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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Vydané v:Emergency radiology Ročník 27; číslo 6; s. 701 - 710
Hlavní autori: Colombi, Davide, Villani, Gabriele D., Maffi, Gabriele, Risoli, Camilla, Bodini, Flavio C., Petrini, Marcello, Morelli, Nicola, Anselmi, Pietro, Milanese, Gianluca, Silva, Mario, Sverzellati, Nicola, Michieletti, Emanuele
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cham Springer International Publishing 01.12.2020
Springer Nature B.V
Predmet:
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
COVID-19
Survival analysis
CT scan
Computer Software Applications
ISSN:1070-3004, 1438-1435, 1438-1435
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Shrnutí: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.
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ISSN:1070-3004
1438-1435
1438-1435
DOI:10.1007/s10140-020-01867-1