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      Risk Prediction Score for Pediatric Patients with Suspected Ebola Virus Disease

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          Abstract

          Rapid diagnostic tools for children with Ebola virus disease (EVD) are needed to expedite isolation and treatment. To evaluate a predictive diagnostic tool, we examined retrospective data (2014–2015) from the International Medical Corps Ebola Treatment Centers in West Africa. We incorporated statistically derived candidate predictors into a 7-point Pediatric Ebola Risk Score. Evidence of bleeding or having known or no known Ebola contacts was positively associated with an EVD diagnosis, whereas abdominal pain was negatively associated. Model discrimination using area under the curve (AUC) was 0.87, which outperforms the World Health Organization criteria (AUC 0.56). External validation, performed by using data from International Medical Corps Ebola Treatment Centers in the Democratic Republic of the Congo during 2018–2019, showed an AUC of 0.70. External validation showed that discrimination achieved by using World Health Organization criteria was similar; however, the Pediatric Ebola Risk Score is simpler to use.

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          Most cited references13

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          Assessing the performance of prediction models: a framework for traditional and novel measures.

          The performance of prediction models can be assessed using a variety of methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic [ROC] curve), and goodness-of-fit statistics for calibration.Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision-analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions.We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration, we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n = 544 for model development, n = 273 for external validation).We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.
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            A Randomized, Controlled Trial of Ebola Virus Disease Therapeutics

            Although several experimental therapeutics for Ebola virus disease (EVD) have been developed, the safety and efficacy of the most promising therapies need to be assessed in the context of a randomized, controlled trial.
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              Presentation of multivariate data for clinical use: The Framingham Study risk score functions.

              The Framingham Heart Study has been a leader in the development and dissemination of multivariable statistical models to estimate the risk of coronary heart disease. These models quantify the impact of measurable and modifiable risk factors on the development of coronary heart disease and can be used to generate estimates of risk of coronary heart disease over a predetermined period, for example the next 10 years. We developed a system, which we call a points system, for making these complex statistical models useful to practitioners. The system is easy to use, it does not require a calculator or computer and it simplifies the estimation of risk based on complex statistical models. This system represents an effort to make available a tool for clinicians to aid in their decision-making process regarding treatment and to assist them in motivating patients toward healthy behaviours. The system is also readily available to patients who can easily estimate their own coronary heart disease risk and monitor this risk over time. Copyright 2004 John Wiley & Sons, Ltd.
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                Author and article information

                Journal
                Emerg Infect Dis
                Emerg Infect Dis
                EID
                Emerging Infectious Diseases
                Centers for Disease Control and Prevention
                1080-6040
                1080-6059
                June 2022
                : 28
                : 6
                : 1189-1197
                Affiliations
                [1]Brown Emergency Medicine, Providence, Rhode Island, USA (A.E. Genisca, H. Vaishnav, A.C. Levine);
                [2]Alpert Medical School of Brown University, Providence (A.E. Genisca, A.C. Levine, I.C. Michelow);
                [3]University of Georgia, Athens, Georgia, USA (T.C. Chu);
                [4]Brown University, Providence (L. Huang, M. Adeniji);
                [5]Rhode Island Hospital, Providence (M. Gainey);
                [6]International Medical Corps, Goma, Democratic Republic of the Congo (E.N. Mbong, R. Laghari, F. Nganga, R.F. Muhayangabo);
                [7]Ministry of Health, Monrovia, Liberia (S.B. Kennedy);
                [8]International Medical Corps, Washington, DC, USA (S.M. Perera);
                [9]University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA (A. Colubri)
                Author notes
                Address for correspondence: Ian C. Michelow, Connecticut Children’s Medical Center, 85 Seymour St, Ste 816, Hartford, CT 06106, USA; email: imichelow@ 123456connecticutchildrens.org ; Alicia E. Genisca, 55 Claverick St, 2nd Fl, Providence, RI 02903, USA; email: alicia.genisca@ 123456brownphysicians.org
                Article
                21-2265
                10.3201/eid2806.212265
                9155869
                35608611
                d3608ece-d9ef-48a7-b0ed-092ab77e14bc
                Copyright @ 2022

                Preventing Chronic Disease is a publication of the U.S. Government. This publication is in the public domain and is therefore without copyright. All text from this work may be reprinted freely. Use of these materials should be properly cited.

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                Research
                Research
                Risk Prediction Score for Pediatric Patients with Suspected Ebola Virus Disease

                Infectious disease & Microbiology
                ebola virus disease,risk prediction score,children,viruses,west africa

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