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COVID-19: A qualitative chest CT model to identify severe form of the disease.
Devie, Antoine; Kanagaratnam, Lukshe; Perotin, Jeanne-Marie; Jolly, Damien; Ravey, Jean-Noël; Djelouah, Manel; Hoeffel, Christine.
  • Devie A; Department of Radiology, Reims University Hospital, 51092 Reims, France. Electronic address: adevie@chu-reims.fr.
  • Kanagaratnam L; Clinical Research Department, Reims University Hospital, 51092 Reims, France.
  • Perotin JM; Department of Respiratory Diseases, INSERM UMRS 1250, Reims University Hospital, 51092 Reims, France.
  • Jolly D; Clinical Research Department, Reims University Hospital, 51092 Reims, France.
  • Ravey JN; Department of Radiology, Grenoble University Hospital, 38700 Grenoble, France.
  • Djelouah M; Department of Radiology, Reims University Hospital, 51092 Reims, France.
  • Hoeffel C; Department of Radiology, Reims University Hospital, 51092 Reims, France; CRESTIC, University of Reims Champagne-Ardenne, 51100 Reims, France.
Diagn Interv Imaging ; 102(2): 77-84, 2021 Feb.
Article in English | MEDLINE | ID: covidwho-987475
ABSTRACT

PURPOSE:

The purpose of this study was to identify clinical and chest computed tomography (CT) features associated with a severe form of coronavirus disease 2019 (COVID-19) and to propose a quick and easy to use model to identify patients at risk of a severe form. MATERIALS AND

METHODS:

A total of 158 patients with biologically confirmed COVID-19 who underwent a chest CT after the onset of the symptoms were included. There were 84 men and 74 women with a mean age of 68±14 (SD) years (range 24-96years). There were 100 non-severe and 58 severe cases. Their clinical data were recorded and the first chest CT examination was reviewed using a computerized standardized report. Univariate and multivariate analyses were performed in order to identify the risk factors associated with disease severity. Two models were built one was based only on qualitative CT features and the other one included a semi-quantitative total CT score to replace the variable representing the extent of the disease. Areas under the ROC curves (AUC) of the two models were compared with DeLong's method.

RESULTS:

Central involvement of lung parenchyma (P<0.001), area of consolidation (P<0.008), air bronchogram sign (P<0.001), bronchiectasis (P<0.001), traction bronchiectasis (P<0.011), pleural effusion (P<0.026), large involvement of either one of the upper lobes or of the middle lobe (P<0.001) and total CT score≥15 (P<0.001) were more often observed in the severe group than in the non-severe group. No significant differences were found between the qualitative model (large involvement of either upper lobes or middle lobe [odd ratio (OR)=2.473], central involvement [OR=2.760], pleural effusion [OR=2.699]) and the semi-quantitative model (total CT score≥15 [OR=3.342], central involvement [OR=2.344], pleural effusion [OR=2.754]) with AUC of 0.722 (95% CI 0.638-0.806) vs. 0.739 (95% CI 0.656-0.823), respectively (P=0.209).

CONCLUSION:

We have developed a new qualitative chest CT-based multivariate model that provides independent risk factors associated with severe form of COVID-19.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Computer Simulation / Tomography, X-Ray Computed / COVID-19 / Lung Type of study: Prognostic study / Qualitative research Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: English Journal: Diagn Interv Imaging Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Computer Simulation / Tomography, X-Ray Computed / COVID-19 / Lung Type of study: Prognostic study / Qualitative research Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: English Journal: Diagn Interv Imaging Year: 2021 Document Type: Article