Sepsis Identification Tools: A Narrative Review.

Recent and historic high-impact events demonstrated coastal risk (Xynthia, Europe, 2010; Katrina, USA, 2005). This is only to get worse, because risk is increasing due to increase in both hazard intensity, frequency and increase in consequences (increased coastal development). Adaptation requires a re-evaluation of coastal disaster risk reduction (DRR) strategies and a new mix of prevention, mitigation (e.g. limiting construction in flood-prone areas) and preparedness (e.g. Early warning systems, EWS) measures. Within the EU funded project RISC-KIT the focus is on preparedness measures and its aim is to demonstrate robustness and applicability of coastal EWS (Early Warning Systems) and DSS (Decision Support Systems). Delft-FEWS, a generic tool for Early Warning Systems has been extended, to be applied at sites all across Europe. The challenges for developing a modern EWS are found in the integration of large data sets, specialised modules to process the data, and open interfaces to allow easy integration of existing modelling capacities. In response to these challenges, Delft-FEWS provides a state of the art EWS framework, which is highly customizable to the specific requirements of an individual organisation. For ten case study sites on all EU regional seas a EWS has been developed, to provide real-time (short-term) forecasts and early warnings. The EWS component is a 2D model framework of hydro-meteo and morphological models which computes hazard intensities. The total expected impact of a hazard can be obtained by using a Bayesian network DSS. This DSS, which is incorporated in the Delft-FEWS platform is a tool that links coastal multi-hazards to their socioeconomic and environmental consequences. An important innovation of the EWS/DSS lies in its application in dual mode: as a forecast and warning system and as a consistent ex-ante planning tool to evaluate the long-term vulnerability due to multiple (low-frequency) coastal hazards, under various climate-related scenarios. Generic tools which can be used to set-up a EWS/DSS for coastal regions regardless of geomorphic settings, forcing or hazard type have been developed and are available via the project website.

Introduction: Early Warning Systems (EWS) are highly vital in the human health since they detect and intervene in the emergence of diseases. As the world health challenges change, now it is relevant to think of utilizing advanced technologies to improve the precision and performance of these systems and to take into account the implementation of Artificial Intelligence (AI), Big Data, and blockchain. This paper will be looking at the international initiatives that have boosted advancement in EWS, including integration of the most recent technologies and systems, including the WHO Early Warning, Alert and Response System (EWARS). Materials and Methods: The current research paper is a literature review on available literature, reports, and case studies, which are concerned with EWS efficacy as a means of detecting disease outbreak. Data have been gained with the help of the peer-reviewed journals, WHO publications, and the latest technological progress in AI, machine learning, and blockchain. In tandem with this, the qualitative investigation of the world health systems and technological amalgamations were carried out, and the achievements and challenges arising in various regions were likewise identified. The performance measures in EWS that include sensitivity, specificity and timeliness have also been compared in the paper. Findings: The findings indicate that the EWARS by WHO has been instrumental in enhancing surveillance and coordination of illnesses response across the globe. AI and Big Data analytics have improved the predictive nature of EWS to a large extent since more accurate predictions of disease outbreaks can be made. Blockchain technology has enhanced data security and transparency and as a result, this has resulted in trust accumulation between the stakeholders. However, the data quality and integration as well as the privacy and lack of resources continue to be a problem, especially in low-resource settings. To overcome these obstacles, upcoming technology is immense such as AI-based real-time analysis and distribution of information through blockchain. Future Direction: To address the current limitations of EWS, in the future, the use of more efficient machine learning models and further development of blockchain applications in the health data management should become a significant part of the matter. As the development of the global-based health activities is also in progress, the global organizations, governments, and technology offered will be of vital importance in the future of scalable and flexible Early Warning Systems, at least in terms of collaboration. The resolution of these problems and the introduction of the technological inclusion will contribute to making EWS more flexible and providing it with more efficient tools to manage health risks at the global scale. Keywords: Early Warning Systems (EWS), Artificial Intelligence (AI), Big Data, Blockchain Technology, Disease Outbreak Detection, Global Health Initiatives, WHO Early Warning, Alert and Response System (EWARS), Machine Learning, Data security, predictive analytics, Health Surveillance.

BackgroundChanges to physiological parameters precede deterioration of ill patients. Early warning and track and trigger systems (TTS) use routine physiological measurements with pre-specified thresholds to identify deteriorating patients and trigger appropriate and timely escalation of care. Patients presenting to the emergency department (ED) are undiagnosed, undifferentiated and of varying acuity, yet the effectiveness and cost-effectiveness of using early warning systems and TTS in this setting is unclear. We aimed to systematically review the evidence on the use, development/validation, clinical effectiveness and cost-effectiveness of physiologically based early warning systems and TTS for the detection of deterioration in adult patients presenting to EDs.MethodsWe searched for any study design in scientific databases and grey literature resources up to March 2016. Two reviewers independently screened results and conducted quality assessment. One reviewer extracted data with independent verification of 50% by a second reviewer. Only information available in English was included. Due to the heterogeneity of reporting across studies, results were synthesised narratively and in evidence tables.ResultsWe identified 6397 citations of which 47 studies and 1 clinical trial registration were included. Although early warning systems are increasingly used in EDs, compliance varies. One non-randomised controlled trial found that using an early warning system in the ED may lead to a change in patient management but may not reduce adverse events; however, this is uncertain, considering the very low quality of evidence. Twenty-eight different early warning systems were developed/validated in 36 studies. There is relatively good evidence on the predictive ability of certain early warning systems on mortality and ICU/hospital admission. No health economic data were identified.ConclusionsEarly warning systems seem to predict adverse outcomes in adult patients of varying acuity presenting to the ED but there is a lack of high quality comparative studies to examine the effect of using early warning systems on patient outcomes. Such studies should include health economics assessments.

ViEWS in the emergency department

Background: The maternal mortality rate in Semarang is 121.5 per 100,000 live births, the second-highest in Central Java. The early warning system with the Early Warning Score and the maternal emergency early warning system (PDKM) still has various shortcomings to reduce MMR.Objective: This study aims to prove the effectiveness of the application of the PDKM Modified Early Obstetric Warning System (MEOWS) as an assessment of the risk of pregnancy in primary health facilities to reduce MMR in Semarang.Methods: The study was conducted on all pregnant women who came to Tlogosari Wetan, Tlogosari Kulon, Bandarharjo, and Bangetayu public health center in Semarang and were willing to participate in the study and were referred to government hospitals using national health assurance BPJS. Sampling was done by cluster random sampling by dividing the intervention and control groups. The study used a pretest-posttest control group design method by comparing the use of the MEOWS and the Poedji Rochjati Scorecard (KSPR) to the number of public health center referrals in Semarang. The data obtained will be analyzed statistically with the bivariate test, Mann-Whitney difference test, relative risk reduction, and absolute risk reduction.Results: The results showed that 21 of 43 (48.8%) patients were referred to the control group and 26 of 36 (72.2%) patients were referred to the intervention group. Mann-Whitney test of the number of referrals after the intervention within 3 months showed significant results (p = 0.033; p <0.05). There was an increase in the number of maternal referrals at the public health center in Semarang after the implementation of the MEOWS score by 1.48 times compared to using the KSPR (RR : 1.48 ; 95% CI : 1.02 – 2.13).Conclusion:The use of the MEOWS score can increase awareness of potential referrals and is associated with complications in patients.Background: The maternal mortality rate in Semarang is 121.5 per 100,000 live births, the second-highest in Central Java. The early warning system with the Early Warning Score and the maternal emergency early warning system (PDKM) still has various shortcomings to reduce MMR.Objective: This study aims to prove the effectiveness of the application of the PDKM Modified Early Obstetric Warning System (MEOWS) as an assessment of the risk of pregnancy in primary health facilities to reduce MMR in Semarang.Methods: The study was conducted on all pregnant women who came to Tlogosari Wetan, Tlogosari Kulon, Bandarharjo, and Bangetayu public health center in Semarang and were willing to participate in the study and were referred to government hospitals using national health assurance BPJS. Sampling was done by cluster random sampling by dividing the intervention and control groups. The study used a pretest-posttest control group design method by comparing the use of the MEOWS and the Poedji Rochjati Scorecard (KSPR) to the number of public health center referrals in Semarang. The data obtained will be analyzed statistically with the bivariate test, Mann-Whitney difference test, relative risk reduction, and absolute risk reduction.Results: The results showed that 21 of 43 (48.8%) patients were referred to the control group and 26 of 36 (72.2%) patients were referred to the intervention group. Mann-Whitney test of the number of referrals after the intervention within 3 months showed significant results (p = 0.033; p <0.05). There was an increase in the number of maternal referrals at the public health center in Semarang after the implementation of the MEOWS score by 1.48 times compared to using the KSPR (RR : 1.48 ; 95% CI : 1.02 – 2.13).Conclusion: The use of the MEOWS score can increase awareness of potential referrals and is associated with complications in patients.

Using big data and artificial intelligence to establish a multi-point monitoring, early warning, and disposal system to achieve early warning and intervention of infectious disease outbreaks is an important means of controlling the spread of the epidemic. Taking Xiaoshan district as an example, this study analyzes the monitoring contents, warning methods, and application effectiveness of the infectious disease monitoring, early warning and disposal system. Based on Xiaoshan's health big data resources, the system starts with syndrome, disease diagnosis and etiology. Through advanced technologies such as artificial intelligence and block chain, it realizes early identification of infectious disease outbreaks, data fusion, multi-cross collaboration, and closed-loop management. It has improved the sensitivity of clustered outbreaks monitoring and the effectiveness of epidemic disposal and provided a reference for grassroots disease prevention and control departments to establish an infectious disease monitoring and early warning system.

ObjectiveTo determine if the introduction of an emergency department (ED) sepsis screening tool and management bundle affects antibiotic prescribing and use.DesignMulticentre, cohort, before-and-after study design.SettingThree tertiary hospitals in Queensland, Australia...

Researchers have found that patients exhibit physiological changes up to 8 hours prior to an arrest event. Deaths have been attributed to a lack of observation, lack of documentation of observations, inability of a caregiver to recognize early signs of deterioration, and lack of communication between healthcare providers. This integrative review examines early warning scoring systems and their effectiveness in predicting a patient's potential for deterioration and considers whether these scoring systems prevent unplanned intensive care unit admissions and/or death. Three databases (MEDLINE, CINAHL [Cumulative Index to Nursing and Allied Health Literature], and the Cochrane Collaboration) were searched to identify the instruments and clinical support systems available to assist healthcare personnel in recognizing early clinical deterioration. Key search words included modified early warning score, early warning score, early warning systems, deteriorating patient, patients at risk, shock index, track and trigger systems, and failure to rescue. Two prior literature reviews examined early warning scoring systems and their effects on patient outcomes; however, the most recent one reviewed only articles published before 2007. This review examined studies of early warning systems and the incorporation of clinical support published from 2007 to 2012. Nine studies fitting the search criteria were included in this review. Early warning scoring systems that interface with electronic medical records and are supplemented with decision aides (algorithms) and clinical support systems produce an effective screening system for early identification of deteriorating patients. This multifaceted approach decreases unplanned intensive care unit admissions and hospital mortality.

Dear Editor, Examinations, laboratory tests, radiology, and clinical experience are required to make the most appropriate clinical decisions. There is no single universal clinical decision-making method advocated in routine medical literature. Often, this process is driven by experience, exploration, and clinical gestalt. Clinician management serves as a subjective decision tool in disease management. It has been extensively studied in the literature, particularly in entities such as pulmonary embolism, difficult airway prediction, and severe COVID-19 (1). By combining parameters such as laboratory-vital parameters and combined hematological parameters, clinical decision-making tools have been developed (2-3). Early warning systems (EWS) help predict which patients will require critical care by evaluating physiological parameters in busy and crowded workspaces. These scores, which can be measured through vital parameters and a simple physical examination, ensure the effective use of resources (4). In intensive care units (ICUs), complex systems are used to predict mortality. APACHE II is the most well-known score used in intensive care, consisting of many parameters that are computationally difficult. Vital parameters, biochemical tests, and blood gas analysis results are used when calculating APACHE II (5). On the other hand, important features of scores used in the emergency department are their quick and easy calculation, requiring no professional knowledge. The VitalPAC Early Warning Score (ViEWS), developed by Prytherch et al. in 2010, is an EWS that includes six physiological parameters. This score records the pulse rate, systolic blood pressure, respiratory minute rate, body temperature, level of consciousness (assessed by AVPU), and peripheral oxygen saturation (SpO2) parameters. ViEWS is created based on the degree of deviation of these six parameters from their normal ranges. Additional points are given if the patient receives oxygen support. The total ViEWS value, calculated by considering each parameter, provides information about the patient's prognosis (6). By adding the rapid lactate level to ViEWS (ViEWS-L), a score that better predicts outcomes in the geriatric population was developed (7). The Modified Early Warning Score (MEWS) is one of the triage scoring methods used to identify patients in need of intensive and critical care, and to expedite their transfer to the intensive care unit. It evaluates systolic blood pressure, heart rate, respiratory rate, body temperature, and neurological status. MEWS is included in routine nursing care procedures in some countries and is routinely assessed by nurses. ICU admission rates and mortality rates increase for those who score 5 or more on this scale, although this threshold may vary in different populations or clinical situations. In a Korean study analyzing sepsis patients, combining lactate with the MEWS score improved the prediction of the need for intensive care (8). The National Early Warning Score (NEWS) is another scoring system used to determine clinical deterioration and follow-up level for all hospitalized patients, as well as to predict clinically high-risk patients. It is calculated by evaluating respiratory rate, oxygen saturation, oxygen support status, blood pressure, body temperature, and neurological status. The NEWS-L score, obtained by combining NEWS with lactate, has been reported to be superior to NEWS alone in geriatric critically ill patients (9). However, contrary to the geriatric patient study, Özkan's study reported that NEWS-L was not superior to NEWS and that they exhibited similar predictive abilities (10). As a result, researchers are actively working on new parameters and scoring systems to optimize resource utilization and predict critical illness. Researchers should be encouraged to investigate studies on EWS lactate combinations or other combinations.

Mitigating disasters—a promising start

Background: Emerging and re-emerging infectious diseases (EIDs and Re-EIDs) cause significant economic crises and public health problems worldwide. Epidemics appear to be more frequent, complex, and harder to prevent. Early warning systems can significantly reduce outbreak response times, contributing to better patient outcomes. Improving early warning systems and methods might be one of the most effective responses. This study employs a bibliometric analysis to dissect the global research hotspots and evolutionary trends in the field of infectious disease early warning, with the aim of providing guidance for optimizing public health emergency management strategies. Methods: Publications related to the role of early warning systems in detecting and responding to infectious disease outbreaks from 1999 to 2024 were retrieved from the Web of Science Core Collection (WoSCC) database. CiteSpace software was used to analyze the datasets and generate knowledge visualization maps. Results: A total of 798 relevant publications are included. The number of annual publications has sharply increased since 2000. The USA produced the highest number of publications and established the most extensive cooperation relationships. The Chinese Center for Disease Control & Prevention was the most productive institution. Drake, John M was the most prolific author, while the World Health Organization and AHMED W were the most cited authors. The top two cited references mainly focused on wastewater surveillance of SARS-CoV-2. The most common keywords were "infectious disease", "outbreak", "transmission", "virus", and "climate change". The basic keyword "climate" ranked the first and long duration with the strongest citation burst. "SARS-CoV-2", "One Health", "early warning system", "artificial intelligence (AI)", and "wastewater-based epidemiology (WBE)" were emerging research foci. Conclusions: Over the past two decades, research on early warning of infectious diseases has focused on climate change, influenza, SARS, virus, machine learning, warning signals and systems, artificial intelligence, and so on. Current research hotspots include wastewater-based epidemiology, sewage, One Health, and artificial intelligence, as well as the early warning and monitoring of COVID-19. Research foci in this area have evolved from focusing on climate-disease interactions to pathogen monitoring systems, and ultimately to the "One Health" integrated framework. Our research findings underscore the imperative for public health policymakers to prioritize investments in real-time surveillance infrastructure, particularly wastewater-based epidemiology and AI-driven predictive models, and strengthen interdisciplinary collaboration frameworks under the One Health paradigm. Developing an integrated human-animal-environment monitoring system will serve as a critical development direction for early warning systems for epidemics.

Our aim was to prospectively determine the predictive capabilities of SEPSIS-1 and SEPSIS-3 definitions in the emergency departments and general wards. Patients with National Early Warning Score (NEWS) of 3 or above and suspected or proven infection were enrolled over a 24-h period in 13 Welsh hospitals. The primary outcome measure was mortality within 30 days. Out of the 5422 patients screened, 431 fulfilled inclusion criteria and 380 (88%) were recruited. Using the SEPSIS-1 definition, 212 patients had sepsis. When using the SEPSIS-3 definitions with Sequential Organ Failure Assessment (SOFA) score ≥ 2, there were 272 septic patients, whereas with quickSOFA score ≥ 2, 50 patients were identified. For the prediction of primary outcome, SEPSIS-1 criteria had a sensitivity (95%CI) of 65% (54-75%) and specificity of 47% (41-53%); SEPSIS-3 criteria had a sensitivity of 86% (76-92%) and specificity of 32% (27-38%). SEPSIS-3 and SEPSIS-1 definitions were associated with a hazard ratio (95%CI) 2.7 (1.5-5.6) and 1.6 (1.3-2.5), respectively. Scoring system discrimination evaluated by receiver operating characteristic curves was highest for Sequential Organ Failure Assessment score (0.69 (95%CI 0.63-0.76)), followed by NEWS (0.58 (0.51-0.66)) (p < 0.001). Systemic inflammatory response syndrome criteria (0.55 (0.49-0.61)) and quickSOFA score (0.56 (0.49-0.64)) could not predict outcome. The SEPSIS-3 definition identified patients with the highest risk. Sequential Organ Failure Assessment score and NEWS were better predictors of poor outcome. The Sequential Organ Failure Assessment score appeared to be the best tool for identifying patients with high risk of death and sepsis-induced organ dysfunction.

BackgroundThe optimal Early Warning System (EWS) scores for identifying patients at risk of clinical deterioration among those transported by ambulance services remain uncertain. This retrospective study compared the performance of...

Early identification of sepsis and initiation of aggressive treatment saves lives. However, the diagnosis of sepsis may be delayed in patients without overt deterioration. Clinical screening tools and lactate levels may help identify sepsis patients at risk for adverse outcomes. The objective was to determine the diagnostic characteristics of a clinical screening tool in combination with measuring early bedside point-of-care (POC) lactate levels in emergency department (ED) patients with suspected sepsis. This was a prospective, observational study set at a suburban academic ED with an annual census of 90,000. A convenience sample of adult ED patients with suspected infection were screened with a sepsis screening tool for the presence of at least one of the following: temperature greater than 38°C or less than 36°C, heart rate greater than 90 beats/min, respiratory rate greater than 20 breaths/min, or altered mental status. Patients meeting criteria had bedside POC lactate testing following triage, which was immediately reported to the treating physician if ≥2.0 mmol/L. Demographic and clinical information, including lactate levels, ED interventions, and final diagnosis, were recorded. Outcomes included presence or absence of sepsis using the American College of Chest Physicians/Society of Critical Care Medicine consensus conference definitions and intensive care unit (ICU) admissions, use of vasopressors, and mortality. Diagnostic test characteristics were calculated using 2-by-2 tables with their 95% confidence intervals (CIs). The association between bedside lactate and ICU admissions, use of vasopressors, and mortality was determined using logistic regression. A total of 258 patients were screened for sepsis. Their mean (± standard deviation [SD]) age was 64 (±19) years; 46% were female, and 82% were white. Lactate levels were 2.0 mmol/L or greater in 80 (31%) patients. Patients were confirmed to meet sepsis criteria in 208 patients (81%). The diagnostic characteristics for sepsis of the combined clinical screening tool and bedside lactates were sensitivity 34% (95% CI = 28% to 41%), specificity 82% (95% CI = 69% to 90%), positive predictive value 89% (95% CI = 80% to 94%), and negative predictive value 23% (95% CI = 17% to 30%). Bedside lactate levels were associated with sepsis severity (p < 0.001), ICU admission (odds ratio [OR] = 2.01; 95% CI = 1.53 to 2.63), and need for vasopressors (OR = 1.54; 95% CI = 1.13 to 2.12). Use of a clinical screening tool in combination with early bedside POC lactates has moderate to good specificity but low sensitivity in adult ED patients with suspected sepsis. Elevated bedside lactate levels are associated with poor outcomes.

The use of preventive diplomacy in conflict prevention can be traced back to various human civilizations where treaties were concluded, alliances formed, inter-ethnic marriages solemnized and various forms of traditional and scientific knowledge employed to prevent inter-state and intra-state conflicts. However, localizing this strategy at community level remains a challenge due to lack of institutional structures and resources to advance capacity of preventive diplomacy in land-based conflicts. The study examines the influence of early warning system on land-based conflicts among the pastoralist communities in Samburu County. The study was guided by conflict prevention theory, and the target population comprised 424 individuals from different institutions involved in peace and security discourses in Samburu County, Kenya. Using Yamane formula to calculate the sample size, stratified random sampling technique was applied to select 206 respondents. Primary data was collected using a questionnaire, and it was processed using descriptive, inferential, and thematic content analysis techniques. Analyzed data was presented using tables, figures, and narratives. Findings indicated that early warning system is used by both the state and the non-state actors to predict trends of violent conflicts from open source information, inter alia, NDMA which publishes early warning system in a monthly bulletin; smart-phone applications that monitor rangeland conditions, and disseminating threats alerts. Complementing scientific methods with traditional knowledge to forecast the future is indispensable, and so are the District Task Forces which work closely with Samburu district peace committees to monitor livestock migratory routes and conduct night watch on herders to prevent them from rearming. The study concluded that the early warning system tool has been applied to predict trends on land-based conflicts in Samburu, but there is need to increase resources in order to increase its capacity. Synergy between multiple actors is recommended to avoid a confused response.