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Pattern of Lung Involvement in Predicting Severity and Sequelae in Patients With COVID-19

Background During the COVID pandemic, high-resolution CT scan has played a pivotal role in detecting lung involvement and severity based on the segments of the lung involved. The pattern of involvement was not considered, and our aim is to observe the pattern of lung involvement in predicting severi...

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Autores principales: Santhanam, Jennie, Agarwal S, Ankush, Mammen, Sarah, K, Arun, Athani, Aishwarya V, K, Subramaniyan, Sundari, Meenakshi, Ibrahim, Hussain, Nila, Uthaya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cureus 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9879588/
https://www.ncbi.nlm.nih.gov/pubmed/36712734
http://dx.doi.org/10.7759/cureus.32973
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author Santhanam, Jennie
Agarwal S, Ankush
Mammen, Sarah
K, Arun
Athani, Aishwarya V
K, Subramaniyan
Sundari, Meenakshi
Ibrahim, Hussain
Nila, Uthaya
author_facet Santhanam, Jennie
Agarwal S, Ankush
Mammen, Sarah
K, Arun
Athani, Aishwarya V
K, Subramaniyan
Sundari, Meenakshi
Ibrahim, Hussain
Nila, Uthaya
author_sort Santhanam, Jennie
collection PubMed
description Background During the COVID pandemic, high-resolution CT scan has played a pivotal role in detecting lung involvement and severity based on the segments of the lung involved. The pattern of involvement was not considered, and our aim is to observe the pattern of lung involvement in predicting severity and guiding management protocol in patients with COVID-19. Methodology It was a prospective observational study conducted with 151 patients admitted with COVID-19 with a positive reverse transcriptase polymerase chain reaction test (RT-PCR) in a single tertiary care hospital in south India. Patients with pre-existing lung pathologies were excluded from the study. Eligible patients were then divided into mild, moderate, and severe categories based on Indian Council of Medical Research (ICMR) guidelines, and high-resolution computed tomography (HRCT) chest was done, findings of which were then categorized based on lung involvement; into ground glass opacities (GGO), interstitial involvement and mixture of both. These were then analyzed to determine their importance with respect to the duration of stay and severity of the disease. Results The data collected was analyzed by IBM SPSS software version 23.0 (IBM Corp., Armonk, NY, USA). The study population included 114 males (75.5%) and 37 females (24.5%). HRCT chest was done which showed 62.3% of patients had GGO, 14.6% had interstitial lung involvement, 18.5% had a mixture of both and 4.6% had normal lung findings. These findings, when compared to clinical categories of severity, showed a significant co-relation between pattern of involvement of the lung and the severity of the disease. It also showed significant co-relation with the duration of stay. Conclusion HRCT chest has proven to be useful in the determination of patient’s severity and can guide with management. We suggest earlier initiation of steroids and anticoagulants in patients with interstitial involvement even for the patients not on oxygen therapy yet. It can be used as a triage modality for screening due to the advantage of presenting with immediate results as opposed to RT-PCR which might take hours and can delay treatment which can prevent worsening.
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spelling pubmed-98795882023-01-27 Pattern of Lung Involvement in Predicting Severity and Sequelae in Patients With COVID-19 Santhanam, Jennie Agarwal S, Ankush Mammen, Sarah K, Arun Athani, Aishwarya V K, Subramaniyan Sundari, Meenakshi Ibrahim, Hussain Nila, Uthaya Cureus Internal Medicine Background During the COVID pandemic, high-resolution CT scan has played a pivotal role in detecting lung involvement and severity based on the segments of the lung involved. The pattern of involvement was not considered, and our aim is to observe the pattern of lung involvement in predicting severity and guiding management protocol in patients with COVID-19. Methodology It was a prospective observational study conducted with 151 patients admitted with COVID-19 with a positive reverse transcriptase polymerase chain reaction test (RT-PCR) in a single tertiary care hospital in south India. Patients with pre-existing lung pathologies were excluded from the study. Eligible patients were then divided into mild, moderate, and severe categories based on Indian Council of Medical Research (ICMR) guidelines, and high-resolution computed tomography (HRCT) chest was done, findings of which were then categorized based on lung involvement; into ground glass opacities (GGO), interstitial involvement and mixture of both. These were then analyzed to determine their importance with respect to the duration of stay and severity of the disease. Results The data collected was analyzed by IBM SPSS software version 23.0 (IBM Corp., Armonk, NY, USA). The study population included 114 males (75.5%) and 37 females (24.5%). HRCT chest was done which showed 62.3% of patients had GGO, 14.6% had interstitial lung involvement, 18.5% had a mixture of both and 4.6% had normal lung findings. These findings, when compared to clinical categories of severity, showed a significant co-relation between pattern of involvement of the lung and the severity of the disease. It also showed significant co-relation with the duration of stay. Conclusion HRCT chest has proven to be useful in the determination of patient’s severity and can guide with management. We suggest earlier initiation of steroids and anticoagulants in patients with interstitial involvement even for the patients not on oxygen therapy yet. It can be used as a triage modality for screening due to the advantage of presenting with immediate results as opposed to RT-PCR which might take hours and can delay treatment which can prevent worsening. Cureus 2022-12-26 /pmc/articles/PMC9879588/ /pubmed/36712734 http://dx.doi.org/10.7759/cureus.32973 Text en Copyright © 2022, Santhanam et al. https://creativecommons.org/licenses/by/3.0/This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Internal Medicine
Santhanam, Jennie
Agarwal S, Ankush
Mammen, Sarah
K, Arun
Athani, Aishwarya V
K, Subramaniyan
Sundari, Meenakshi
Ibrahim, Hussain
Nila, Uthaya
Pattern of Lung Involvement in Predicting Severity and Sequelae in Patients With COVID-19
title Pattern of Lung Involvement in Predicting Severity and Sequelae in Patients With COVID-19
title_full Pattern of Lung Involvement in Predicting Severity and Sequelae in Patients With COVID-19
title_fullStr Pattern of Lung Involvement in Predicting Severity and Sequelae in Patients With COVID-19
title_full_unstemmed Pattern of Lung Involvement in Predicting Severity and Sequelae in Patients With COVID-19
title_short Pattern of Lung Involvement in Predicting Severity and Sequelae in Patients With COVID-19
title_sort pattern of lung involvement in predicting severity and sequelae in patients with covid-19
topic Internal Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9879588/
https://www.ncbi.nlm.nih.gov/pubmed/36712734
http://dx.doi.org/10.7759/cureus.32973
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