Qualitative and quantitative chest CT parameters as predictors of specific mortality in COVID-19 patients
Purpose To test the association between death and both qualitative and quantitative CT parameters obtained visually and by software in coronavirus disease (COVID-19) early outbreak. Methods The study analyzed retrospectively patients underwent chest CT at hospital admission for COVID-19 pneumonia su...
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| Vydáno v: | Emergency radiology Ročník 27; číslo 6; s. 701 - 710 |
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| Hlavní autoři: | , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Cham
Springer International Publishing
01.12.2020
Springer Nature B.V |
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| ISSN: | 1070-3004, 1438-1435, 1438-1435 |
| On-line přístup: | Získat plný text |
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| Abstract | Purpose
To test the association between death and both qualitative and quantitative CT parameters obtained visually and by software in coronavirus disease (COVID-19) early outbreak.
Methods
The study analyzed retrospectively patients underwent chest CT at hospital admission for COVID-19 pneumonia suspicion, between February 21 and March 6, 2020. CT was performed in case of hypoxemia or moderate-to-severe dyspnea. CT scans were analyzed for quantitative and qualitative features obtained visually and by software. Cox proportional hazards regression analysis examined the association between variables and overall survival (OS). Three models were built for stratification of mortality risk: clinical, clinical/visual CT evaluation, and clinical/software-based CT assessment. AUC for each model was used to assess performance in predicting death.
Results
The study included 248 patients (70% males, median age 68 years). Death occurred in 78/248 (32%) patients. Visual pneumonia extent > 40% (HR 2.15, 95% CI 1.2–3.85,
P
= 0.01), %high attenuation area – 700 HU > 35% (HR 2.17, 95% CI 1.2–3.94,
P
= 0.01), exudative consolidations (HR 2.85–2.93, 95% CI 1.61–5.05/1.66–5.16,
P
< 0.001), visual CAC score > 1 (HR 2.76–3.32, 95% CI 1.4–5.45/1.71–6.46,
P
< 0.01/
P
< 0.001), and CT classified as COVID-19 and other disease (HR 1.92–2.03, 95% CI 1.01–3.67/1.06–3.9,
P
= 0.04/
P
= 0.03) were significantly associated with shorter OS. Models including CT parameters (AUC 0.911–0.913, 95% CI 0.873–0.95/0.875–0.952) were better predictors of death as compared to clinical model (AUC 0.869, 95% CI 0.816–0.922;
P =
0.04 for both models).
Conclusions
In COVID-19 patients, qualitative and quantitative chest CT parameters obtained visually or by software are predictors of mortality. Predictive models including CT metrics were better predictors of death in comparison to clinical model. |
|---|---|
| AbstractList | Purpose
To test the association between death and both qualitative and quantitative CT parameters obtained visually and by software in coronavirus disease (COVID-19) early outbreak.
Methods
The study analyzed retrospectively patients underwent chest CT at hospital admission for COVID-19 pneumonia suspicion, between February 21 and March 6, 2020. CT was performed in case of hypoxemia or moderate-to-severe dyspnea. CT scans were analyzed for quantitative and qualitative features obtained visually and by software. Cox proportional hazards regression analysis examined the association between variables and overall survival (OS). Three models were built for stratification of mortality risk: clinical, clinical/visual CT evaluation, and clinical/software-based CT assessment. AUC for each model was used to assess performance in predicting death.
Results
The study included 248 patients (70% males, median age 68 years). Death occurred in 78/248 (32%) patients. Visual pneumonia extent > 40% (HR 2.15, 95% CI 1.2–3.85,
P
= 0.01), %high attenuation area – 700 HU > 35% (HR 2.17, 95% CI 1.2–3.94,
P
= 0.01), exudative consolidations (HR 2.85–2.93, 95% CI 1.61–5.05/1.66–5.16,
P
< 0.001), visual CAC score > 1 (HR 2.76–3.32, 95% CI 1.4–5.45/1.71–6.46,
P
< 0.01/
P
< 0.001), and CT classified as COVID-19 and other disease (HR 1.92–2.03, 95% CI 1.01–3.67/1.06–3.9,
P
= 0.04/
P
= 0.03) were significantly associated with shorter OS. Models including CT parameters (AUC 0.911–0.913, 95% CI 0.873–0.95/0.875–0.952) were better predictors of death as compared to clinical model (AUC 0.869, 95% CI 0.816–0.922;
P =
0.04 for both models).
Conclusions
In COVID-19 patients, qualitative and quantitative chest CT parameters obtained visually or by software are predictors of mortality. Predictive models including CT metrics were better predictors of death in comparison to clinical model. To test the association between death and both qualitative and quantitative CT parameters obtained visually and by software in coronavirus disease (COVID-19) early outbreak.PURPOSETo test the association between death and both qualitative and quantitative CT parameters obtained visually and by software in coronavirus disease (COVID-19) early outbreak.The study analyzed retrospectively patients underwent chest CT at hospital admission for COVID-19 pneumonia suspicion, between February 21 and March 6, 2020. CT was performed in case of hypoxemia or moderate-to-severe dyspnea. CT scans were analyzed for quantitative and qualitative features obtained visually and by software. Cox proportional hazards regression analysis examined the association between variables and overall survival (OS). Three models were built for stratification of mortality risk: clinical, clinical/visual CT evaluation, and clinical/software-based CT assessment. AUC for each model was used to assess performance in predicting death.METHODSThe study analyzed retrospectively patients underwent chest CT at hospital admission for COVID-19 pneumonia suspicion, between February 21 and March 6, 2020. CT was performed in case of hypoxemia or moderate-to-severe dyspnea. CT scans were analyzed for quantitative and qualitative features obtained visually and by software. Cox proportional hazards regression analysis examined the association between variables and overall survival (OS). Three models were built for stratification of mortality risk: clinical, clinical/visual CT evaluation, and clinical/software-based CT assessment. AUC for each model was used to assess performance in predicting death.The study included 248 patients (70% males, median age 68 years). Death occurred in 78/248 (32%) patients. Visual pneumonia extent > 40% (HR 2.15, 95% CI 1.2-3.85, P = 0.01), %high attenuation area - 700 HU > 35% (HR 2.17, 95% CI 1.2-3.94, P = 0.01), exudative consolidations (HR 2.85-2.93, 95% CI 1.61-5.05/1.66-5.16, P < 0.001), visual CAC score > 1 (HR 2.76-3.32, 95% CI 1.4-5.45/1.71-6.46, P < 0.01/P < 0.001), and CT classified as COVID-19 and other disease (HR 1.92-2.03, 95% CI 1.01-3.67/1.06-3.9, P = 0.04/P = 0.03) were significantly associated with shorter OS. Models including CT parameters (AUC 0.911-0.913, 95% CI 0.873-0.95/0.875-0.952) were better predictors of death as compared to clinical model (AUC 0.869, 95% CI 0.816-0.922; P = 0.04 for both models).RESULTSThe study included 248 patients (70% males, median age 68 years). Death occurred in 78/248 (32%) patients. Visual pneumonia extent > 40% (HR 2.15, 95% CI 1.2-3.85, P = 0.01), %high attenuation area - 700 HU > 35% (HR 2.17, 95% CI 1.2-3.94, P = 0.01), exudative consolidations (HR 2.85-2.93, 95% CI 1.61-5.05/1.66-5.16, P < 0.001), visual CAC score > 1 (HR 2.76-3.32, 95% CI 1.4-5.45/1.71-6.46, P < 0.01/P < 0.001), and CT classified as COVID-19 and other disease (HR 1.92-2.03, 95% CI 1.01-3.67/1.06-3.9, P = 0.04/P = 0.03) were significantly associated with shorter OS. Models including CT parameters (AUC 0.911-0.913, 95% CI 0.873-0.95/0.875-0.952) were better predictors of death as compared to clinical model (AUC 0.869, 95% CI 0.816-0.922; P = 0.04 for both models).In COVID-19 patients, qualitative and quantitative chest CT parameters obtained visually or by software are predictors of mortality. Predictive models including CT metrics were better predictors of death in comparison to clinical model.CONCLUSIONSIn COVID-19 patients, qualitative and quantitative chest CT parameters obtained visually or by software are predictors of mortality. Predictive models including CT metrics were better predictors of death in comparison to clinical model. PurposeTo test the association between death and both qualitative and quantitative CT parameters obtained visually and by software in coronavirus disease (COVID-19) early outbreak.MethodsThe study analyzed retrospectively patients underwent chest CT at hospital admission for COVID-19 pneumonia suspicion, between February 21 and March 6, 2020. CT was performed in case of hypoxemia or moderate-to-severe dyspnea. CT scans were analyzed for quantitative and qualitative features obtained visually and by software. Cox proportional hazards regression analysis examined the association between variables and overall survival (OS). Three models were built for stratification of mortality risk: clinical, clinical/visual CT evaluation, and clinical/software-based CT assessment. AUC for each model was used to assess performance in predicting death.ResultsThe study included 248 patients (70% males, median age 68 years). Death occurred in 78/248 (32%) patients. Visual pneumonia extent > 40% (HR 2.15, 95% CI 1.2–3.85, P = 0.01), %high attenuation area – 700 HU > 35% (HR 2.17, 95% CI 1.2–3.94, P = 0.01), exudative consolidations (HR 2.85–2.93, 95% CI 1.61–5.05/1.66–5.16, P < 0.001), visual CAC score > 1 (HR 2.76–3.32, 95% CI 1.4–5.45/1.71–6.46, P < 0.01/P < 0.001), and CT classified as COVID-19 and other disease (HR 1.92–2.03, 95% CI 1.01–3.67/1.06–3.9, P = 0.04/P = 0.03) were significantly associated with shorter OS. Models including CT parameters (AUC 0.911–0.913, 95% CI 0.873–0.95/0.875–0.952) were better predictors of death as compared to clinical model (AUC 0.869, 95% CI 0.816–0.922; P = 0.04 for both models).ConclusionsIn COVID-19 patients, qualitative and quantitative chest CT parameters obtained visually or by software are predictors of mortality. Predictive models including CT metrics were better predictors of death in comparison to clinical model. To test the association between death and both qualitative and quantitative CT parameters obtained visually and by software in coronavirus disease (COVID-19) early outbreak. The study analyzed retrospectively patients underwent chest CT at hospital admission for COVID-19 pneumonia suspicion, between February 21 and March 6, 2020. CT was performed in case of hypoxemia or moderate-to-severe dyspnea. CT scans were analyzed for quantitative and qualitative features obtained visually and by software. Cox proportional hazards regression analysis examined the association between variables and overall survival (OS). Three models were built for stratification of mortality risk: clinical, clinical/visual CT evaluation, and clinical/software-based CT assessment. AUC for each model was used to assess performance in predicting death. The study included 248 patients (70% males, median age 68 years). Death occurred in 78/248 (32%) patients. Visual pneumonia extent > 40% (HR 2.15, 95% CI 1.2-3.85, P = 0.01), %high attenuation area - 700 HU > 35% (HR 2.17, 95% CI 1.2-3.94, P = 0.01), exudative consolidations (HR 2.85-2.93, 95% CI 1.61-5.05/1.66-5.16, P < 0.001), visual CAC score > 1 (HR 2.76-3.32, 95% CI 1.4-5.45/1.71-6.46, P < 0.01/P < 0.001), and CT classified as COVID-19 and other disease (HR 1.92-2.03, 95% CI 1.01-3.67/1.06-3.9, P = 0.04/P = 0.03) were significantly associated with shorter OS. Models including CT parameters (AUC 0.911-0.913, 95% CI 0.873-0.95/0.875-0.952) were better predictors of death as compared to clinical model (AUC 0.869, 95% CI 0.816-0.922; P = 0.04 for both models). In COVID-19 patients, qualitative and quantitative chest CT parameters obtained visually or by software are predictors of mortality. Predictive models including CT metrics were better predictors of death in comparison to clinical model. |
| Author | Sverzellati, Nicola Milanese, Gianluca Petrini, Marcello Michieletti, Emanuele Morelli, Nicola Silva, Mario Maffi, Gabriele Villani, Gabriele D. Colombi, Davide Risoli, Camilla Bodini, Flavio C. Anselmi, Pietro |
| Author_xml | – sequence: 1 givenname: Davide orcidid: 0000-0002-2794-5237 surname: Colombi fullname: Colombi, Davide email: D.Colombi@ausl.pc.it organization: Department of Radiological Functions, Radiology Unit, “Guglielmo da Saliceto” Hospital – sequence: 2 givenname: Gabriele D. surname: Villani fullname: Villani, Gabriele D. organization: Department of Radiological Functions, Radiology Unit, “Guglielmo da Saliceto” Hospital – sequence: 3 givenname: Gabriele surname: Maffi fullname: Maffi, Gabriele organization: Department of Radiological Functions, Radiology Unit, “Guglielmo da Saliceto” Hospital – sequence: 4 givenname: Camilla surname: Risoli fullname: Risoli, Camilla organization: Department of Radiological Functions, Radiology Unit, “Guglielmo da Saliceto” Hospital – sequence: 5 givenname: Flavio C. surname: Bodini fullname: Bodini, Flavio C. organization: Department of Radiological Functions, Radiology Unit, “Guglielmo da Saliceto” Hospital – sequence: 6 givenname: Marcello surname: Petrini fullname: Petrini, Marcello organization: Department of Radiological Functions, Radiology Unit, “Guglielmo da Saliceto” Hospital – sequence: 7 givenname: Nicola surname: Morelli fullname: Morelli, Nicola organization: Department of Radiological Functions, Radiology Unit, “Guglielmo da Saliceto” Hospital – sequence: 8 givenname: Pietro surname: Anselmi fullname: Anselmi, Pietro organization: Department of Radiological Functions, Radiology Unit, “Guglielmo da Saliceto” Hospital – sequence: 9 givenname: Gianluca surname: Milanese fullname: Milanese, Gianluca organization: Department of Medicine and Surgery (DiMeC), Unit “Scienze Radiologiche”, University of Parma, Padiglione Barbieri – sequence: 10 givenname: Mario surname: Silva fullname: Silva, Mario organization: Department of Medicine and Surgery (DiMeC), Unit “Scienze Radiologiche”, University of Parma, Padiglione Barbieri – sequence: 11 givenname: Nicola surname: Sverzellati fullname: Sverzellati, Nicola organization: Department of Medicine and Surgery (DiMeC), Unit “Scienze Radiologiche”, University of Parma, Padiglione Barbieri – sequence: 12 givenname: Emanuele surname: Michieletti fullname: Michieletti, Emanuele organization: Department of Radiological Functions, Radiology Unit, “Guglielmo da Saliceto” Hospital |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33119835$$D View this record in MEDLINE/PubMed |
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| Copyright | American Society of Emergency Radiology 2020 American Society of Emergency Radiology 2020. |
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| Keywords | COVID-19 Survival analysis CT scan Computer Software Applications |
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| License | This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
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To test the association between death and both qualitative and quantitative CT parameters obtained visually and by software in coronavirus disease... To test the association between death and both qualitative and quantitative CT parameters obtained visually and by software in coronavirus disease (COVID-19)... PurposeTo test the association between death and both qualitative and quantitative CT parameters obtained visually and by software in coronavirus disease... |
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| SubjectTerms | Aged Attenuation Betacoronavirus Chest Computed tomography Coronavirus Infections - diagnostic imaging Coronavirus Infections - mortality Coronaviruses COVID-19 Death Dyspnea Emergency Medicine Exudation Female Humans Hypoxemia Imaging Male Mathematical models Medicine Medicine & Public Health Mortality Original Original Article Pandemics Parameters Performance prediction Pneumonia Pneumonia, Viral - diagnostic imaging Pneumonia, Viral - mortality Prediction models Predictive Value of Tests Qualitative analysis Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic - methods Radiology Regression analysis Retrospective Studies SARS-CoV-2 Software Tomography, X-Ray Computed - methods Viral diseases |
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| Title | Qualitative and quantitative chest CT parameters as predictors of specific mortality in COVID-19 patients |
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