Comparing 11 early warning scores and three shock indices in early sepsis prediction in the emergency department

BACKGROUND:This study aimed to evaluate the discriminatory performance of 11 vital sign-based early warning scores(EWSs)and three shock indices in early sepsis prediction in the emergency department(ED).METHODS:We performed a retrospective study on consecutive adult patients with an infection over 3 months in a public ED in Hong Kong.The primary outcome was sepsis(Sepsis-3 definition)within 48 h of ED presentation.Using c-statistics and the DeLong test,we compared 11 EWSs,including the National Early Warning Score 2(NEWS2),Modified Early Warning Score,and Worthing Physiological Scoring System(WPS),etc.,and three shock indices(the shock index[SI],modified shock index[MSI],and diastolic shock index[DSI]),with Systemic Inflammatory Response Syndrome(SIRS)and quick Sequential Organ Failure Assessment(qSOFA)in predicting the primary outcome,intensive care unit admission,and mortality at different time points.RESULTS:We analyzed 601 patients,of whom 166(27.6%)developed sepsis.NEWS2 had the highest point estimate(area under the receiver operating characteristic curve[AUROC]0.75,95%CI 0.70-0.79)and was significantly better than SIRS,qSOFA,other EWSs and shock indices,except WPS,at predicting the primary outcome.However,the pooled sensitivity and specificity of NEWS2≥5 for the prediction of sepsis were 0.45(95%CI 0.37-0.52)and 0.88(95%CI 0.85-0.91),respectively.The discriminatory performance of all EWSs and shock indices declined when used to predict mortality at a more remote time point.CONCLUSION:NEWS2 compared favorably with other EWSs and shock indices in early sepsis prediction but its low sensitivity at the usual cut-off point requires further modification for sepsis screening.

Outcome prediction value of National Early Warning Score in septic patients with community-acquired pneumonia in emergency department: A single-center retrospective cohort study

BACKGROUND:To evaluate the accuracy of National Early Warning Score(NEWS)in predicting clinical outcomes(28-day mortality,intensive care unit[ICU]admission,and mechanical ventilation use)for septic patients with community-acquired pneumonia(CAP)compared with other commonly used severity scores(CURB65,Pneumonia Severity Index[PSI],Sequential Organ Failure Assessment[SOFA],quick SOFA[qSOFA],and Mortality in Emergency Department Sepsis[MEDS])and admission lactate level.METHODS:Adult patients diagnosed with CAP admitted between January 2017 and May 2019 with admission SOFA≥2 from baseline were enrolled.Demographic characteristics were collected.The primary outcome was the 28-day mortality after admission,and the secondary outcome included ICU admission and mechanical ventilation use.Outcome prediction value of parameters above was compared using receiver operating characteristics(ROC)curves.Cox regression analyses were carried out to determine the risk factors for the 28-day mortality.Kaplan-Meier survival curves were plotted and compared using optimal cut-off values of qSOFA and NEWS.RESULTS:Among the 340 enrolled patients,90 patients were dead after a 28-day follow-up,62 patients were admitted to ICU,and 84 patients underwent mechanical ventilation.Among single predictors,NEWS achieved the largest area under the receiver operating characteristic(AUROC)curve in predicting the 28-day mortality(0.861),ICU admission(0.895),and use of mechanical ventilation(0.873).NEWS+lactate,similar to MEDS+lactate,outperformed other combinations of severity score and admission lactate in predicting the 28-day mortality(AUROC 0.866)and ICU admission(AUROC 0.905),while NEWS+lactate did not outperform other combinations in predicting mechanical ventilation(AUROC 0.886).Admission lactate only improved the predicting performance of CURB65 and qSOFA in predicting the 28-day mortality and ICU admission.CONCLUSIONS:NEWS could be a valuable predictor in septic patients with CAP in emergency departments.Admission lactate did not predict well the outcomes or improve the severity scores.A qSOFA≥2 and a NEWS≥9 were strongly associated with the 28-day mortality,ICU admission,and mechanical ventilation of septic patients with CAP in the emergency departments.

Development and Validation of a Prediction Model on Adult Emergency Department Patients for Early Identification of Fulminant Myocarditis

Objective It is difficult to predict fulminant myocarditis at an early stage in the emergency department.The objective of this study was to construct and validate a simple prediction model for the early identification of fulminant myocarditis.Methods A total of 61 patients with fulminant myocarditis and 160 patients with acute myocarditis were enrolled in the training and internal validation cohorts.LASSO regression and multivariate logistic regression were selected to develop the prediction model.The selection of the model was based on overall performance and simplicity.A nomogram based on the optimal model was built,and its clinical usefulness was evaluated by decision curve analysis.The predictive model was further validated in an external validation group.Results The resulting prediction model was based on 4 factors:systolic blood pressure,troponin I,left ventricular ejection fraction,and ventricular wall motion abnormality.The Brier scores of the final model were 0.078 in the training data set and 0.061 in the internal testing data set,respectively.The C-indexes of the training data set and the testing data set were 0.952 and 0.968,respectively.Decision curve analysis showed that the nomogram model developed based on the 4 predictors above had a positive net benefit for predicting probability thresholds.In the external validation cohort,the model also showed good performance(Brier score=0.007,and C-index=0.989).Conclusion We developed and validated an early prediction model consisting of 4 clinical factors(systolic blood pressure,troponin I,left ventricular ejection fraction,and ventricular wall motion abnormality)to identify potential fulminant myocarditis patients in the emergency department.

Poor performance of the modified early warning score for predicting mortality in critically ill patients presenting to an emergency department

BACKGROUND:This study was undertaken to validate the use of the modified early warning score(MEWS) as a predictor of patient mortality and intensive care unit(ICU)/ high dependency(HD)admission in an Asian population.METHODS:The MEWS was applied to a retrospective cohort of 1 024 critically ill patients presenting to a large Asian tertiary emergency department(ED) between November 2006 and December2007.Individual MEWS was calculated based on vital signs parameters on arrival at ED.Outcomes of mortality and ICU/HD admission were obtained from hospital records.The ability of the composite MEWS and its individual components to predict mortality within 30 days from ED visit was assessed.Sensitivity,specificity,positive and negative predictive values were derived and compared with values from other cohorts.A MEWS of ≥4 was chosen as the cut-off value for poor prognosis based on previous studies.RESULTS:A total of 311(30.4%) critically ill patients were presented with a MEWS ≥4.Their mean age was 61.4 years(SD 18.1) with a male to female ratio of 1.10.Of the 311 patients,53(17%)died within 30 days,64(20.6%) were admitted to ICU and 86(27.7%) were admitted to HD.The area under the receiver operating characteristic curve was 0.71 with a sensitivity of 53.0%and a specificity of 72.1%in addition to a positive predictive value(PPV) of 17.0%and a negative predictive value(NPV)of 93.4%(MEWS cut-off of ≥4) for predicting mortality.CONCLUSION:The composite MEWS did not perform well in predicting poor patient outcomes for critically ill patients presenting to an ED.

Biological Early Warning and Emergency Management Support System for Water Pollution Accident

Water pollution accidents such as chemical spill and toxicants leach have become a serious issue in the world, which brings great threats to the aquatic ecosystem and drinking water safety. To detect unknown pollutions in time and take proper emergency actions, this study developed a system which integrated an online intelligent water quality monitoring device with a high efficient water quality model. The device is based on water quality probes and biological sensors which use fish motion as indicator. The numerical model is a combination of Euler and Lagrangian approaches. Finite element method is applied to simulate the flow field in an unstructured grid, and the pollutant be- haviours are traced through particle tracking. Oracle is used to manage the basic and monitoring data, and ArcSDE as well as ArcIMS technologies are applied to make the system WebGIS-based so as to improve the data receiving and dispatching efficiency. The developed system has been successfully operated in Douhe Reservoir and Taihu Lake, China, where drinking water intakes are installed. It is seen from the applications that the online intelligent water qual- ity monitoring and emergency support system can be of great support to emergency management.

Comparative research on the prognostic ability of improved early warning and APACHE Ⅱ evaluation for hospitalized patients in the emergency department

Objective： To compare the feasibility and applicability of predicting the prognosis of patients using the Early Warning Score（MEWS） system and the Acute Physiology and Chronic Health Evaluation（APACHE Ⅱ） system in the Emergency Department.Methods： Using a prospective study method, the APACHE Ⅱ and MEWS data for 640 patients hospitalized in the Emergency Internal Medicine Department were collected. The prognoses, two scores to predict the corresponding prediction index of sensitivity, specificity and positive predictive value for the prognosis,the negative predictive value and the ROC curve for predicting the prognosis were analyzed for all patients.Results： In the prediction of the risk of mortality, the MEWS system had a high resolution. The MEWS area under the ROC curve was 0.93. The area under the ROC curve for the APACHE score was 0.79, and the difference was statistically significant（Z =4.348, P 〈 0.01）.Conclusions： Both the MEWS and APACHE Ⅱ systems can be used to determine the severity of emergency patients and have a certain predictive value for the patient＇s mortality risk. However, the MEWS system is simple and quick to operate, making it a useful supplement for APACHE Ⅱ score.

A Novel Early Warning Model for Hand, Foot and Mouth Disease Prediction Based on a Graph Convolutional Network

Objectives Hand,foot and mouth disease(HFMD)is a widespread infectious disease that causes a significant disease burden on society.To achieve early intervention and to prevent outbreaks of disease,we propose a novel warning model that can accurately predict the incidence of HFMD.Methods We propose a spatial-temporal graph convolutional network(STGCN)that combines spatial factors for surrounding cities with historical incidence over a certain time period to predict the future occurrence of HFMD in Guangdong and Shandong between 2011 and 2019.The 2011-2018 data served as the training and verification set,while data from 2019 served as the prediction set.Six important parameters were selected and verified in this model and the deviation was displayed by the root mean square error and the mean absolute error.Results As the first application using a STGCN for disease forecasting,we succeeded in accurately predicting the incidence of HFMD over a 12-week period at the prefecture level,especially for cities of significant concern.Conclusions This model provides a novel approach for infectious disease prediction and may help health administrative departments implement effective control measures up to 3 months in advance,which may significantly reduce the morbidity associated with HFMD in the future.

Overview of China’s Emergency Early Warning in 2021

This research uses the 59th-70th national emergency early warning information release monthly report data of China to study the total number of emergency early warnings in 2021, the general situation of early warning categories and regional distribution differences. The results show that: 1) there are 387,075 early warning information in China in 2021, and the early warning has obvious seasonality. 2) The issuance of early warnings is obviously related to the seasons, and the issuance of early warnings varies from month to month. The issuance of one kind of early warning can easily lead to the issuance of another kind of early warning, that is, the early warnings are closely related, and some specific disasters are easy to cause secondary disasters. 3) There are differences in the number of early warnings in each province. 4) The reason for the peak of early warnings in July and the large proportion of red early warnings in Henan Province is the sudden heavy rainstorm in Henan Province in July;the physical mechanism of this heavy rainstorm is sufficient water vapor, strong uplifting movement and stable situation and a long existence time.

Recognition of Warning Signs by the Patient with Ischemic Stroke and Impact on Emergency Department Admission Times

The general objective of this study was to assess the impact of the recognition of warning, signs of ischemic stroke, on the reduction of emergency admission times and the improvement of the prognosis of the patient suffering from ischemic stroke in the emergency department of the Yaounde central hospital. Methods: This was a prospective study with a descriptive and analytical aim carried out in the medical emergency department of the central hospital of Yaounde over a period of 3 months, going from October to December 2020. All patients admitted to emergency departement, during this period, for a diagnosis of ischemic stroke confirmed by a brain CT scan were included in the study. The data was collected on a survey sheet divided into 3 sections: the patient, the assessment of his knowledge and his reaction to the stroke. The variables studied were socio-demographic data, cardiovascular risk factors and warning signs of stroke. Data analysis was conducted with Census and Survey Processing System (CSPRO). Results: During the study period, 62 patients met the inclusion criteria. Adults under 50 (41.94%) were the most represented age group. The mean age of the patients was 52.3 years with ranges ranging from 36 to 82 years. The sex ratio was 1.38 in favor of women. The most common unmodifiable cardiovascular risk factor was age over 55 years 44%. High blood pressure 61% was the most common modifiable risk factor. More than half of the study population had no knowledge of the warning signs of ischemic stroke. The most well-known warning sign was 40% mouth deformation. The anamnesis noted that the weakness of the hemibody, the limb or the leg was found in our cohort in 77% of cases, followed by balance disorder (73%) and speech disorder (50%). The main associated sign was asthenia 32%. Direct admission to hospital through the emergency department was the first call for alert 58%, followed by self-medication 32% and seeking help from a family member 31%. The emergency department admission time was over 4 hours 30 minutes in more than half of the cases. Conclusion: Ischemic stroke is an important cause of death in sub-Saharan Africa in the acute phase. Information, education and communication about the warning signs of ischemic stroke reduce the time to emergency room visits and improve the prognosis of these patients.

Forage Monitoring and Prediction Model for Early Warning Application over the East of Africa Region

Rangelands dominate arid and semi-arid lands of the Greater Horn of Africa(GHA)region,whereby pastoralism being the primary source of livelihood.The pastoral livelihood is affected by the seasonal variability of pasture and water resources.This research sought to design a grid-based forage monitoring and prediction model for the cross-border areas of the GHA region.A technique known as Geographically Weighted Regression was used in developing the model with monthly rainfall,temperature,soil moisture,and the Normalized Difference Vegetation Index(NDVI).Rainfall and soil moisture had a high correlation with NDVI,and thus formed the model development parameters.The model performed well in predicting the available forage biomass at each grid-cell with March-May and October-December seasons depicting a similar pattern but with a different magnitude in ton/ha.The output is critical for actionable early warning over the GHA region’s rangeland areas.It is expected that this mode can be used operationally for forage monitoring and prediction over the eastern Africa region and further guide the regional,national,sub-national actors and policymakers on issuing advisories before the season.

Continuous prediction method of earthquake early warning magnitude for high-speed railway based on support vector machine

Purpose–Using the strong motion data ofK-net in Japan,the continuous magnitude prediction method based on support vector machine(SVM)was studied.Design/methodology/approach–In the range of 0.5–10.0 s after the P-wave arrival,the prediction time window was established at an interval of 0.5 s.12 P-wave characteristic parameters were selected as the model input parameters to construct the earthquake early warning(EEW)magnitude prediction model(SVM-HRM)for high-speed railway based on SVM.Findings–The magnitude prediction results of the SVM-HRM model were compared with the traditional magnitude prediction model and the high-speed railway EEW current norm.Results show that at the 3.0 s time window,themagnitude prediction error of the SVM-HRMmodel is obviously smaller than that of the traditionalτc method and Pd method.The overestimation of small earthquakes is obviously improved,and the construction of the model is not affected by epicenter distance,so it has generalization performance.For earthquake events with themagnitude range of 3–5,the single station realization rate of the SVM-HRMmodel reaches 95%at 0.5 s after the arrival of P-wave,which is better than the first alarm realization rate norm required by“The TestMethod of EEW andMonitoring Systemfor High-Speed Railway.”For earthquake eventswithmagnitudes ranging from3 to 5,5 to 7 and 7 to 8,the single station realization rate of the SVM-HRM model is at 0.5 s,1.5 s and 0.5 s after the P-wave arrival,respectively,which is better than the realization rate norm of multiple stations.Originality/value–At the latest,1.5 s after the P-wave arrival,the SVM-HRM model can issue the first earthquake alarm that meets the norm of magnitude prediction realization rate,which meets the accuracy and continuity requirements of high-speed railway EEW magnitude prediction.

Yellow River Valley flood and drought disaster：spatial－temporal distribution prediction and early－warning

By means of analysing the historical data of flood-drought grade series in the past 2000 years(A.D.0-1900),especially in the last 5000 years (1470-1900) , this paper revealed the spatial-temporaldistribution features of severe flood and drought in Yellow River Valley. Statistical methods of varianceanalysis, probability transition and the principles of scale correspondence were employed tocomprehensively predicate 90's tendency of severe flood and drought in the Yellow River Valley. In addi-tion, this paper pointed out the possible breaching dikes, sectors and the flooding ranges by future's se-vere flood, meanwhile estimating the associated economic losses and impact to environment.

Real-time prediction of earthquake potential damage:A case study for the January 8,2022 M_(S) 6.9 Menyuan earthquake in Qinghai,China

It is critical to determine whether a site has potential damage in real-time after an earthquake occurs,which is a challenge in earthquake disaster reduction.Here,we propose a real-time Earthquake Potential Damage predictor(EPDor)based on predicting peak ground velocities(PGVs)of sites.The EPDor is composed of three parts:(1)predicting the magnitude of an earthquake and PGVs of triggered stations based on the machine learning prediction models;(2)predicting the PGVs at distant sites based on the empirical ground motion prediction equation;(3)generating the PGV map through predicting the PGV of each grid point based on an interpolation process of weighted average based on the predicted values in(1)and(2).We apply the EPDor to the 2022 M_(S) 6.9 Menyuan earthquake in Qinghai Province,China to predict its potential damage.Within the initial few seconds after the first station is triggered,the EPDor can determine directly whether there is potential damage for some sites to a certain degree.Hence,we infer that the EPDor has potential application for future earthquakes.Meanwhile,it also has potential in Chinese earthquake early warning system.

Preliminary studies on the dynamic prediction method of rainfall-triggered landslide

Rainfall-triggered landslides have posed significant threats to human lives and property each year in China. This paper proposed a meteorologicalgeotechnical early warning system GRAPES-LFM(GRAPES: Global and Regional Assimilation and Pr Ediction System; LFM: Landslide Forecast Model),basing on the GRAPES model and the landslide predicting model TRIGRS(Transient Rainfall Infiltration and Grid-based Regional Slope-Stability Model) for predicting rainfall-triggered landslides.This integrated system is evaluated in Dehua County,Fujian Province, where typhoon Bilis triggered widespread landslides in July 2006. The GRAPES model runs in 5 km×5 km horizontal resolution, and the initial fields and lateral boundaries are provided by NCEP(National Centers for Environmental Prediction) FNL(Final) Operational Global Analysis data. Quantitative precipitation forecasting products of the GRAPES model are downscaled to 25 m×25 m horizontal resolution by bilinear interpolation to drive the TRIGRS model. Results show that the observed areas locate in the high risk areas, and the GRAPES-LFM model could capture about 74% of the historical landslides with the rainfall intense 30mm/h. Meanwhile, this paper illustrates the relationship between the factor of safety(FS) and different rainfall patterns. GRAPES-LFM model enables us to further develop a regional, early warning dynamic prediction tool of rainfall-induced landslides.

Useful predictions ahead of large earthquakes and lessons learned for future progress

A significant basis for this article is the outcome of 4 multinational research projects which were carried out in South-West Iceland from 1988-2006,focusing on crustal processes that preceded two magnitude-6.6 earthquakes in June 2000.The seismic activity that preceded a magnitude 6.3 double-earthquake in the same seismic zone in 2008 is also significant,as well as other research work which helps to understand how observable crustal processes lead to earthquakes.A significant outcome is that it cannot be assumed that any two earthquakes have the same precursory processes.Therefore,statistical analysis of precursors of past earthquakes is of limited value for predicting future earthquakes.On the other hand,with highly sensitive seismic monitoring it is possible to observe the nucleation process for each specific large earthquake for long enough time for earth-realistic modeling of it and extrapolating towards the earthquake in time and space.In the Iceland crust,with its fluid-rock interactions,pre-earthquake activity on a scale of years is expected.This allows a long-term approach to prediction.We apply historical information and sensor-based data to find probable sources of earthquake nucleation.We monitor the nucleation process for a possibly impending earthquake at these sources,and then model the process to find its governing factors and extrapolate those in time and space,aiming towards finding hypocenter,fault-size,impact,and time of the impending earthquake.We refine our models by predicting frequent medium-sized earthquakes and compare the predictions with measurements.We predict how the possibly impending large earthquake would trigger earthquakes at other locations.Given the complexity of the crust,we must take all observed changes into account when developing models of pre-earthquake processes.The development of a continuously operating geo-watching system is discussed to link scientific evaluations to warnings that can be used by emergency authorities.

Effects of Estrogen Contamination on Human Cells: Modeling and Prediction Based on Michaelis-Menten Kinetics

In this paper, we propose a novel prevention strategy to alert citizens when water is contaminated by estro-gen. Epidemiological studies have shown that chronic exposure to high blood level of estrogen is associated with the development of breast cancer. The preventive strategy proposed in this paper is based on the predic-tion of estrogen effects on human living cells. Based on first principle insights, we develop in this work, a mathematical model for this prediction purpose. Dynamic measurements of cell proliferation response to es-trogen stimulation were continuously monitored by a real-time cell electronic sensor (RT-CES) and used in order to estimate the parameters of the model developed.

Non-destructive testing and pre-warning analysis on the quality of bolt support in deep roadways of mining districts

The bolt support quality of coal roadways is one of the important factors for the efficiency and security of coal production. By means of a self-developed technique and equipment of random non-destructive testing, non-destructive detection and pre-warning analysis on the quality of bolt support in deep roadways of mining districts were performed in a number of mining areas. The measured data were obtained in the detection instances of abnormal in-situ stress and support invalidation etc. The corresponding relation between axial bolt load variation and roadway surrounding rock deformation and stability was summarized in different mining service stages. Pre-warning technology of roadway surrounding rock stability is proposed based on the detection of axial bolt load. Meanwhile, pre-warning indicators of axial bolt load in different mining service stages are offered and some successful pre-warning cases are also illustrated.The research results show that the change rules of axial bolt load in different mining service stages are quite similar in different mining areas. The change of axial bolt load is in accord with the adjustment of surrounding rock stress, which can consequently reflect the deformation and stability state of roadway surrounding rock. Through the detection of axial bolt load in different sections of roadways, the status of real-time bolt support quality can be reflected; meanwhile, the rationality of bolt support design can be evaluated which provides reference for bolting parameters optimization.

Real-Time Warning System of Regional Landslides Supported by WEGISB and Its Application in Typhoon Rananim,Zhejiang Province,China

As one of the provinces of highest economic growth in coastal China,Zhejiang Province is experiencing serious geological disasters during the past development of economy.The main kinds of geo-hazards include landslides,rock falls and debris-flows in Zhejiang Province,which are mainly induced by intensive rainfall during typhoon season or by long-term rainfall from May to June every year.Thus,

Early Warning of Foundation Settlement Deformation for Ballastless High-Speed Railway Tracks

There has been rapid development of high-speed railway lines, especially passenger-dedicated railway lines, in China. Trains are traveling at speeds exceeding 250 km per hour and they require highly smooth tracks to ensure safety. However, there have been no in-depth studies on the early warning of the settlement of high-speed railway lines in China or abroad. Most methods use a simple model based on data processing and decision rules. The core issues of early warning lie in the science and rationality of decision rules. The present paper therefore investigates novel and critical indexes for the warning of settlement under high-speed railway lines according to existing norms and field data, and several essential indexes of deformation warning are suggested through theoretical and experimental analysis.

Services and Applications Based on Mobile User’s Location Detection and Prediction

Many wireless applications are deployed and available to customers via their mobile phones. Variety of these applications and services are based on determination of the current or future location of mobile user. Location based services (LBS) are one of the vital applications which are subdivided into two main categories: economical category and public category. Economic applications include mobile marketing, entertainment and tracking applications. Whereas, emergency cases, safety, traffic management, Muslims’ applications and public information applications are sort of public applications. The first part of the paper presents a new proposed system with developed procedure to recreate public and economic applications with high positioning accuracy and good authentication of users’ data. The developed system is created to enhance both location based services and network allocation resources within mobile network platform using either normal or GPS supported mobile equipment. The second part of the paper introduces future location prediction of mobile user dependent applications. New algorithm is developed depending on utilizing both intra-cell Movement Pattern algorithm (ICMP) [1] and hybrid uplink time Difference of Arrival and Assisted GPS technique (UTDOA_AGPS) [2]. It has been noticed that ICMP algorithm outperforms other future location prediction algorithms with high precision and within suitable time (less than 220) msec. However, UTDOA_AGPS guarantees high precession of mobile user independent of the surrounding environment. The proposed technique is used to enhance reliability and efficiency of location based services using cellular network platform.