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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
Novel coronavirus disease 2019: predicting prognosis with a computed tomography-based disease severity score and
Ali Sabri1, Amir H Davarpanah2, Arash Mahdavi3
1Department of Radiology, McMaster University, Niagara Health, St. Catharines, Ontario, Canada. sabri.ali@gmail.com
Insights
Predicting COVID-19 mortality and ICU admission is crucial. Lower oxygen saturation and more lung lobes affected by diffuse patterns indicate higher mortality risk in novel coronavirus disease 2019 patients.
Area of Science:
- Radiology and Imaging
- Infectious Diseases
- Critical Care Medicine
Background:
- Predictive models for novel coronavirus disease 2019 (COVID-19) mortality and intensive care unit (ICU) admission are lacking.
- Computed tomography (CT) and clinical data may offer insights into disease severity.
Purpose of the Study:
- To identify risk factors for mortality and ICU admission in COVID-19 patients.
- To explore the combined utility of CT imaging and clinical laboratory data for predicting outcomes.
Main Methods:
- Retrospective analysis of 63 polymerase chain reaction-confirmed COVID-19 patients.
- CT scans were analyzed for total CT score and number of involved lung lobes.
- Univariable and multivariable proportional hazard analyses were performed correlating CT findings, clinical data, and outcomes (ICU admission, in-hospital death).
Main Results:
- In-hospital mortality was associated with low oxygen saturation (<88%) and extensive lung lobe involvement with diffuse patterns.
- Multivariable analysis confirmed low oxygen saturation and diffuse lung involvement as mortality predictors.
- ICU admission risk was elevated by comorbidities (hypertension, ischemic heart disease), low oxygen saturation, and pericardial effusion.
Conclusions:
- CT findings combined with clinical data can identify patients at high risk for mortality and ICU admission in COVID-19.
- These findings can aid clinical decision-making and strategic healthcare planning during pandemics.
Introduction:
Currently, there are known contributing factors but no comprehensive methods for predicting the mortality risk or intensive care unit (ICU) admission in patients with novel coronavirus disease 2019 (COVID‑19).
Objectives:
The aim of this study was to explore risk factors for mortality and ICU admission in patients with COVID‑19, using computed tomography (CT) combined with clinical laboratory data.
Patients And Methods:
Patients with polymerase chain reaction-confirmed COVID‑19 (n = 63) from university hospitals in Tehran, Iran, were included. All patients underwent CT examination. Subsequently, a total CT score and the number of involved lung lobes were calculated and compared against collected laboratory and clinical characteristics. Univariable and multivariable proportional hazard analyses were used to determine the association among CT, laboratory and clinical data, ICU admission, and in‑hospital death.
Results:
By univariable analysis, in‑hospital mortality was higher in patients with lower oxygen saturation on admission (below 88%), higher CT scores, and a higher number of lung lobes (more than 4) involved with a diffuse parenchymal pattern. By multivariable analysis, in‑hospital mortality was higher in those with oxygen saturation below 88% on admission and a higher number of lung lobes involved with a diffuse parenchymal pattern. The risk of ICU admission was higher in patients with comorbidities (hypertension and ischemic heart disease), arterial oxygen saturation below 88%, and pericardial effusion.
Conclusions:
We can identify factors affecting in‑hospital death and ICU admission in COVID-19. This can help clinicians to determine which patients are likely to require ICU admission and to inform strategic healthcare planning in critical conditions such as the COVID‑19 pandemic.
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