Overall, the percent agreement was 75%, with 77% of epochs of sleep correctly characterized as sleep; however, the agreement between the 2 monitors varied depending on the sleep stages being evaluated (Table 2). The overall weighted kappa was 0.62, 95% confidence interval, 0.58 to 0.64. A greater percentage agreement was seen in the wake stage, non-REM, and REM stages and lowest for the N3 stage.
The detailed characteristics and admitting diagnoses of the study patients are shown in Supplemental Table 1 (Supplemental Digital Content, http://links.lww.com/AA/B417). The mean age of the patients was 68 ± 11 years. Forty-three percent of the patients were admitted for medical reasons, and the remainder were postoperative patients. During the study period, 9 of 23 (39%) patients received intermittent opioids for pain control, and 3 of 23 patients (13%) received intermittent benzodiazepines (midazolam, zolpidem, or temazepam) for sedation. None of the patients received continuous sedation with opioids, benzodiazepines, propofol, or dexmedetomidine (full list of medications can be found in Supplemental Table 2, Supplemental Digital Content, http://links.lww.com/AA/B417). Clinically, all patients had a sedation level targeted at an RASS score of 0 or −1.
In all enrolled subjects, we were able to continuously monitor and retrieve their EEG for analysis with a mean recording time for the 23 enrolled patients of 19.10 hours, suggesting that SedLine was well tolerated among our target population. A sleep report, with traditional variables, such as TST and SE, was generated for the entire recording period for each patient (Supplemental Digital Content, Supplemental Table 3, http://links.lww.com/AA/B417). There were several signs indicative of poor-quality sleep, with reduced time spent in REM (6.4 ± 12 minutes, 1.38% ± 2.74% of TST, where 20% to 25% of TST is the normal range for this age group31) and stage N3 (5.4 ± 11.6 minutes, 2.17% ± 5.53% of TST, where 2.4% to 35.6% is the normal range for this age group32,33) coupled with a high arousal index (34.63 ± 19.04 arousals per hour, where normal is 23 arousals per hour for this age group26,35,36).
We further compared the sleep data between patients who developed delirium versus those who did not (Table 3). Of the 23 patients studied in this cohort, 8 developed delirium (35%), as measured by a positive score on CAM-ICU. Overall, there was no statistical difference on polysomnographic variables between the 2 groups although both groups had a small percentage of N3 rates and elevated arousal indices. The full results are shown in Supplemental Table 3 (Supplemental Digital Content, http://links.lww.com/AA/B417).
A preliminary analysis to validate SedLine recordings, compared with full PSG, indicated that the strengths of the SedLine monitor include capturing changes in the EEG, such as discerning awake from sleep compared with data from the PSG as reflected by the SE measurements. The brain function monitor was able to capture arousals and transitions between sleep stages in patients who were monitored in the sleep laboratory but was less accurate in determining sleep stages than in distinguishing sleep from wakefulness.
We demonstrated the feasibility of measuring sleep and wake EEG data over an extended period of time in a manner that did not interfere with the overall nursing care and management of ICU patients. In general, there was good agreement between SedLine and in-laboratory PSG for scoring all subjects; however, the accuracy in measuring different sleep stages by the SedLine monitor varies, specifically, the percent agreement on N1 epochs is only 29%. It appears that when the PSG sleep stages were N1, the SedLine scores were incorrectly reported as N2 or wake. The overlap with wake might represent periods of transition when the patient is going in and out of wakefulness, and thus, the determination of which stage is dominant on a particular epoch can sometimes be difficult to assess. Similarly, slow rolling eye movements that are typically seen in N1 can appear in wakefulness as a patient is approaching N1 sleep and perhaps an overreliance on the eye movements led to misscoring with a decreased sensitivity to the alpha waves of wakefulness that are more prominent occipitally. Furthermore, both N1 and N2 have a background EEG activity described as theta waves. One of the defining features of N2 is the sleep spindle. These phenomena are more challenging to detect using solely frontal leads, as is the case with SedLine, since sleep spindles have a central-parietal peak.37 Nonetheless, frontal leads have the benefit of good visualization of K-complexes, slow waves, and eye movements. A decreased ability to detect sleep spindles may have led to poorer differentiation between N1 and N2. The lowest agreement occurs in N3 sleep stage. Because the N3 stages count for only 3% of sleep epochs, however, a larger sample size will be needed to further discern the reasons for misclassification for this sleep stage. Potential difficulties with this technology include the use of frontal poles as a proxy to assess EOG and the absence of occipital leads. Using frontal poles rather than the traditionally placed EOG led to decreased accuracy in determining eye movements. Similarly, as noted previously, alpha waves, which indicate the state of wakefulness, are less prominent in frontal leads, so an underscoring of wake may occur. There was decreased concordance in N3 when one would have predicted the reverse because slow waves are generally best seen in frontal leads although our results may not be representative because of the small number of epochs or other artifacts in the signal.
Through this pilot study in the ICU, we successfully studied 23 patients, with a mean recording time of 19.10 hours, suggesting that the device was well tolerated by critically ill patients, giving insight into the sleep cycle dynamics experienced by patients in the ICU. To our knowledge, this is the first study using the SedLine monitor to measure sleep in critically ill patients.
Sleep in normal healthy adults is distributed as stage 1, 2% to 5%; stage 2, 45% to 55%; stage 3, 2% to 36%, and REM sleep, 20% to 25%.31,32,34 The stages of sleep are fragmented in ICU patients.37,38 Our data show trends consistent with previously published reports on sleep among ICU patients. Specifically, (1) sleep was distributed throughout the day and night rather than being consolidated at night, (2) there was a notable reduction in proportion of REM sleep, and (3) patients did not have normal sleep cycle architecture, where individuals transition from lighter to deeper sleep and alternate between non-REM and REM sleep in approximately 90-minute cycles (Fig. 1).
Sleep quality and quantity in the ICU is reduced by factors that include a patient’s acute illness, pain, discomfort, medical procedures, nursing-related care, ambient light, noise, mechanical ventilation, and other circadian disrupters, and medications.39,40 Full-montage EEG is generally not practical in the ICU setting, and these results confirm the utility of a portable monitor to measure different sleep stages. The ease of use, comfortability, small size, and portability of brain function monitors make their use feasible for the monitoring and care of critically ill patients, providing a potential use for research. Our results need further confirmation by a larger study. Although our study provided a mean recording duration of 19.10 hours, future studies should include longer duration of monitoring to capture >1 day-night cycle. However, the utility of monitoring sleep with a portable brain monitor to improve sleep hygiene and patient outcomes will need to be further evaluated because our pilot study did not discern any significant differences in polysomnographic variables in patients with delirium versus those without.
Our results confirm the feasibility of a portable EEG monitor, such as SedLine, to assess the sleep stages with reasonable accuracy and utility within the ICU setting. Further research should also focus on improving a processed EEG monitor that has greater agreement with PSG. Having the ability to continuously monitor sleep in the ICU setting will facilitate clinical trials with goal-directed interventions that might help understand, address, and rectify sleep disruption. Targeting modifiable risk factors such as sleep disruption may ultimately decrease delirium and associated adverse events in critically ill patients, a hypothesis that remains to be tested. Specifically, tracking sleep in real time in the clinical setting may lead to increased use of other nonpharmacologic treatments to improve sleep hygiene such as the use of earplugs, eyeshades, noise reduction, and others.
The authors would like to thank Dr. David Claman, Director of the Sleep Disorders Center at University of California, San Francisco, for his assistance in the setup of the studies in the Sleep Lab.
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