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To compare the number of alarms, interventions and nurses’ acceptance of automated ventilation with INTELLiVENT-ASV versus conventional ventilation strategy in patients receiving postoperative ventilation after cardiac surgery. This preplanned secondary analysis of the ‘POSITiVE’ randomized clinical trial compared INTELLiVENT-ASV (automated ventilation) with conventional ventilation in postoperative cardiac surgery patients. The number of critical alarms and manual ventilator interventions were compared during the first three hours of ventilation or until extubation. Nurses’ acceptance was assessed using a Technology Acceptance Model 2-based questionnaire and a user acceptance score from 1 to 10. POSITiVE randomized 220 patients (109 to automated and 111 to conventional ventilation). The average number of critical alarms per monitoring hour was similar between the automated and conventional group (5.6 vs 5.7; p = 0.823). The automated group required fewer manual interventions per monitoring hour for both ventilation control (0.7 vs 1.9; p < 0.001) and alarm management (2.0 vs 2.8; p < 0.001). The automated ventilation mode scored higher for perceived usefulness (2.6 vs 2.1; p < 0.001) and user acceptance (8.0 vs 7.0; p < 0.001), but similar for perceived ease of use. Automated ventilation for postoperative cardiac surgery patients had similar alarm frequencies as conventional ventilation, but reduced the number of interventions and showed higher nurses’ acceptance, indicating its potential to optimize patient care and reduce nurses’ workload. Our findings suggest that automated ventilation modes like INTELLiVENT-ASV can reduce the frequency of manual interventions and improve nurses’ acceptance, which may help alleviate nurses’ workload for postoperative cardiac surgery patients.

IntroductionAcute kidney injury (AKI) is common among hospitalised patients and under-recognised by providers and yet carries a significant risk of morbidity and mortality. Electronic alerts for AKI have become more common despite a lack of strong evidence of their benefits. We designed a multicentre, randomised, controlled trial to evaluate the effectiveness of AKI alerts. Our aim is to highlight several challenges faced in the design of this trial, which uses electronic screening, enrolment, randomisation, intervention and data collection.Methods and analysisThe design and implementation of an electronic alert system for AKI was a reiterative process involving several challenges and limitations set by the confines of the electronic medical record system. The trial will electronically identify and randomise 6030 adults with AKI at six hospitals over a 1.5–2 year period to usual care versus an electronic alert containing an AKI-specific order set. Our primary outcome will be a composite of AKI progression, inpatient dialysis and inpatient death within 14 days of randomisation. During a 1-month pilot in the medical intensive care unit of Yale New Haven Hospital, we have demonstrated feasibility of automating enrolment and data collection. Feedback from providers exposed to the alerts was used to continually improve alert clarity, user friendliness and alert specificity through refined inclusion and exclusion criteria.Ethics and disseminationThis study has been approved by the appropriate ethics committees for each of our study sites. Our study qualified for a waiver of informed consent as it presents no more than minimal risk and cannot be feasibly conducted in the absence of a waiver. We are committed to open dissemination of our data through clinicaltrials.gov and submission of results to the NIH data sharing repository. Results of our trial will be submitted for publication in a peer-reviewed journal.Trial registration number NCT02753751; Pre-results.

PurposeTo investigate the application value of “electronic alerts” (“e-alerts”) for acute kidney injury (AKI) among high-risk wards of hospitals.MethodsA prospective, randomized, controlled study was conducted. We developed an e-alert system for AKI and ran the system in intensive care units and divisions focusing on cardiovascular disease. The e-alert system diagnosed AKI automatically based on serum creatinine levels. Patients were assigned randomly to an e-alert group (467 patients) or non-e-alert group (408 patients). Only the e-alert group could receive pop-up messages.ResultsThe sensitivity, specificity, Youden Index and accuracy of the AKI e-alert system were 99.8, 97.7, 97.5 and 98.1%, respectively. The prevalence of the diagnosis for AKI and expanded-AKI (AKI or multiple-organ failure) in the e-alert group was higher than that in the non-e-alert group (AKI 7.9 and 2.7%, P = 0.001; expanded-AKI 16.3 and 6.1%, P < 0.001). The prevalence of nephrology consultation in the e-alert group was higher than that in the non-e-alert group (9.0 and 3.7%, P = 0.001). There was no significant difference in the prevalence dialysis, rehabilitation of renal function or death in the two groups.ConclusionThe e-alert system described here was a reliable tool to make an accurate diagnosis of AKI.

Background: In conditions of intensive therapy, where the patients treated are in a critical condition, alarms are omnipresent. Nurses, as they spend most of their time with patients, monitoring their condition 24 h, are particularly exposed to so-called alarm fatigue. The purpose of this study is to review the literature available on the perception of clinical alarms by nursing personnel and its impact on work in the ICU environment. Methods: A systematic review of the literature was carried out according to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol. The content of electronic databases was searched through, i.e., PubMed, OVID, EBSCO, ProQuest Nursery, and Cochrane Library. The keywords used in the search included: “intensive care unit,” “nurse,” “alarm fatigue,” “workload,” and “clinical alarm.” The review also covered studies carried out among nurses employed at an adult intensive care unit. Finally, seven publications were taken into consideration. Data were analyzed both descriptively and quantitatively, calculating a weighted average for specific synthetized data. Results: In the analyzed studies, 389 nurses were tested, working in different intensive care units. Two studies were based on a quality model, while the other five described the problem of alarms in terms of quantity, based on the HTF (Healthcare Technology Foundation) questionnaire. Intensive care nurses think that alarms are burdensome and too frequent, interfering with caring for patients and causing reduced trust in alarm systems. They feel overburdened with an excessive amount of duties and a continuous wave of alarms. Having to operate modern equipment, which is becoming more and more advanced, takes time that nurses would prefer to dedicate to their patients. There is no clear system for managing the alarms of monitoring devices. Conclusion: Alarm fatigue may have serious consequences, both for patients and for nursing personnel. It is necessary to introduce a strategy of alarm management and for measuring the alarm fatigue level.

Physiologic monitors are plagued with alarms that create a cacophony of sounds and visual alerts causing \"alarm fatigue\" which creates an unsafe patient environment because a life-threatening event may be missed in this milieu of sensory overload. Using a state-of-the-art technology acquisition infrastructure, all monitor data including 7 ECG leads, all pressure, SpO(2), and respiration waveforms as well as user settings and alarms were stored on 461 adults treated in intensive care units. Using a well-defined alarm annotation protocol, nurse scientists with 95% inter-rater reliability annotated 12,671 arrhythmia alarms. A total of 2,558,760 unique alarms occurred in the 31-day study period: arrhythmia, 1,154,201; parameter, 612,927; technical, 791,632. There were 381,560 audible alarms for an audible alarm burden of 187/bed/day. 88.8% of the 12,671 annotated arrhythmia alarms were false positives. Conditions causing excessive alarms included inappropriate alarm settings, persistent atrial fibrillation, and non-actionable events such as PVC's and brief spikes in ST segments. Low amplitude QRS complexes in some, but not all available ECG leads caused undercounting and false arrhythmia alarms. Wide QRS complexes due to bundle branch block or ventricular pacemaker rhythm caused false alarms. 93% of the 168 true ventricular tachycardia alarms were not sustained long enough to warrant treatment. The excessive number of physiologic monitor alarms is a complex interplay of inappropriate user settings, patient conditions, and algorithm deficiencies. Device solutions should focus on use of all available ECG leads to identify non-artifact leads and leads with adequate QRS amplitude. Devices should provide prompts to aide in more appropriate tailoring of alarm settings to individual patients. Atrial fibrillation alarms should be limited to new onset and termination of the arrhythmia and delays for ST-segment and other parameter alarms should be configurable. Because computer devices are more reliable than humans, an opportunity exists to improve physiologic monitoring and reduce alarm fatigue.

The purpose of this study is to establish a model correlating the number of alarms and effective alarms on a monitor, and to use this model to optimise night-time alarm issues in intensive care units in order to reduce alarm fatigue among night shift nurses. A retrospective study method was used to track 1,843 samples. Based on partial experimental design analysis, a model for ‘alarm frequency’ and ‘effective alarms’ was established for the monitor, which was then optimised using the composite centre factorial (CCF) method. The performance of the model was evaluated using random sampling and night-time model application. We can model based on three factors: ‘APACHE II score,’ ‘Alarm time period,’ and ‘Nurse ICU work years.’ After using this model, the average number of alarms decreased by 11.86%, and the average proportion of effective alarms increased by 4%. We can use CCF modeling to manage monitors and help reduce patient and nurse fatigue. The number of monitor alarms and effective alarms related to the patient’s condition, working time period, and the nurse’s experience. We can tailor the management strategy of the monitor based on clinical conditions, reducing the number of night-time alarms while ensuring patient safety, increasing the effectiveness of alarms, and reducing nurse alarm fatigue. The longer the length of service in critical care, the less significant the improvement in monitor alarm performance.

Previous studies have shown that electrocardiographic (ECG) alarms have high sensitivity and low specificity, have underreported adverse events, and may cause neonatal intensive care unit (NICU) staff fatigue or alarm ignoring. Moreover, prolonged noise stimuli in hospitalized neonates can disrupt neonatal development. The aim of the study is to conduct a nationwide, multicenter, large-sample cross-sectional survey to identify current practices and investigate the decision-making requirements of health care providers regarding ECG alarms. We conducted a nationwide, cross-sectional survey of NICU staff working in grade III level A hospitals in 27 Chinese provinces to investigate current clinical practices, perceptions, decision-making processes, and decision-support requirements for clinical ECG alarms. A comparative analysis was conducted on the results using the chi-square, Kruskal-Wallis, or Mann-Whitney U tests. In total, 1019 respondents participated in this study. NICU staff reported experiencing a significant number of nuisance alarms and negative perceptions as well as practices regarding ECG alarms. Compared to nurses, physicians had more negative perceptions. Individuals with higher education levels and job titles had more negative perceptions of alarm systems than those with lower education levels and job titles. The mean difficulty score for decision-making about ECG alarms was 2.96 (SD 0.27) of 5. A total of 62.32% (n=635) respondents reported difficulty in resetting or modifying alarm parameters. Intelligent module-assisted decision support systems were perceived as the most popular form of decision support. This study highlights the negative perceptions and strong decision-making requirements of NICU staff related to ECG alarm handling. Health care policy makers must draw attention to the decision-making requirements and provide adequate decision support in different forms.

ObjectiveTo investigate the frequency and cause of neonatal ventilator alarms. Neonatal ventilators frequently alarm and also disturb babies, parents and nurses. If frequent they may cause alarm fatigue and be ignored. The number, frequency and details of neonatal ventilator alarms are unreported.MethodsWe developed programs for retrieving and analysing ventilator data each second on alarms and ventilation parameters from 46 babies ventilated with Dräger Babylog VN500 ventilators using various modes.ResultsA mean of 60 hours was recorded per baby. Over 116 days, 27 751 alarms occurred. On average, that was 603 per baby and 10 per hour. Median (IQR) alarm duration was 10 (4–21) s. Type, frequency and duration varied between infants. Some babies had >10% of their time with alarms. Eight alarm types caused ~99% of all alarms. Three alarms, ‘MV high limit’ and ‘respiratory rate >high limit’, caused 46.6%, often due to inappropriate settings. 49.9% were due to a low expired tidal volume during volume guarantee ventilation, often due to the maximum pressure being set too low. 26 106 (94.1%) of all alarms lasted <1 min. However, 86 alarms lasted >10 min and 16 alarms >1 hour. Similar alarms were frequently clustered, sometimes >100/hour.ConclusionsFrequent ventilator alarms are caused by physiological variability in the respiratory rate or minute volume, inappropriate alarm limits or too low maximum peak inflating pressure during volume-targeted ventilation. While most alarms were very short, sometimes alarms were ignored by neonatal intensive care unit staff for long periods.

Background Despite significant advances in type 1 diabetes (T1D) management, achieving targeted glycemic control in pediatric patients remains a struggle. Continuous glucose monitoring (CGM) with remote access holds the promise to address this challenge by allowing caregivers to monitor glucose, even when the child is not directly under their supervision. Objective To explore real‐time and remote CGM practices in homes and schools, including caregiver expectations regarding this technology. Subjects Parents and daytime caregivers. Methods Respondents answered an anonymous survey assessing characteristics of CGM use. Cross‐sectional data were collected and analyzed using quantitative and qualitative methods. Results Thirty‐three parents and 17 daytime caregivers responded. Threshold alerts (alerts when patients reached certain pre‐set high or low limits) were used most frequently, followed by rate of change alerts. Most parents and daytime caregivers responded to low‐ and high‐threshold CGM alerts by confirming with a glucose meter prior to treatment; while about one‐third endorsed treating lows without a confirmatory test. Most parents expected their child's daytime caregiver to respond to CGM alerts and daytime caregivers felt the parent's expectations of them were reasonable. All parents and most caregivers reported decreased overall worry/stress. Parents felt positive about CGM use and daytime caregivers felt comfortable with CGM. Conclusion The positive and collaborative management reported by parents and daytime caregivers sets the stage for CGM to play an important role in the management of children with T1D both in the home and in the school settings.

Studies reveal that the false alarm rate (FAR) demonstrated by intensive care unit (ICU) vital signs monitors ranges from 0.72 to 0.99. We applied machine learning (ML) to ICU multi-sensor information to imitate a medical specialist in diagnosing patient condition. We hypothesized that applying this data-driven approach to medical monitors will help reduce the FAR even when data from sensors are missing. An expert-based rules algorithm identified and tagged in our dataset seven clinical alarm scenarios. We compared a random forest (RF) ML model trained using the tagged data, where parameters (e.g., heart rate or blood pressure) were (deliberately) removed, in detecting ICU signals with the full expert-based rules (FER), our ground truth, and partial expert-based rules (PER), missing these parameters. When all alarm scenarios were examined, RF and FER were almost identical. However, in the absence of one to three parameters, RF maintained its values of the Youden index (0.94–0.97) and positive predictive value (PPV) (0.98–0.99), whereas PER lost its value (0.54–0.8 and 0.76–0.88, respectively). While the FAR for PER with missing parameters was 0.17–0.39, it was only 0.01–0.02 for RF. When scenarios were examined separately, RF showed clear superiority in almost all combinations of scenarios and numbers of missing parameters. When sensor data are missing, specialist performance worsens with the number of missing parameters, whereas the RF model attains high accuracy and low FAR due to its ability to fuse information from available sensors, compensating for missing parameters.