Classification of daily mental status in critically ill patients for research purposes.

PURPOSE: The purpose of this study is to develop a reliable tool for daily mental status classification in intensive care unit (ICU) patients for research purposes. Secondly, to identify patients with single, 1-day episodes of delirium and to compare them with patients having more delirium days or episodes. PATIENTS AND METHODS: A 5-step algorithm was designed, which includes Richmond Agitation Sedation Scale and Confusion Assessment Method for the ICU scores from bedside nurses, initiation of delirium treatment, chart review, and the Confusion Assessment Method for the ICU administered by researchers. This algorithm was validated against a reference standard of delirium experts. Subsequently, a cohort study was performed in patients admitted to a mixed ICU. RESULTS: In 65 paired observations, the algorithm had 0.75 sensitivity and 0.85 specificity. Applying the algorithm, interobserver agreement was high with mean Fleiss κ of 0.94 (5 raters) and 0.97 (4 raters). In the cohort study, 1112 patients were included of whom 535 (48%) became delirious. Single, 1-day episodes occurred in 43% of the delirious patients, whom were characterized by lower age compared with those with more delirium days. CONCLUSIONS: The algorithm for daily mental status classification seems to be a valid tool. In a substantial proportion of patients, delirium occurs only once during ICU admission lasting only 1 day.

Opportunities and methodological challenges in EEG and MEG resting state functional brain network research.

Electroencephalogram (EEG) and magnetoencephalogram (MEG) recordings during resting state are increasingly used to study functional connectivity and network topology. Moreover, the number of different analysis approaches is expanding along with the rising interest in this research area. The comparison between studies can therefore be challenging and discussion is needed to underscore methodological opportunities and pitfalls in functional connectivity and network studies. In this overview we discuss methodological considerations throughout the analysis pipeline of recording and analyzing resting state EEG and MEG data, with a focus on functional connectivity and network analysis. We summarize current common practices with their advantages and disadvantages; provide practical tips, and suggestions for future research. Finally, we discuss how methodological choices in resting state research can affect the construction of functional networks. When taking advantage of current best practices and avoid the most obvious pitfalls, functional connectivity and network studies can be improved and enable a more accurate interpretation and comparison between studies.

Sleep monitoring by actigraphy in short-stay ICU patients.

Sleep deprivation is common in intensive care unit (ICU) patients. The criterion standard for sleep monitoring, polysomnography, is impractical in ICU. Actigraphy (a wrist watch indicating amount of sleep) proved to be a good alternative in non-ICU patients, but not in prolonged mechanically ventilated patients, probably due to ICU-acquired weakness. Short-stay ICU patients do not suffer from ICU-acquired weakness. However, the accuracy of actigraphy is unknown in these patients. Therefore, we compared actigraphy to polysomnography in short-stay ICU patients. Sleep measurements were conducted in 7 postcardiothoracic surgery patients. The sensitivity (percentage of actigraphy data that agreed with sleep determined using polysomnography) and specificity (percentage of actigraphy data that agreed with awake determined using polysomnography) were calculated. The result showed that actigraphy underestimated the amount of wake time and overestimated the amount of sleep. The median specificity for actigraphy was always less than 19% and sensitivity more than 94%. Therefore, actigraphy is not reliable for sleep monitoring in short-stay ICU patients.