13
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Related collections

          Most cited references26

          • Record: found
          • Abstract: found
          • Article: not found

          Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases

          Background Chest CT is used for diagnosis of 2019 novel coronavirus disease (COVID-19), as an important complement to the reverse-transcription polymerase chain reaction (RT-PCR) tests. Purpose To investigate the diagnostic value and consistency of chest CT as compared with comparison to RT-PCR assay in COVID-19. Methods From January 6 to February 6, 2020, 1014 patients in Wuhan, China who underwent both chest CT and RT-PCR tests were included. With RT-PCR as reference standard, the performance of chest CT in diagnosing COVID-19 was assessed. Besides, for patients with multiple RT-PCR assays, the dynamic conversion of RT-PCR results (negative to positive, positive to negative, respectively) was analyzed as compared with serial chest CT scans for those with time-interval of 4 days or more. Results Of 1014 patients, 59% (601/1014) had positive RT-PCR results, and 88% (888/1014) had positive chest CT scans. The sensitivity of chest CT in suggesting COVID-19 was 97% (95%CI, 95-98%, 580/601 patients) based on positive RT-PCR results. In patients with negative RT-PCR results, 75% (308/413) had positive chest CT findings; of 308, 48% were considered as highly likely cases, with 33% as probable cases. By analysis of serial RT-PCR assays and CT scans, the mean interval time between the initial negative to positive RT-PCR results was 5.1 ± 1.5 days; the initial positive to subsequent negative RT-PCR result was 6.9 ± 2.3 days). 60% to 93% of cases had initial positive CT consistent with COVID-19 prior (or parallel) to the initial positive RT-PCR results. 42% (24/57) cases showed improvement in follow-up chest CT scans before the RT-PCR results turning negative. Conclusion Chest CT has a high sensitivity for diagnosis of COVID-19. Chest CT may be considered as a primary tool for the current COVID-19 detection in epidemic areas. A translation of this abstract in Farsi is available in the supplement. - ترجمه چکیده این مقاله به فارسی، در ضمیمه موجود است.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR

            Summary In a series of 51 patients with chest CT and RT-PCR assay performed within 3 days, the sensitivity of CT for COVID-19 infection was 98% compared to RT-PCR sensitivity of 71% (p<.001). Introduction In December 2019, an outbreak of unexplained pneumonia in Wuhan [1] was caused by a new coronavirus infection named COVID-19 (Corona Virus Disease 2019). Noncontrast chest CT may be considered for early diagnosis of viral disease, although viral nucleic acid detection using real-time polymerase chain reaction (RT-PCR) remains the standard of reference. Chung et al. reported that chest CT may be negative for viral pneumonia of COVID-19 [2] at initial presentation (3/21 patients). Recently, Xie reported 5/167 (3%) patients who had negative RT-PCR for COVID-19 at initial presentation despite chest CT findings typical of viral pneumonia [3]. The purpose of this study was to compare the sensitivity of chest CT and viral nucleic acid assay at initial patient presentation. Materials and Methods The retrospective analysis was approved by institutional review board and patient consent was waived. Patients at Taizhou Enze Medical Center (Group) Enze Hospital were evaluated from January 19, 2020 to February 4, 2020. During this period, chest CT and RT-PCR (Shanghai ZJ Bio-Tech Co, Ltd, Shanghai, China) was performed for consecutive patients who presented with a history of 1) travel or residential history in Wuhan or local endemic areas or contact with individuals with individuals with fever or respiratory symptoms from these areas within 14 days and 2) had fever or acute respiratory symptoms of unknown cause. In the case of an initial negative RT-PCR test, repeat testing was performed at intervals of 1 day or more. Of these patients, we included all patients who had both noncontrast chest CT scan (slice thickness, 5mm) and RT-PCR testing within an interval of 3 days or less and who had an eventual confirmed diagnosis of COVID-19 infection by RT-PCR testing (Figure 1). Typical and atypical chest CT findings were recorded according to CT features previously described for COVD-19 (4,5). The detection rate of COVID-19 infection based on the initial chest CT and RT-PCR was compared. Statistical analysis was performed using McNemar Chi-squared test with significance at the p <.05 level. Figure 1: Flowchart for patient inclusion. Results 51 patients (29 men and 22 women) were included with median age of 45 (interquartile range, 39- 55) years. All patients had throat swab (45 patients) or sputum samples (6 patients) followed by one or more RT-PCR assays. The average time from initial disease onset to CT was 3 +/- 3 days; the average time from initial disease onset to RT-PCR testing was 3 +/- 3 days. 36/51 patients had initial positive RT-PCR for COVID-19. 12/51 patients had COVID-19 confirmed by two RT-PCR nucleic acid tests (1 to 2 days), 2 patients by three tests (2-5 days) and 1 patient by four tests (7 days) after initial onset. 50/51 (98%) patients had evidence of abnormal CT compatible with viral pneumonia at baseline while one patient had a normal CT. Of 50 patients with abnormal CT, 36 (72%) had typical CT manifestations (e.g. peripheral, subpleural ground glass opacities, often in the lower lobes (Figure 2) and 14 (28%) had atypical CT manifestations (Figure 3) [2]. In this patient sample, difference in detection rate for initial CT (50/51 [98%, 95% CI 90-100%]) patients was greater than first RT-PCR (36/51 [71%, 95%CI 56-83%]) patients (p<.001). Figure 2a: Examples of typical chest CT findings compatible with COVID-19 pneumonia in patients with epidemiological and clinical presentation suspicious for COVID-19 infection. A, male, 74 years old with fever and cough for 5 days. Axial chest CT shows bilateral subpleural ground glass opacities (GGO). B, female, 55 years old, with fever and cough for 7 days. Axial chest CT shows extensive bilateral ground glass opacities and consolidation; C, male, 43 years old, presenting with fever and cough for 1 week. Axial chest CT shows small bilateral areas of peripheral GGO with minimal consolidation; D, female, 43 years old presenting with fever with cough for 5 days. Axial chest CT shows a right lung region of peripheral consolidation. Figure 2b: Examples of typical chest CT findings compatible with COVID-19 pneumonia in patients with epidemiological and clinical presentation suspicious for COVID-19 infection. A, male, 74 years old with fever and cough for 5 days. Axial chest CT shows bilateral subpleural ground glass opacities (GGO). B, female, 55 years old, with fever and cough for 7 days. Axial chest CT shows extensive bilateral ground glass opacities and consolidation; C, male, 43 years old, presenting with fever and cough for 1 week. Axial chest CT shows small bilateral areas of peripheral GGO with minimal consolidation; D, female, 43 years old presenting with fever with cough for 5 days. Axial chest CT shows a right lung region of peripheral consolidation. Figure 2c: Examples of typical chest CT findings compatible with COVID-19 pneumonia in patients with epidemiological and clinical presentation suspicious for COVID-19 infection. A, male, 74 years old with fever and cough for 5 days. Axial chest CT shows bilateral subpleural ground glass opacities (GGO). B, female, 55 years old, with fever and cough for 7 days. Axial chest CT shows extensive bilateral ground glass opacities and consolidation; C, male, 43 years old, presenting with fever and cough for 1 week. Axial chest CT shows small bilateral areas of peripheral GGO with minimal consolidation; D, female, 43 years old presenting with fever with cough for 5 days. Axial chest CT shows a right lung region of peripheral consolidation. Figure 2d: Examples of typical chest CT findings compatible with COVID-19 pneumonia in patients with epidemiological and clinical presentation suspicious for COVID-19 infection. A, male, 74 years old with fever and cough for 5 days. Axial chest CT shows bilateral subpleural ground glass opacities (GGO). B, female, 55 years old, with fever and cough for 7 days. Axial chest CT shows extensive bilateral ground glass opacities and consolidation; C, male, 43 years old, presenting with fever and cough for 1 week. Axial chest CT shows small bilateral areas of peripheral GGO with minimal consolidation; D, female, 43 years old presenting with fever with cough for 5 days. Axial chest CT shows a right lung region of peripheral consolidation. Figure 3a: Examples of chest CT findings less commonly reported in COVID-19 infection (atypical) in patients with epidemiological and clinical presentation suspicious for COVID-19 infection. A, male, 36 years old with cough for 3 days. Axial chest CT shows a small focal and central ground glass opacity (GGO) in the right upper lobe; B, female, 40 years old. Axial chest CT shows small peripheral linear opacities bilaterally. C, male, 38 years old. Axial chest CT shows a GGO in the central left lower lobe; D, male, 31 years old with fever for 1 day. Axial chest CT shows a linear opacity in the left lower lateral mid lung. Figure 3b: Examples of chest CT findings less commonly reported in COVID-19 infection (atypical) in patients with epidemiological and clinical presentation suspicious for COVID-19 infection. A, male, 36 years old with cough for 3 days. Axial chest CT shows a small focal and central ground glass opacity (GGO) in the right upper lobe; B, female, 40 years old. Axial chest CT shows small peripheral linear opacities bilaterally. C, male, 38 years old. Axial chest CT shows a GGO in the central left lower lobe; D, male, 31 years old with fever for 1 day. Axial chest CT shows a linear opacity in the left lower lateral mid lung. Figure 3c: Examples of chest CT findings less commonly reported in COVID-19 infection (atypical) in patients with epidemiological and clinical presentation suspicious for COVID-19 infection. A, male, 36 years old with cough for 3 days. Axial chest CT shows a small focal and central ground glass opacity (GGO) in the right upper lobe; B, female, 40 years old. Axial chest CT shows small peripheral linear opacities bilaterally. C, male, 38 years old. Axial chest CT shows a GGO in the central left lower lobe; D, male, 31 years old with fever for 1 day. Axial chest CT shows a linear opacity in the left lower lateral mid lung. Figure 3d: Examples of chest CT findings less commonly reported in COVID-19 infection (atypical) in patients with epidemiological and clinical presentation suspicious for COVID-19 infection. A, male, 36 years old with cough for 3 days. Axial chest CT shows a small focal and central ground glass opacity (GGO) in the right upper lobe; B, female, 40 years old. Axial chest CT shows small peripheral linear opacities bilaterally. C, male, 38 years old. Axial chest CT shows a GGO in the central left lower lobe; D, male, 31 years old with fever for 1 day. Axial chest CT shows a linear opacity in the left lower lateral mid lung. Discussion In our series, the sensitivity of chest CT was greater than that of RT-PCR (98% vs 71%, respectively, p<.001). The reasons for the low efficiency of viral nucleic acid detection may include: 1) immature development of nucleic acid detection technology; 2) variation in detection rate from different manufacturers; 3) low patient viral load; or 4) improper clinical sampling. The reasons for the relatively lower RT-PCR detection rate in our sample compared to a prior report are unknown (3). Our results support the use of chest CT for screening for COVD-19 for patients with clinical and epidemiologic features compatible with COVID-19 infection particularly when RT-PCR testing is negative.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              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.
                Bookmark

                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
                : 8994
                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
                8aa574bd-84c8-4fc7-a56d-d747c89522e9
                © 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/.

                History
                : 2 October 2020
                : 1 April 2021
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                diseases,health care,risk factors,mathematics and computing
                Uncategorized
                diseases, health care, risk factors, mathematics and computing

                Comments

                Comment on this article