ArticleEmergency radiology2020
Qualitative and quantitative chest CT parameters as predictors of specific mortality in COVID-19 patients.
Article in Emergency radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review and meta-analysis on coronary calcifications in COVID-19.Emergency radiology · 2022Pooled it
- Quantitative HRCT-Derived Fibrosis Burden as an Independent Predictor of Mortality in Patients with Idiopathic Pulmonary Fibrosis: A Retrospective Observational Study.Journal of clinical medicine · 2026Article
- Automated quantitative chest CT for mortality prediction in COVID-19 patients in a resource-limited emergency setting in Gaza: a retrospective study using LungCTAnalyzer.Emergency radiology · 2025Article
- Consolidation-to-ground-glass opacity ratio on chest CT as a prognostic marker for critical outcomes in COVID-19: a retrospective cohort study.BMC pulmonary medicine · 2025Article
- Quantification and Analysis of Lung Involvement by Artificial Intelligence in Patients with Progressive Pulmonary Fibrosis Treated with Nintedanib.Medicina (Kaunas, Lithuania) · 2025Article
- Effect of slice thickness on quantitative analysis of interstitial lung disease: a retrospective volumetric chest CT study.La Radiologia medica · 2025Article
- Prognostication and integration of bedside lung ultrasound and computed tomography imaging findings with clinical features to Predict COVID-19 In-hospital mortality and ICU admission.Emergency radiology · 2025Article
- Insights from 3D modeling and fluid dynamics in COVID-19 pneumonia.Medical & biological engineering & computing · 2024Article
- Quantitative CT at Follow-Up of COVID-19 Pneumonia: Relationship with Pulmonary Function Tests.Diagnostics (Basel, Switzerland) · 2023Article
- Clinical utilization of artificial intelligence-based COVID-19 pneumonia quantification using chest computed tomography - a multicenter retrospective cohort study in Japan.Respiratory research · 2023Article
- A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia.European radiology experimental · 2023Article
- Clinical Characteristics and Predictors of In-Hospital Mortality of Patients Hospitalized with COVID-19 Infection.Journal of clinical medicine · 2022Article
- Quantitative chest computed tomography combined with plasma cytokines predict outcomes in COVID-19 patients.Heliyon · 2022Article
- Co-Administration of Remdesivir and Azithromycin May Protect against Intensive Care Unit Admission in COVID-19 Pneumonia Requiring Hospitalization: A Real-Life Observational Study.Antibiotics (Basel, Switzerland) · 2022Article
- Chest CT Total Severity Score on Admission to Predict In-Hospital Mortality in COVID-19 Patients with Acute and Chronic Renal Impairment.Diagnostics (Basel, Switzerland) · 2022Article
- Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients.Diagnostics (Basel, Switzerland) · 2022Article
- Predictors of intubation and mortality in COVID-19 patients: a retrospective study.Journal of anesthesia, analgesia and critical care · 2021Article
- Automated Quantitative Lung CT Improves Prognostication in Non-ICU COVID-19 Patients beyond Conventional Biomarkers of Disease.Diagnostics (Basel, Switzerland) · 2021Article
- Cardiovascular findings on chest computed tomography associated with COVID-19 adverse clinical outcomes.American heart journal plus : cardiology research and practice · 2021Article
- Quantitative chest CT combined with plasma cytokines predict outcomes in COVID-19 patients.medRxiv : the preprint server for health sciences · 2021Article
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12 authors.
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No grant is acknowledged in the PubMed record.
Abstract
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.
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