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      Practical clinical and radiological models to diagnose COVID-19 based on a multicentric teleradiological emergency chest CT cohort

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          Abstract

          Our aim was to develop practical models built with simple clinical and radiological features to help diagnosing Coronavirus disease 2019 [COVID-19] in a real-life emergency cohort. To do so, 513 consecutive adult patients suspected of having COVID-19 from 15 emergency departments from 2020-03-13 to 2020-04-14 were included as long as chest CT-scans and real-time polymerase chain reaction (RT-PCR) results were available (244 [47.6%] with a positive RT-PCR). Immediately after their acquisition, the chest CTs were prospectively interpreted by on-call teleradiologists (OCTRs) and systematically reviewed within one week by another senior teleradiologist. Each OCTR reading was concluded using a 5-point scale: normal, non-infectious, infectious non-COVID-19, indeterminate and highly suspicious of COVID-19. The senior reading reported the lesions’ semiology, distribution, extent and differential diagnoses. After pre-filtering clinical and radiological features through univariate Chi-2, Fisher or Student t-tests (as appropriate), multivariate stepwise logistic regression (Step-LR) and classification tree (CART) models to predict a positive RT-PCR were trained on 412 patients, validated on an independent cohort of 101 patients and compared with the OCTR performances (295 and 71 with available clinical data, respectively) through area under the receiver operating characteristics curves (AUC). Regarding models elaborated on radiological variables alone, best performances were reached with the CART model (i.e., AUC = 0.92 [versus 0.88 for OCTR], sensitivity = 0.77, specificity = 0.94) while step-LR provided the highest AUC with clinical-radiological variables (AUC = 0.93 [versus 0.86 for OCTR], sensitivity = 0.82, specificity = 0.91). Hence, these two simple models, depending on the availability of clinical data, provided high performances to diagnose positive RT-PCR and could be used by any radiologist to support, modulate and communicate their conclusion in case of COVID-19 suspicion. Practically, using clinical and radiological variables (GGO, fever, presence of fibrotic bands, presence of diffuse lesions, predominant peripheral distribution) can accurately predict RT-PCR status.

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          Most cited references 30

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          Decision curve analysis: a novel method for evaluating prediction models.

          Diagnostic and prognostic models are typically evaluated with measures of accuracy that do not address clinical consequences. Decision-analytic techniques allow assessment of clinical outcomes but often require collection of additional information and may be cumbersome to apply to models that yield a continuous result. The authors sought a method for evaluating and comparing prediction models that incorporates clinical consequences,requires only the data set on which the models are tested,and can be applied to models that have either continuous or dichotomous results. The authors describe decision curve analysis, a simple, novel method of evaluating predictive models. They start by assuming that the threshold probability of a disease or event at which a patient would opt for treatment is informative of how the patient weighs the relative harms of a false-positive and a false-negative prediction. This theoretical relationship is then used to derive the net benefit of the model across different threshold probabilities. Plotting net benefit against threshold probability yields the "decision curve." The authors apply the method to models for the prediction of seminal vesicle invasion in prostate cancer patients. Decision curve analysis identified the range of threshold probabilities in which a model was of value, the magnitude of benefit, and which of several models was optimal. Decision curve analysis is a suitable method for evaluating alternative diagnostic and prognostic strategies that has advantages over other commonly used measures and techniques.
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            A simple, step-by-step guide to interpreting decision curve analysis

            Background Decision curve analysis is a method to evaluate prediction models and diagnostic tests that was introduced in a 2006 publication. Decision curves are now commonly reported in the literature, but there remains widespread misunderstanding of and confusion about what they mean. Summary of commentary In this paper, we present a didactic, step-by-step introduction to interpreting a decision curve analysis and answer some common questions about the method. We argue that many of the difficulties with interpreting decision curves can be solved by relabeling the y-axis as “benefit” and the x-axis as “preference.” A model or test can be recommended for clinical use if it has the highest level of benefit across a range of clinically reasonable preferences. Conclusion Decision curves are readily interpretable if readers and authors follow a few simple guidelines.
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              Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China

               C Huang,  Y. Wang,  X Li (2020)
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                Author and article information

                Contributors
                g.gorincour@imadis.fr
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                26 April 2021
                26 April 2021
                2021
                : 11
                Affiliations
                [1 ]Imadis Teleradiology, 48 Rue Quivogne, 69002 Lyon, France
                [2 ]Centre Aquitain d’Imagerie, 64 rue de Canolle, 33000 Bordeaux, France
                [3 ]Modelisation in Oncology (MOnc) Team, UMR 5251, INRIA Bordeaux-Sud-Ouest, CNRS, Université de Bordeaux, 33405 Talence, France
                [4 ]Deeplink Medical, 22 rue Seguin, 69002 Lyon, France
                [5 ]Norimagerie, Caluire et Cuire, France
                [6 ]Imagerie Médicale du Mâconnais, Mâcon, France
                [7 ]Ramsay Générale de Santé, Clinique de la Sauvegarde, Lyon, France
                [8 ]Centre d’Imagerie Médicale Pourcel, Bergson, et de la clinique du Parc, Saint Etienne, France
                [9 ]GRID grid.492693.3, ISNI 0000 0004 0622 4363, Ramsay Générale de Santé, Hôpital Privé Jean Mermoz, ; Lyon, France
                [10 ]Service d’imagerie Diagnostique et Interventionnelle de l’adulte, Groupe Hospitalier Pellegrin, Place Amélie-Raba-Léon, 33076 Bordeaux cedex, France
                [11 ]GRID grid.489921.f, Department of Diagnostic and Interventional Imaging, , Centre Hospitalier Saint-Joseph Saint-Luc, ; 20 Quai Claude Bernard, 69007 Lyon, France
                [12 ]GRID grid.418116.b, ISNI 0000 0001 0200 3174, Department of Radiology, Centre Léon Bérard, ; Lyon, France
                [13 ]Ramsay Générale de Santé, Clinique Convert, Bourg-en-Bresse, France
                [14 ]Department of Radiology, Hopital Nord-Ouest, Villefranche-sur-Saône, France
                [15 ]ELSAN, Clinique Bouchard, Marseille, France
                Article
                88053
                10.1038/s41598-021-88053-6
                8076229
                33903624
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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                diseases, health care, risk factors, mathematics and computing

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