Predictive Monitoring of Critical Cardiorespiratory Alarms in Neonates Under Intensive Care

Artificial intelligence in critical care nursing: A scoping review

BACKGROUND

The integration of artificial intelligence (AI) into health care has been rapidly advancing, driven by its potential to enhance nursing care quality through improved decision-making and efficiency. Within critical care nursing, where the complexity and urgency of patient data are paramount, AI technologies offer significant advantages, such as enhanced patient monitoring and support in clinical decision-making.

AIM/OBJECTIVE

The aim of this scoping review was to synthesise existing literature on AI applications in critical care nursing and their impact on patient outcomes and nursing practice.

METHODS

Following Arksey and O'Malley's framework, we conducted a systematic search across seven electronic databases including PubMed, CINAHL, and Embase. Studies were included if they involved AI applications in critical care nursing or reported on AI's impact on patient outcomes and clinical decision-making in critical care settings. A synthesis of the literature was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews checklist.

RESULTS

Thirty-five studies that addressed this topic were included. The review identified six distinct domains of AI applications: monitoring, nursing intervention, clinical decision support systems, documentation, resource allocation, and predictive analytics. Predictive analytics emerged as the most prevalent application, particularly in forecasting complications such as pressure injuries and sepsis onset. Notably, narrowly focussed AI applications demonstrated superior performance compared to broader applications in clinical decision support systems, particularly in specific tasks like neonatal pain classification. AI-driven documentation systems showed promise in reducing administrative burden and improving accuracy, while resource allocation tools enhanced staffing optimisation and workflow management in intensive care units.

CONCLUSIONS

Our findings demonstrate AI's significant potential to enhance critical care nursing practice while highlighting implementation challenges. Future research should focus on developing standardised implementation strategies and clear guidelines for AI integration in nursing workflow while maintaining the balance between technological advancement and human expertise.

REGISTRATION

Open Science Framework Registries http://osf.io/t2y43/.

Minimising alarm pressure on a single room NICU through automated withdrawal of resolved alarms

AIM

In 2017, the Leiden University Medical Centre implemented a secondary alarm system using handheld devices to ensure accurate patient monitoring on the single room NICU. Initially, alarms remained active on the handheld devices until one of the caregivers in the alarm chain accepted the alarm. In 2020, a bidirectional communication protocol (BCP) was implemented, enabling automated withdrawal of resolved alarms. The aim of this study was to evaluate the effect of this implementation on the alarm duration and pressure.

METHODS

Data of all alarms of the secondary alarm chain in the 90 days before and after the implementation were analysed and compared between both periods.

RESULTS

Following the implementation of the BCP, 60% of the alarms were withdrawn before the designated nurse responded. Despite a significant higher total number of alarms, the median alarm duration decreased from 9 (7-14) to 6 (4-10) s, the acceptance rate of the designated nurse increased from 93% to 95% and the median time of alarm sounding per phone per hour significantly decreased from 71 (51-101) to 51 (35-69) s following implementation of the BCP.

CONCLUSION

This study showed that automated withdrawal of resolved alarms significantly reduces alarm duration and pressure on a NICU.

Neonatal Disease Prediction Using Machine Learning Techniques

Neonatal diseases are among the main causes of morbidity and a significant contributor to underfive mortality in the world. There is an increase in understanding of the pathophysiology of the diseases and the implementation of different strategies to minimize their burden. However, improvements in outcomes are not adequate. Limited success is due to different factors, including the similarity of symptoms, which can lead to misdiagnosis, and the inability to detect early for timely intervention. In resource-limited countries like Ethiopia, the challenge is more severe. Low access to diagnosis and treatment due to the inadequacy of neonatal health professionals is one of the shortcomings. Due to the shortage of medical facilities, many neonatal health professionals are forced to decide the type of disease only based on interviews. They may not have a complete picture of all variables that have a contributing effect on neonatal disease from the interview. This can make the diagnosis inconclusive and may lead to a misdiagnosis. Machine learning has great potential for early prediction if relevant historical data is available. We have applied a classification stacking model for the following four main neonatal diseases: sepsis, birth asphyxia, necrotizing enter colitis (NEC), and respiratory distress syndrome. These diseases account for 75% of neonatal deaths. The dataset has been obtained from the Asella Comprehensive Hospital. It has been collected between 2018 and 2021. The developed stacking model was compared to three related machine-learning models XGBoost (XGB), Random Forest (RF), and Support Vector Machine (SVM). The proposed stacking model outperformed the other models, with an accuracy of 97.04%. We believe that this will contribute to the early detection and accurate diagnosis of neonatal diseases, especially for resource-limited health facilities.

Artificial intelligence-driven wearable technologies for neonatal cardiorespiratory monitoring. Part 2: artificial intelligence

BACKGROUND

With the development of Artificial Intelligence (AI) techniques, smart health monitoring, particularly neonatal cardiorespiratory monitoring with wearable devices, is becoming more popular. To this end, it is crucial to investigate the trend of AI and wearable sensors being developed in this domain.

METHODS

We performed a review of papers published in IEEE Xplore, Scopus, and PubMed from the year 2000 onwards, to understand the use of AI for neonatal cardiorespiratory monitoring with wearable technologies. We reviewed the advances in AI development for this application and potential future directions. For this review, we assimilated machine learning (ML) algorithms developed for neonatal cardiorespiratory monitoring, designed a taxonomy, and categorised the methods based on their learning capabilities and performance.

RESULTS

For AI related to wearable technologies for neonatal cardio-respiratory monitoring, 63% of studies utilised traditional ML techniques and 35% utilised deep learning techniques, including 6% that applied transfer learning on pre-trained models.

CONCLUSIONS

A detailed review of AI methods for neonatal cardiorespiratory wearable sensors is presented along with their advantages and disadvantages. Hierarchical models and suggestions for future developments are highlighted to translate these AI technologies into patient benefit.

IMPACT

State-of-the-art review in artificial intelligence used for wearable neonatal cardiorespiratory monitoring. Taxonomy design for artificial intelligence methods. Comparative study of AI methods based on their advantages and disadvantages.

The role of artificial intelligence in enhancing clinical nursing care: A scoping review

AIM

To present an overview of how artificial intelligence has been used to improve clinical nursing care.

BACKGROUND

Artificial intelligence has been reshaping the healthcare industry but little is known about its applicability in enhancing nursing care.

EVALUATION

A scoping review was conducted. Seven electronic databases (CINAHL, Cochrane Library, EMBASE, IEEE Xplore, PubMed, Scopus, and Web of Science) were searched from 1 January 2010 till 20 December 2020. Grey literature and reference lists of included articles were also searched.

KEY ISSUES

Thirty-seven studies encapsulating the use of artificial intelligence in improving clinical nursing care were included in this review. Six use cases were identified - documentation, formulating nursing diagnoses, formulating nursing care plans, patient monitoring, patient care prediction such as falls prediction (most common) and wound management. Various techniques of machine learning and classification were used for predictive analyses and to improve nurses' preparedness and management of patients' conditions CONCLUSION: This review highlighted the potential of artificial intelligence in improving the quality of nursing care. However, more randomized controlled trials in real-life healthcare settings should be conducted to enhance the rigor of evidence.

IMPLICATIONS FOR NURSING MANAGEMENT

Education in the application of artificial intelligence should be promoted to empower nurses to lead technological transformations and not passively trail behind others.

Application and potential of artificial intelligence in neonatal medicine

Neonatal care is becoming increasingly complex with large amounts of rich, routinely recorded physiological, diagnostic and outcome data. Artificial intelligence (AI) has the potential to harness this vast quantity and range of information and become a powerful tool to support clinical decision making, personalised care, precise prognostics, and enhance patient safety. Current AI approaches in neonatal medicine include tools for disease prediction and risk stratification, neurological diagnostic support and novel image recognition technologies. Key to the integration of AI in neonatal medicine is the understanding of its limitations and a standardised critical appraisal of AI tools. Barriers and challenges to this include the quality of datasets used, performance assessment, and appropriate external validation and clinical impact studies. Improving digital literacy amongst healthcare professionals and cross-disciplinary collaborations are needed to harness the full potential of AI to help take the next significant steps in improving neonatal outcomes for high-risk infants.

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.

Effect of Algoplaque Hydrocolloid Dressing Combined with Nanosilver Antibacterial Gel under Predictive Nursing in the Treatment of Medical Device-Related Pressure Injury

It was aimed at the clinical value of predictive nursing and Algoplaque hydrocolloid dressing (AHD) combined with nanosilver antibacterial gel in treating medical device-related pressure injury (MDRPI). 100 patients, who underwent surgery in Chongqing Qijiang District People's Hospital from February 2019 to February 2020, were selected as the research objects and were randomly divided into the experimental group (50 cases) and the control group (50 cases). For the characterization test, a nanosilver antibacterial gel was created first. Patients in both groups received predictive nursing, but those in the experimental group received AHD and nanosilver antibacterial gel, and those in the control group received gauzes. MDRPI incidence, pressed skin injury severity, comfort level, clothing changes, nursing satisfaction, and other factors were all compared. The particle size of the nanosilver gel was 45-85 nm, with a relatively homogeneous distribution with the medium size, according to the findings. The incidence of MDRPI in the experimental group was lower than that in the control group significantly (6% vs. 30%, P < 0.05). The degree of injury of pressured skin in the experimental group was milder than that in the control group (P < 0.05), the degree of comfort and nursing satisfaction was higher in the experimental group than in the control group (P < 0.05), and dressing change count was lower than that in the control group (P < 0.05). In the treatment of MDRPI, predictive nursing and AHD using nanosilver antibacterial gel showed high clinical application value.

Automated Classification of Whole Body Plethysmography Waveforms to Quantify Breathing Patterns

Whole body plethysmography (WBP) monitors respiratory rate and depth but conventional analysis fails to capture the diversity of waveforms. Our first purpose was to develop a waveform cluster analysis method for quantifying dynamic changes in respiratory waveforms. WBP data, from adult Sprague-Dawley rats, were sorted into time domains and principle component analysis was used for hierarchical clustering. The clustering method effectively sorted waveforms into categories including sniffing, tidal breaths of varying duration, and augmented breaths (sighs). We next used this clustering method to quantify breathing after opioid (fentanyl) overdose and treatment with ampakine CX1942, an allosteric modulator of AMPA receptors. Fentanyl caused the expected decrease in breathing, but our cluster analysis revealed changes in the temporal appearance of inspiratory efforts. Ampakine CX1942 treatment shifted respiratory waveforms toward baseline values. We conclude that this method allows for rapid assessment of breathing patterns across extended data recordings. Expanding analyses to include larger portions of recorded WBP data may provide insight on how breathing is affected by disease or therapy.