SummaryBackgroundExcessive daytime sleepiness (EDS) is the main complaint in many neurological sleep disorders, such as idiopathic hypersomnia, narcolepsy, or obstructive sleep apnea/hypopnea syndrome (OSAS). The validity of the Epworth Sleepiness Scale (ESS) as a screening tool for EDS remains controversial. We therefore investigated (1) the interrelation of the ESS total score and the mean sleep latency (MSL) during the multiple sleep latency test (MSLT) and (2) the diagnostic accuracy of the ESS total score to detect EDS in patients with the chief complaint of subjective EDS.MethodsA total of 94 patients (48 males) with subjective EDS were included in this study. Regression analyses and ROC curve analyses were carried out to assess the predictive value of the ESS score for MSL.ResultsThe ESS score significantly predicted a shortened MSL (p = 0.01, β = −0.29). After dichotomizing into two groups, the ESS score predicted MSL only in patients with hypersomnia or narcolepsy (p = 0.01, β = −0.33), but not in patients with other clinical diagnoses (e. g. OSAS; p = 0.36, β = −0.15). The ROC curve analyses indicated an optimal ESS cut-off value of 16 with a sensitivity of 70%; however, specificity remained unsatisfactory (55.6%).ConclusionsOur results suggest that the predictive value of the ESS score in patients with subjective EDS is low and patient subgroup-specific (superior in hypersomnia/narcolepsy vs. other diagnoses) and that the commonly used cut-off of 11 points may be insufficient for clinical practice.

BackgroundCardiovascular disease is the main cause of death in Austria. However, no systematic information exists regarding characteristics and treatments of contemporary patients with stable coronary artery disease (CAD) in Austria. We assembled two retrospective physicians’ databases to describe demographics, clinical profiles, and therapeutic strategies in patients with stable CAD. In addition, we compared patient profiles of secondary care internists and hospital-based cardiologists with those of general practitioners in a primary care setting outside of hospital.MethodsThe study population was identified from retrospective chart review of 1020 patients from 106 primary care physicians in Austria (ProCor II registry), and was merged with a previous similar database of 1280 patients under secondary care (ProCor I registry) to yield a total patient number of 2300.ResultsFemale patients with stable CAD were older, had more angina and/or heart failure symptoms, and more depression than males. Female gender, type 2 diabetes mellitus, higher CCS class and asthma/COPD were predictors of elevated heart rate, while previous coronary events/revascularization predicted a lower heart rate in multivariate analysis. There were no significant differences with regard to characteristics and management of patients of general practitioners in the primary care setting versus internists in secondary care.ConclusionsCharacteristics and treatments of unselected patients with stable ischemic heart disease in Austria resemble the pattern of large international registries of stable ischemic heart disease, with the exception that diabetes and systemic hypertension were more prevalent.

Consider a parallel group trial for the comparison of an experimental treatment to a control, where the second-stage sample size may depend on the blinded primary endpoint data as well as on additional blinded data from a secondary endpoint. For the setting of normally distributed endpoints, we demonstrate that this may lead to an inflation of the type I error rate if the null hypothesis holds for the primary but not the secondary endpoint. We derive upper bounds for the inflation of the type I error rate, both for trials that employ random allocation and for those that use block randomization. We illustrate the worst-case sample size reassessment rule in a case study. For both randomization strategies, the maximum type I error rate increases with the effect size in the secondary endpoint and the correlation between endpoints. The maximum inflation increases with smaller block sizes if information on the block size is used in the reassessment rule. Based on our findings, we do not question the well-established use of blinded sample size reassessment methods with nuisance parameter estimates computed from the blinded interim data of the primary endpoint. However, we demonstrate that the type I error rate control of these methods relies on the application of specific, binding, pre-planned and fully algorithmic sample size reassessment rules and does not extend to general or unplanned sample size adjustments based on blinded data.