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Early Warning Information for Severe and Critical Patients With COVID-19 Based on Quantitative CT Analysis of Lung Segments.

ABSTRACT: Background: The coronavirus disease 2019 (COVID-19) outbreak is spreading rapidly around the world. Purpose: We aimed to explore early warning information for patients with severe/critical COVID-19 based on quantitative analysis of chest CT images at the lung segment level. Materials and Methods: A dataset of 81 patients with coronavirus disease 2019 (COVID-19) treated at Wuhan Wuchang hospital in Wuhan city from 21 January 2020 to 14 February 2020 was retrospectively analyzed, including ordinary and severe/critical cases. The time course of all subjects was divided into four stages. The differences in each lobe and lung segment between the two groups at each stage were quantitatively analyzed using the percentage of lung involvement (PLI) in order to investigate the most important segment of lung involvement in the severe/critical group and its corresponding time point. Results: Lung involvement in the ordinary and severe/critical groups reached a peak on the 18th and 14th day, respectively. In the first stage, PLIs in the right middle lobe and the left superior lobe between the two groups were significantly different. In the second stage and the fourth stage, there were statistically significant differences between the two groups in the whole lung, right superior lobe, right inferior lobe and left superior lobe. The rapid progress of the lateral segment of the right middle lobe on the second day and the anterior segment of the right upper lobe on the 13th day may be a warning sign for severe/critical patients. Age was the most important demographic characteristic of the severe/critical group. Conclusion: Quantitative assessment based on the lung segments of chest CT images provides early warning information for potentially severe/critical patients.

SUBMITTER: Yuyun X

PROVIDER: S-EPMC8155286 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveThe aim of this study was to investigate the clinical and computed tomography (CT) features associated with severe and critical coronavirus disease 2019 (COVID-19) pneumonia.Materials and methodsEighty-three patients with COVID-19 pneumonia including 25 severe/critical cases and 58 ordinary cases were enrolled. The chest CT images and clinical data of them were reviewed and compared. The risk factors associated with disease severity were analyzed.ResultsCompared with the ordinary patients, the severe/critical patients had older ages, higher incidence of comorbidities, cough, expectoration, chest pain, and dyspnea. The incidences of consolidation, linear opacities, crazy-paving pattern, and bronchial wall thickening in severe/critical patients were significantly higher than those of the ordinary patients. Besides, severe/critical patients showed higher incidences of lymph node enlargement, pericardial effusion, and pleural effusion than the ordinary patients. The CT scores of severe/critical patients were significantly higher than those of the ordinary patients (P < 0.001). Receiver operating characteristic curve showed that the sensitivity and specificity of CT score were 80.0% and 82.8%, respectively, for the discrimination of the 2 types. The clinical factors of age older than 50 years, comorbidities, dyspnea, chest pain, cough, expectoration, decreased lymphocytes, and increased inflammation indicators were risk factors for severe/critical COVID-19 pneumonia. Computed tomography findings of consolidation, linear opacities, crazy-paving pattern, bronchial wall thickening, high CT scores, and extrapulmonary lesions were features of severe/critical COVID-19 pneumonia.ConclusionsThere are significant differences in clinical symptoms, laboratory examinations, and CT manifestations between the ordinary patients and the severe/critical patients. Many factors are related to the severity of the disease, which can help clinicians to judge the severity of the patient and evaluate the prognosis.

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Early Warning Information for Severe and Critical Patients With COVID-19 Based on Quantitative CT Analysis of Lung Segments.

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