Clinical Alarms in a Gynaecological Surgical Unit: A Retrospective Data Analysis.

Alarm fatigue refers to the desensitisation of medical staff to patient monitor clinical alarms, which may lead to slower response time or total ignorance of alarms and thereby affects patient safety. The reasons behind alarm fatigue are complex; the main contributing factors include the high number of alarms and the poor positive predictive value of alarms. The study was performed in the Surgery and Anaesthesia Unit of the Women's Hospital, Helsinki, by collecting data from patient monitoring device clinical alarms and patient characteristics from surgical operations. We descriptively analysed the data and statistically analysed the differences in alarm types between weekdays and weekends, using chi-squared, for a total of eight monitors with 562 patients. The most common operational procedure was caesarean section, of which 149 were performed (15.7%). Statistically significant differences existed in alarm types and procedures between weekdays and weekends. The number of alarms produced was 11.7 per patient. In total, 4698 (71.5%) alarms were technical and 1873 (28.5%) were physiological. The most common physiological alarm type was low pulse oximetry, with a total of 437 (23.3%). Of all the alarms, the number of alarms either acknowledged or silenced was 1234 (18.8%). A notable phenomenon in the study unit was alarm fatigue. Greater customisation of patient monitors for different settings is needed to reduce the number of alarms that do not have clinical significance.

Clinical alarms and alarm fatigue in a University Hospital Emergency Department-A retrospective data analysis.

BACKGROUND: Alarm fatigue is hypothesized to be caused by vast amount of patient monitor alarms. Objectives were to study the frequency and types of patient monitor alarms, to evaluate alarm fatigue, and to find unit specific alarm threshold values in a university hospital emergency department. METHODS: We retrospectively gathered alarm data from 9 September to 6 October 2019, in Jorvi Hospital Emergency department, Finland. The department treats surgical, internal and general medicine patients aged 16 and older. The number of patients is on average 4600 to 5000 per month. Eight out of 46 monitors were used for data gathering and the monitored modalities included electrocardiography, respiratory rate, blood pressure, and pulse oximetry. RESULTS: Total number of alarms in the study monitors was 28 176. Number of acknowledged alarms (ie acknowledgement indicator pressed in the monitor) was 695 (2.5%). The most common alarm types were: Respiratory rate high, 9077 (32.2%), pulse oximetry low, 4572 (16.2%) and pulse oximetry probe off, 4036 (14.3%). Number of alarms with duration under 10 s was 14 936 (53%). Number of individual alarm sounds was 105 000, 469 per monitor per day. Of respiratory rate high alarms, 2846 (31.4%) had initial value below 30 breaths min-1 . Of pulse oximetry low alarms, 2421 (53.0%) had initial value above 88%. CONCLUSIONS: Alarm sound load, from individual alarm sounds, was nearly continuous in an emergency department observation room equipped with nine monitors. Intervention by the staff to the alarms was infrequent. More than half of the alarms were momentary.

Staff education, regular sedation and analgesia quality feedback, and a sedation monitoring technology for improving sedation and analgesia quality for critically ill, mechanically ventilated patients: a cluster randomised trial.

BACKGROUND: Optimal sedation of patients in intensive care units (ICUs) requires the avoidance of pain, agitation, and unnecessary deep sedation, but these outcomes are challenging to achieve. Excessive sedation can prolong ICU stay, whereas light sedation can increase pain and frightening memories, which are commonly recalled by ICU survivors. We aimed to assess the effectiveness of three interventions to improve sedation and analgesia quality: an online education programme; regular feedback of sedation-analgesia quality data; and use of a novel sedation-monitoring technology (the Responsiveness Index [RI]). METHODS: We did a cluster randomised trial in eight ICUs, which were randomly allocated to receive education alone (two ICUs), education plus sedation-analgesia quality feedback (two ICUs), education plus RI monitoring technology (two ICUs), or all three interventions (two ICUs). Randomisation was done with computer-generated random permuted blocks, stratified according to recruitment start date. A 45 week baseline period was followed by a 45 week intervention period, separated by an 8 week implementation period in which the interventions were introduced. ICU and research staff were not masked to study group assignment during the intervention period. All mechanically ventilated patients were potentially eligible. We assessed patients' sedation-analgesia quality for each 12 h period of nursing care, and sedation-related adverse events daily. Our primary outcome was the proportion of care periods with optimal sedation-analgesia, defined as being free from excessive sedation, agitation, poor limb relaxation, and poor ventilator synchronisation. Analysis used multilevel generalised linear mixed modelling to explore intervention effects in a single model taking clustering and patient-level factors into account. A concurrent mixed-methods process evaluation was undertaken to help understand the trial findings. The trial is registered with ClinicalTrials.gov, number NCT01634451. FINDINGS: Between June 1, 2012, and Dec 31, 2014, we included 881 patients (9187 care periods) during the baseline period and 591 patients (6947 care periods) during the intervention period. During the baseline period, optimal sedation-analgesia was present for 5150 (56%) care periods. We found a significant improvement in optimal sedation-analgesia with RI monitoring (odds ratio [OR] 1·44 [95% CI 1·07-1·95]; p=0·017), which was mainly due to increased periods free from excessive sedation (OR 1·59 [1·09-2·31]) and poor ventilator synchronisation (OR 1·55 [1·05-2·30]). However, more patients experienced sedation-related adverse events (OR 1·91 [1·02-3·58]). We found no improvement in overall optimal sedation-analgesia with education (OR 1·13 [95% CI 0·86-1·48]), but fewer patients experienced sedation-related adverse events (OR 0·56 [0·32-0·99]). The sedation-analgesia quality data feedback did not improve quality (OR 0·74 [95% CI 0·54-1·00]) or sedation-related adverse events (OR 1·15 [0·61-2·15]). The process evaluation suggested many clinicians found the RI monitoring useful, but it was often not used for decision making as intended. Education was valued and considered useful by staff. By contrast, sedation-analgesia quality feedback was poorly understood and thought to lack relevance to bedside nursing practice. INTERPRETATION: Combination of RI monitoring and online education has the potential to improve sedation-analgesia quality and patient safety in mechanically ventilated ICU patients. The RI monitoring seemed to improve sedation-analgesia quality, but inconsistent adoption by bedside nurses limited its impact. The online education programme resulted in a clinically relevant improvement in patient safety and was valued by nurses, but any changes to behaviours did not seem to alter other measures of sedation-analgesia quality. Providing sedation-analgesia quality feedback to ICUs did not appear to improve any quality metrics, probably because staff did not think it relevant to bedside practice. FUNDING: Chief Scientist Office, Scotland; GE Healthcare.

A randomized controlled proof-of-concept trial of early sedation management using Responsiveness Index monitoring in mechanically ventilated critically ill patients.

INTRODUCTION: Deep sedation is associated with adverse patient outcomes. We recently described a novel sedation-monitoring technology, the Responsiveness Index (RI), which quantifies patient arousal using processed frontal facial EMG data. We explored the potential effectiveness and safety of continuous RI monitoring during early intensive care unit (ICU) care as a nurse decision-support tool. METHODS: In a parallel-group controlled single centre proof of concept trial, patients requiring mechanical ventilation and sedation were randomized via sequential sealed envelopes following ICU admission. Control group patients received hourly clinical sedation assessment and daily sedation holds; the RI monitor was connected but data were concealed from clinical staff. The intervention group received control group care, but RI monitoring was visible and nurses were asked to adjust sedation to maintain patients with an RI>20 whenever possible. Traffic-light colour coding (RI<20, Red; 20-40, Amber; >40, Green) simplified decision-making. The intervention lasted up to 48 hours. Sixteen nurses were interviewed to explore their views of the novel technology. RESULTS: We analysed 74 patients treated per protocol (36 intervention; 38 control). The proportion of patients with RI<20 was identical at the start of monitoring (54% both groups). Overall, the proportion of time with RI<20 trended to lower values for the intervention group (median 16% (1-3rd quartile 8-30%) versus 33% (10-54%); P = 0.08); sedation and analgesic use was similar. A post hoc analysis restricted to patients with RI<20 when monitoring started, found intervention patients spent less time with low RI value (16% (11-45%) versus 51% (33-72%); P = 0.02), cumulative propofol use trended to lower values (median 1090 mg versus 2390 mg; P = 0.14), and cumulative alfentanil use was lower (21.2 mg versus 32.3 mg; P = 0.01). RASS scores were similar for both groups. Sedation related adverse event rates were similar (7/36 versus 5/38). Similar proportions of patients had sedation holds (83% versus 87 %) and were extubated (47% versus 44%) during the intervention period. Nurses valued the objective visible data trends and simple colour prompts, and found RI monitoring a useful adjunct to existing practice. CONCLUSIONS: RI monitoring was safe and acceptable. Data suggested potential to modify sedation decision-making. Larger trials are justified to explore effects on patient-centred outcomes. TRIAL REGISTRATION: NCT01361230 (registered April 19, 2010).

Algorithm for Quantifying Frontal EMG Responsiveness for Sedation Monitoring.

INTRODUCTION: To study stimulation-related facial electromyographic (FEMG) activity in intensive care unit (ICU) patients, develop an algorithm for quantifying the FEMG activity, and to optimize the algorithm for monitoring the sedation state of ICU patients. METHODS: First, the characteristics of FEMG response patterns related to vocal stimulation of 17 ICU patients were studied. Second, we collected continuous FEMG data from 30 ICU patients. Based on these data, we developed the Responsiveness Index (RI) algorithm that quantifies FEMG responses. Third, we compared the RI values with clinical sedation level assessments and adjusted algorithm parameters for best performance. RESULTS: In patients who produced a clinically observed response to the vocal stimulus, the poststimulus FEMG power was 0.33 µV higher than the prestimulus power. In nonresponding patients, there was no difference. The sensitivity and specificity of the developed RI for detecting deep sedation in the subgroup with low probability of encephalopathy were 0.90 and 0.79, respectively. CONCLUSION: Consistent FEMG patterns were found related to standard stimulation of ICU patients. A simple and robust algorithm was developed and good correlation with clinical sedation scores achieved in the development data.

An evaluation of the validity and potential utility of facial electromyelogram Responsiveness Index for sedation monitoring in critically ill patients.

PURPOSE: The purpose of this study is to explore the validity of a novel sedation monitoring technology based on facial electromyelography (EMG) in sedated critically ill patients. MATERIALS AND METHODS: The Responsiveness Index (RI) integrates the preceding 60 minutes of facial EMG data. An existing data set was used to derive traffic light cut-offs for low (red), intermediate (amber), and higher (green) states of patient arousal. The validity of these was prospectively evaluated in 30 sedated critically ill patients against hourly Richmond Agitation Sedation Scale (RASS) assessments with concealment of RI data from clinical staff. RESULTS: With derivation data, an RI less than or equal to 35 had best discrimination for a Ramsay score of 5/6 (sensitivity, 90%; specificity, 79%). For traffic lights, we chose RI less than or equal to 20 as red, 20 to 40 as amber, and more than 40 as green. In the prospective study, RI values were red/amber for 76% of RASS -5/-4 assessments, but RI varied dynamically over time in many patients, and discordance with RASS may have resulted from the use of 1 hour of data for RI calculations. We also noted that red/amber values resulted from sleep, encephalopathy, and low levels of stimulation. CONCLUSIONS: Responsiveness Index is not directly comparable with clinical sedation scores but is a potential continuous alert to possible deep sedation in critically ill patients.

Dissociation of face-selective cortical responses by attention.

We studied attentional modulation of cortical processing of faces and houses with functional MRI and magnetoencephalography (MEG). MEG detected an early, transient face-selective response. Directing attention to houses in "double-exposure" pictures of superimposed faces and houses strongly suppressed the characteristic, face-selective functional MRI response in the fusiform gyrus. By contrast, attention had no effect on the M170, the early, face-selective response detected with MEG. Late (>190 ms) category-related MEG responses elicited by faces and houses, however, were strongly modulated by attention. These results indicate that hemodynamic and electrophysiological measures of face-selective cortical processing complement each other. The hemodynamic signals reflect primarily late responses that can be modulated by feedback connections. By contrast, the early, face-specific M170 that was not modulated by attention likely reflects a rapid, feed-forward phase of face-selective processing.

Comparison of minimum current estimate and dipole modeling in the analysis of simulated activity in the human visual cortices.

Magnetoencephalographic(MEG) data are typically interpreted using source models because of the nonunique inverse problem. Although single current dipoles, adequately representing local active areas, can be identified accurately, multiple and overlapping sources form a challenge for MEG modeling. We tested the performances of multidipole modeling and minimum current estimate (MCE) in the analysis of complicated source configurations. Simulated current sources were placed to physiologically meaningful areas of the human visual cortices. Ten volunteers from the laboratory staff analyzed four different simulations with both dipole modeling and MCE without prior information of the sources. In general, the same sources were found using both modeling methods. The subjects tended to report more false sources with MCE than with dipole model, in part due to their inexperience with the method. Dipole model was more accurate than MCE both in time and space for nonsimultaneous sources but both methods performed similarly when sources overlapped in time. For all source configurations, considerably smaller source amplitudes were reported with MCE than with dipole model.