration of all apneas and hypopneas. In this study, we aimed to investigate the association of mean apnea-hypopnea duration in patients with obstructive sleep apnea with clinical and polysomnographic parameters. METHODS: In our hospital, a total of 764 patients were diagnosed with OSA by polysomnography in 2017. Age, body mass index, and the current diseases were recorded. Sleep structures obtained from polysomnography readings, blood oxygen levels, apnea-hypopnea index, and mean average duration were recorded. Patients with mean average duration of 20 s or more were assigned to the long average duration group and those with less than 20 s were assigned to the short average duration group. Groups were compared in terms of clinical and polysomnographic parameters. RESULTS: Snoring, witnessed apnea, morning tiredness, and hypertension were significantly higher in the long average duration group. There was statistically significantly more male patients and higher neck circumference in the MAD group. Total wake duration, percentage of sleep, stage 3, stage 1, and mean oxygen saturation percentage of the long average duration group were significantly reduced. CONCLUSION: In present study, the patients with obstructive sleep apnea with long average duration were found to have more negative effects of sleep apnea than the patients with short average duration. We think that the use of mean apnea-hypopnea duration as an indicator with apnea-hypopnea index will be beneficial for the follow-up and treatment of the disease.

Profiles of Subjective Daytime Sleepiness through Cluster Analysis.

Sleepiness propensity and sleepiness perception are two relevant dimensions of the general multidimensional sleepiness construct. In the current study, the aim was to identify eventual homogeneous subgroups when sleepiness propensity and sleepiness perception measures are combined. Data from 678 undergraduate students (aged 17 to 24 years) from a medical school were analyzed. A Two-Step Cluster Analysis was performed taken into consideration sleepiness propensity and sleepiness perception measures as clustering variables. Four different clusters were identified. Additionally, the two groups comprising higher levels of sleepiness perception (i.e., "high perception, low propensity" and "high subjective daytime sleepiness" clusters) had the most compromised results in some of the sleep-related variables examined - sleep needs, sleep latency, sleep quality, sleep sufficiency, self-reported insomnia, sleep reactivity to stress, and cognitive arousal - and in some of non-sleep related variables, such as neuroticism and affect. As to non-sleep variables, those groups reported higher scores in neuroticism, arousability, self-reported mental health and affective states. The identification of distinct groups concerning self-reported sleepiness may bring new avenues for research and understanding of the specific and differential role of sleepiness and its dimensions in sleep health and sleep disturbances, in particular, insomnia disorder.

Identifying subtypes of Hypersomnolence Disorder: a clustering analysis.

BACKGROUND: Patient heterogeneity is problematic for the accurate assessment and effective treatment of Hypersomnolence Disorder. Clustering analysis is a preferred approach for establishing homogenous subclassifications. Thus, this investigation aimed to identify more homogeneous subclassifications of Hypersomnolence Disorder through clustering analysis. METHODS: Patients undergoing polysomnography (PSG) and multiple sleep latency test (MSLT) assessment for hypersomnolence were recruited as part of a larger investigation. A sample of patients with Hypersomnolence Disorder was determined based on a post hoc chart review protocol. After removing persons with missing data, 62 participants were included in the analyses. Self-report total sleep time, Epworth Sleepiness Scale (ESS) score, and Sleep Inertia Questionnaire (SIQ) score were chosen as clustering variables to mirror Hypersomnolence Disorder diagnostic traits. A statistically-driven clustering process produced two clusters using Ward's D hierarchical approach. Clusters were compared across characteristics, self-report measures, PSG/MSLT results, and additional objective measures. RESULTS: The resulting clusters differed across a variety of hypersomnolence-related subjective metrics and objective measurements. A more severe hypersomnolence phenotype was identified in a cluster that also had elevated depressive symptoms. This cluster endorsed significantly greater daytime sleepiness, sleep inertia, and functional impairment, while displaying longer sleep duration and worse vigilance. CONCLUSIONS: These results provide growing support for a nosological reformulation of hypersomnolence associated with psychiatric disorders. Future research is necessary to solidify the conceptualization and characterization of unexplained hypersomnolence presenting with-and-without psychiatric illness.

Recognizable clinical subtypes of obstructive sleep apnea across international sleep centers: a cluster analysis.

Study Objectives: A recent study of patients with moderate-severe obstructive sleep apnea (OSA) in Iceland identified three clinical clusters based on symptoms and comorbidities. We sought to verify this finding in a new cohort in Iceland and examine the generalizability of OSA clusters in an international ethnically diverse cohort. Methods: Using data on 972 patients with moderate-severe OSA (apnea-hypopnea index [AHI] ≥ 15 events per hour) recruited from the Sleep Apnea Global Interdisciplinary Consortium (SAGIC), we performed a latent class analysis of 18 self-reported symptom variables, hypertension, cardiovascular disease, and diabetes. Results: The original OSA clusters of disturbed sleep, minimally symptomatic, and excessively sleepy replicated among 215 SAGIC patients from Iceland. These clusters also generalized to 757 patients from five other countries. The three clusters had similar average AHI values in both Iceland and the international samples, suggesting clusters are not driven by OSA severity; differences in age, gender, and body mass index were also generally small. Within the international sample, the three original clusters were expanded to five optimal clusters: three were similar to those in Iceland (labeled disturbed sleep, minimal symptoms, and upper airway symptoms with sleepiness) and two were new, less symptomatic clusters (labeled upper airway symptoms dominant and sleepiness dominant). The five clusters showed differences in demographics and AHI, although all were middle-aged (44.6-54.5 years), obese (30.6-35.9 kg/m2), and had severe OSA (42.0-51.4 events per hour) on average. Conclusions: Results confirm and extend previously identified clinical clusters in OSA. These clusters provide an opportunity for a more personalized approach to the management of OSA.

Proposal of cutoff points for pediatric daytime sleepiness scale to identify excessive daytime sleepiness.

The objective of the present study was to propose cutoff points for the Pediatric Daytime Sleepiness Scale (PDSS) through sensitivity and specificity analyses in order to identify excessive daytime sleepiness, considering parameters such as duration and quality of sleep, health perception, stress control and depressive moods (feelings of sadness) in adolescents. A total of 1,132 adolescents, aged 14-19 years old, of both sexes, from the public high school of São José - SC, answered the questionnaire with information on age, daytime sleepiness, sleep duration, health perception, stress management, depressive moods (feelings of sadness) and quality of sleep. The Receiver Operating Characteristic (ROC) curve was used to estimate cutoff points considering the sensitivity and specificity values ​​that best identify adolescents with excessive daytime sleepiness, using independent variables as a reference. The majority of the sample was female (54.2%), aged 14-16 years. The girls presented worse quality of sleep (66.4%), and the boys had a more positive perception of health (74.8%), better stress control (64.8%) and lower depressive moods (feelings of sadness) (63.3%). The largest area in the ROC curve was the one that considered sleep quality as a parameter in both sexes (area of the curve = 0.709 and 0.659, respectively, for boys and girls, p < 0.001). Considering sleep quality as a reference, the cutoff point for excessive daytime sleepiness was 15 points. The other parameters used were also significant (p < 0.005). Poor sleep quality was the parameter most strongly related to daytime sleepiness, and a cutoff of 15 points for the PDSS for both sexes should be used in the definition of excessive daytime sleepiness. For the other parameters, stress management, depressive mood (feelings of sadness) and health perception, different cutoff points are suggested for boys and girls.

Impact of sleep-related symptoms on clinical motor subtypes and disability in Parkinson's disease: a multicentre cross-sectional study.

OBJECTIVES: To investigate the impact of sleep disturbances on Parkinson's disease (PD) clinical motor subtypes and disease-related disability in a multicentre setting. METHODS: We report a cross-sectional relationship between sleep-related symptoms and clinical motor subtypes (tremor dominant (TD); intermediate; postural instability and gait disturbances (PIGDs)) identified in a multicentre study, including 436 patients with PD and 401 age-matched controls. PD-related sleep problems (PD-SP), excessive daytime sleepiness (EDS) and probable REM sleep behaviour disorder (pRBD) were evaluated using the PD sleep scale (PDSS)-2, Epworth Sleepiness Scale (ESS) and RBD screening questionnaire-Japanese version (RBDSQ-J), respectively. RESULTS: PD-SP (PDSS-2 ≥18; 35.1% vs 7.0%), EDS (ESS ≥10; 37.8% vs 15.5%) and pRBD (RBDSQ-J ≥5; 35.1% vs 7.7%) were more common in patients with PD than in controls. The prevalence of restless legs syndrome did not differ between patients with PD and controls (3.4% vs 2.7%). After adjusting for age, sex, disease duration and Movement Disorder Society-Unified PD Rating Scale (MDS-UPDRS) part III score, the PIGD group had higher PDSS-2 and ESS scores than the TD group. The RBDSQ-J scores did not differ among the TD, intermediate and PIGD groups. A stepwise regression model predicting the MDS-UPDRS part II score identified the Hoehn and Yahr stage, followed by the number of sleep-related symptoms (PD-SP, EDS and pRBD), disease duration, MDS-UPDRS part III score, PIGD subtype, depression and MDS-UPDRS part IV score as significant predictors. CONCLUSION: Our study found a significant relationship between sleep disturbances and clinical motor subtypes. An increased number of sleep-related symptoms had an impact on disease-related disability.

[Unconsciousness and clouded awareness]. / Bewusstseinsstörung oder -trübung.

Stupor and coma are clinical states in which patients have impaired responsiveness or are unresponsive to external stimulation and are either difficult to arouse or are unarousable. The term stupor refer to states between alertness and coma. An alteration in arousal represents an acute life-threatening emergency, requiring prompt intervention for preservation of life and brain function.

Insufficient sleep rather than the apnea-hypopnea index can be associated with sleepiness-related driving problems of Japanese obstructive sleep apnea syndrome patients residing in metropolitan areas.

OBJECTIVE: Obstructive sleep apnea syndrome (OSAS) and insufficient sleep might increase the risk of drowsy driving and sleepiness-related vehicular accidents. This study retrospectively investigated the factors associated with these driving problems, particularly addressing OSAS severity and sleep amounts of affected drivers. METHODS: This study examined 161 patients (146 male and 15 female) with OSAS (apnea-hypopnea index [AHI] ≥ 5) who drove on a routine basis and who completed study questionnaires. To investigate factors associated with drowsy driving during the prior year and sleepiness-related vehicular accidents or near-miss events during the prior five years, logistic regression analyses were performed with age, body mass index, monthly driving distance, habitual sleep duration on weekdays, the Japanese version of Epworth Sleepiness Scale score, AHI, and periodic limb movement index as independent variables. RESULTS: Of the patients, 68 (42.2%) reported drowsy driving experiences, and 86 (53.4%) reported sleepiness-related vehicular accidents or near-miss events. Analyses revealed the following: older age (46-65 years, ≥66 years) was negatively associated with drowsy driving (p <0.05, p <0.05), and habitually shorter sleep duration on weekdays (≤6 hours) was positively associated with drowsy driving (p <0.01). Habitual sleep duration of ≤6 hours (p <0.01) and Epworth Sleepiness Scale score of ≥11 (p <0.01) were positively associated with sleepiness-related vehicular accidents and near-miss events. However, AHI was not associated with these driving problems. CONCLUSION: Insufficient sleep, rather than severity of OSAS, was associated with sleepiness-related driving problems in these Japanese OSAS patients.

Clinical features in patients with positional obstructive sleep apnea according to its subtypes.

PURPOSE: This study aimed to determine the prevalence of positional obstructive sleep apnea (OSA) and its subtypes in Korean adults with newly diagnosed OSA and document the clinical characteristics of positional OSA and its subtypes compared to non-positional OSA METHODS: In this cross-sectional study, we evaluated 1052 OSA adults. Positional OSA was defined as an overall apnea-hypopnea index (AHI) ≥5 and supine AHI to non-supine AHI ratio of ≥2. Positional OSA was subtyped depending on the degree of AHI while in the non-supine position: subtype I (a non-supine AHI <5/h), subtype II (a non-supine AHI ≥5/h and <15/h), and subtype III (a non-supine AHI ≥15/h). To compare clinical characteristics between patient groups depending on the positional tendency of OSA, statistical analyses were performed. RESULTS: The prevalence of positional OSA was 75.6 % with 39.9 % having AHI normalized <5/h while in non-supine position. Positional OSA patients had milder OSA, older age, and lower BMI than did non-positional OSA patients. However, having positional OSA did not influence daytime sleepiness, depressive symptoms, anxiety, and health-related quality of life. Unlike the subtype I and II positional OSAs, subtype III did not differ in clinical features from non-positional OSA. There were significant differences in supine sleep time depending on the positional tendency of OSA. Subtype III positional OSA had the shortest supine sleep time whereas subtype I positional OSA and non-positional OSA had the longest supine sleep time. CONCLUSIONS: Positional OSA subtypes have different clinical characteristics. Subtyping of positional OSA is helpful for developing specific treatment strategies according to positional tendency.

Amsterdam positional OSA classification: the AASM 2012 recommended hypopnoea criteria increases the number of positional therapy candidates.

PURPOSE: This study examined the effect of hypopnoea criteria on the prevalence of positional obstructive sleep apnoea (pOSA) identified under the Amsterdam Positional OSA Classification (APOC) system. METHODS: Three hundred three consecutive patients undertaking polysomnography (PSG) for the suspicion of OSA were included in this retrospective investigation. PSGs were scored using both the 2007 American Academy of Sleep Medicine (AASM) recommended hypopnoea criteria (AASM2007Rec) and the 2012 AASM recommended hypopnoea criteria (AASM2012Rec). For each hypopnoea criteria, OSA patients were grouped according to the APOC categories (I, II or II) or else deemed non-APOC if they did not meet the APOC criteria. Outcome measures, such as Functional Outcomes of Sleep Questionnaire (FOSQ), MOS 36-item short-form health survey (SF-36) and psychomotor vigilance task (PVT), were also compared between the groups. RESULTS: The AASM2012Rec increased the prevalence of OSA compared to AASM2007Rec. The AASM2012Rec trebled the number of APOC I patients compared to AASM2007Rec (297% increase) as well as increased the proportion of females in the APOC I group. AASM2012Rec did not change the number of APOC II and APOC III patients. In fact, the same patients were present in these categories irrespective of hypopnoea criteria. The proportion of non-APOC patients proportionally decreased with the AASM2012Rec criteria. There were no differences in outcome measures between the AASM2012Rec and AASM2007Rec groups. CONCLUSIONS: This study demonstrates that, compared to AASM2007Rec, AASM2012Rec increases the prevalence of who could be successfully treated with positional therapy. The proportion of females with pOSA also increases as a consequence of AASM2012Rec.

Risk factors for automobile accidents caused by falling asleep while driving in obstructive sleep apnea syndrome.

PURPOSE: We examined the risk factors for automobile accidents caused by falling asleep while driving in subjects with obstructive sleep apnea syndrome (OSAS). METHODS: We asked licensed drivers with history of snoring and excessive daytime sleepiness who had undergone polysomnography (PSG) at the Department of Sleep Medicine/Sleep Disorders Center at Aichi Medical University Hospital to complete the questionnaires on accidents caused by falling asleep while driving. As a subjective measure of sleepiness, we used the Epworth sleepiness scale (ESS). Based on PSG results, 2387 subjects diagnosed with OSAS were divided into three groups according to apnea-hypopnea index (AHI): mild-to-moderate (5 ≤ AHI < 30), severe (30 ≤ AHI < 60), and very severe (AHI ≥ 60). We performed univariate and multivariate logistic regression on variables that might explain falling asleep at the wheel. RESULTS: We compared results between each group and simple snorers (394 subjects with AHI < 5) and found the group with very severe OSAS reported significantly higher rates of driving when drowsy and having accidents in the past 5 years due to falling asleep. CONCLUSIONS: Our multivariate analysis suggests that scores on the ESS and patient-reported frequency of feeling drowsy while regular driving and working are related to automobile accidents caused by falling asleep while driving.

Narcolepsy with and without cataplexy, idiopathic hypersomnia with and without long sleep time: a cluster analysis.

BACKGROUND: The successive editions of the International Classification of Sleep Disorders (ICSD) reflect the evolution of the concepts of various sleep disorders. This is particularly the case for central disorders of hypersomnolence, with continuous changes in terminology and divisions of narcolepsy, idiopathic hypersomnia, and recurrent hypersomnia. According to the ICSD 2nd Edition (ICSD-2), narcolepsy with cataplexy (NwithC), narcolepsy without cataplexy (Nw/oC), idiopathic hypersomnia with long sleep time (IHwithLST), and idiopathic hypersomnia without long sleep time (IHw/oLST) are four, well-defined hypersomnias of central origin. However, in the absence of biological markers, doubts have been raised as to the relevance of a division of idiopathic hypersomnia into two forms, and it is not yet clear whether Nw/oC and IHw/oLST are two distinct entities. With this in mind, it was decided to empirically review the ICSD-2 classification by using a hierarchical cluster analysis to see whether this division has some relevance, even though the terms "with long sleep time" and "without long sleep time" are inappropriate. RESULTS: The cluster analysis differentiated three main clusters: Cluster 1, "combined monosymptomatic hypersomnia/narcolepsy type 2" (people initially diagnosed with IHw/oLST and Nw/oC); Cluster 2 "polysymptomatic hypersomnia" (people initially diagnosed with IHwithLST); and Cluster 3, narcolepsy type 1 (people initially diagnosed with NwithC). CONCLUSIONS: Cluster analysis confirmed that narcolepsy type 1 and polysymptomatic hypersomnia are independent sleep disorders. People who were initially diagnosed with Nw/oC and IHw/oLST formed a single cluster, referred to as "combined monosymptomatic hypersomnia/narcolepsy type 2."

The daytime impact of DSM-5 insomnia disorder: comparative analysis of insomnia subtypes from the Great British Sleep Survey.

OBJECTIVE: To profile the daytime impact of the proposed DSM-5 insomnia disorder diagnosis, with and without mental health, physical health, or other sleep disorder comorbidities; to better understand how specific daytime symptom patterns are associated with nighttime sleep in insomnia; and to compare childhood-onset and adulthood-onset insomnia disorder with respect to daytime dysfunction. METHOD: Data were derived from the Great British Sleep Survey (GBSS), an open-access online population survey completed by adults who had a valid postcode and were residents of the United Kingdom. The primary variables of interest were the 6 areas that, according to the proposed DSM-5 criteria, may be impacted in the daytime by insomnia disorder: energy, concentration, relationships, ability to stay awake, mood, and ability to get through work. These variables were compared for those with versus those without insomnia disorder and across 5 insomnia subtypes (difficulty initiating sleep, difficulty maintaining sleep, early morning awakening, a combination of these 3 core symptoms, or nonrestorative sleep). Clinically comorbid insomnia presentations (insomnia disorder with poor mental health/poor physical health/additional sleep disorder symptoms) and insomnia disorder of childhood versus adult onset were also evaluated. RESULTS: A total of 11,129 participants (72% female; mean age = 39 years) completed the GBSS between March 2010 and April 2011, of whom 5,083 screened as having possible insomnia disorder. Compared with those who did not have insomnia disorder, those with insomnia disorder reported greater impairment in all areas of daytime functioning (Cohen d range, 0.68-1.30). The greatest effects reflected negative impact on energy and mood. Participants with a combination of insomnia symptoms tended to be the most impaired (Cohen d range, 0.10-0.23), whereas no consistent differences emerged between the other 4 subtypes. Finally, individuals who had both insomnia disorder and poor mental health were consistently the most impaired comorbid group (Cohen d range, 0.15-0.65), and childhood-onset insomnia disorder had greater daytime impact than adult-onset insomnia disorder (P < .05 for energy; P < .01 for mood, concentration, and getting through work). CONCLUSIONS: The severity of daytime impact of DSM-5 insomnia disorder varies by insomnia type. This finding has implications for the evaluation and management of insomnia in clinical practice.

Classification algorithms for predicting sleepiness and sleep apnea severity.

Identifying predictors of subjective sleepiness and severity of sleep apnea are important yet challenging goals in sleep medicine. Classification algorithms may provide insights, especially when large data sets are available. We analyzed polysomnography and clinical features available from the Sleep Heart Health Study. The Epworth Sleepiness Scale and the apnea-hypopnea index were the targets of three classifiers: k-nearest neighbor, naive Bayes and support vector machine algorithms. Classification was based on up to 26 features including demographics, polysomnogram, and electrocardiogram (spectrogram). Naive Bayes was best for predicting abnormal Epworth class (0-10 versus 11-24), although prediction was weak: polysomnogram features had 16.7% sensitivity and 88.8% specificity; spectrogram features had 5.3% sensitivity and 96.5% specificity. The support vector machine performed similarly to naive Bayes for predicting sleep apnea class (0-5 versus >5): 59.0% sensitivity and 74.5% specificity using clinical features and 43.4% sensitivity and 83.5% specificity using spectrographic features compared with the naive Bayes classifier, which had 57.5% sensitivity and 73.7% specificity (clinical), and 39.0% sensitivity and 82.7% specificity (spectrogram). Mutual information analysis confirmed the minimal dependency of the Epworth score on any feature, while the apnea-hypopnea index showed modest dependency on body mass index, arousal index, oxygenation and spectrogram features. Apnea classification was modestly accurate, using either clinical or spectrogram features, and showed lower sensitivity and higher specificity than common sleep apnea screening tools. Thus, clinical prediction of sleep apnea may be feasible with easily obtained demographic and electrocardiographic analysis, but the utility of the Epworth is questioned by its minimal relation to clinical, electrocardiographic, or polysomnographic features.

[Classification of sleep disorders]. / Clasificación de los trastornos del sueño.

Sleep disorders are frequent processes, both as a symptom associated with other diseases and as independent disorders. However, only in the last 4 decades has Sleep medicine gained its position among the medical specialties. In fact, it was only in these years that significant advances were obtained in the study of the etiology and treatment of these disorders. Similarly, the different classifications have been evolving over the years. First, they were based upon the clinical symptom; later on, more emphasis was given to the diseases. Finally, in 2005, the new classification was once again based on the symptoms. More than 90 disorders are listed in this latest classification, and an attempt is made to include the symptoms and the diseases of sleep, as well as those in which sleep disorders are fundamental. It is essential to have a clear idea of this complete classification of sleep disorders in order to deal with these patients appropriately.

[Hypersomnia: diagnosis, classification and treatment]. / Las hipersomnias: diagnóstico, clasificación y tratamiento.

Hypersomnia or excessive daytime sleepiness is common in neurological practice and may have different etiologies. Hypersomnia may be defined as sleepiness at an inappropriate time or in an inappropriate situation. It is important to consider that hypersomnia is at times referred to as tiredness or fatigue. A detailed clinical history is essential to reach an accurate diagnosis. A correct diagnosis is necessary to initiate the appropriate treatment considering the negative social and occupational consequences of hypersomnia. Excessive daytime sleepiness syndromes include primary sleep disorders like narcolepsy and hypersomnia secondary to several neurological and psychiatric disorders and also as an adverse effect of numerous drugs.

Daytime polysomnography for early diagnosis and treatment of patients with suspected sleep-disordered breathing.

Excessive daytime sleepiness (EDS) is a common complaint among patients with sleep-disordered breathing (SDB). Population-based studies on traffic and industrial accidents suggest a relationship between EDS and life-threatening events, and adults with EDS have cognitive and memory problems. Nocturnal polysomnography (nPSG) is essential for diagnosing SDB but it is time and energy consuming. We examined the usefulness of daytime polysomnography (dPSG) for the early diagnosis and treatment of patients with suspected SDB. We studied 108 consecutive patients aged 51.9 +/- 13.5 years (mean+/-SD). All patients underwent dPSG and nPSG. The number of apnea/hypopnea episodes per hour (apnea/hypopnea index: AHI) and the number of 3% desaturation episodes per hour (desaturation index: DSI) were calculated. All patients were classified into two groups. The REM group consisted of subjects who had an AHI < or = 25/h, AHI(REM)/AHI(NREM) > 2, and AHI(NREM) < 15/h. Those who did not satisfy these criteria were placed in the NREM group. Continuous positive airway pressure (CPAP) titration was performed for patients whose AHI was > or =20/h on dPSG. Using the international classification of sleep disorders, 96 patients were diagnosed as obstructive sleep apnea [including five upper airway resistance syndrome (UARS) patients], six patients were snoring, four had idiopathic hypersomnia due to a medical condition, and two had circadian rhythm sleep disorders. The sensitivity of dPSG for AHI was 81.0%, specificity was 100%, and accuracy was 83.5%. The sensitivity and accuracy of dPSG for AHI in the REM group were considerably lower than in the NREM group. There was no significant difference for optimal CPAP between dPSG and nPSG. In the five patients with UARS, their AHI, DSI, and arousal index on dPSG were 0.92 +/- 1.2/h, 2.9 +/- 3.4/h, and 29.3 +/- 3.5/h, respectively, and their AHI and DSI on nPSG were 3.2 +/- 2.5/h and 2.8 +/- 2.4/h, respectively. However, their respiratory effort-related arousals were 37.9 +/- 7.4/h, and their arousal index was 33.2 +/- 6.3/h. The five patients with UARS were also treated with CPAP, and their daytime sleepiness was improved. Although dPSG has limitations, these results indicate that dPSG recording is clinically useful for the diagnosis of and determination of types of treatment in patients with suspected SDB.