Computational Tools to Facilitate Early Warning of New Emerging Risk Chemicals.

Innovative tools suitable for chemical risk assessment are being developed in numerous domains, such as non-target chemical analysis, omics, and computational approaches. These methods will also be critical components in an efficient early warning system (EWS) for the identification of potentially hazardous chemicals. Much knowledge is missing for current use chemicals and thus computational methodologies complemented with fast screening techniques will be critical. This paper reviews current computational tools, emphasizing those that are accessible and suitable for the screening of new and emerging risk chemicals (NERCs). The initial step in a computational EWS is an automatic and systematic search for NERCs in literature and database sources including grey literature, patents, experimental data, and various inventories. This step aims at reaching curated molecular structure data along with existing exposure and hazard data. Next, a parallel assessment of exposure and effects will be performed, which will input information into the weighting of an overall hazard score and, finally, the identification of a potential NERC. Several challenges are identified and discussed, such as the integration and scoring of several types of hazard data, ranging from chemical fate and distribution to subtle impacts in specific species and tissues. To conclude, there are many computational systems, and these can be used as a basis for an integrated computational EWS workflow that identifies NERCs automatically.

Avian Influenza: Lessons from Past Outbreaks and an Inventory of Data Sources, Mathematical and AI Models, and Early Warning Systems for Forecasting and Hotspot Detection to Tackle Ongoing Outbreaks.

BACKGROUND/OBJECTIVES: The ongoing avian influenza (H5N1) outbreak, one of the most widespread and persistent in recent history, has significantly impacted public health and the poultry and dairy cattle industries. This review covers lessons from past outbreaks, risk factors for transmission, molecular epidemiology, clinical features, surveillance strategies, and socioeconomic impacts. Since 1997, H5N1 has infected over 900 individuals globally, with a fatality rate exceeding 50%. Key factors influencing infection rates include demographic, socioeconomic, environmental, and ecological variables. The virus's potential for sustained human-to-human transmission remains a concern. The current outbreak, marked by new viral clades, has complicated containment efforts. METHODS: This review discusses how to integrate technological advances, such as mathematical modeling and artificial intelligence (AI), to improve forecasting, hotspot detection, and early warning systems. RESULTS: We provide inventories of data sources, covering both conventional and unconventional data streams, as well as those of mathematical and AI models, which can be vital for comprehensive surveillance and outbreak responses. CONCLUSION: In conclusion, integrating AI, mathematical models, and technological innovations into a One-Health approach is essential for improving surveillance, forecasting, and response strategies to mitigate the impacts of the ongoing avian influenza outbreak. Strengthening international collaboration and biosecurity measures will be pivotal in controlling future outbreaks and protecting both human and animal populations from this evolving global threat.

A cluster of multi-drug intoxications involving xylazine, benzimidazole opioids (nitazenes) and novel benzodiazepines in South Australia.

OBJECTIVE: To highlight the detection of xylazine and nitrazolam in conjunction with benzimidazole opioids (nitazenes) and other novel benzodiazepines in a cluster of patients after putative heroin use. METHODS: Deidentified clinical and analytical data were gathered from patients enrolled in the Emerging Drugs Network of Australia system. RESULTS: In December 2023, three adults presented to the ED with sedation following putative use of heroin. Toxicological analysis of blood samples identified xylazine, protonitazene, metonitazene, bromazolam and nitrazolam. Local health authorities subsequently issued an alert. CONCLUSION: This is the first signal of xylazine and nitrazolam use in Australia. These results demonstrate toxico-surveillance programmes with analytical confirmation of drugs are invaluable in monitoring illicit drug use.

Designing and testing clinical simulations of an early warning system for implementation in acute care settings.

Objectives: Conducting simulation testing with end-users is essential for facilitating successful implementation of new health information technologies. This study designed a standardized simulation testing process with a system prototype prior to implementation to help study teams identify the system's interpretability and feasibility from the end-user perspective and to effectively integrate new innovations into real-world clinical settings and workflows. Materials and Methods: A clinical simulation model was developed to test a new Clinical Decision Support (CDS) system outside of the clinical environment while maintaining high fidelity. A web-based CDS prototype, the "CONCERN Smart Application," which leverages clinical data to measure and express a patient's risk of deterioration on a 3-level scale ("low," "moderate," or "high"), and audiovisual-integrated materials, were used to lead simulation sessions. Results: A total of 6 simulation sessions with 17 nurses were held to investigate how nurses interact with the CONCERN Smart application and how it influences their critical thinking, and clinical responses. Four themes were extracted from the simulation debriefing sessions and used to inform implementation strategies. The strategies include how the CDS should be improved for practical real-world use. Discussion and Conclusions: Standardized simulation testing procedures identified and informed the necessary CDS improvements, the enhancements needed for real-world use, and the training requirements to effectively prepare end-users for system go-live.

Wearable devices as part of postoperative early warning score systems: a scoping review.

Postoperative deterioration is often preceded by abnormalities in vital parameters, but limited resources prevent their continuous monitoring in patients with no indication to ICU admission. The development of new technologies allowed the introduction of wearable devices (WDs), enabling the possibility of postoperative monitoring in surgical wards. We performed a Scoping Review to determine the current use of wearable devices as part of Continuous Remote Early Warning Score (CREWS) systems and their efficiency during postoperative period. This Scoping Review was conducted according to PRISMA-ScR guidelines. PICO framework was used before the search to define the review protocol. Systematic literature research has been performed on PubMed, MeSH, MEDLINE and Embase, considering a period between 2018 and February 2024. Prospective and retrospective studies involving patients undergoing cardiac and non-cardiac surgery are included. A total of 10 articles were included in the review. 11 different CE/FDA approved wearable devices were used in the studies analyzed. In all studies the WDs were applied the day of the surgery. The use of WDs as part of CREWS systems is feasible and safe. Furthermore, with the aid of other technologies (LoRa and Artificial Intelligence), they shorten Length of Stay (LOS) and reduce the number of ICU admissions with a reduction in healthcare costs. Continuous monitoring in surgical departments can facilitate the correct and timely identification of postoperative complications. This article is a starting point for the development of new protocols and for the application of these monitoring systems in clinical practice.

Suitable temperature indicator for adverse health impacts in sub-tropical cities: a case study in Hong Kong from 2010-2019.

Heat-health warning systems and services are important preventive actions for extreme heat, however, global evidence differs on which temperature indicator is more informative for heat-health outcomes. We comprehensively assessed temperature predictors on their summer associations with adverse health impacts in a high-density subtropical city. Maximum, mean, and minimum temperatures were examined on their associations with non-cancer mortality and hospital admissions in Hong Kong during summer seasons 2010-2019 using Generalized Additive Models and Distributed Lag Non-linear Models. In summary, mean and minimum temperatures were identified as strong indicators for mortality, with a relative risk(RR) and 95% confidence interval(CI) of 1.037 (1.006-1.069) and 1.055 (1.019-1.092), respectively, at 95th percentile vs. optimal temperature. Additionally, minimum temperatures captured the effects of hospital admissions, RR1.009 (95%CI: 1.000- 1.018). In stratified analyses, significant associations were found for older adults, female sex, and respiratory-related outcomes. For comparison, there was no association between maximum temperature and health outcomes. With climate change and projected increase of night-time warming, the findings from this comprehensive assessment method are useful to strengthen heat prevention strategies and enhance heat-health warning systems. Other locations could refer to this comprehensive method to evaluate their heat risk, especially in highly urbanized environments and subtropical cities.

Syndromic Surveillance in Public Health Emergencies: A Systematic Analysis of Cases Related to Exposure to 2023 Floodwaters in Romagna, Italy.

BACKGROUND: In May 2023, Romagna, Italy, faced a devastating flood resulting in 16 fatalities, forced displacement of 26,000 citizens, and significant economic losses. Due to potential water contamination, implementing public health strategies became imperative for the Local Health Authority to mitigate the health consequences, analyze the flood's impact on the local population's health, and detect early anomalies requiring timely public health interventions. METHODS: Between June and July 2023, general practitioners who were part of the RespiVirNet surveillance network completed weekly structured forms. These forms collected data on individuals exposed or not to floodwaters and clinical syndromes. Rates per 1000 resident population aged > 14 were stratified by district, week of observation, and symptomatology. Missing data were addressed by imputation using second-order autoregressive modeling. RESULTS: An incidence of 3.52 syndromes potentially related to flood water exposure per 1000 individuals (95% CI 2.82-4.35) was estimated. Ravenna, the city most affected by the flood, recorded the highest rate (6.05 per 1000, 95% CI 4.59-7.82). Incidence decreased in the weeks post-event. Anxiety, or trauma and stress symptoms, exhibited higher rates among the exposed, diminishing over weeks. The incidence for the non-exposed (12.76 per 1000, 95% CI 10.55-15.29) showed no significant territorial differences compared to the exposed ones. CONCLUSIONS: Syndromic surveillance provided timely information on the flood's health impact, revealing a higher incidence of individual syndromes among the non-exposed. This study contributes to guiding the implementation of future public health preparedness and response strategies for populations facing similar natural disasters.

Early detection of deteriorating patients in general wards through continuous contactless vital signs monitoring.

Objective: To assess the efficacy of continuous contactless vital signs monitoring with an automated Early Warning System (EWS) in detecting clinical deterioration among patients in general wards. Methods: A prospective observational cohort study was conducted in the medical unit of a tertiary care hospital in India, involving 706 patients over 84,448 monitoring hours. The study used a contactless ballistocardiography system (Dozee system) to continuously monitor heart rate, respiratory rate, and blood pressure. The study assessed total, mean, and median alerts at 24, 48, 72, 96, 120 h, and length of stay (LOS) before patient deterioration or discharge. It analyzed alert sensitivity and specificity, average time from initial alert to deterioration, and healthcare practitioners (HCP) activity. Study was registered with the Clinical Trials Registry-India CTRI/2022/10/046404. Results: Out of 706 patients, 33 (5%) experienced clinical deterioration, while 673 (95%) did not. The deterioration group consistently had a higher number of alerts compared to those who were discharged normally, across all time-points. On average, the time between the initial alert and clinical deterioration was 16 h within the last 24 h preceding the event. The sensitivity of the Dozee-EWS varied between 67% and 94%. HCP spend 10% of their time on vital signs check and documentation. Conclusions: This study suggests that utilizing contactless continuous vital signs monitoring with Dozee-EWS in general ward holds promise for enhancing the early detection of clinical deterioration. Further research is essential to evaluate the effectiveness across a wider range of clinical settings.

Enhancing early warning: A DNA biosensor with polyaniline/graphene nanocomposite for label-free voltammetric detection of saxitoxin-producing harmful algae.

Yearly reports of detrimental effects resulting from harmful algal blooms (HAB) are still received in Malaysia and other countries, particularly concerning fish mortality and seafood contamination, both of which bear consequences for the fisheries industry. The underlying reason is the absence of a dependable early warning system. Hence, this research aims to develop a single DNA biosensor that can detect a group of HAB species known for producing saxitoxin (SXT), which is commonly found in Malaysian waters. The screen-printed carbon electrode (SPCE)-based DNA biosensor was fabricated by covalent grafting of the 3' aminated DNA probe of the sxtA4 conserved domain in SXT-producing dinoflagellates on the reverse-phase polymerized polyaniline/graphene (PGN) nanocomposite electrode via carbodiimide linkage. The introduction of a carboxyphenyl layer to the PGN nanotransducing element was essential to augment the carboxylic groups on the graphene (RGO), facilitating attachment with the aminated DNA. The synergistic effect of the asynthesized nanocomposite of PANI and RGO, tremendously enhanced the electron transfer rate of the ferri/ferrocyanide redox probe at the SPCE transducer surface, allowing for the label-free bioanalytical assay of complementary DNA targets. The developed DNA biosensor featuring the capacity to detect a broad range of Alexandrium minutum (A. minutum) cell concentrations, ranging from 10 to 10,000,000 cells L-1. The quantification of A. minutum cells from pure algal culture by the electrochemical DNA biosensor has been well-validated with traditional microscopic techniques. Furthermore, Alexandrium tamiyavanichii, another toxigenic HAB species, exhibited a similar electrochemical characteristic signal to those observed with A. minutum, whilst the biosensor yielded appreciably distinctive results when subjected to a non-toxigenic microalgae species as a negative control, i.e. Isochrysis galbana. A compendium DNA biosensor design and electrochemical detection strategy at laboratory scale serves as a precursor to the potential development of portable device for on-site detection, thus expanding the utility and scope of biosensor technology.

Development of measured hydrodynamic information-based flood early warning system for small streams.

In general, small stream basins, characterized by narrow channels and steep slopes, face heightened vulnerability to climate change-induced flooding, posing challenges for evacuation procedures. With the increasing intensity of floods and typhoons in recent years, urgent measures are necessary to mitigate damage in such areas. This research endeavors to address these challenges by developing a novel small stream flood early warning system (SSFEWS) tailored to small streams and piloting its application. The proposed system integrates real-time hydrodynamic data collection, flood probability forecasting, and proactive warning issuance through an amalgamation of IoT-based sensor networks, statistical models leveraging measurement data, a robust constrained nonlinear optimization algorithm (RCNOA), and four-parameter logistic method (4PL). Moreover, system accuracy and reliability are enhanced by an automated iterative process that continuously refines forecasting model parameters via a user-defined rainfall-discharge nomograph and rating curve using RCNOA and 4PL. The developed SSFEWS is expected to contribute to the safety of the community as well as prevent possible small stream-related casualties by enabling efficient disaster response. © 2024 Elsevier Ltd. All rights reserved.

A crisis awareness chain warning management model for patients with difficulties cooperating with magnetic resonance examinations.

OBJECTIVE: This case-control study tested a crisis awareness-based chain warning management model for patients with difficulties cooperating with magnetic resonance imaging (MRI) examinations. METHODS: All participants experienced difficulties cooperating with MRI examinations and underwent cranial magnetic resonance spectroscopy (MRS) and MRI at the same hospital in China. The control group (n = 1233) underwent examinations from January to June 2023 and received routine nursing care (pre-examination safety notification, instructions on cooperating during the examination, post-examination observation). A crisis awareness chain warning management model was implemented for the intervention group (n = 1352), who underwent examinations from July to December 2023. The groups were compared on average time for examination completion, quality of care and occurrence of complications. Data were collected using a self-devised data collection form. RESULTS: The average length of time to complete MRS and MRI was shorter for intervention group patients than for control group patients. The intervention group showed better pre-examination preparation, examination success rate, image quality attainment rate, and one-time examination success rate, and lower incidence of examination-related complications. CONCLUSION: This management model could increase MRI examination efficiency, improve quality of care, reduce complications and increase nurses' understanding of nursing continuity and crisis awareness.

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.

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.

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.

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.

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: 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.

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.

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.

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.

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.