Cargando…
CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study
Objective: Early identification of coronavirus disease 2019 (COVID-19) patients with worse outcomes may benefit clinical management of patients. We aimed to quantify pneumonia findings on CT at admission to predict progression to critical illness in COVID-19 patients. Methods: This retrospective stu...
Autores principales: | , , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8245676/ https://www.ncbi.nlm.nih.gov/pubmed/34222293 http://dx.doi.org/10.3389/fmed.2021.689568 |
_version_ | 1783716159479087104 |
---|---|
author | Pang, Baoguo Li, Haijun Liu, Qin Wu, Penghui Xia, Tingting Zhang, Xiaoxian Le, Wenjun Li, Jianyu Lai, Lihua Ou, Changxing Ma, Jianjuan Liu, Shuai Zhou, Fuling Wang, Xinlu Xie, Jiaxing Zhang, Qingling Jiang, Min Liu, Yumei Zeng, Qingsi |
author_facet | Pang, Baoguo Li, Haijun Liu, Qin Wu, Penghui Xia, Tingting Zhang, Xiaoxian Le, Wenjun Li, Jianyu Lai, Lihua Ou, Changxing Ma, Jianjuan Liu, Shuai Zhou, Fuling Wang, Xinlu Xie, Jiaxing Zhang, Qingling Jiang, Min Liu, Yumei Zeng, Qingsi |
author_sort | Pang, Baoguo |
collection | PubMed |
description | Objective: Early identification of coronavirus disease 2019 (COVID-19) patients with worse outcomes may benefit clinical management of patients. We aimed to quantify pneumonia findings on CT at admission to predict progression to critical illness in COVID-19 patients. Methods: This retrospective study included laboratory-confirmed adult patients with COVID-19. All patients underwent a thin-section chest computed tomography (CT) scans showing evidence of pneumonia. CT images with severe moving artifacts were excluded from analysis. Patients' clinical and laboratory data were collected from medical records. Three quantitative CT features of pneumonia lesions were automatically calculated using a care.ai Intelligent Multi-disciplinary Imaging Diagnosis Platform Intelligent Evaluation System of Chest CT for COVID-19, denoting the percentage of pneumonia volume (PPV), ground-glass opacity volume (PGV), and consolidation volume (PCV). According to Chinese COVID-19 guidelines (trial version 7), patients were divided into noncritical and critical groups. Critical illness was defined as a composite of admission to the intensive care unit, respiratory failure requiring mechanical ventilation, shock, or death. The performance of PPV, PGV, and PCV in discrimination of critical illness was assessed. The correlations between PPV and laboratory variables were assessed by Pearson correlation analysis. Results: A total of 140 patients were included, with mean age of 58.6 years, and 85 (60.7%) were male. Thirty-two (22.9%) patients were critical. Using a cutoff value of 22.6%, the PPV had the highest performance in predicting critical illness, with an area under the curve of 0.868, sensitivity of 81.3%, and specificity of 80.6%. The PPV had moderately positive correlation with neutrophil (%) (r = 0.535, p < 0.001), erythrocyte sedimentation rate (r = 0.567, p < 0.001), d-Dimer (r = 0.444, p < 0.001), high-sensitivity C-reactive protein (r = 0.495, p < 0.001), aspartate aminotransferase (r = 0.410, p < 0.001), lactate dehydrogenase (r = 0.644, p < 0.001), and urea nitrogen (r = 0.439, p < 0.001), whereas the PPV had moderately negative correlation with lymphocyte (%) (r = −0.535, p < 0.001). Conclusions: Pneumonia volume quantified on initial CT can non-invasively predict the progression to critical illness in advance, which serve as a prognostic marker of COVID-19. |
format | Online Article Text |
id | pubmed-8245676 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82456762021-07-02 CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study Pang, Baoguo Li, Haijun Liu, Qin Wu, Penghui Xia, Tingting Zhang, Xiaoxian Le, Wenjun Li, Jianyu Lai, Lihua Ou, Changxing Ma, Jianjuan Liu, Shuai Zhou, Fuling Wang, Xinlu Xie, Jiaxing Zhang, Qingling Jiang, Min Liu, Yumei Zeng, Qingsi Front Med (Lausanne) Medicine Objective: Early identification of coronavirus disease 2019 (COVID-19) patients with worse outcomes may benefit clinical management of patients. We aimed to quantify pneumonia findings on CT at admission to predict progression to critical illness in COVID-19 patients. Methods: This retrospective study included laboratory-confirmed adult patients with COVID-19. All patients underwent a thin-section chest computed tomography (CT) scans showing evidence of pneumonia. CT images with severe moving artifacts were excluded from analysis. Patients' clinical and laboratory data were collected from medical records. Three quantitative CT features of pneumonia lesions were automatically calculated using a care.ai Intelligent Multi-disciplinary Imaging Diagnosis Platform Intelligent Evaluation System of Chest CT for COVID-19, denoting the percentage of pneumonia volume (PPV), ground-glass opacity volume (PGV), and consolidation volume (PCV). According to Chinese COVID-19 guidelines (trial version 7), patients were divided into noncritical and critical groups. Critical illness was defined as a composite of admission to the intensive care unit, respiratory failure requiring mechanical ventilation, shock, or death. The performance of PPV, PGV, and PCV in discrimination of critical illness was assessed. The correlations between PPV and laboratory variables were assessed by Pearson correlation analysis. Results: A total of 140 patients were included, with mean age of 58.6 years, and 85 (60.7%) were male. Thirty-two (22.9%) patients were critical. Using a cutoff value of 22.6%, the PPV had the highest performance in predicting critical illness, with an area under the curve of 0.868, sensitivity of 81.3%, and specificity of 80.6%. The PPV had moderately positive correlation with neutrophil (%) (r = 0.535, p < 0.001), erythrocyte sedimentation rate (r = 0.567, p < 0.001), d-Dimer (r = 0.444, p < 0.001), high-sensitivity C-reactive protein (r = 0.495, p < 0.001), aspartate aminotransferase (r = 0.410, p < 0.001), lactate dehydrogenase (r = 0.644, p < 0.001), and urea nitrogen (r = 0.439, p < 0.001), whereas the PPV had moderately negative correlation with lymphocyte (%) (r = −0.535, p < 0.001). Conclusions: Pneumonia volume quantified on initial CT can non-invasively predict the progression to critical illness in advance, which serve as a prognostic marker of COVID-19. Frontiers Media S.A. 2021-06-17 /pmc/articles/PMC8245676/ /pubmed/34222293 http://dx.doi.org/10.3389/fmed.2021.689568 Text en Copyright © 2021 Pang, Li, Liu, Wu, Xia, Zhang, Le, Li, Lai, Ou, Ma, Liu, Zhou, Wang, Xie, Zhang, Jiang, Liu and Zeng. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Medicine Pang, Baoguo Li, Haijun Liu, Qin Wu, Penghui Xia, Tingting Zhang, Xiaoxian Le, Wenjun Li, Jianyu Lai, Lihua Ou, Changxing Ma, Jianjuan Liu, Shuai Zhou, Fuling Wang, Xinlu Xie, Jiaxing Zhang, Qingling Jiang, Min Liu, Yumei Zeng, Qingsi CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study |
title | CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study |
title_full | CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study |
title_fullStr | CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study |
title_full_unstemmed | CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study |
title_short | CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study |
title_sort | ct quantification of covid-19 pneumonia at admission can predict progression to critical illness: a retrospective multicenter cohort study |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8245676/ https://www.ncbi.nlm.nih.gov/pubmed/34222293 http://dx.doi.org/10.3389/fmed.2021.689568 |
work_keys_str_mv | AT pangbaoguo ctquantificationofcovid19pneumoniaatadmissioncanpredictprogressiontocriticalillnessaretrospectivemulticentercohortstudy AT lihaijun ctquantificationofcovid19pneumoniaatadmissioncanpredictprogressiontocriticalillnessaretrospectivemulticentercohortstudy AT liuqin ctquantificationofcovid19pneumoniaatadmissioncanpredictprogressiontocriticalillnessaretrospectivemulticentercohortstudy AT wupenghui ctquantificationofcovid19pneumoniaatadmissioncanpredictprogressiontocriticalillnessaretrospectivemulticentercohortstudy AT xiatingting ctquantificationofcovid19pneumoniaatadmissioncanpredictprogressiontocriticalillnessaretrospectivemulticentercohortstudy AT zhangxiaoxian ctquantificationofcovid19pneumoniaatadmissioncanpredictprogressiontocriticalillnessaretrospectivemulticentercohortstudy AT lewenjun ctquantificationofcovid19pneumoniaatadmissioncanpredictprogressiontocriticalillnessaretrospectivemulticentercohortstudy AT lijianyu ctquantificationofcovid19pneumoniaatadmissioncanpredictprogressiontocriticalillnessaretrospectivemulticentercohortstudy AT lailihua ctquantificationofcovid19pneumoniaatadmissioncanpredictprogressiontocriticalillnessaretrospectivemulticentercohortstudy AT ouchangxing ctquantificationofcovid19pneumoniaatadmissioncanpredictprogressiontocriticalillnessaretrospectivemulticentercohortstudy AT majianjuan ctquantificationofcovid19pneumoniaatadmissioncanpredictprogressiontocriticalillnessaretrospectivemulticentercohortstudy AT liushuai ctquantificationofcovid19pneumoniaatadmissioncanpredictprogressiontocriticalillnessaretrospectivemulticentercohortstudy AT zhoufuling ctquantificationofcovid19pneumoniaatadmissioncanpredictprogressiontocriticalillnessaretrospectivemulticentercohortstudy AT wangxinlu ctquantificationofcovid19pneumoniaatadmissioncanpredictprogressiontocriticalillnessaretrospectivemulticentercohortstudy AT xiejiaxing ctquantificationofcovid19pneumoniaatadmissioncanpredictprogressiontocriticalillnessaretrospectivemulticentercohortstudy AT zhangqingling ctquantificationofcovid19pneumoniaatadmissioncanpredictprogressiontocriticalillnessaretrospectivemulticentercohortstudy AT jiangmin ctquantificationofcovid19pneumoniaatadmissioncanpredictprogressiontocriticalillnessaretrospectivemulticentercohortstudy AT liuyumei ctquantificationofcovid19pneumoniaatadmissioncanpredictprogressiontocriticalillnessaretrospectivemulticentercohortstudy AT zengqingsi ctquantificationofcovid19pneumoniaatadmissioncanpredictprogressiontocriticalillnessaretrospectivemulticentercohortstudy |