1. Qualitative and quantitative chest CT parameters as predictors of specific mortality in COVID-19 patients
- Author
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Marcello Petrini, Flavio Cesare Bodini, Camilla Risoli, Gianluca Milanese, Emanuele Michieletti, Nicola Morelli, Gabriele Maffi, Pietro Anselmi, Mario Silva, Nicola Sverzellati, Davide Colombi, and Gabriele D. Villani
- Subjects
Male ,CT scan ,medicine.medical_specialty ,Coronavirus disease 2019 (COVID-19) ,Pneumonia, Viral ,Chest ct ,030218 nuclear medicine & medical imaging ,Hypoxemia ,03 medical and health sciences ,Betacoronavirus ,0302 clinical medicine ,Predictive Value of Tests ,Internal medicine ,Medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Pandemics ,Survival analysis ,Aged ,Retrospective Studies ,business.industry ,SARS-CoV-2 ,COVID-19 ,030208 emergency & critical care medicine ,Retrospective cohort study ,Specific mortality ,Computer Software Applications ,medicine.disease ,Pneumonia ,Radiology Nuclear Medicine and imaging ,Predictive value of tests ,Emergency Medicine ,Radiographic Image Interpretation, Computer-Assisted ,Female ,Radiography, Thoracic ,Original Article ,medicine.symptom ,business ,Coronavirus Infections ,Tomography, X-Ray Computed ,Software - 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. Supplementary Information The online version contains supplementary material available at 10.1007/s10140-020-01867-1.
- Published
- 2020