Little is known about sleep/wake abnormalities in intensive care and less is known about the mechanisms responsible for these abnormalities. We studied 22 (20 mechanically ventilated) medical intensive care unit (ICU) patients with continuous polysomnography (PSG) and environmental noise measurements for 24–48 h to characterize sleep–wake patterns and objectively determine the effect of environmental noise on sleep disruption. All 22 patients demonstrated sleep–wake cycle abnormalities. There were large variations in total sleep time (TST) with the mean total sleep time per 24-h study period of 8.8 ± 5.0 h. Sleep–wake cycles were fragmented and nonconsolidated with a mean of 57 ± 18% and 43 ± 18% of the TST occurring during the day and night, respectively. Environmental noise was responsible for 11.5 and 17% of the overall arousals and awakenings from sleep, respectively. The mean noise arousal index was 1.9 ± 2.1 arousals/h sleep. Conclusions: (1) ICU patients are qualitatively, but not necessarily quantitatively, sleep deprived; and (2) although environmental noise is in part responsible for sleep–wake abnormalities, it is not responsible for the majority of the sleep fragmentation and may therefore not be as disruptive to sleep as the previous literature suggests.
Although previous investigators evaluating sleep patterns in ICU patients have demonstrated altered sleep architecture and sleep deprivation (1-4), little is actually known about sleep in the critically ill. Most of our current knowledge is based on studies evaluating only nocturnal sleep, rather than over 24-h periods (1, 2). Two studies have monitored polysomnography continuously for ⩾ 24 h, albeit in only a total of 19 ICU patients (3, 4). Hilton (4) demonstrated a mean total sleep time per 24-h period of 5.5 ± 3.4 h (range 0.1–13.3) in 10 ventilated patients with respiratory insufficiency. Aurell and Elmquist (3) found the mean total sleep time per 24-h, to be 4.6 ± 1.6 h (range 0–7) in 9 postoperative patients. In addition to the reduction in total sleep time, these studies demonstrated altered sleep architecture with a predominance of stage 1 and 2 sleep, decreased or absent stage 3, stage 4, and rapid eye movement (REM) sleep, shortened REM periods, and sleep fragmentation. Sleep distribution was also abnormal, as up to 50% of the total sleep time occurred during the day.
The etiologies of these sleep disturbances in the ICU are presumed to be multifactorial, although little is actually known about the mechanisms responsible for sleep–wake cycle disturbances in the ICU. Environmental stimuli are proposed to be the most disruptive factors to achieving sleep in the ICU (5-11). The environmental stimulus most often cited in the literature to disturb sleep is noise (6, 7, 12). Several studies have shown that noise levels in the ICU are substantially higher than the Environmental Protection Agency (EPA) recommendations for maximum hospital room noise levels, both at night and during the day (6, 7, 11-14). Polysomnographic studies evaluating the effect of nocturnal ICU noise on sleep in normal individuals in a sleep laboratory demonstrated decreased total sleep time, total REM time, and sleep efficiency, and increased REM latency and arousal index (number of arousals per hour of sleep) (8, 9). However, nocturnal polysomnographic studies of ICU patients have only indirectly linked noise to sleep disruption by attempting to correlate environmental noise levels with arousals from nocturnal sleep (3, 5). These studies had small sample sizes and were not designed to determine the specific etiologies of the sleep disruption.
Our previous research has demonstrated that although ICU patients subjectively experienced significantly poorer sleep quality in the ICU than at home, ICU noise was not perceived as the most disruptive environmental stimulus (15). ICU patients perceived frequent interruptions from vital signs and diagnostic testing to be as disruptive to achieving quality sleep as noise, although statistically no single environmental factor was perceived as significantly more disruptive than any other (15). These data led us to hypothesize that other factors besides noise are important in mediating sleep disruption in the ICU.
The main goals of this study were to gain a better understanding of the underlying mechanisms of altered sleep–wake patterns in ICU patients and, specifically, to objectively determine the effect of ICU environmental noise on sleep fragmentation. Our study, unlike previous studies, was designed to objectively evaluate the effects of noise on sleep disruption by relaying the output of the noise meter to the polysomnograph so that environmental noise and sleep patterns could be evaluated simultaneously with real time recordings.
Our primary aim was to objectively determine the disruptive nature of environmental noise on sleep in ICU patients. Secondary aims of the study were to (1) characterize sleep– wake patterns in a group of primarily mechanically ventilated medical ICU patients and (2) gain insight into the effect of severity of illness on sleep.
This study was performed between March 1997 and February 1999 at the University of Pennsylvania Medical Center and the Presbyterian Medical Center. The study was approved by the Institutional Review Board (IRB) of the University of Pennsylvania.
The medical intensive care unit (MICU) at the University of Pennsylvania Medical Center is a 24-bed ICU with 12 intermediate level of care/step down beds and 12 acute care beds. All of the acute care and step down patient rooms are single patient rooms that are enclosed on three sides and can be isolated from the nurses station by a sliding glass door. The ICU at the Presbyterian Medical Center is a 15-bed mixed medical/surgical ICU. All beds are acute care beds. For this study, all of the patient rooms that were utilized were single-patient rooms that were enclosed on three sides. Seven of these rooms could be totally enclosed by a sliding glass door while the remaining four beds only had a curtain for patient privacy at the entrance to the room.
All patients were in the ICU for primarily medical problems. Patients were excluded if, prior to the initiation of the study, they were receiving continuous heavy sedation, were stuporous or comatose, and/or had a previous history of dementia. Heavy sedation was defined as the inability to arouse the patient or inability of the patient to follow verbal commands. Heavy sedation was a criterion for exclusion based upon the inability to classify the patients' level of consciousness as sleep versus wakefulness. Patients with a diagnosis of dementia were excluded because of the known abnormal EEG patterns described in demented patients, which make it difficult to accurately determine sleep versus wakefulness by EEG criteria even in non-critically ill demented patients (16). Patients and/or their families gave written consent prior to their participation. Patients were volunteers who received no remuneration for their participation.
The gold standard for determining sleep onset and maintenance is polysomnography (17). All subjects were monitored with continuous (24 to 48 h) standard polysomnography utilizing a Sandman portable polysomnograph (Nellcor-Puritan Bennet, Pleasenton, CA) or a Biologic portable polysomnograph (Mundelein, IL). Electrode leads were placed on the subject's head in the C3, C4, and 0z positions according to the International 10/20 system of electrode placement. These leads were referenced to two reference electrodes A1 and A2 over the subjects' mastoid regions. Two extraocular (EOG) leads and two chin electromyogram (EMG) leads were utilized to assess ocular movements and muscle tone to differentiate REM sleep from NREM sleep and wakefulness. The polysomnograms were scored according to the criteria of Rechtschaffen and Kales (18).
Arousals from sleep were scored according to the American Sleep Disorders Association (ASDA) definition (19). Arousals from sleep specifically due to noise were defined according to the ASDA definition of an arousal and if the arousal occurred during or within 3 s after the completion of an environmental noise increase of > 10 dB (A) (see Figure 1).
Environmental noise was assessed by a Quest 1900 portable integrating/logging sound level meter (Quest Technologies, Oconomowoc, WI), which is accurate to within 0.5 dB (factory manual). A microphone was secured to the head of the bed and positioned so that the microphone was within 3 in. of the patient's head. This technique allowed the microphone to move in harmony with the patient's head in an attempt to measure the noise that the patient was experiencing. Environmental noise was continuously recorded in decibels (dB) on the decibel A (dB[A]) scale. The decibel A scale is a frequency weighting method that simulates the reception characteristics of the human ear (7). The sound meter was calibrated prior to each study with a Quest model QC-20 calibrator at 1,000 Hz at 94 dB as a reference output. The sound level meter decibel range was set between 40 and 100 dB(A), based on our preliminary data as well as prior studies assessing ICU noise levels (5, 6, 20). The output of the sound meter was simultaneously recorded on the polysomnograph to assess the effect of noise on arousals from sleep (see Figure 1). Noise data for the entire study period were logged and stored at 1-min intervals (the sampling frequency was at 1-s intervals).
Each patient had an Acute Physiology, Age, and Chronic Health Evaluation (APACHE) III score calculated for each 24-h period in an attempt to correlate severity of illness with degree of sleep disruption (21).
Unpaired Student's t tests were utilized to compare daytime versus nighttime (average, maximum, and peak) noise levels, sleep versus wakefulness (average, maximum, and peak) noise levels, and the nonnoise sleep arousal index versus the noise-specific arousal index. Unpaired Student's t tests were also utilized to determine if differences existed between sexes and a patient's window status (rooms with versus without windows) with respect to (1) sleep stages (1, 2, 3/4, and REM), (2) arousal indexes (noise, nonnoise, and total), (3) daytime sleep (total time and percentage of total sleep time), (4) nighttime sleep (total time and percentage of total sleep time), and (5) total sleep time (TST).
Pearson's correlation analysis and one-way analysis of variance were used to determine the relationship of patient age, duration of stay, and APACHE III score to the following factors: sleep stages (1, 2, 3/4, and REM), arousal indexes (noise, nonnoise, and total), daytime sleep (total time and percentage of total sleep time), nighttime sleep (total time and percentage of total sleep time), and total sleep time (TST). Spearman's correlation analysis was used to confirm the Pearson's analysis. Only the Pearson's correlation coefficients were reported if there was an agreement between these latter two analyses.
A total of 24 ICU patients were enrolled between March 1997 and February 1999. Two patients withdrew from the study prior to its initiation at the request from the patient's family members after the initial consent was given. Twenty-two patients completed the study for a total of 30 24-h periods (8 patients were studied for 48 h continuously and the remaining 14 patients were studied for 24 h). The study population was comprised of 12 males and 10 females with a mean age of 61 ± 16 yr (range 20–83), mean APACHE III score of 57 ± 28 (range 7–132), and a mean duration of ICU stay prior to the study of 18 ± 20 d (range 3–80). Twenty of the 22 patients were mechanically ventilated at the time of the study, and remained mechanically ventilated for the entire study period. Primary reasons for mechanical ventilation included pneumonia (7 patients), sepsis (5 patients), chronic obstructive pulmonary disease (COPD) exacerbation (3 patients), acute respiratory distress syndrome (ARDS) (3 patients), and myasthenia gravis (2 patients). Fourteen patients received no sedation during the study period. Of the remaining 8 patients, 4 patients received intermittent, as needed, intravenous lorezepam and 4 patients received intermittent, as needed, intravenous doses of fentanyl. No patients were on a combination of benzodiazepines and narcotics during the study period. No patients received tricyclic or other types of antidepressant medications during the study period. All patients had a Glasgow coma score of 14 or greater upon entrance into the study.
Seventeen (77.3%) of the 22 patients had scorable EEG data according to the criteria of Rechtschaffen and Kales (18). From this group, there were a total of 21 separate 24-h day/ night periods (13 patients with 24 h of scorable data and 4 patients with 48 h of scorable data). The other 5 patients (22.7%) demonstrated evidence of septic encephalopathy throughout the majority or all of the study period, and were therefore unable to be scored according to standard criteria of sleep versus wake (see section on sepsis and sleep). Of the 17 ICU patients with scorable sleep–wake polysomnograms, all demonstrated abnormal sleep architecture. Although the mean total sleep time per 24 h period was within the normal range (8.8 ± 5.0 h) (17) there were large individual variations in total sleep time (range 1.7 to 19.4 h).
There was a predominance of stage 1 sleep (mean 59 ± 33%) with decreased or absent stages 2 (mean 26 ± 28%), 3/4 (mean 9 ± 18%), and REM sleep (mean 6 ± 9%). Twelve of these 17 patients demonstrated no REM sleep. Of the 8 patients who were studied continuously for 48 h, 5 had scorable EEG data. There were no significant differences between study Day 1 and 2 with respect to mean total time spent in sleep stages 1, 3/4, and REM, day versus night total sleep times, or the overall arousal index. There were no significant differences (p > 0.05) between sexes or window status with respect to TST/24-h period in time in any sleep stage. There were no significant correlations (p > 0.05) between TST/24-h period or time in any sleep stage with age, duration of ICU stay, or APACHE III score.
All 17 patients with scorable sleep–wake stages demonstrated nonconsolidated sleep that was distributed throughout the 24-h day–night study period (see Figure 2). Fifty-seven percent ± 18% of the total sleep time occurred during the daytime (6:00 a.m.–10:00 p.m.) and 43 ± 18% of the TST occurred during the nighttime hours (10:00 p.m.–6:00 a.m.). The mean number of sleep periods per 24-h study period was 41 ± 28 (range 5–100). The mean length of each sleep bout was 15 ± 9 min (range 5.5–40 min). REM sleep, when it occurred, was equally distributed between day (50 ± 5%) and night (50 ± 5%). The overall arousal index (number of arousals per hour of sleep) was normal (mean 11.6 ± 5.0; range 4.6–21.2) (22). There were no significant differences (p > 0.05) between sexes or window status with respect to daytime versus nighttime sleep or the overall arousal index. Age, duration of ICU stay, and APACHE III score were not significantly correlated (p > 0.05) with daytime versus nighttime sleep or the overall arousal index.
The mean, mean maximum, and mean peak ICU environmental noise levels exceeded EPA recommendation during both the daytime and nighttime hours. There were no statistically significant differences (p > 0.05) in mean (59.1 ± 6.1 dB[A] versus 56.8 ± 4.9 dB[A]), mean maximum (68.5 ± 7.7 dB versus 64.6 ± 7.5 dB), or mean peak (85.9 ± 5.1 dB versus 82.8 ± 5.3 dB) noise levels between the day and night, respectively. There were no significant differences (p > 0.05) between mean (58.9 ± 6.0 dB versus 57.1 ± 5.2 dB), mean maximum (68.3 ± 7.5 dB versus 64.9 ± 7.6 dB), and mean peak (85.6 ± 5.0 dB versus 84.9 ± 4.8 dB) noise levels during periods of sleep and wakefulness.
Overall, 11.5% of the arousals from sleep for the entire population studied were secondary to environmental noise. Arousals related to environmental noise comprised an average of 11.5 ± 11.8% of the total arousals from sleep per subject. The mean arousal index specifically related to environmental noise was 1.9 ± 2.1. This was significantly less (p < 0.0001) than the mean spontaneous (nonnoise) arousal index of 9.6 ± 4.9. Overall, environmental noise was responsible for 17% of the awakenings from sleep. Awakenings related to environmental noise comprised an average of 26.2 ± 24.8% (range 0–75%) of the total awakenings from sleep per subject. There were no significant differences (p > 0.05) between sexes or window status with respect to the noise specific arousal index or awakenings secondary to noise. Age, duration of ICU stay, and APACHE III score were not significantly correlated (p > 0.05) with the noise specific arousal index or awakenings secondary to noise.
As stated earlier, 5 patients either developed sepsis and/or positive blood cultures during the study period (4 of 5) or were recovering from sepsis (1 of 5) during the study period. None of the patients was receiving continuous sedative medications immediately before (previous 24 h) or during the study period. All 5 patients demonstrated similar EEG patterns of a baseline of low-voltage mixed-frequency waves with intermittent and variable amounts of theta and delta waveform activity (see Figure 3). In the 4 patients without known sepsis prior to the study period, this EEG pattern appeared up to 8 h prior to these patients demonstrating clinical signs of sepsis (fever, hypotension). This EEG pattern was present both when the patients' eyes were open as well as when they were closed. For this reason, we were unable to define the patients' state of consciousness into definitive sleep or wake states, by current EEG criteria. These 5 patients did not demonstrate any evidence of clearly definable sleep throughout the study period. There was no evidence of spindles, K-complexes, or REM activity, all hallmarks or normal sleep.
All medical ICU patients studied demonstrated sleep–wake cycle abnormalities. Nonseptic ICU patients demonstrated abnormal sleep architecture with a predominance of stage 1 sleep and decreased or absent stages 2, 3, 4, and REM sleep. These patients tended to sleep for frequent, short periods that were nonconsolidated and abnormally distributed over the 24-h day. ICU patients with sepsis demonstrated varying degrees of encephalopathy, with no definable sleep or wake periods. Although environmental noise was in part responsible for sleep– wake abnormalities, our data suggested that other factors must be responsible for sleep disruption in this patient population. Finally, although further research needs to be performed, we have observed that continuous EEG monitoring may be useful as an early marker of the onset of sepsis.
Our study design had several limitations, which should be reviewed. Our results are not generalizable to all ICU patient populations as the majority of the patients that we evaluated were nonsedated mechanically ventilated medical ICU patients. However, this is a very important patient population to study since many ICU patients are mechanically ventilated. There may also have been a selection bias as the patients were selected to participate based on their likelihood of remaining in the ICU for a continuous 48-h period and were therefore not randomly selected. We did not control for underlying disease state as we were interested in evaluating disorders of sleep and wake in a heterogeneous population of ICU patients. We excluded patients under heavy sedation in this initial study because we thought it would be difficult to classify a given patient's level of consciousness as wakefulness versus sleep. Also, sedatives themselves can affect sleep and/or the EEG potentially confounding our results (23). However, the majority of our patients were not treated with sedatives. We were unable to determine if all of the patients' sleep–wake cycles changed on a day-to-day basis or improved over time as we evaluated each patient only for 24–48 h and did not control for length of stay in the ICU. However, the abnormalities in sleep architecture were evident regardless of length of stay, suggesting that length of stay may not be an important contributor to sleep alterations in the ICU. Also, there were no significant differences in the total sleep time, time in the various sleep stages, or day versus night total sleep times between study Day 1 and 2 in the 5 patients with scorable EEG data who were studied for 48 continuous hours. This suggests that there is little day-to-day variation in sleep architecture.
Environmental noise clearly plays a role in disrupting sleep in medical ICU patients. The finding that environmental noise was not responsible for the majority of arousals and awakenings from sleep is contrary to the current literature, which considers noise to be the major etiologic factor responsible for sleep disruption in the ICU (5-11). We have objectively demonstrated that environmental noise was responsible for 11.5 and 17% of the arousals and awakenings from sleep, respectively. These findings confirm our previous research that demonstrated that although ICU patients subjectively experienced significantly poorer sleep quality in the ICU than at home, ICU noise was not perceived as the most disruptive environmental stimulus (15).
We believe our results are valid for several reasons. First, our technique of simultaneously monitoring environmental noise and polysomnography allowed for the objective measurement of effects of noise on sleep. Second, there were no significant differences in mean, maximum, or peak noise levels during periods of wake and sleep, so our results cannot be explained by differences in noise levels during sleep versus wake periods. Finally, it cannot be argued that our ICUs were quieter than other ICUs as the noise levels recorded in our ICUs appear to be comparable to those in other publications (7, 24).
The finding that environmental noise was not as disruptive to ICU patient sleep as previously described in the literature was not completely surprising. Previous studies show that there are wide variations in individual sensitivity to sounds during sleep and that the meaning of the sound is also critical (25, 26). Although studies on the acute effect of noise on sleep have consistently demonstrated a sleep disrupting effect, normal individuals rapidly adapt to the disruptive effects of environmental noise on sleep (27). Studies in normals have demonstrated that individuals habituate to sound by increasing their arousal threshold for noise over time, with some individuals being able to increase their arousal threshold for noise to more than 80 dB(A) (28-30). This finding is also consistent with the results of Aurell and Elmquist (3) who demonstrated that sleep architecture and distribution were significantly altered in 9 postsurgical ICU patients, despite a concerted effort by the staff to keep environmental disturbances (noise, interruption, and light) to a minimum.
From the standpoint of the ICU environment, we previously described that ICU patients subjectively perceived human interventions and diagnostic testing to be as disruptive to sleep as noise (15). In this predominantly mechanically ventilated group of patients, it is possible that sleep was disrupted by patient/ventilator dyssynchrony as well as from human interventions such as suctioning and the administration of respiratory treatments. This study was not designed to evaluate the effects of human interventions or other factors (light, noxious odors, etc.) on sleep–wake disturbances. Future research should focus on objectively determining the effects of these factors on sleep disturbances in the ICU population.
It was remarkable that the abnormal sleep–wake patterns and sleep disruptions attributable to noise demonstrated by this group of patients were not affected by patient age, sex, duration of ICU stay, or severity of illness. These findings confirm our previous research (15), which demonstrated that the patient characteristics of age, sex, and duration of stay were not associated with an ICU patient's poor subjective sleep quality. This indicates that all mechanically ventilated medical ICU patients are at risk for sleep disturbances. This also indicates that other factors, environmental (human interventions and light) and/or patient specific (medications, pain/anxiety, underlying disease, inflammatory mediators, and/or circadian rhythm disturbances), must be responsible for sleep–wake disturbances in the ICU.
Although the majority of the critical care literature (1-4) has suggested that ICU patients are sleep deprived, we have demonstrated that mechanically ventilated medical ICU patients are not necessarily quantitatively sleep deprived. Our patients demonstrated large variations in total sleep time per 24-h period, with a mean total sleep time per 24-h period of 8.8 ± 5.0 h. Many patients are selectively deprived of certain stages of sleep, but our patients were not totally sleep deprived for any given 24-h period.
Although ICU patients may not be quantitatively sleep deprived, they all demonstrated fragmentation and nonconsolidation of their sleep–wake cycles as well as a predominance of stage 1 sleep. Thus, they may be functionally sleep deprived. The sleep continuity theory demonstrates that consolidation of sleep is as important as total sleep time, as decrements in daytime function increase as the length of periods of consolidated sleep decreases (28-30). It is likely that many of these patients suffer from chronic daytime and/or nighttime sleepiness as well as neurocognitive performance deficits that are common in individuals experiencing sleep fragmentation (28-30).
ICU patients appear to lose the ability to maintain the normal circadian night/day distribution of sleep and wake. Our data indicate that total sleep time is redistributed over a 24-h period with large individual variations in total sleep time. These data are in agreement with other studies evaluating sleep in the ICU with continuous polysomnography (7, 31). Our results reinforce the importance of performing sleep studies over a 24-h period to adequately characterize sleep–wake patterns in this patient population as it is evident that studying nocturnal sleep alone is insufficient.
Decreased or absent REM sleep as well as its equal distribution across the day and night is abnormal. REM sleep typically occupies 20–25% of nocturnal sleep time in normal individuals and unlike delta sleep, REM distribution remains relatively stable throughout the life span (17). The mechanisms responsible for the absence or abnormal distribution of REM sleep in critically ill patients is unknown, but may be explained by (1) inadequate time during short bouts of sleep to cycle into REM sleep (17), (2) disturbances in the circadian system that normally controls the timing of REM sleep in normal individuals (32, 33), and (3) underlying disease and specifically mediators (endotoxin) that may be released with inflammatory states/sepsis, which may decrease or inhibit REM sleep (34, 35). This may help to explain why our septic subjects did not demonstrate any REM activity. We do not believe that medications were responsible for the REM suppression demonstrated by this group of patients as the majority (84%) of the patients studied were not on REM-suppressing medications.
This study also provides insight into the effects of sepsis on sleep and wake states. The finding of EEG slowing with sepsis states is most consistent with septic encephalopathy (35). Interestingly, our patients with sepsis demonstrated no evidence of clearly definable sleep or wake states by standard monitoring criteria over the 24- to 48-h monitoring period. Patients with underlying sepsis appear to be in a dissociated state of consciousness, deprived of both normal sleep and wake states. Although not previously described in older studies evaluating sleep in ICU patients (2-4), Cooper and coworkers (31) recently described similar findings in a group of mechanically ventilated patients with acute lung injury. It is likely that many ICU patients may demonstrate a similar state of dissociated consciousness.
Although EEG changes with previously diagnosed sepsis have been described in the literature (36-38), the observation that these EEG changes may precede other clinical features of sepsis (fever, tachycardia, hypotension) is a novel finding. Five of the 22 patients demonstrated EEG findings that were consistent with mild to moderate encephalopathy, before other signs of sepsis were present. None of the nonseptic patients demonstrated this EEG pattern. Future studies will need to be prospectively performed to determine if continuous EEG will be a valid, noninvasive marker of early sepsis.
Our data indicate that mechanically ventilated medical ICU patients manifest sleep–wake pattern abnormalities. ICU patients demonstrate large individual variations in total sleep time and, in general, are not quantitatively sleep deprived. Sleep architecture and distribution are severely altered as illustrated by excessive sleep fragmentation, selective sleep stage deprivation, and loss of the normal circadian night–day, sleep–wake cycle distribution. Although all of the mechanisms responsible for these sleep–wake abnormalities are not yet elucidated, it appears that the impact of environmental noise on sleep disruption is much less important than previously described. Our data also indicate that all patients are at risk for sleep–wake disturbances as age, sex, severity of illness, and duration of stay were not associated with sleep–wake abnormalities. Further research is needed to better define the mechanisms responsible for sleep abnormalities in patients who are critically ill.
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This investigation was supported by the National Institutes of Health Grant HL-03124.