A theoretical framework for identifying sociocultural factors that influence nurses' compliance with early warning systems for acute clinical deterioration: A cross-sectional survey.

BACKGROUND: Systematic adoption of early warning systems in healthcare settings is dependent on the optimal and reliable application by the user. Psychosocial issues and hospital culture influence clinicians' patient safety behaviours. OBJECTIVE: (i) To examine the sociocultural factors that influence nurses' EWS compliance behaviours, using a theory driven behavioural model and (ii) to propose a conceptual model of sociocultural factors for EWS compliance behaviour. DESIGN: A cross-sectional survey. SETTING: Nurses employed in public hospitals across Queensland, Australia. PARTICIPANTS: Using convenience and snowball sampling techniques eligible nurses accessed a dedicated web site and survey containing closed and open-ended questions. 291 nurses from 60 hospitals completed the survey. METHODS: Quantitative data were analysed using ANOVA or t-tests to test differences in means. A series of path models based on the theory were conducted to develop a new model. Directed or theory driven content analysis informed qualitative data analysis. RESULTS: Nurses report high levels of previous compliance behaviour and strong intentions to continue complying in the future (M=4.7; SD 0.48). Individual compliance attitudes (ß 0.29, p<.05), perceived value of escalation (ß 0.24, p<.05) and perceived ease or difficulty complying with documentation (ß -0.31, p<.05) were statistically significant, predicting 24% of variation in compliance behaviour. Positive personal charting beliefs (ß 0.14, p<.05) and subjective norms both explain higher behavioural intent indirectly through personal attitudes. High ratings of peer charting beliefs indirectly explain attitudes through subjective norms (ß 0.20, p<.05). Perceptions of control over one's clinical actions (ß -0.24, p<.05) and early warning system training (ß -0.17, p<.05) directly contributed to fewer difficulties complying with documentation requirements. Prior difficulties when escalating care (ß -0.31, p<.05) directly influenced the perceived value of escalating. CONCLUSIONS: The developed theory-based conceptual model identified sociocultural variables that inform compliance behaviour (documenting and escalation protocols). The model highlights areas of clinical judgement, education, interprofessional trust, workplace norms and cultural factors that directly or indirectly influence nurses' intention to comply with EWS protocols. Extending our understanding of the sociocultural and system wide factors that hamper nurses' use of EWSs and professional accountability has the potential to improve the compliance behaviour of staff and subsequently enhance the safety climate attitudes of hospitals. TWEETABLE ABSTRACT: A newly developed model reports nurse's personal attitudes, peer influence, perceived difficulties encountered documenting and escalation beliefs all predict early warning system compliance behaviour.

Toward a more resilient Thailand: Developing a machine learning-powered forest fire warning system.

Forest fires in Thailand are a recurring and formidable challenge, inflicting widespread damage and ranking among the nation's most devastating natural disasters. Most detection methods are labor-intensive, lack speed for early detection, or result in high infrastructure costs. An essential approach to mitigating this issue involves establishing an efficient forest fire warning system based on amalgamating diverse available data sources and optimized algorithms. This research endeavors to develop a binary machine-learning classifier based on Thailand's forest fire occurrences from January 2019 to October 2022 using data acquired from satellite resources, including the Google Earth engine. We use four gas variables including carbon monoxide, sulfur dioxide, nitrogen dioxide, and ozone. The study explores a range of classification models, encompassing linear classifiers, gradient-boosting classifiers, and artificial neural networks. The XGBoost model is the top-performing option across various classification evaluation metrics. The model provides the accuracy of 99.6 % and ROC-AUC score of 0.939. These findings underscore the necessity for a comprehensive forest fire warning system that integrates gas measurement sensor devices and geospatial data. A feedback mechanism is also imperative to enable model retraining post-deployment, thereby diminishing reliance on geospatial attributes. Moreover, given that decision-tree-based algorithms consistently yield superior results, future research in machine learning for forest fire prediction should prioritize these approaches.

Protonitazene detection in two cases of opioid toxicity following the use of tetrahydrocannabinol vape products in Australia.

INTRODUCTION: Protonitazene is an opioid belonging to the 2-benzylbenzimidazole structural class. We describe two cases of opioid toxicity involving the reported inhalation of a delta-9-tetrahydrocannabinol vape product in which protonitazene was detected. CASE REPORTS: Case 1 was a young male found unconscious after the reported use of a delta-9-tetrahydrocannabinol vape. He suffered two subsequent apnoeic episodes requiring bag-valve-mask ventilation before eventual recovery. Only protonitazene was detected in blood at a concentration of 0.74 µg/L. Case 2 was a young male who died shortly after being found unresponsive. The postmortem femoral blood concentrations of protonitazene and delta-9-tetrahydrocannabinol were 0.33 µg/L and 2 µg/L, respectively. Analysis of a pod vaping device found in the decedent's hand and a separate e-liquid bottle labelled as delta-9-tetrahydrocannabinol showed a mixture of protonitazene and delta-9-tetrahydrocannabinol. DISCUSSION: The opioid effects of protonitazene are mediated through ß-arrestin2 and mu opioid receptor signalling pathways. Benzimidazole opioids are lipophilic and, when mixed with a suitable solvent, can be used in a vape device. It is anticipated that naloxone would have provided effective reversal of toxicity in our cases. CONCLUSIONS: Novel routes of opioid administration, like vaping, may appear relatively innocuous in comparison to intravenous administration, but opioids may still be absorbed at high concentrations, resulting in severe opioid toxicity or death.

Wastewater surveillance of severe acute respiratory syndrome coronavirus-2 in open drains of two Indian megacities captures evolutionary lineage transitions: a zonation approach.

Wastewater-based environmental surveillance (WBES) has been proven as proxy tool for monitoring nucleic acids of pathogens shed by infected population before clinical outcomes. The poor sewershed network of low to middle-income countries (LMICs) leads to most of the wastewater flow through open drains. We studied the effectiveness of WBES using open drain samples to monitor the emergence of the SARS-CoV-2 variants in 2 megacities of India having dense population through zonation approach. Samples from 28 locations spanned into 5 zones of Pune region, Maharashtra, India, were collected on a weekly basis during October 2021 to July 2022. Out of 1115 total processed samples, 303 (~ 27%) tested positive for SARS-CoV-2. The periodical rise and fall in the percentage positivity of the samples was found to be in sync with the abundance of SARS-CoV-2 RNA and the reported COVID-19 active cases for Pune city. Sequencing of the RNA obtained from wastewater samples confirmed the presence of SARS-CoV-2. Of 337 sequences, lineage identification for 242 samples revealed 265 distinct SARS-CoV-2 variants including 10 highly transmissible ones. Importantly, transition from Delta to Omicron variant could be detected in wastewater samples 2 weeks prior to any clinically reported Omicron cases in India. Thus, this study demonstrates the usefulness of open drain samples for real-time monitoring of a viral pathogen's evolutionary dynamics and could be implemented in LMICs.

Multisite Pragmatic Cluster-Randomized Controlled Trial of the CONCERN Early Warning System.

Importance: Late predictions of hospitalized patient deterioration, resulting from early warning systems (EWS) with limited data sources and/or a care team's lack of shared situational awareness, contribute to delays in clinical interventions. The COmmunicating Narrative Concerns Entered by RNs (CONCERN) Early Warning System (EWS) uses real-time nursing surveillance documentation patterns in its machine learning algorithm to identify patients' deterioration risk up to 42 hours earlier than other EWSs. Objective: To test our a priori hypothesis that patients with care teams informed by the CONCERN EWS intervention have a lower mortality rate and shorter length of stay (LOS) than the patients with teams not informed by CONCERN EWS. Design: One-year multisite, pragmatic controlled clinical trial with cluster-randomization of acute and intensive care units to intervention or usual-care groups. Setting: Two large U.S. health systems. Participants: Adult patients admitted to acute and intensive care units, excluding those on hospice/palliative/comfort care, or with Do Not Resuscitate/Do Not Intubate orders. Intervention: The CONCERN EWS intervention calculates patient deterioration risk based on nurses' concern levels measured by surveillance documentation patterns, and it displays the categorical risk score (low, increased, high) in the electronic health record (EHR) for care team members. Main Outcomes and Measures: Primary outcomes: in-hospital mortality, LOS; survival analysis was used. Secondary outcomes: cardiopulmonary arrest, sepsis, unanticipated ICU transfers, 30-day hospital readmission. Results: A total of 60 893 hospital encounters (33 024 intervention and 27 869 usual-care) were included. Both groups had similar patient age, race, ethnicity, and illness severity distributions. Patients in the intervention group had a 35.6% decreased risk of death (adjusted hazard ratio [HR], 0.644; 95% confidence interval [CI], 0.532-0.778; P<.0001), 11.2% decreased LOS (adjusted incidence rate ratio, 0.914; 95% CI, 0.902-0.926; P<.0001), 7.5% decreased risk of sepsis (adjusted HR, 0.925; 95% CI, 0.861-0.993; P=.0317), and 24.9% increased risk of unanticipated ICU transfer (adjusted HR, 1.249; 95% CI, 1.093-1.426; P=.0011) compared with patients in the usual-care group. Conclusions and Relevance: A hospital-wide EWS based on nursing surveillance patterns decreased in-hospital mortality, sepsis, and LOS when integrated into the care team's EHR workflow. Trial Registration: ClinicalTrials.gov Identifier: NCT03911687.

A Proposed Systematic Approach to Safe Inter Hospital Patient Transfer in Interventional Radiology: From Referral to Repatriation.

WHAT THIS PAPER ADDS: There is no reference in the literature regarding the transfer of patients between hospitals for interventional radiology procedures. This paper outlines an approach to assist with the safe assessment, reassessment and repatriation of patients requiring urgent procedures in a different hospital.

Effects of driver's braking behavior by the real-time pedestrian scale warning system.

A driver warning system can improve pedestrian safety by providing drivers with alerts about potential hazards. Most driver warning systems have primarily focused on detecting the presence of pedestrians, without considering other factors, such as the pedestrian's gender and speed, and whether pedestrians are carrying luggage, that can affect driver braking behavior. Therefore, this study aims to investigate how driver braking behavior changes based on the information about the number of pedestrians in a crowd and examine if a developed warning system based on this information can induce safe braking behavior. For this purpose, an experiment scenario was conducted using a virtual reality-based driving simulator and an eye tracker. The collected driver data were analyzed using mixed ANOVA to derive meaningful conclusions. The research findings indicate that providing information about the number of pedestrians in a crowd has a positive impact on driver braking behavior, including deceleration, yielding intention, and attention. Particularly, It was found that in scenarios with a larger number of pedestrians, the Time to Collision (TTC) and distance to the crosswalk were increased by 12%, and the pupil diameter was increased by 9%. This research also verified the applicability of the proposed warning system in complex road environments, especially under conditions with poor visibility such as nighttime. The system was able to induce safe braking behavior even at night and exhibited consistent performance regardless of gender. In conclusion, considering various factors that influence driver behavior, this research provides a comprehensive understanding of the potential and effectiveness of a driver warning system based on information about the number of pedestrians in a crowd in complex road environments.

Wastewater-Based Epidemiology for SARS-CoV-2 in Northern Italy: A Spatiotemporal Model.

The study investigated the application of Wastewater-Based Epidemiology (WBE) as a tool for monitoring the SARS-CoV-2 prevalence in a city in northern Italy from October 2021 to May 2023. Based on a previously used deterministic model, this study proposed a variation to account for the population characteristics and virus biodegradation in the sewer network. The model calculated virus loads and corresponding COVID-19 cases over time in different areas of the city and was validated using healthcare data while considering viral mutations, vaccinations, and testing variability. The correlation between the predicted and reported cases was high across the three waves that occurred during the period considered, demonstrating the ability of the model to predict the relevant fluctuations in the number of cases. The population characteristics did not substantially influence the predicted and reported infection rates. Conversely, biodegradation significantly reduced the virus load reaching the wastewater treatment plant, resulting in a 30% reduction in the total virus load produced in the study area. This approach can be applied to compare the virus load values across cities with different population demographics and sewer network structures, improving the comparability of the WBE data for effective surveillance and intervention strategies.

European survey on Paediatric Early Warning Systems, and other processes used to aid the recognition and response to children's deterioration on hospital wards.

BACKGROUND: Internationally, there is an increasing trend in using Rapid Response Systems (RRS) to stabilize in-patient deterioration. Despite a growing evidence base, there remains limited understanding of the processes in place to aid the early recognition and response to deteriorating children in hospitals across Europe. AIM/S: To describe the processes in place for early recognition and response to in-patient deterioration in children in European hospitals. STUDY DESIGN: A cross-sectional opportunistic multi-centre European study, of hospitals with paediatric in-patients, using a descriptive self-reported, web-based survey, was conducted between September 2021 and March 2022. The sampling method used chain referral through members of European and national societies, led by country leads. The survey instrument was an adaptation to the survey of Recognition and Response Systems in Australia. The study received ethics approval. Descriptive analysis and Chi-squared tests were performed to compare results in European regions. RESULTS: A total of 185 questionnaires from 21 European countries were received. The majority of respondents (n = 153, 83%) reported having written policies, protocols, or guidelines, regarding the measurement of physiological observations. Over half (n = 120, 65%) reported that their hospital uses a Paediatric Early Warning System (PEWS) and 75 (41%) reported having a Rapid Response Team (RRT). Approximately one-third (38%) reported that their hospital collects specific data about the effectiveness of their RRS, while 100 (54%) reported providing regular training and education to support it. European regional differences existed in PEWS utilization (North = 98%, Centre = 25%, South = 44%, p < .001) and process evaluation (North = 49%, Centre = 6%, South = 36%, p < .001). CONCLUSIONS: RRS practices in European hospitals are heterogeneous. Differences in the uptake of PEWS and RRS process evaluation emerged across Europe. RELEVANCE TO CLINICAL PRACTICE: It is important to scope practices for the safe monitoring and management of deteriorating children in hospital across Europe. To reduce variance in practice, a consensus statement endorsed by paediatric and intensive care societies could provide guidance and resources to support PEWS implementation and for the operational governance required for continuous quality improvement.

Advancing Cycling Safety: On-Bike Alert System Utilizing Multi-Layer Radar Point Cloud Clustering for Coarse Object Classification.

Cyclists are considered to be vulnerable road users (VRUs) and need protection from potential collisions with cars and other vehicles induced by unsafe driving, dangerous road conditions, or weak cycling infrastructure. Integrating mmWave radars into cycling safety measures presents an efficient solution to this problem given their compact size, low power consumption, and low cost compared to other sensors. This paper introduces an mmWave radar-based bike safety system designed to offer real-time alerts to cyclists. The system consists of a low-power radar sensor affixed to the bicycle, connected to a micro-controller, and delivering a preliminary classification of detected obstacles. An efficient two-level clustering based on the accumulation of radar point clouds from multiple frames with a temporal projection from previous frames into the current frame is proposed. The clustering is followed by a coarse classification algorithm in which we use relevant features extracted from the resulting clusters. An annotated RadBike dataset composed of radar point cloud data synchronized with RGB camera images is developed to evaluate our system. The two-level clustering outperforms the DBSCAN algorithm, achieving a v-measure score of 0.91, compared to 0.88 with classical DBSCAN. Different classifiers, including decision trees, random forests, support vector machines (SVMs), and AdaBoost, have been assessed, with an overall accuracy of 87% for the three main object classes: four-wheeled, two-wheeled, and others. The system has the ability to improve rider safety on the road and substantially reduce the frequency of incidents involving cyclists.

Improving patient outcomes following vital sign monitoring protocol failure: A retrospective cohort study.

Background and Aims: Vital sign monitoring needs to be timely and correct to recognize deteriorating patients early and trigger the relevant clinical response. The aim of this study is to retrospectively evaluate compliance specifically toward the regional vital sign monitoring protocol the so called early warning score protocol (EWS-protocol) 72 h before a medical emergency team response (MET-response) and thereby illuminate whether poor compliance translates into a worse patient outcome. Methods: It was investigated all eligible patients that underwent MET responses during the calendar year 2019. The inclusion criteria encompassed somatic patients above 18 years of age admitted to the hospital and detailed evaluations of the medical records of the included patients were conducted. Results: Four hundred and twenty-nine MET-responses were included in the final analysis. EWS-protocol failure was observed for more than half the patients within all the time frames assessed. Thirty-day mortality was significantly higher for patients subject to EWS protocol failure in the timeframes 24-16, 16-8, 8-0 h before MET response. Adjusting for admission length, age, and gender, patients subject to EWS-protocol failure had an odds ratio (OR) of 1.9, 2.0, 2.1, 2.3 for mortality in the time frames 72-48, 24-16, 16-8, and 8-0 h before the MET-response, respectively. The adjusted OR for ICU-admission was 1.7, and 1.6 for patients subject to EWS-protocol failure in the time frames 16-8 and 8-0 h before MET-response, respectively. Conclusion: According to all the data analysis in this article, there is evidence that compliance toward the NEWS-protocol is poor. EWS-protocol failure is associated with a significant higher mortality and ICU-admission rate.

Composition of factors for local heat adaptation measures at the local level in cities of the mid-latitude - An approach for the south-west of Germany.

Traditional heat health warning systems focus on severe and extreme heat events at the district or regional level, often overlooking localized risk and protective factors such as healthcare access and urban green spaces. This approach considers less the varying impacts of heat within cities, including the phenomenon of Urban Heat Islands (UHIs) and the diverse needs of different populations. To address these shortcomings, a need for the development of an Urban Heat Health Warning and Information System (UHHWIS) that operates within the framework of Heat Health Action Plans is needed. Such a system integrates national acute heat health warnings with city-specific assessments of UHI effects and other relevant factors. The technical implementation of the UHHWIS involves the calculation and preprocessing of basic factors such as the Normalised Difference Vegetation Index (NDVI), imperviousness, and UHI intensity. Additionally, further factors are assessed, spatially processed, and provided in accordance with Open Geospatial Consortium (OGC) standards. An iso-area analysis is conducted to evaluate the accessibility of protective factors, such as urban green spaces, drinking wells, hospitals, physicians, and pharmacies, based on the city's road topology. One crucial factor considered in the system is the casting of shadows, which is influenced by both time and location and facilitated through deck.gl. The developed template encompasses all these components into a unified system aimed at protecting vulnerable and risk groups, such as the elderly, through resilient, climate-adapted urban planning. The system provides warnings and information tailored to the urban morphology and prevailing conditions, complemented by a catalogue of potential short- to long-term measures focused on behavioral changes and climate-resilient urban planning strategies. The template can be adapted for use in various European cities, offering valuable insights to decision-makers in city administration for mitigating thermal stress and enhancing resilience against urban heat nowadays and in future.

Nurse by numbers: The impact of early warning systems on nurses' higher-order thinking, a quantitative study.

AIM: To evaluate registered nurses' perceptions of whether the mandated use of the early warning system vital signs tool impacts the development of nurses' higher-order thinking skills. DESIGN: A concurrent mixed methods study design. METHOD: Using an online survey, registered nurses' perceptions were elucidated on whether early warning system algorithmic tools affected the development of their higher-order thinking. Likert-type matrix questions with additional qualitative fields were used to obtain information on nurse's perceptions of the tool's usefulness, clinical confidence in using the tool, compliance with escalation protocols, work environment and perceived compliance barriers. RESULTS: Most of the 305 (91%) participants included in the analysis had more than 5 years of nursing experience. Most nurses supported the early warning tool and were happy to comply with escalation protocols if the early warning score concurred with their assessment of the patient (63.6%). When the score and the nurse's higher-order thinking did not align, some had the confidence to override the escalation protocol (40.0%), while others omitted (69.4%) or inaccurately documented vital signs (63.3%) to achieve the desired score. Very few nurses (3.6%) believe using early warning tools did not impede the development of higher-order thinking. CONCLUSION: Although experienced nurses appreciate the support of early warning tools, most value patient safety above the tools and rely on their higher-order thinking. The sustained development and use of nurses' higher-order thinking should be encouraged, possibly by adding a critical thinking criterion to existing algorithmic tools. IMPACT: The study has implications for all nurses who utilize algorithmic tools, such as early warning systems, in their practice. Relying heavily on algorithmic tools risks impeding the development of higher-order thinking. Most experienced nurses prioritize their higher-order thinking in decision-making but believe early warning tools can impede higher-order thinking. PATIENT OR PUBLIC CONTRIBUTION: Registered nurses participated as survey respondents.

Predicting the potentially exacerbation of severe viral pneumonia in hospital by MuLBSTA score joint CD4 + and CD8 +T cell counts: construction and verification of risk warning model.

PURPOSE: This study mainly focuses on the immune function and introduces CD4+, CD8+ T cells and their ratios based on the MuLBSTA score, a previous viral pneumonia mortality risk warning model, to construct an early warning model of severe viral pneumonia risk. METHODS: A retrospective single-center observational study was operated from January 2021 to December 2022 at the People's Hospital of Liangjiang New Area, Chongqing, China. A total of 138 patients who met the criteria for viral pneumonia in hospital were selected and their data, including demographic data, comorbidities, laboratory results, CT scans, immunologic and pathogenic tests, treatment regimens, and clinical outcomes, were collected and statistically analyzed. RESULTS: Forty-one patients (29.7%) developed severe or critical illness. A viral pneumonia severe risk warning model was successfully constructed, including eight parameters: age, bacterial coinfection, CD4+, CD4+/CD8+, multiple lung lobe infiltrations, smoking, hypertension, and hospital admission days. The risk score for severe illness in patients was set at 600 points. The model had good predictive performance (AUROC = 0.94397), better than the original MuLBSTA score (AUROC = 0.8241). CONCLUSION: A warning system constructed based on immune function has a good warning effect on the risk of severe conversion in patients with viral pneumonia.

Temporal assessment of SARS-CoV-2 detection in wastewater and its epidemiological implications in COVID-19 case dynamics.

This research evaluated the relationship between daily new Coronavirus Disease 2019 (COVID-19) cases and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) concentrations in wastewater, followed by effects of differential SARS-CoV-2 shedding loads across various COVID-19 outbreaks. Linear regression analyses were utilized to examine the lead time of the SARS-CoV-2 signal in wastewater relative to new COVID-19 clinical cases. During the Delta wave, no lead time was evident, highlighting limited predictive capability of wastewater monitoring during this phase. However, significant lead times were observed during the Omicron wave, potentially attributed to testing capacity overload and subsequent case reporting delays or changes in shedding patterns. During the Post-Omicron wave (Febuary 23 to May 19, 2022), no lead time was discernible, whereas following the lifting of the COVID-19 state of emergency (May 30, 2022 to May 30, 2023), the correlation coefficient increased and demonstrated the potential of wastewater surveillance as an early warning system. Subsequently, we explored the virus shedding in wastewater through feces, operationalized as the ratio of SARS-CoV-2 concentrations to daily new COVID-19 cases. This ratio varied significantly across the Delta, Omicron, other variants and post-state-emergency phases, with the Kruskal-Wallis H test confirming a significant difference in medians across these stages (P < 0.0001). Despite its promise, wastewater surveillance of COVID-19 disease prevalence presents several challenges, including virus shedding variability, data interpretation complexity, the impact of environmental factors on viral degradation, and the lack of standardized testing procedures. Overall, our findings offer insights into the correlation between COVID-19 cases and wastewater viral concentrations, potential variation in SARS-CoV-2 shedding in wastewater across different pandemic phases, and underscore the promise and limitations of wastewater surveillance as an early warning system for disease prevalence trends.

Analyzing retraction responses of Tubifex tubifex (Oligochaeta: Naididae) colonies to copper treatments through a digital image analysis approach for developing early biological warning systems.

Biological monitoring and assessments are commonly used for sustainable ecosystem management. Oligochaetes are found in various freshwater ecosystems and have been used as indicators of water quality and for the biological assessment of aquatic ecosystems. Among aquatic oligochaetes, the sludge worm Tubifex tubifex (Oligochaeta, Naididae) is tolerant to organic pollution and has been used as a biomonitoring indicator of toxicity and organic pollution. In this study, we investigated the response of worm colonies to copper (CuSO4) treatments (0.01, 0.05, 0.1, 0.5, and 1.0 mg/L) in an observation cage (100 mL beaker) for 30 min. Using a digital image analysis approach, we measured the changes in the colony image area between pre- and post-copper treatment. After copper treatment, the colony image area tended to decrease, even at low copper concentrations. In addition, the colony areas did not recover to their original levels at high concentrations, although those at low concentrations did. Area decreased proportional to the logarithm of the copper concentration. Finally, our results present the possible use of the retraction responses of Tubifex tubifex colonies to chemical disturbances as early biological warning systems.

Effectiveness of Implementing Modified Early Warning System and Rapid Response Team for General Ward Inpatients.

This retrospective study assessed the effectiveness and impact of implementing a Modified Early Warning System (MEWS) and Rapid Response Team (RRT) for inpatients admitted to the general ward (GW) of a medical center. This study included all inpatients who stayed in GWs from Jan. 2017 to Feb. 2022. We divided inpatients into GWnon-MEWS and GWMEWS groups according to MEWS and RRT implementation in Aug. 2019. The primary outcome, unexpected deterioration, was defined by unplanned admission to intensive care units. We defined the detection performance and effectiveness of MEWS according to if a warning occurred within 24 h before the unplanned ICU admission. There were 129,039 inpatients included in this study, comprising 58,106 GWnon-MEWS and 71,023 GWMEWS. The numbers of inpatients who underwent an unplanned ICU admission in GWnon-MEWS and GWMEWS were 488 (.84%) and 468 (.66%), respectively, indicating that the implementation significantly reduced unexpected deterioration (p < .0001). Besides, 1,551,525 times MEWS assessments were executed for the GWMEWS. The sensitivity, specificity, positive predicted value, and negative predicted value of the MEWS were 29.9%, 98.7%, 7.09%, and 99.76%, respectively. A total of 1,568 warning signs accurately occurred within the 24 h before an unplanned ICU admission. Among them, 428 (27.3%) met the criteria for automatically calling RRT, and 1,140 signs necessitated the nursing staff to decide if they needed to call RRT. Implementing MEWS and RRT increases nursing staff's monitoring and interventions and reduces unplanned ICU admissions.

Can public opinions improve the effect of financial early warning ? -- an empirical study on the new energy industry.

Public opinion will significantly affect investor decision-making and stock prices, which ultimately has an impact on the long-term development of the new energy industry. This paper mainly aims to delve in the impact of public opinion on the efficacy of financial risk early warning effect and try to establish an enhanced financial risk early warning model for the new energy list companies. To achieve this, we collect the financial data and public evaluation texts of 185 new energy listed companies, converting the text into emotional indicators which are combined with financial indicators to build a financial risk early warning model for new energy listed companies. The contributions of this paper are as follows: (1) The experiment validation demonstrates that the combination of 7 deep learning models and Bagging algorithm highly improves the accuracy of the sentiment analysis model, achieving an accuracy of 84.09%. (2) The accuracy of financial early warning models is generally enhanced after adding sentiment indicators, among which the accuracy of the BP neural network model reached 95.78%. (3) Through clustering analysis, the evaluation models can productively divide the warning intervals, thereby bolstering the interpretability and applicability of early warning results. Therefore, we suggest that when establishing the financial early warning system, it's necessary to take public opinions into consideration. Aside from improving the early warning effect, it also can be used as a separate indicator for daily monitoring.

Combining biomarkers of BNIP3 L, S100B, NSE, and accessible measures to predict sepsis-associated encephalopathy: a prospective observational study.

BACKGROUND: Accurate identification of delirium in sepsis patients is crucial for guiding clinical diagnosis and treatment. However, there are no accurate biomarkers and indicators at present. We aimed to identify which combinations of cognitive impairment-related biomarkers and other easily accessible assessments best predict delirium in sepsis patients. METHODS: One hundred and one sepsis patients were enrolled in a prospective study cohort. S100B, NSE, and BNIP3 L biomarkers were detected in plasma and cerebrospinal fluid and patients' optic nerve sheath diameter (ONSD). The optimal biomarkers identified by Logistic regression are combined with other factors such as ONSD to filter out the perfect model to predict delirium in sepsis patients through Logistic regression, Naïve Bayes, decision tree, and neural network models. MAIN RESULTS: Among all biomarkers, compared with BNIP3 L (AUC = .706, 95% CI = .597-.815) and NSE (AUC = .711, 95% CI = .609-.813) in cerebrospinal fluid, plasma S100B (AUC = .729, 95% CI = .626-.832) had the best discrimination performance for delirium in sepsis patients. Logistic regression analysis showed that the combination of cerebrospinal fluid BNIP3 L with plasma S100B, ONSD, neutrophils, and age provided the best discrimination to cognitive impairment in sepsis patients (accuracy = .901, specificity = .923, sensitivity = .911), which was better than Naïve Bayes, decision tree, and neural network models. Neutrophils, ONSD, and cerebrospinal fluid BNIP3 L were consistently the major contributors in a few models. CONCLUSIONS: The logistic regression showed that the combination model was strongly correlated with cognitive dysfunction in sepsis patients.

Strengthening global health security through health early warning systems: A literature review and case study.

Disease transmission is dependent on a variety of factors, including the characteristics of an event, such as crowding and shared accommodations, the potential of participants having prolonged exposure and close contact with infectious individuals, the type of activities, and the characteristics of the participants, such as their age and immunity to infectious agents [1-3]. Effective control of outbreaks of infectious diseases requires rapid diagnosis and intervention in high-risk settings. As a result, syndromic and event-based surveillance may be used to enhance the responsiveness of the surveillance system [1]. In public health, surveillance is collecting, analyzing, and interpreting data across time to inform decision-making and aid policy implementation [1]. In this review article we aimed to provide an overview of the principles, types, uses, advantages, and limitations of surveillance systems and to highlight the importance of early warning systems in response to the information received by disease surveillance. The study conducted a comprehensive literature search using several databases, selecting, and reviewing 78 articles that covered different types of surveillance systems, their applications, and their impact on controlling infectious diseases. The article also presents a case study from the Hajj gathering, which highlighted the development, evaluation, and impact of early warning systems on response to the information received by disease surveillance. The study concludes that ongoing disease surveillance should be accompanied by well-designed early warning and response systems, and continuous efforts should be invested in evaluating and validating these systems to minimize the risk of reporting delays and reducing the risk of outbreaks.