Perceived Severity of Stressors in the Intensive Care Unit: A Systematic Review and Semi-Quantitative Analysis of the Literature on the Perspectives of Patients, Health Care Providers and Relatives

The aim of this study was to synthesize quantitative research that identified ranking lists of the most severe stressors of patients in the intensive care unit, as perceived by patients, relatives, and health care professionals (HCP). We conducted a systematic literature search in PubMed, MEDLINE, EMBASE, PsycInfo, CINAHL, and Cochrane Library from 1989 to 15 May 2020. Data were analyzed with descriptive and semi-quantitative methods to yield summarizing ranking lists of the most severe stressors. We synthesized the results of 42 prospective cross-sectional observational studies from different international regions. All investigations had assessed patient ratings. Thirteen studies also measured HCP ratings, and four studies included ratings of relatives. Data indicated that patients rate the severity of stressors lower than HCPs and relatives do. Out of all ranking lists, we extracted 137 stressor items that were most frequently ranked among the most severe stressors. After allocation to four domains, a group of clinical ICU experts sorted these stressors with good to excellent agreement according to their stress levels. Our results may contribute to improve HCPs' and relatives' understanding of patients' perceptions of stressors in the ICU. The synthesized stressor rankings can be used for the development of new assessment instruments of stressors

Enhancing Inhibition-Induced Plasticity in Tinnitus – Spectral Energy Contrasts in Tailor-Made Notched Music Matter

Chronic tinnitus seems to be caused by reduced inhibition among frequency selective neurons in the auditory cortex. One possibility to reduce tinnitus perception is to induce inhibition onto over-activated neurons representing the tinnitus frequency via tailor-made notched music (TMNM). Since lateral inhibition is modifiable by spectral energy contrasts, the question arises if the effects of inhibition-induced plasticity can be enhanced by introducing increased spectral energy contrasts (ISEC) in TMNM. Eighteen participants suffering from chronic tonal tinnitus, pseudo randomly assigned to either a classical TMNM or an ISEC-TMNM group, listened to notched music for three hours on three consecutive days. The music was filtered for both groups by introducing a notch filter centered at the individual tinnitus frequency. For the ISEC-TMNM group a frequency bandwidth of 3/8 octaves on each side of the notch was amplified, additionally, by about 20 dB. Before and after each music exposure, participants rated their subjectively perceived tinnitus loudness on a visual analog scale. During the magnetoencephalographic recordings, participants were stimulated with either a reference tone of 500 Hz or a test tone with a carrier frequency representing the individual tinnitus pitch. Perceived tinnitus loudness was significantly reduced after TMNM exposure, though TMNM type did not influence the loudness ratings. Tinnitus related neural activity in the N1m time window and in the so called tinnitus network comprising temporal, parietal and frontal regions was reduced after TMNM exposure. The ISEC-TMNM group revealed even enhanced inhibition-induced plasticity in a temporal and a frontal cortical area. Overall, inhibition of tinnitus related neural activity could be strengthened in people affected with tinnitus by increasing spectral energy contrast in TMNM, confirming the concepts of inhibition-induced plasticity via TMNM and spectral energy contrasts

Patient Monitoring Alarms in an Intensive Care Unit: Observational Study With Do-It-Yourself Instructions

Background: As one of the most essential technical components of the intensive care unit (ICU), continuous monitoring of patients' vital parameters has significantly improved patient safety by alerting staff through an alarm when a parameter deviates from the normal range. However, the vast number of alarms regularly overwhelms staff and may induce alarm fatigue, a condition recently exacerbated by COVID-19 and potentially endangering patients. Objective: This study focused on providing a complete and repeatable analysis of the alarm data of an ICU's patient monitoring system. We aimed to develop do-it-yourself (DIY) instructions for technically versed ICU staff to analyze their monitoring data themselves, which is an essential element for developing efficient and effective alarm optimization strategies. Methods: This observational study was conducted using alarm log data extracted from the patient monitoring system of a 21-bed surgical ICU in 2019. DIY instructions were iteratively developed in informal interdisciplinary team meetings. The data analysis was grounded in a framework consisting of 5 dimensions, each with specific metrics: alarm load (eg, alarms per bed per day, alarm flood conditions, alarm per device and per criticality), avoidable alarms, (eg, the number of technical alarms), responsiveness and alarm handling (eg alarm duration), sensing (eg, usage of the alarm pause function), and exposure (eg, alarms per room type). Results were visualized using the R package ggplot2 to provide detailed insights into the ICU's alarm situation. Results: We developed 6 DIY instructions that should be followed iteratively step by step. Alarm load metrics should be (re)defined before alarm log data are collected and analyzed. Intuitive visualizations of the alarm metrics should be created next and presented to staff in order to help identify patterns in the alarm data for designing and implementing effective alarm management interventions. We provide the script we used for the data preparation and an R-Markdown file to create comprehensive alarm reports. The alarm load in the respective ICU was quantified by 152.5 (SD 42.2) alarms per bed per day on average and alarm flood conditions with, on average, 69.55 (SD 31.12) per day that both occurred mostly in the morning shifts. Most alarms were issued by the ventilator, invasive blood pressure device, and electrocardiogram (ie, high and low blood pressure, high respiratory rate, low heart rate). The exposure to alarms per bed per day was higher in single rooms (26%, mean 172.9/137.2 alarms per day per bed). Conclusions: Analyzing ICU alarm log data provides valuable insights into the current alarm situation. Our results call for alarm management interventions that effectively reduce the number of alarms in order to ensure patient safety and ICU staff's work satisfaction. We hope our DIY instructions encourage others to follow suit in analyzing and publishing their ICU alarm data

A brief questionnaire for measuring alarm fatigue in nurses and physicians in intensive care units

Abstract When exposed to hundreds of medical device alarms per day, intensive care unit (ICU) staff can develop “alarm fatigue” (i.e., desensitisation to alarms). However, no standardised way of quantifying alarm fatigue exists. We aimed to develop a brief questionnaire for measuring alarm fatigue in nurses and physicians. After developing a list of initial items based on a literature review, we conducted 15 cognitive interviews with the target group (13 nurses and two physicians) to ensure that the items are face valid and comprehensible. We then asked 32 experts on alarm fatigue to judge whether the items are suited for measuring alarm fatigue. The resulting 27 items were sent to nurses and physicians from 15 ICUs of a large German hospital. We used exploratory factor analysis to further reduce the number of items and to identify scales. A total of 585 submissions from 707 participants could be analysed (of which 14% were physicians and 64% were nurses). The simple structure of a two-factor model was achieved within three rounds. The final questionnaire (called Charité Alarm Fatigue Questionnaire; CAFQa) consists of nine items along two scales (i.e., the “alarm stress scale” and the “alarm coping scale”). The CAFQa is a brief questionnaire that allows clinical alarm researchers to quantify the alarm fatigue of nurses and physicians. It should not take more than five minutes to administer

Staff perspectives on the influence of patient characteristics on alarm management in the intensive care unit: a cross-sectional survey study

Abstract Background High rates of clinical alarms in the intensive care unit can result in alarm fatigue among staff. Individualization of alarm thresholds is regarded as one measure to reduce non-actionable alarms. The aim of this study was to investigate staff’s perceptions of alarm threshold individualization according to patient characteristics and disease status. Methods This is a cross-sectional survey study (February-July 2020). Intensive care nurses and physicians were sampled by convenience. Data was collected using an online questionnaire. Results Staff view the individualization of alarm thresholds in the monitoring of vital signs as important. The extent to which alarm thresholds are adapted from the normal range varies depending on the vital sign monitored, the reason for clinical deterioration, and the professional group asked. Vital signs used for hemodynamic monitoring (heart rate and blood pressure) were most subject to alarm individualizations. Staff are ambivalent regarding the integration of novel technological features into alarm management. Conclusions All relevant stakeholders, including clinicians, hospital management, and industry, must collaborate to establish a “standard for individualization,” moving away from ad hoc alarm management to an intelligent, data-driven alarm management. Making alarms meaningful and trustworthy again has the potential to mitigate alarm fatigue – a major cause of stress in clinical staff and considerable hazard to patient safety. Trial registration The study was registered at ClinicalTrials.gov (NCT03514173) on 02/05/2018

Power spectral density of Brownian noise filtered with both procedures.

<p>Brownian noise was chosen to illustrate the filter procedure, as it resembles the typical energy spectrum of music (lower energy in higher frequencies, 1/f behavior). Black line: frequency spectrum of Brownian noise with 3 s duration. Blue line: frequency spectrum of 3 s of Brownian noise filtered with the applied online filter for classical tailor-made notched music (TMNM). First, spectral energy was re-distributed from low to high frequency ranges (“flattening”). Second, a frequency band of 1/2 octave width centered at 6 kHz was removed from the energy spectrum of Brownian noise (classical TMNM). Green line: frequency spectrum of 3 s of Brownian noise filtered with the applied online filter for increased spectral energy contrast TMNM (ISEC-TMNM). In addition to the filtering procedure of classical TMNM, 3/8 octaves bandwidth of the edge frequency bands around the notch were amplified by 20 dB.</p

Tinnitus loudness ratings.

<p>Averaged ratings (across all three days) on a visual analog scale (VAS) of the subjectively perceived tinnitus loudness before (grey bar) and after (red bar) tailor-made notched music (TMNM) exposure for the increased spectral energy (ISEC) TMNM (left) and the classical TMNM (right) group. Error bars denote one standard error of the mean. Stars denote significance values of <i>F</i>-test depicting a significant main effect (** = <i>p</i> <. 01) of <i>Session</i> (pre vs. post music exposure) and a non-significant (<i>ns</i>) interaction effect <i>Session</i> x <i>Group</i>.</p

Study procedure.

<p>Tinnitus frequency was estimated before beginning of the training. The training session consisted of a hearing threshold determination for the reference tone of 500 Hz (RT). Ratings on the visual analog scale (VAS) were done before and after MEG measurements. Classical or ISEC tailor-made notched music (TMNM) was presented for 3 hours in between two VAS/MEG/VAS sessions. The training session was repeated on the next two days.</p

Results for the main effect <i>Session</i>.

<p>A: the right part depicts Statistical Parametric Maps of <i>F</i>-values for the main effect <i>Session</i> (pre vs. post tailor-made notched music (TMNM) exposure) in a temporal (B: parietal, C: orbitofrontal, D: occipital-temporal) cluster. Only spatiotemporal clusters which survived the cluster-based permutation test are colorized. Black cylinders reflect the source locations within the significant clusters. The right part framed with a circle disentangles the direction of the effect by depicting the mean activity evoked by the tinnitus tone before and after TMNM exposure within the significant temporal (B: parietal, C: orbitofrontal, D: occipital-temporal) cluster. Error bars denote one standard error of the mean. Stars denote significance-values of the <i>F</i>-test (** = <i>p</i> <. 01, * = <i>p</i> <. 05, <i>ns</i> = non-significant).</p

Global source power plot of the source waveforms averaged across all subjects.

<p>Neural activities evoked by the tinnitus tone pre (dotted line) and post (solid line) tailor-made notched music (TMNM) exposure. The a priori N1 time interval of interest (IOI) is highlighted in grey.</p