Noise in the intensive care unit and its influence on sleep quality: a multicenter observational study in Dutch intensive care units.

BACKGROUND: High noise levels in the intensive care unit (ICU) are a well-known problem. Little is known about the effect of noise on sleep quality in ICU patients. The study aim is to determine the effect of noise on subjective sleep quality. METHODS: This was a multicenter observational study in six Dutch ICUs. Noise recording equipment was installed in 2-4 rooms per ICU. Adult patients were eligible for the study 48 h after ICU admission and were followed up to maximum of five nights in the ICU. Exclusion criteria were presence of delirium and/or inability to be assessed for sleep quality. Sleep was evaluated using the Richards Campbell Sleep Questionnaire (range 0-100 mm). Noise recordings were used for analysis of various auditory parameters, including the number and duration of restorative periods. Hierarchical mixed model regression analysis was used to determine associations between noise and sleep. RESULTS: In total, 64 patients (68% male), mean age 63.9 (± 11.7) years and mean Acute Physiology And Chronic Health Evaluation (APACHE) II score 21.1 (± 7.1) were included. Average sleep quality score was 56 ± 24 mm. The mean of the 24-h average sound pressure levels (LAeq, 24h) was 54.0 dBA (± 2.4). Mixed-effects regression analyses showed that background noise (ß = - 0.51, p < 0.05) had a negative impact on sleep quality, whereas number of restorative periods (ß = 0.53, p < 0.01) and female sex (ß = 1.25, p < 0.01) were weakly but significantly correlated with sleep. CONCLUSIONS: Noise levels are negatively associated and restorative periods and female gender are positively associated with subjective sleep quality in ICU patients. TRIAL REGISTRATION: www.ClinicalTrials.gov, NCT01826799 . Registered on 9 April 2013.

The influence of APACHE II score on the average noise level in an intensive care unit: an observational study.

BACKGROUND: Noise levels in hospitals, especially in intensive care units (ICUs) are known to be high, potentially affecting not only the patients' well-being but also their clinical outcomes. In an observational study, we made a long-term measurement of noise levels in an ICU, and investigated the influence of various factors on the noise level, including the acute physiology and chronic health evaluation II (APACHE II) score. METHODS: The average noise level was continuously measured for three months in all (eight) patient rooms in an ICU, while the patient data were also registered, including the APACHE II score. The 24-hour trend of the noise level was obtained for the patients of length-of-stay (LOS) ≥1 day, which was compared to the timeline of the ICU routine events. For the patients with LOS ≥4 days, the average noise levels in the first four days were analyzed, and regression models were established using the stepwise search method based on the Akaike information criterion. RESULTS: Features identified in the 24-hour trends (n = 55) agreed well with the daily routine events in the ICU, where regular check-ups raised the 10-minute average noise level by 2~3 dBA from the surrounding values at night, and the staff shift changes consistently increased the noise level by 3~5 dBA. When analyzed in alignment with the patient's admission (n=22), the daytime acoustic condition improved from Day 1 to 2, but worsened from Day 2 to 4, most likely in relation to the various phases of patient's recovery. Regression analysis showed that the APACHE II score, room location, gender, day of week and the ICU admission type could explain more than 50% of the variance in the daily average noise level, LAeq,24h. Where these factors were argued to have causal relations to LAeq,24h, the APACHE II score was found to be most strongly correlated: LAeq,24h increased by 1.3~1.5 dB when the APACHE II score increased by 10 points. CONCLUSIONS: Patient's initial health condition is one important factor that influences the acoustic environment in an ICU, which needs to be considered in observational and interventional studies where the noise in healthcare environments is the subject of investigation.

Analysis of the soundscape in an intensive care unit based on the annotation of an audio recording.

The acoustic environments in hospitals, particularly in intensive care units (ICUs), are characterized by frequent high-level sound events which may negatively affect patient outcome. Many studies performed acoustic surveys, but the measurement protocol was not always reported in detail, and the scope of analysis was limited by the selected mode of sound level meters. Fewer studies systematically investigated the noise sources in ICUs by employing an observer in the patient room, which may potentially bias the measurement. In the current study, the soundscape of an ICU was evaluated where acoustic parameters were extracted from a ∼67-h audio recording, and a selected 24-h recording was annotated off-line for a source-specific analysis. The results showed that the patient-involved noise accounted for 31% of the acoustic energy and 11% of the predicted loudness peaks (PLPs). Excluding the patient-involved noise, the remaining acoustic energy was attributed to staff members (57%), alarms (30%), and the operational noise of life-supporting devices (13%). Furthermore, the contribution of each noise category to the PLPs was found to be more uneven: Staff (92%), alarms (6%), and device noise (2%). The current study suggests that most of the noise sources in ICUs may be associated with modifiable human factors.

Comment on "Short-term acoustic forecasting via artificial neural networks for neonatal intensive care units" [J. Acoust. Soc. Am. 132, 3234-3239 (2012)].

In contrast to common expectations, the noise levels measured in hospital wards are known to be high with little day-night variation, potentially having negative effects on the patient outcomes and the work performance of the staff members, and considerable research attention has been drawn to such adverse acoustic conditions in healthcare environments. Recently, Young et al. [J. Acoust. Soc. Am. 132(5), 3234-3239 (2012)] proposed to use an artificial neural network (ANN) to predict the hourly energy-equivalent sound pressure level (Leq, 1h), particularly targeting neonatal intensive care units. Despite the timeliness of the study and the potential benefits of an "acoustic forecasting" model, the proposed scheme appears to be underdeveloped in a few important aspects, which this letter attempts to address. In particular, the prediction of a simpler time-series smoothing technique was equally or more accurate compared to that of the ANN. In addition, the percentage error used to indicate the prediction accuracy was not only perceptually irrelevant but also misleading given the narrow distribution of test data. Furthermore, this letter raises the more general question whether the sound pressure level may meaningfully be modeled solely based on the past time-series.

Irrelevant speech effect under stationary and adaptive masking conditions.

The irrelevant speech effect was investigated in this study where the serial-recall task was performed under six different conditions: Silence, speech-only, noise-only, speech masked by a stationary noise at two different signal-to-noise ratios (SNRs), and speech masked by an adaptive noise. Measured in five test blocks distributed throughout the four test days, the error rate of the serial-recall task under the silence condition sharply decreased in the first few test blocks, halved after completing about seven blocks. When the adaptive masking scheme was used, the error rate of the serial-recall test was reduced compared to the speech-only condition (by 9%) and to the lower-SNR stationary noise (by 4.4%). However, the serial-recall performance was not significantly different between the stationary and the adaptive maskers when the average sound level was carefully matched. Speech Transmission Index (STI) and the correlation coefficient of power spectra were used as the estimators of the temporal and spectral distinctiveness between sound tokens, respectively. The comparison to the test results implied that the frequency-domain estimator may be a better predictor of the relative ISE especially for a non-stationary masker, although it was also suggested that such estimators may have to be combined possibly with an appropriate weighting.

Pattern-matching analysis of fine echo delays by the spectrogram correlation and transformation receiver.

Among a few previous attempts to model the outstanding echolocation capability of bats, the work by Saillant et al. [J. Acoust. Soc. Am. 94, 2691-2712 (1993)] is, arguably, one of the most frequently referenced studies in which the predictions of spectrogram correlation and transformation (SCAT) model were compared to the results of relevant behavioral experiments. The SCAT model consists of cochlear, spectrogram correlation and spectrogram transformation blocks, where the latter two processes estimate the overall and the fine time delays between the animal's call and the echoes, given the neural representation of the acoustic signals generated by the cochlear block. This paper first provides a rigorous account of the spectrogram transformation (ST) block. By approximating the neural signals in analytic forms, many aspects of the ST block are explained and discussed in relation to the predictive scope of the model. Furthermore, based on these analytical arguments, the ST block is investigated from a different point of view, interpreted as a pattern-matching process which may operate at the high level of the animal's auditory pathway.