Automated False Alarm Reduction in a Real-Life Intensive Care Setting Using Motion Detection

A customised down-sampling machine learning approach for sepsis prediction

OBJECTIVE

Sepsis is a life-threatening condition in the ICU and requires treatment in time. Despite the accuracy of existing sepsis prediction models, insufficient focus on reducing alarms could worsen alarm fatigue and desensitisation in ICUs, potentially compromising patient safety. In this retrospective study, we aim to develop an accurate, robust, and readily deployable method in ICUs, only based on the vital signs and laboratory tests.

METHODS

Our method consists of a customised down-sampling process and a specific dynamic sliding window and XGBoost to offer sepsis prediction. The down-sampling process was applied to the retrospective data for training the XGBoost model. During the testing stage, the dynamic sliding window and the trained XGBoost were used to predict sepsis on the retrospective datasets, PhysioNet and FHC.

RESULTS

With the filtered data from PhysioNet, our method achieved 80.74% accuracy (77.90% sensitivity and 84.42% specificity) and 83.95% (84.82% sensitivity and 82.00% specificity) on the test set of PhysioNet-A and PhysioNet-B, respectively. The AUC score was 0.89 for both datasets. On the FHC dataset, our method achieved 92.38% accuracy (88.37% sensitivity and 95.16% specificity) and 0.98 AUC score on the test set of FHC.

CONCLUSION

Our results indicate that the down-sampling process and the dynamic sliding window with XGBoost brought robust and accurate performance to give sepsis prediction under various hospital settings. The localisation and robustness of our method can assist in sepsis diagnosis in different ICU settings.

An adaptive metaheuristic optimization approach for Tennessee Eastman process for an industrial fault tolerant control system

The Tennessee Eastman Process (TEP) is widely recognized as a standard reference for assessing the effectiveness of fault detection and false alarm tracking methods in intricate industrial operations. This paper presents a novel methodology that employs the Adaptive Crow Search Algorithm (ACSA) to improve fault identification capabilities and mitigate the occurrence of false alarms in the TEP. The ACSA is an optimization approach that draws inspiration from the observed behavior of crows in their natural environment. This algorithm possesses the capability to adapt its search behavior in response to the changing dynamics of the optimization process. The primary objective of our research is to devise a monitoring strategy that is adaptable in nature, with the aim of efficiently identifying faults within the TEP while simultaneously minimizing the occurrence of false alarms. The ACSA is applied in order to enhance the optimization of monitoring variables, alarm thresholds, and decision criteria selection and configuration. When compared to traditional static approaches, the ACSA-based monitoring strategy is better at finding faults and reducing false alarms because it adapts well to changes in process dynamics and disturbances. In order to assess the efficacy of our suggested methodology, we have conducted comprehensive simulations on the TEP dataset. The findings suggest that the monitoring strategy based on ACSA demonstrates superior fault identification rates while concurrently mitigating the frequency of false alarms. In addition, the flexibility of ACSA allows it to efficiently manage process variations, disturbances, and uncertainties, thereby enhancing its robustness and reliability in practical scenarios. To validate the effectiveness of our proposed approach, extensive simulations were conducted on the TEP dataset. The results indicate that the ACSA-based monitoring strategy achieves higher fault detection rates while simultaneously reducing the occurrence of false alarms. Moreover, the adaptability of ACSA enables it to effectively handle process variations, disturbances, and uncertainties, making it robust and reliable for real-world applications. The contributions of this research extend beyond the TEP, as the adaptive monitoring strategy utilizing ACSA can be applied to other complex industrial processes. The findings of this study provide valuable insights into the development of advanced fault detection and false alarm monitoring techniques, offering significant benefits in terms of process safety, reliability, and operational efficiency.

Continuous sensing and quantification of body motion in infants: A systematic review

Abnormal body motion in infants may be associated with neurodevelopmental delay or critical illness. In contrast to continuous patient monitoring of the basic vitals, the body motion of infants is only determined by discrete periodic clinical observations of caregivers, leaving the infants unattended for observation for a longer time. One step to fill this gap is to introduce and compare different sensing technologies that are suitable for continuous infant body motion quantification. Therefore, we conducted this systematic review for infant body motion quantification based on the PRISMA method (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). In this systematic review, we introduce and compare several sensing technologies with motion quantification in different clinical applications. We discuss the pros and cons of each sensing technology for motion quantification. Additionally, we highlight the clinical value and prospects of infant motion monitoring. Finally, we provide suggestions with specific needs in clinical practice, which can be referred by clinical users for their implementation. Our findings suggest that motion quantification can improve the performance of vital sign monitoring, and can provide clinical value to the diagnosis of complications in infants.

The impact of an intervention to improve intrapartum maternal vital sign monitoring and reduce alarm fatigue

BACKGROUND

The infrastructure of many labor and delivery units in the United States may dispose clinicians to overuse continuous and automated maternal physiological monitors. Overmonitoring low-risk patients can negatively affect patient care, primarily through generating alarm fatigue.

OBJECTIVE

Given the national attention to reducing alarm fatigue across healthcare settings and the concern for vital sign monitoring overuse on our labor and delivery unit, this quality improvement study aimed to evaluate vital sign monitoring patterns and alarm rates, and nursing experiences of alarm fatigue, before and after implementing a vital sign monitoring guideline for low-risk obstetrical patients.

STUDY DESIGN

This was a quality improvement study conducted on the labor and delivery unit of an urban, academic, tertiary hospital. The lack of guidance for maternal vital sign assessment in low-risk patients was identified as a potential safety challenge. A vital sign guideline was developed with multidisciplinary input, followed by a pre-post-implementation study evaluating vital sign volume and alarm rates. Total vital signs and alarm rates for all patients delivered during designated calendar days were assessed as a rate of vital signs per patient and compared across baseline, peri-intervention, and follow-up periods. Data were examined in p-type statistical process control charts and with time-series analysis. Patient characteristics and severe maternal morbidity, as a balancing metric, were compared across periods. Nursing perceptions of vital sign monitoring and experience of alarm fatigue were assessed via survey before and after implementation of the guideline.

RESULTS

A total of 35 individual 24-hour periods were evaluated with regard to vital sign and alarm volume. There was a decrease in vital signs per patient from a mean of 208.34 to 135.46 (incidence rate ratio, 0.65) and in alarms per patient from a mean of 14.31 to 10.51 (incidence rate ratio, 0.73) after implementation, with no difference in severe maternal morbidity. There were 85 total respondents to the nursing surveys, and comparison of modified task-load index scores before and after implementation demonstrated overall lower scores in the postperiod, although these were not statistically significant.

CONCLUSION

Introducing a maternal vital sign guideline for low-risk patients on the labor and delivery unit decreased vital signs measured as well as alarms, which may ultimately reduce alarm fatigue. This strategy should be considered on labor and delivery units widely to improve patient safety and optimize outcomes.

Computational approaches to alleviate alarm fatigue in intensive care medicine: A systematic literature review

Patient monitoring technology has been used to guide therapy and alert staff when a vital sign leaves a predefined range in the intensive care unit (ICU) for decades. However, large amounts of technically false or clinically irrelevant alarms provoke alarm fatigue in staff leading to desensitisation towards critical alarms. With this systematic review, we are following the Preferred Reporting Items for Systematic Reviews (PRISMA) checklist in order to summarise scientific efforts that aimed to develop IT systems to reduce alarm fatigue in ICUs. 69 peer-reviewed publications were included. The majority of publications targeted the avoidance of technically false alarms, while the remainder focused on prediction of patient deterioration or alarm presentation. The investigated alarm types were mostly associated with heart rate or arrhythmia, followed by arterial blood pressure, oxygen saturation, and respiratory rate. Most publications focused on the development of software solutions, some on wearables, smartphones, or headmounted displays for delivering alarms to staff. The most commonly used statistical models were tree-based. In conclusion, we found strong evidence that alarm fatigue can be alleviated by IT-based solutions. However, future efforts should focus more on the avoidance of clinically non-actionable alarms which could be accelerated by improving the data availability.

Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021233461, identifier: CRD42021233461.

Evaluation of alarm fatigue of nurses working in the COVID-19 Intensive Care Service: A mixed methods study

AIMS AND OBJECTIVES

To reveal the existence of alarms in COVID-19 intensive care units, where medical devices with alarm function are frequently used, the effects of alarms on nurses, especially their on-the-job performances and social lives, and their coping methods.

METHODOLOGY/METHODS

This was a mixed design, including descriptive and qualitative research methods with two stages, and was carried out between March and April 2021. The study adhered to the STROBE checklist for cross-sectional studies and the COREQ guidelines for qualitative studies.

SETTING

Nurses in the COVID-19 intensive care unit of a university hospital constituted the sample. 58 nurses participated in the quantitative data phase, and 18 nurses in the qualitative interviews.

RESULTS

More than half of the nurses worked in the COVID-19 intensive care unit for more than 5 months and overtime, and 87.9 had day and night shifts. The monthly income level of 65.5% was between the hunger and poverty lines, and 12.1 % received psychiatric support in the last 6 months. A positive and significant relationship was found between the mean score obtained from the alarm fatigue questionnaire and the level of discomfort felt due to the alarms (1-10 points) (p = 0.001). Five themes and thirty sub-themes were emerged in the focus group interviews.

CONCLUSION

The number of alarms of the medical devices in the COVID-19 intensive care units was more than the other intensive care units, resulting in fatigue in nurses. Since alarm fatigue is directly related to patient safety, the effective management of medical device alarms can reduce alarm fatigue and prevent potentially dangerous outcomes.

RELEVANCE TO CLINICAL PRACTICE

Nurses care for patients with severe clinical conditions in COVID-19 intensive care units. This situation caused them to be exposed to more alarms. Nurses should make efforts to reduce their alarm intensity.

OUP accepted manuscript

ICU Cockpit: a secure, fast, and scalable platform for collecting multimodal waveform data, online and historical data visualization, and online validation of algorithms in the intensive care unit. We present a network of software services that continuously stream waveforms from ICU beds to databases and a web-based user interface. Machine learning algorithms process the data streams and send outputs to the user interface. The architecture and capabilities of the platform are described. Since 2016, the platform has processed over 89 billion data points (N = 979 patients) from 200 signals (0.5-500 Hz) and laboratory analyses (once a day). We present an infrastructure-based framework for deploying and validating algorithms for critical care. The ICU Cockpit is a Big Data platform for critical care medicine, especially for multimodal waveform data. Uniquely, it allows algorithms to seamlessly integrate into the live data stream to produce clinical decision support and predictions in clinical practice.

Multichannel esophageal signals to monitor respiratory rate in preterm infants

BACKGROUND

Apnea of prematurity cannot be reliably measured with current monitoring techniques. Instead, indirect parameters such as oxygen desaturation or bradycardia are captured. We propose a Kalman filter-based detection of respiration activity and hence apnea using multichannel esophageal signals in neonatal intensive care unit patients.

METHODS

We performed a single-center observational study with moderately preterm infants. Commercially available nasogastric feeding tubes containing multiple electrodes were used to capture signals with customized software. Multichannel esophageal raw signals were manually annotated, processed using extended Kalman filter, and compared with standard monitoring data including chest impedance to measure respiration activity.

RESULTS

Out of a total of 405.4 h captured signals in 13 infants, 100 episodes of drop in oxygen saturation or heart rate were examined. Median (interquartile range) difference in respiratory rate was 0.04 (-2.45 to 1.48)/min between esophageal measurements annotated manually and with Kalman filter and -3.51 (-7.05 to -1.33)/min when compared to standard monitoring, suggesting an underestimation of respiratory rate when using the latter.

CONCLUSIONS

Kalman filter-based estimation of respiratory activity using multichannel esophageal signals is safe and feasible and results in respiratory rate closer to visual annotation than that derived from chest impedance of standard monitoring.

Alarms in a neurocritical care unit: a prospective study

The contemporary practice of monitoring physiologic parameters in the critical care setting is based on alarm systems with high sensitivity but low specificity. A natural consequence of this approach is a massive amount of alarms, which potentially leads to fatigue in the personnel and negatively impacts the quality of care provided. The study objective is to determine the prevalence, types, and determinants of alarms in a neurological critical care unit (NCCU) prototype. During a one-month period corresponding to 272 days of monitoring in 34 patients, nursing staff recorded the type and number of sounding alarms in a university NCCU. Alarms were categorized into three types as type-A alarms that were merely handled by the nursing staff, type-B alarms that were primarily managed by nurses, but the physician was also notified, and type-C alarms that were principally handled by NCCU physicians. There were a total of 9439 alarms, with an average of daily 34.7 alarms per bed, corresponding to one alarm every 41.4 min. Most of the alarms were type-A (57.7%), followed by type-B (39.2%) and type-C (3.1%) alarms. Alarms originated from electrocardiogram (34.6%), pulse oximeter (33.7%), noninvasive blood pressure monitoring (9.8%), respiratory monitoring (9.7%), intravenous fluid pumps (4.5%), ventilator (3.9%), enteral pumps (2.1%) and invasive blood pressure systems (1.7%). A noticeable diurnal variation was observed for type-A pulse oximeter, type-A and -B ECG alarms (increase during morning shifts), and type-A ventilator alarms (decrease during morning shifts). Alarms are highly prevalent in NCCUs and can correspond to an important portion of the workload.

Multichannel Esophageal Heart Rate Monitoring of Preterm Infants

OBJECTIVE

Autonomic dysregulation in preterm infants requires continuous monitoring of vital signs such as heart rate over days to months. Unfortunately, common surface electrodes are prone to electrocardiography (ECG) signal artifacts and cause serious skin irritations during long-term use. In contrast, esophageal ECG is known to be very sensitive due to the proximity of electrodes and heart and insensitive to external influences. This study addresses if multichannel esophageal ECG qualifies for heart rate monitoring in preterm infants.

METHODS

We recorded esophageal leads with a multi-electrode gastric feeding tube in a clinical study with 13 neonates and compared the heartbeat detection performance with standard surface leads. A computationally simple and versatile ECG wave detection algorithm was used.

RESULTS

Multichannel esophageal ECG manifested heartbeat sensitivity and positive predictive value greater than 98.5% and significant less false negative (FN) ECG waves as compared to surface ECG due to site-typical electrode motion artifacts. False positive bradycardia as indicated with more than 13 consecutive FN ECG waves was equally expectable in esophageal and surface channels. No adverse events were reported for the multi-electrode gastric feeding tube.

CONCLUSION

Heart rate monitoring of preterm infants with multiple esophageal electrodes is considered as feasible and reliable. Less signal artifacts will improve the detection of bradycardia, which is crucial for immediate interventions, and reduce alarm fatigue.

SIGNIFICANCE

Due to the possibility to integrate the multichannel ECG into a gastric feeding tube and meanwhile omit harmful skin electrodes, the presented system has great potential to facilitate future intensive care of preterm infants.