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. 2022:9:100431.
doi: 10.1016/j.ejro.2022.100431. Epub 2022 Jun 24.

Comparison of temporal evolution of computed tomography imaging features in COVID-19 and influenza infections in a multicenter cohort study

Tim Fischer  1 , Yassir El Baz  1 , Giulia Scanferla  2 , Nicole Graf  3 , Frederike Waldeck  4 , Gian-Reto Kleger  5 , Thomas Frauenfelder  6 , Jens Bremerich  7 , Sabine Schmidt Kobbe  8 , Jean-Luc Pagani  9 , Sebastian Schindera  10 , Anna Conen  11 , Simon Wildermuth  1 , Sebastian Leschka  1 , Carol Strahm  2 , Stephan Waelti  1 , Tobias Johannes Dietrich  1 , Werner C Albrich  2
Affiliations

Comparison of temporal evolution of computed tomography imaging features in COVID-19 and influenza infections in a multicenter cohort study

Tim Fischer et al. Eur J Radiol Open. 2022.

Abstract

Purpose: To compare temporal evolution of imaging features of coronavirus disease 2019 (COVID-19) and influenza in computed tomography and evaluate their predictive value for distinction.

Methods: In this retrospective, multicenter study 179 CT examinations of 52 COVID-19 and 44 influenza critically ill patients were included. Lung involvement, main pattern (ground glass opacity, crazy paving, consolidation) and additional lung and chest findings were evaluated by two independent observers. Additional findings and clinical data were compared patient-wise. A decision tree analysis was performed to identify imaging features with predictive value in distinguishing both entities.

Results: In contrast to influenza patients, lung involvement remains high in COVID-19 patients > 14 days after the diagnosis. The predominant pattern in COVID-19 evolves from ground glass at the beginning to consolidation in later disease. In influenza there is more consolidation at the beginning and overall less ground glass opacity (p = 0.002). Decision tree analysis yielded the following: Earlier in disease course, pleural effusion is a typical feature of influenza (p = 0.007) whereas ground glass opacities indicate COVID-19 (p = 0.04). In later disease, particularly more lung involvement (p < 0.001), but also less pleural (p = 0.005) and pericardial (p = 0.003) effusion favor COVID-19 over influenza. Regardless of time point, less lung involvement (p < 0.001), tree-in-bud (p = 0.002) and pericardial effusion (p = 0.01) make influenza more likely than COVID-19.

Conclusions: This study identified differences in temporal evolution of imaging features between COVID-19 and influenza. These findings may help to distinguish both diseases in critically ill patients when laboratory findings are delayed or inconclusive.

Keywords: COPD, Chronic obstructive pulmonary disease; COVID-19; COVID-19, Coronavirus disease 2019; CT, Computed tomography; Computed tomography; GGO, Ground glass opacity; HIV, Human immunodeficiency virus; HSCT, Haematopoietic stem cell transplantation; ICC, Intraclass correlation coefficient; ICU, Intensive care unit; IQR, Interquartile range; Influenza; Lung; PCR, Polymerase chain reaction; Pneumonia; SD, Standard deviation; SOT, Solid organ transplantation.

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Conflict of interest statement

The authors declare that they have no conflict of interests.

Figures

Fig. 1
Fig. 1
Mean degree of involvements of total lung (A), upper lobes (B), and lower lobes (C) in COVID-19 (red) and influenza patients (green) by time. Each time bin comprises only one (the first) measurement per patient due to the otherwise dependent nature of the data. Error bars show mean and bootstrapped 95 % confidence intervals for days − 7 to 0 (n = 17 and 23 for COVID-19 and influenza), days 1–7 (n = 33 and 18 for COVID-19 and influenza), days 8–14 (n = 23 and 11 for COVID-19 and influenza) and > 14 days after diagnosis (n = 20 and 14 for COVID-19 and influenza). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 2
Fig. 2
Predominant main pattern (ground glass opacity, A, crazy paving, B and consolidation, C) in COVIOD-19 (red) and influenza patients (green) by time. Each time bin comprises only one (the first) measurement per patient due to the otherwise dependent nature of the data. Error bars show mean and bootstrapped 95 % confidence intervals for days − 7 to 0 (n = 17 and 23 for COVID-19 and influenza), days 1–7 (n = 33 and 18 for COVID-19 and influenza), days 8–14 (n = 23 and 11 for COVID-19 and influenza) and > 14 days after diagnosis (n = 20 and 14 for COVID-19 and influenza). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 3
Fig. 3
Classification tree for the prediction of COVID-19 for entire observation time (with Bonferroni correction). The tree illustrates how the data set was split at specific cut points of one of the predictors. Resulting data subsets represent groups of patients with COVID-19 diagnosis (dark parts of bar plots). P-values at each split indicate the significance of the relationship between the predictor and COVID-19 diagnosis among the patients considered at this split. Splitting criteria are indicated on the branches. Involvement in percent (%) of total lung, pericardial effusion in millimeter (mm).
Fig. 4
Fig. 4
Classification tree for the prediction of COVID-19 for different time points: at ≤ 0 days after diagnosis (A), 0–7 days after diagnosis (B), 7–14 days after diagnosis (C) and ≥ 14 days after diagnosis (D) without Bonferroni correction. For each time bin, only one (the first) measurement per patient was included in the analysis. The trees illustrate how the data set was split at specific cut points of one of the predictors. Resulting data subsets represent groups of patients with COVID-19 diagnosis (dark parts of bar plots). P-values at each split indicate the significance of the relationship between the predictor and COVID-19 diagnosis among the patients considered at this split. Splitting criteria are indicated on the branches. Involvement in percent (%) of total lung, pericardial effusion in millimeter (mm), pleural effusion in millimeter (mm).
Fig. 5
Fig. 5
Examples of imaging features associated with influenza: Tree-in-bud (A) a in the left upper and lower lobe in a 60- years old male patient, one day after symptom onset at the day of the influenza diagnosis and at the day of ICU admission. Pleural effusion (B) in a 41- years old female patient 12 days after symptom onset, seven days after influenza diagnosis and six days after ICU admission. Pericardial effusion (C) in a 76- years old male patient, 27 days after symptom onset, 17 days after influenza diagnosis and 11 days after ICU admission.
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References

    1. Lauring A.S., Hodcroft E.B. Genetic variants of SARS-CoV-2-what do they mean? JAMA. 2021;325:529–531. doi: 10.1001/jama.2020.27124. - DOI - PubMed
    1. Deng L.-S., Yuan J., Ding L., Chen Y.-L., Zhao C.-H., Chen G.-Q., Li X.-H., Li X.-H., Luo W.-T., Lan J.-F., Tan G.-Y., Tang S.-H., Xia J.-Y., Liu X. Comparison of patients hospitalized with COVID-19, H7N9 and H1N1. Infect. Dis. Poverty. 2020;9:163. doi: 10.1186/s40249-020-00781-5. - DOI - PMC - PubMed
    1. Kim S.-H., Wi Y.M., Lim S., Han K.-T., Bae I.-G. Differences in clinical characteristics and chest images between coronavirus disease 2019 and influenza-associated pneumonia. Diagnostic. 2021;11:261. doi: 10.3390/diagnostics11020261. - DOI - PMC - PubMed
    1. Liu M., Zeng W., Wen Y., Zheng Y., Lv F., Xiao K. COVID-19 pneumonia: CT findings of 122 patients and differentiation from influenza pneumonia. Eur. Radiol. 2020;30:5463–5469. doi: 10.1007/s00330-020-06928-0. - DOI - PMC - PubMed
    1. Zhou M., Yang D., Chen Y., Xu Y., Xu J.-F., Jie Z., Yao W., Jin X., Pan Z., Tan J., Wang L., Xia Y., Zou L., Xu X., Wei J., Guan M., Yan F., Feng J., Zhang H., Qu J. Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia. Ann. Transl. Med. 2021;9:111. doi: 10.21037/atm-20-5328. - DOI - PMC - PubMed