Epworth Sleepiness Scale

About: Epworth Sleepiness Scale is a research topic. Over the lifetime, 4742 publications have been published within this topic receiving 155088 citations.

Excessive daytime sleepiness assessed by the Epworth Sleepiness Scale and its association with health related quality of life: a population-based study in China

Abstract: Excessive daytime sleepiness (EDS) is a common condition worldwide that has many negative effects on people who were afflicted with it, especially on their health-related quality of life (HRQOL). The Epworth Sleepiness Scale (ESS) is a commonly used method for evaluating EDS in English-speaking countries. This paper reported the prevalence of subjective EDS in China as assessed by the Mandarin version of the ESS; tested the scale’s response rate, reliability and validity; and investigated the relationship between ESS scores and HRQOL. A population-based sample of 3600 residents was selected randomly in five cities in China. The demographic information was collected, subjective EDS was assessed by the Mandarin version of the ESS (ESS scores >10), and HRQOL was evaluated by the Mandarin version of the 36-item Short Form Health Survey (SF-36). The Mandarin version of ESS had very few missing responses, and the average response rate of its eight items was 97.92%. The split-half reliability coefficient and Cronbach’s α coefficient were 0.81 and 0.80, respectively. One factor was identified by factor analysis with an eigenvalue of 2.78. The ESS scores showed positive skewness in the selected sample, with a median (Q1, Q3) of 6 (3, 0). 644 (22.16%) respondents reported subjective EDS, and all of the scores of the eight dimensions of the SF-36 were negatively correlated with ESS scores. The Mandarin version of ESS is an acceptable, reliable, and valid tool for measuring EDS. In addition, subjective EDS is common in China, based on the ESS results, and impairs HRQOL.

Classification algorithms for predicting sleepiness and sleep apnea severity.

Abstract: 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.

Rationale and Methodology of the Impact of Continuous Positive Airway Pressure on Patients With ACS and Nonsleepy OSA: The ISAACC Trial

Abstract: Background Obstructive sleep apnea (OSA) is common in acute coronary syndrome (ACS) and a possible cause of increased morbidity and mortality. Objectives The main objective is to determine in patients with ACS and OSA if CPAP treatment reduces the incidence of cardiovascular events (cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, hospitalization for heart failure, and hospitalization for unstable angina or transient ischemic attack). The secondary objectives are to determine the prevalence of nonsleepy OSA in patients with ACS; assess the effect of CPAP on the incidence of newly diagnosed diabetes mellitus, symptoms, and quality of life; identify biomarkers of risk involved in cardiovascular complications in these patients; and conduct a cost-effectiveness analysis of diagnosis and treatment. Population and Methodology Multicenter, prospective, randomized and controlled study. Patients are admitted to the coronary care unit with diagnosis of ACS and without daytime sleepiness (Epworth Sleepiness Scale ≤10) at 15 teaching hospitals in Spain. All patients undergo a sleep study by cardiorespiratory polygraphy. Patients with an apnea-hypopnea index ≥15/hour will be randomized to treatment with CPAP (group 1, 632 patients) or conservative treatment (group 2, 632 patients). Patients with an apnea-hypopnea index <15/hour (group 3, 600 patients) will be followed as a reference group. Patients will be monitored at baseline (T0), 1 month (T1), 3 months (T2), 6 months (T3), 12 months (T4), and every 6 months thereafter (where applicable) during the follow-up period. Conclusions The ISAACC trial will contribute to evaluating the effect of CPAP treatment on cardiovascular events in patients with ACS and OSA.