Related Experiment Video
Updated: Jul 30, 2025
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
Chest CT opportunistic biomarkers for phenotyping high-risk COVID-19 patients: a retrospective multicentre study
Anna Palmisano1,2, Chiara Gnasso1,2, Alberto Cereda3
1Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, Italy.
Insights
Opportunistic biomarkers from chest CT scans can identify high-risk COVID-19 patients. These CT-derived markers improve risk stratification, aiding in predicting severe outcomes and guiding treatment decisions for SARS-CoV-2 infection.
Area of Science:
- Radiology and Imaging
- Infectious Diseases
- Cardiology
Background:
- Chest computed tomography (CT) is routinely performed on hospitalized COVID-19 patients.
- Opportunistic findings on these CT scans may offer valuable prognostic information.
- Phenotypization of high-risk COVID-19 patients is crucial for effective management.
Purpose of the Study:
- To evaluate the utility of opportunistic biomarkers from admission chest CT scans for phenotyping high-risk COVID-19 patients.
- To determine if these biomarkers can enhance the prediction of severe outcomes in COVID-19.
Main Methods:
- A multicentre retrospective study analyzed 1669 COVID-19 patients with chest CTs within 72 hours of admission.
- Opportunistic data on atherosclerotic profile, liver steatosis, myosteatosis, and osteoporosis were extracted from CT scans.
- Multivariate logistic regression models were used to assess prediction capabilities.
Main Results:
- CT-derived biomarkers (atherosclerosis, steatosis, osteoporosis) were more prevalent in patients with severe disease, ICU admission, and in-hospital death (p < 0.05).
- A model incorporating clinical and CT variables improved prediction of non-critical pneumonia (AUC 0.801 vs 0.789) and patient death (AUC 0.815 vs 0.800) compared to clinical variables alone.
Conclusions:
- Opportunistic biomarkers from chest CT scans significantly enhance the characterization and risk stratification of COVID-19 patients.
- These biomarkers can identify subclinical comorbidities, improving prognostic accuracy even without extensive clinical data.
- CT-derived opportunistic findings offer a valuable tool for managing COVID-19 patients and predicting outcomes.
Objective:
To assess the value of opportunistic biomarkers derived from chest CT performed at hospital admission of COVID-19 patients for the phenotypization of high-risk patients.
Methods:
In this multicentre retrospective study, 1845 consecutive COVID-19 patients with chest CT performed within 72 h from hospital admission were analysed. Clinical and outcome data were collected by each center 30 and 80 days after hospital admission. Patients with unknown outcomes were excluded. Chest CT was analysed in a single core lab and behind pneumonia CT scores were extracted opportunistic data about atherosclerotic profile (calcium score according to Agatston method), liver steatosis (≤ 40 HU), myosteatosis (paraspinal muscle F < 31.3 HU, M < 37.5 HU), and osteoporosis (D12 bone attenuation < 134 HU). Differences according to treatment and outcome were assessed with ANOVA. Prediction models were obtained using multivariate binary logistic regression and their AUCs were compared with the DeLong test.
Results:
The final cohort included 1669 patients (age 67.5 [58.5-77.4] yo) mainly men 1105/1669, 66.2%) and with reduced oxygen saturation (92% [88-95%]). Pneumonia severity, high Agatston score, myosteatosis, liver steatosis, and osteoporosis derived from CT were more prevalent in patients with more aggressive treatment, access to ICU, and in-hospital death (always p < 0.05). A multivariable model including clinical and CT variables improved the capability to predict non-critical pneumonia compared to a model including only clinical variables (AUC 0.801 vs 0.789; p = 0.0198) to predict patient death (AUC 0.815 vs 0.800; p = 0.001).
Conclusion:
Opportunistic biomarkers derived from chest CT can improve the characterization of COVID-19 high-risk patients.
Clinical Relevance Statement:
In COVID-19 patients, opportunistic biomarkers of cardiometabolic risk extracted from chest CT improve patient risk stratification.
Key Points:
• In COVID-19 patients, several information about patient comorbidities can be quantitatively extracted from chest CT, resulting associated with the severity of oxygen treatment, access to ICU, and death. • A prediction model based on multiparametric opportunistic biomarkers derived from chest CT resulted superior to a model including only clinical variables in a large cohort of 1669 patients suffering from SARS- CoV2 infection. • Opportunistic biomarkers of cardiometabolic comorbidities derived from chest CT may improve COVID-19 patients' risk stratification also in absence of detailed clinical data and laboratory tests identifying subclinical and previously unknown conditions.
More Related Videos
Related Concept Videos
01:30Pneumonia III: Complications and Assessment
01:20COPD: Pathogenesis and Clinical Features
The primary cause for the onset of COPD is cigarette smoking and exposure to air pollution. These hazardous factors initiate a chain reaction within the lungs, resulting in chronic inflammation, damage to the airways, and a...
01:27Chronic Obstructive Pulmonary Disease-IV: Assessement and Diagnostic Studies
Medical History
01:29Pulmonary Embolism II: Diagnostic Studies and Interprofessional Care
01:05Single Nucleotide Polymorphisms-SNPs