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Predicting lung fibrosis in post-COVID-19 patients after discharge with follow-up chest CT findings

BACKGROUND: Coronavirus disease has spread widely all over the world since the beginning of 2020, and this required rapid adequate management. High-resolution computed tomography (HRCT) has become an initial valuable tool for screening, diagnosis, and assessment of disease severity. This study aimed...

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Autores principales: Yasin, Rabab, Gomaa, Ahmed Abdelhakim Kamel, Ghazy, Tamer, Hassanein, Shaimaa Abdelhamid, Ibrahem, Reda Abdel latif, Khalifa, Mohamed Hossameldin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8090913/
http://dx.doi.org/10.1186/s43055-021-00495-0
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author Yasin, Rabab
Gomaa, Ahmed Abdelhakim Kamel
Ghazy, Tamer
Hassanein, Shaimaa Abdelhamid
Ibrahem, Reda Abdel latif
Khalifa, Mohamed Hossameldin
author_facet Yasin, Rabab
Gomaa, Ahmed Abdelhakim Kamel
Ghazy, Tamer
Hassanein, Shaimaa Abdelhamid
Ibrahem, Reda Abdel latif
Khalifa, Mohamed Hossameldin
author_sort Yasin, Rabab
collection PubMed
description BACKGROUND: Coronavirus disease has spread widely all over the world since the beginning of 2020, and this required rapid adequate management. High-resolution computed tomography (HRCT) has become an initial valuable tool for screening, diagnosis, and assessment of disease severity. This study aimed to assess the clinical, radiographic, and laboratory findings of COVID-19 with HRCT follow-up in discharged patients to predict lung fibrosis after COVID-19 infection in survived patients. RESULTS: This study included two-hundred and ten patients who were tested positive for the novel coronavirus by nasopharyngeal swap, admitted to the hospital, and discharged after recovery. Patients with at least a one-time chest CT scan after discharge were enrolled. According to the presence of fibrosis on follow-up CT after discharge, patients were classified into two groups and assigned as the “non-fibrotic group” (without evident fibrosis) and “fibrotic group” (with evident fibrosis). We compared between these two groups based on the recorded clinical data, patient demographic information (i.e., sex and age), length of stay (LOS) in the hospital, admission to the ICU, laboratory results (peak C-reactive protein [CRP] level, lowest lymphocyte level, serum ferritin, high-sensitivity troponin, d-dimer, administration of steroid), and CT features (CT severity score and CT consolidation/crazy-paving score). CT score includes the CT during the hospital stay with peak opacification and follow-up CT after discharge. The average CT follow-up time after discharge is 41.5 days (range, 20 to 65 days). There was a statistically significant difference between both groups (p ˂0.001). Further, a multivariate analysis was performed and found that the age of the patients, initial CT severity score, consolidation/crazy-paving score, and ICU admission were independent risk factors associated with the presence of post-COVID-19 fibrosis (p<0.05). Chest CT severity score shows a sensitivity of 86.1%, a specificity of 78%, and an accuracy of 81.9% at a cutoff point of 10.5. CONCLUSION: The residual pulmonary fibrosis in COVID-19 survivors after discharge depends on many factors with the patient’s age, CT severity, consolidation/crazy-paving scores, and ICU admission as independent risk factors associated with the presence of post-COVID-19 fibrosis.
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spelling pubmed-80909132021-05-03 Predicting lung fibrosis in post-COVID-19 patients after discharge with follow-up chest CT findings Yasin, Rabab Gomaa, Ahmed Abdelhakim Kamel Ghazy, Tamer Hassanein, Shaimaa Abdelhamid Ibrahem, Reda Abdel latif Khalifa, Mohamed Hossameldin Egypt J Radiol Nucl Med Research BACKGROUND: Coronavirus disease has spread widely all over the world since the beginning of 2020, and this required rapid adequate management. High-resolution computed tomography (HRCT) has become an initial valuable tool for screening, diagnosis, and assessment of disease severity. This study aimed to assess the clinical, radiographic, and laboratory findings of COVID-19 with HRCT follow-up in discharged patients to predict lung fibrosis after COVID-19 infection in survived patients. RESULTS: This study included two-hundred and ten patients who were tested positive for the novel coronavirus by nasopharyngeal swap, admitted to the hospital, and discharged after recovery. Patients with at least a one-time chest CT scan after discharge were enrolled. According to the presence of fibrosis on follow-up CT after discharge, patients were classified into two groups and assigned as the “non-fibrotic group” (without evident fibrosis) and “fibrotic group” (with evident fibrosis). We compared between these two groups based on the recorded clinical data, patient demographic information (i.e., sex and age), length of stay (LOS) in the hospital, admission to the ICU, laboratory results (peak C-reactive protein [CRP] level, lowest lymphocyte level, serum ferritin, high-sensitivity troponin, d-dimer, administration of steroid), and CT features (CT severity score and CT consolidation/crazy-paving score). CT score includes the CT during the hospital stay with peak opacification and follow-up CT after discharge. The average CT follow-up time after discharge is 41.5 days (range, 20 to 65 days). There was a statistically significant difference between both groups (p ˂0.001). Further, a multivariate analysis was performed and found that the age of the patients, initial CT severity score, consolidation/crazy-paving score, and ICU admission were independent risk factors associated with the presence of post-COVID-19 fibrosis (p<0.05). Chest CT severity score shows a sensitivity of 86.1%, a specificity of 78%, and an accuracy of 81.9% at a cutoff point of 10.5. CONCLUSION: The residual pulmonary fibrosis in COVID-19 survivors after discharge depends on many factors with the patient’s age, CT severity, consolidation/crazy-paving scores, and ICU admission as independent risk factors associated with the presence of post-COVID-19 fibrosis. Springer Berlin Heidelberg 2021-05-03 2021 /pmc/articles/PMC8090913/ http://dx.doi.org/10.1186/s43055-021-00495-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research
Yasin, Rabab
Gomaa, Ahmed Abdelhakim Kamel
Ghazy, Tamer
Hassanein, Shaimaa Abdelhamid
Ibrahem, Reda Abdel latif
Khalifa, Mohamed Hossameldin
Predicting lung fibrosis in post-COVID-19 patients after discharge with follow-up chest CT findings
title Predicting lung fibrosis in post-COVID-19 patients after discharge with follow-up chest CT findings
title_full Predicting lung fibrosis in post-COVID-19 patients after discharge with follow-up chest CT findings
title_fullStr Predicting lung fibrosis in post-COVID-19 patients after discharge with follow-up chest CT findings
title_full_unstemmed Predicting lung fibrosis in post-COVID-19 patients after discharge with follow-up chest CT findings
title_short Predicting lung fibrosis in post-COVID-19 patients after discharge with follow-up chest CT findings
title_sort predicting lung fibrosis in post-covid-19 patients after discharge with follow-up chest ct findings
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8090913/
http://dx.doi.org/10.1186/s43055-021-00495-0
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