Longitudinal Chest CT Features in Severe/Critical COVID-19 Cases and the Predictive Value of the Initial CT for Mortality

Hailan Li,1,* Shiyong Luo,2,* Youming Zhang,3 Xiaoyi Xiao,4 Huaping Liu4 1Department of Radiology, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, 410000, Hunan Province, People’s Republic of China; 2Department of Radiology...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Li H, Luo S, Zhang Y, Xiao X, Liu H
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2021
Materias:	covid-19 sars-cov-2 mortality multivariate combined analysis chest ct score risk factors Pathology RB1-214 Therapeutics. Pharmacology RM1-950
Acceso en línea:	https://doaj.org/article/4f4fd8954793480782ba3586d282ae04
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:4f4fd8954793480782ba3586d282ae04
record_format	dspace
institution	DOAJ
collection	DOAJ
language	EN
topic	covid-19 sars-cov-2 mortality multivariate combined analysis chest ct score risk factors Pathology RB1-214 Therapeutics. Pharmacology RM1-950
spellingShingle	covid-19 sars-cov-2 mortality multivariate combined analysis chest ct score risk factors Pathology RB1-214 Therapeutics. Pharmacology RM1-950 Li H Luo S Zhang Y Xiao X Liu H Longitudinal Chest CT Features in Severe/Critical COVID-19 Cases and the Predictive Value of the Initial CT for Mortality
description	Hailan Li,1,* Shiyong Luo,2,* Youming Zhang,3 Xiaoyi Xiao,4 Huaping Liu4 1Department of Radiology, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, 410000, Hunan Province, People’s Republic of China; 2Department of Radiology, Wuhan Third Hospital (Tongren Hospital of Wuhan University), Wuhan, 430060, Hubei Province, People’s Republic of China; 3Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan Province, People’s Republic of China; 4Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan Province, People’s Republic of China*These authors contributed equally to this workCorrespondence: Huaping LiuDepartment of Radiology, The Third Xiangya Hospital, Central South University, Tongzipo Road 138, Changsha, 410013, Hunan Province, People’s Republic of ChinaTel +86-731-88638888Fax +86-731-84327332Email huaziyiranshuai@126.comPurpose: To evaluate longitudinal computed tomography (CT) features and the predictive value of the initial CT and clinical characteristics for mortality in patients with severe/critical coronavirus disease 2019 (COVID-19) pneumonia.Methods: A retrospective analysis was performed on patients with COVID-19 pneumonia confirmed by laboratory. By excluding mild and common patients, 155 severe/critical patients with definite outcome were finally enrolled. A total of 516 CTs of 147 patients were divided into four stages according to the time after onset (stage 1, 1– 7 days; stage 2, 8– 14 days; stage 3, 15– 21 days, and stage 4, > 21 days). The evolving imaging features between the survival and non-survival groups were compared by using Chi-square, Fisher’s exact test, student’s t-test or Mann–Whitney U-test, as appropriate. The predictive value of clinical and CT features at admission for mortality was analysed through logistic regression analysis. To avoid overfitting caused by CT scores, CT scores were divided into two parts, which were combined with clinical variables, respectively, to construct the models.Results: Ground-glass opacities (GGO) patterns were predominant for stages 1 and 2 for both groups (both P> 0.05). The numbers of consolidation lesions increased in stage 3 in both groups (P=0.857), whereas the linear opacity increased in the survival group but decreased in the non-survival group (P=0.0049). In stage 4, the survival group predominantly presented linear opacity patterns, whereas the non-survival group mainly showed consolidation patterns (P=0.007). Clinical and imaging characteristics correlated with mortality; multivariate analyses revealed age > 71 years, neutrophil count > 6.38 × 109/L, aspartate aminotransferase (AST) > 58 IU/L, and CT score (total lesions score > 17 in model 1, GGO score > 14 and consolidation score > 2 in model 2) as independent risk factors (all P< 0.05). The areas under the curve of the six independent risk factors alone ranged from 0.65 to 0.75 and were 0.87 for model 2, 0.89 for model 1, and 0.92 for the six variables combined. Statistical differences were observed between Kaplan Meier curves of groups separated by cut-off values of these six variables (all P< 0.01).Conclusion: Longitudinal imaging features demonstrated differences between the two groups, which may help determine the patient’s prognosis. The initial CT score combined with age, AST, and neutrophil count is an excellent predictor for mortality in COVID-19 patients.Keywords: COVID-19, SARS-CoV-2, mortality, multivariate combined analysis, chest CT score, risk factors
format	article
author	Li H Luo S Zhang Y Xiao X Liu H
author_facet	Li H Luo S Zhang Y Xiao X Liu H
author_sort	Li H
title	Longitudinal Chest CT Features in Severe/Critical COVID-19 Cases and the Predictive Value of the Initial CT for Mortality
title_short	Longitudinal Chest CT Features in Severe/Critical COVID-19 Cases and the Predictive Value of the Initial CT for Mortality
title_full	Longitudinal Chest CT Features in Severe/Critical COVID-19 Cases and the Predictive Value of the Initial CT for Mortality
title_fullStr	Longitudinal Chest CT Features in Severe/Critical COVID-19 Cases and the Predictive Value of the Initial CT for Mortality
title_full_unstemmed	Longitudinal Chest CT Features in Severe/Critical COVID-19 Cases and the Predictive Value of the Initial CT for Mortality
title_sort	longitudinal chest ct features in severe/critical covid-19 cases and the predictive value of the initial ct for mortality
publisher	Dove Medical Press
publishDate	2021
url	https://doaj.org/article/4f4fd8954793480782ba3586d282ae04
work_keys_str_mv	AT lih longitudinalchestctfeaturesinseverecriticalcovid19casesandthepredictivevalueoftheinitialctformortality AT luos longitudinalchestctfeaturesinseverecriticalcovid19casesandthepredictivevalueoftheinitialctformortality AT zhangy longitudinalchestctfeaturesinseverecriticalcovid19casesandthepredictivevalueoftheinitialctformortality AT xiaox longitudinalchestctfeaturesinseverecriticalcovid19casesandthepredictivevalueoftheinitialctformortality AT liuh longitudinalchestctfeaturesinseverecriticalcovid19casesandthepredictivevalueoftheinitialctformortality
_version_	1718393093567283200
spelling	oai:doaj.org-article:4f4fd8954793480782ba3586d282ae042021-12-02T13:24:28ZLongitudinal Chest CT Features in Severe/Critical COVID-19 Cases and the Predictive Value of the Initial CT for Mortality1178-7031https://doaj.org/article/4f4fd8954793480782ba3586d282ae042021-03-01T00:00:00Zhttps://www.dovepress.com/longitudinal-chest-ct-features-in-severecritical-covid-19-cases-and-th-peer-reviewed-article-JIRhttps://doaj.org/toc/1178-7031Hailan Li,1,* Shiyong Luo,2,* Youming Zhang,3 Xiaoyi Xiao,4 Huaping Liu4 1Department of Radiology, Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, 410000, Hunan Province, People’s Republic of China; 2Department of Radiology, Wuhan Third Hospital (Tongren Hospital of Wuhan University), Wuhan, 430060, Hubei Province, People’s Republic of China; 3Department of Radiology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan Province, People’s Republic of China; 4Department of Radiology, The Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan Province, People’s Republic of China*These authors contributed equally to this workCorrespondence: Huaping LiuDepartment of Radiology, The Third Xiangya Hospital, Central South University, Tongzipo Road 138, Changsha, 410013, Hunan Province, People’s Republic of ChinaTel +86-731-88638888Fax +86-731-84327332Email huaziyiranshuai@126.comPurpose: To evaluate longitudinal computed tomography (CT) features and the predictive value of the initial CT and clinical characteristics for mortality in patients with severe/critical coronavirus disease 2019 (COVID-19) pneumonia.Methods: A retrospective analysis was performed on patients with COVID-19 pneumonia confirmed by laboratory. By excluding mild and common patients, 155 severe/critical patients with definite outcome were finally enrolled. A total of 516 CTs of 147 patients were divided into four stages according to the time after onset (stage 1, 1– 7 days; stage 2, 8– 14 days; stage 3, 15– 21 days, and stage 4, > 21 days). The evolving imaging features between the survival and non-survival groups were compared by using Chi-square, Fisher’s exact test, student’s t-test or Mann–Whitney U-test, as appropriate. The predictive value of clinical and CT features at admission for mortality was analysed through logistic regression analysis. To avoid overfitting caused by CT scores, CT scores were divided into two parts, which were combined with clinical variables, respectively, to construct the models.Results: Ground-glass opacities (GGO) patterns were predominant for stages 1 and 2 for both groups (both P> 0.05). The numbers of consolidation lesions increased in stage 3 in both groups (P=0.857), whereas the linear opacity increased in the survival group but decreased in the non-survival group (P=0.0049). In stage 4, the survival group predominantly presented linear opacity patterns, whereas the non-survival group mainly showed consolidation patterns (P=0.007). Clinical and imaging characteristics correlated with mortality; multivariate analyses revealed age > 71 years, neutrophil count > 6.38 × 109/L, aspartate aminotransferase (AST) > 58 IU/L, and CT score (total lesions score > 17 in model 1, GGO score > 14 and consolidation score > 2 in model 2) as independent risk factors (all P< 0.05). The areas under the curve of the six independent risk factors alone ranged from 0.65 to 0.75 and were 0.87 for model 2, 0.89 for model 1, and 0.92 for the six variables combined. Statistical differences were observed between Kaplan Meier curves of groups separated by cut-off values of these six variables (all P< 0.01).Conclusion: Longitudinal imaging features demonstrated differences between the two groups, which may help determine the patient’s prognosis. The initial CT score combined with age, AST, and neutrophil count is an excellent predictor for mortality in COVID-19 patients.Keywords: COVID-19, SARS-CoV-2, mortality, multivariate combined analysis, chest CT score, risk factorsLi HLuo SZhang YXiao XLiu HDove Medical Pressarticlecovid-19sars-cov-2mortalitymultivariate combined analysischest ct scorerisk factorsPathologyRB1-214Therapeutics. PharmacologyRM1-950ENJournal of Inflammation Research, Vol Volume 14, Pp 1111-1124 (2021)

Longitudinal Chest CT Features in Severe/Critical COVID-19 Cases and the Predictive Value of the Initial CT for Mortality

Ejemplares similares