Trends and prediction of antimicrobial susceptibility in urinary bacteria isolated in European emergency departments: the EuroUTI 2010-2016 Study

2019 ◽  
Vol 74 (10) ◽  
pp. 3069-3076 ◽  
Author(s):  
Alice Quaegebeur ◽  
Loïc Brunard ◽  
François Javaudin ◽  
Marie-Anne Vibet ◽  
Pascale Bemer ◽  
...  
Keyword(s):  
Time Series ◽  
Positive Predictive Value ◽  
Time Series Analysis ◽  
Predictive Value ◽  
Emergency Departments ◽  
High Probability ◽  
Logistic Models ◽  
Multivariate Models ◽  
Urinary Isolates ◽  
Age And Sex

Abstract Objectives To assess recent trends in susceptibility to antibiotics among urinary isolates isolated in European emergency departments (EDs) and to identify isolates with a high (90% or more) predicted probability of susceptibility to fluoroquinolones or third-generation cephalosporins (3GCs). Methods In this cross-sectional study, we included urine cultures obtained from adult patients between 2010 and 2016 in 24 European EDs. Temporal trends were assessed using time-series analysis and multivariate logistic models. Multivariate logistic models were also used to predict susceptibility to fluoroquinolones or 3GCs from patient age and sex, year, month and ED. Results We included 88242 isolates. Time-series analysis found a significant increase in susceptibility to fluoroquinolones and no significant trend for susceptibility to 3GCs. Adjusting for patient age and sex, ED and organism, multivariate models showed that susceptibility to 3GCs decreased from 2014 to 2016, while susceptibility to fluoroquinolones increased in 2015 and 2016. Among isolates from 2016, multivariate models predicted high probability of susceptibility to fluoroquinolones in 11% of isolates (positive predictive value 91%) and a high probability of susceptibility to 3GCs in 35% of isolates (positive predictive value 94%). Conclusions Susceptibility of ED urinary isolates to fluoroquinolones increased from 2014, while susceptibility to 3GCs decreased from 2015. Predictive models identified isolates with a high probability of susceptibility to fluoroquinolones or 3GCs. The ability of such models to guide the empirical treatment of pyelonephritis in the ED remains to be determined.

scholarly journals Modeling and Prediction of COVID-19 Outbreak in India

2020 ◽  
Author(s):  
Saurabh Kumar ◽  
Varun Agiwal ◽  
Ashok Kumar ◽  
Jitendra Kumar
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Predictive Models ◽  
Predictive Value ◽  
Arima Models ◽  
Epidemiological Modeling ◽  
Modeling And Prediction ◽  
Propagation Rule ◽  
Administrative Strategies ◽  
Indian Data

As the outbreak of coronavirus disease 2019 (COVID-19) is continuously increasing in India, so epidemiological modeling of COVID-19 data is urgently required for administrative strategies. Time series and is capable to predict future observations by modeling the data based on past and present data. Here, we have modeled the epidemiological COVID-19 Indian data using various models. Based on the collected COVID-19 outbreak data, we try to find the propagation rule of this outbreak disease and predict the outbreak situations in India. For India data, the time series model gives the best results in the form of predication as compared to other models for all variables of COVID-19. For new cases, new deaths, total cases and total deaths, the best fitted ARIMA models are as follows: ARIMA(0,2,3), ARIMA(0,1,1), ARIMA(0,2,0) and ARIMA(0,2,1). Based on time series analysis, we predict all variables for next month and conclude that the predictive value of Indian COVID-19 data of total cases is more than 20 lakhs with more than 43 thousand total deaths. The present chapter recommended that a comparison between various predictive models provide the accurate and better forecast value of the COVID-19 outbreak for all study variables.

A Measure of Landscapes

1996 ◽  
Vol 4 (4) ◽  
pp. 335-360 ◽  
Author(s):  
Wim Hordijk
Keyword(s):  
Time Series ◽  
Random Walk ◽  
Time Series Analysis ◽  
Point Mutation ◽  
Predictive Value ◽  
Fitness Landscape ◽  
Fitness Landscapes ◽  
Landscape Analysis ◽  
Correlation Structure ◽  
Fitness Landscape Analysis

This paper introduces a statistical fitness landscape analysis, based on Weinberger's random walk method and on a time series analysis known as the Box-Jenkins approach, to measure and express the correlation structure of fitness landscapes. The analysis has some additions to and advantages over previous methods for measuring this structure. The analysis is demonstrated on fitness landscapes constructed with Kauffman's NK-model, using two operators (point mutation and a form of crossover) and a combination of the two. Furthermore, the predictive value of the method is shown.

scholarly journals Presentations to Alberta Emergency Departments for Asthma: A Time Series Analysis

2015 ◽  
Vol 22 (8) ◽  
pp. 942-949 ◽  
Author(s):  
Rhonda J. Rosychuk ◽  
Erik Youngson ◽  
Brian H. Rowe
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Emergency Departments ◽  
Series Analysis

scholarly journals Usefulness of the 5MPB-Toledo model to predict bacteremia in patients with community-acquired pneumonia in the Emergency Department

2021 ◽  
Author(s):  
Agustín Julián-Jiménez ◽  
◽  
Juan González del Castillo ◽  
Eric Jorge García-Lamberechts ◽  
Rafael Rubio Díaz ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Predictive Value ◽  
Emergency Departments ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Community Acquired Pneumonia ◽  
Prognostic Performance ◽  
Operating Characteristic Curve

Objective. To analyse a new risk score to predict bacteremia in the patients with Community-acquired Pneumonia (CAP) in the emergency departments. Patients and methods. Prospective and multicenter observational cohort study of the blood cultures ordered in 74 Spanish emergency departments for patients with CAP seen from November 1, 2019, to March 31, 2020. The predictive ability of the model was analyzed with the area under the Receiver Operating Characteristic curve (AUC-ROC). The prognostic performance for true bacteremia was calculated with the chosen cut-off for getting the sensitivity, specificity, positive predictive value and negative predictive value. Results. A total of 1,020 blood samples wered cultured. True cases of bacteremia were confirmed in 162 (15.9%). The remaining 858 cultures (84.1%) wered negative. And, 59 (5.8%) were judged to be contaminated. The model´s area under the receiver operating characteristic curve was 0.915 (95% CI, 0.898-0.933). The prognostic performance with a model´s cut-off value of ≥ 5 points achieved 97.5% (95% CI, 95.1-99.9) sensitivity, 73.2% (95% CI, 70.2-76.2) specificity, 40.9% (95% CI, 36.4-45.1) positive predictive value and 99.4% (95% CI, 99.1-99.8) negative predictive value. Conclusion. The 5MPB-Toledo score is useful for predicting bacteremia in the patients with CAP seen in the emergency departments.

scholarly journals HINTS protocole and it’s accuracy to identify posterior circulation stroke: a review

2021 ◽  
Author(s):  
Eduardo Sales Loureiro ◽  
Luna Vasconcelos Felippe ◽  
Ana Luiza Cotta Mourão Guimarães ◽  
Anna Carolina Dockhorn de Menezes Carvalho Costa
Keyword(s):  
Hearing Loss ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Emergency Departments ◽  
Posterior Circulation ◽  
Clinical Use ◽  
Emergency Physicians ◽  
Practical Application ◽  
Posterior Circulation Stroke ◽  
Acute Vestibular Syndrome

Introduction: The HINTS protocol is important to differentiate peripheral from central vertigo in Acute Vestibular Syndrome (AVS). There are studies that show almost 1/3 of patients have posterior circulation stroke. It is important to investigate why this happens. Objectives: Review the accuracy of HINTS test in the diagnosis of posterior circulation stroke. Methods: Review conducted in PubMed using key words “HINTS AND ACCURACY AND STROKE”. The search found 9 articles, 7 of which were included. Results: Tehrani et al. (2014) studied HINTS’s accuracy when associated with hearing loss, resulting on a bigger accuracy than MRI. Newman- Toker et al. (2013) found that HINTS score was superior than ABCD2 in identifying AVS caused by stroke. From Carmona et al. (2016), HINTS had 100 % sensibility and 94,4% specificity and emphasized ataxia evaluation’s importance, once all patients with central etiology had a grade of ataxia. Krishnan et al. (2019) HINTS had 59,9% Negative Predictive Value (NPV) and 97,2 % Positive Predictive Value (PPV) related to stroke, same PPV was found by Sankalia et al. (2021). Ohle et al. (2020) observed that HINTS done by a neurologist was more accurate than studies that mixed neurologists and emergency physicians. Dmitriew et al. (2021) saw that HINTS was used wrongly in non-specific emergency departments, once only 3,1% patients tested had AVS and 96,9% wrongly tested had false positives. Conclusion: The HINTS is a valuable instrument in the clinical use and the training for better practical application needs incentive in emergency departments.

scholarly journals Evaluation of a Candida Antigen Detection Method (Cand-Tec): Experience from a University Teaching Hospital

1992 ◽  
Vol 3 (4) ◽  
pp. 167-172 ◽  
Author(s):  
Todd J Anderson ◽  
Heather E Bryant ◽  
Deirdre L Church
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Sensitivity And Specificity ◽  
Invasive Candidiasis ◽  
Predictive Value ◽  
High Probability ◽  
University Teaching ◽  
Candidal Infection

The usefulness of a rapid latex agglutination method for the detection of Candida antigen (Cand-Tec; Ramco Laboratories. Texas) was retrospectively assessed in a university teaching hospital over a one year period. Patients were enrolled when the managing physician requested Cand-Tec testing for confirmation of possible invasive candidal infection. The majority of patients were critically ill; 56% were in the intensive care unit, and 30% subsequently died. Analyses were available from 79 patients and 125 samples. Thirty-three patients were diagnosed as having either definite invasive candidiasis or a high probability of infection based upon clinical, microbiological and tissue criteria. The sensitivity and specificity of Candida antigen detection was determined for doubling titres ≥1:4 to ≥1:16. If a titre of ≥1:4 was used as the diagnostic cut-off level for definite candidal infection, overall sensitivity and specificity were 77% and 69%, respectively, while the positive predictive value was 48%, and the negative predictive value 89%. When patients with a high probability of invasive candidal infection were included in analyses for a cut-off titre of ≥1:4, overall sensitivity and specificity were 70% and 69%, respectively, while the positive predictive value was 68%, and the negative predictive value 71%. The usefulness of the Cand-Tec test was not improved further for any subgroup of patients (including those in the intensive care unit), nor by following serial titres in individual patients. In this experience, the Cand-Tec test did not add enough information to include definitively or exclude invasive candidiasis in this high risk general patient population.

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