Multicenter cohort study demonstrates more consolidation in upper lungs on initial CT increases the risk of adverse clinical outcome in COVID-19 patients

Abstract

Rationale: Chest computed tomography (CT) has been used for the coronavirus disease 2019 (COVID-19) monitoring. However, the imaging risk factors for poor clinical outcomes remain unclear. In this study, we aimed to assess the imaging characteristics and risk factors associated with adverse composite endpoints in patients with COVID-19 pneumonia. Methods: This retrospective cohort study enrolled patients with laboratory-confirmed COVID-19 from 24 designated hospitals in Jiangsu province, China, between 10 January and 18 February 2020. Clinical and initial CT findings at admission were extracted from medical records. Patients aged < 18 years or without available clinical or CT records were excluded. The composite endpoints were admission to ICU, acute respiratory failure occurrence, or shock during hospitalization. The volume, density, and location of lesions, including ground-glass opacity (GGO) and consolidation, were quantitatively analyzed in each patient. Multivariable logistic regression models were used to identify the risk factors among age and CT parameters associated with the composite endpoints. Results: In this study, 625 laboratory-confirmed COVID-19 patients were enrolled; among them, 179 patients without an initial CT at admission and 25 patients aged < 18 years old were excluded and 421 patients were included in analysis. The median age was 48.0 years and the male proportion was 53% (224/421). During the follow-up period, 64 (15%) patients had a composite endpoint. There was an association of older age (odds ratio [OR], 1.04; 95% confidence interval [CI]: 1.01-1.06; P = 0.003), larger consolidation lesions in the upper lung (Right: OR, 1.13; 95%CI: 1.03-1.25, P =0.01; Left: OR,1.15; 95%CI: 1.01-1.32; P = 0.04) with increased odds of adverse endpoints. Conclusion:There was an association of older age and larger consolidation in upper lungs on admission with higher odds of poor outcomes in patients with COVID-19.

Lung ultrasound predicts clinical course and outcomes in COVID-19 patients

“…This is in concordance with previously described evidence in patients with decompensated heart failure, in which semi-quantitative B-line assessment was shown to be a prognostic indicator of adverse outcomes and mortality [9]. Moreover, our results are in line with a publication regarding chest CT in COVID-19 patients, in which the total burden of lung involvement and anterior segment involvement at admission were associated with higher rates of adverse clinical composite endpoints of ICU admission, respiratory failure and shock [38]. The peripheral distribution of lung infiltrates in COVID-19 makes LUS a reliable imaging study, and can reduce the number of CT scans performed [17,39], with their associated risks of infection spread, radiation exposure and the need to disinfect the CT room [22].…”

A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images

“…Noteworthy, radiologists across the world have provided new insights by accessing the lung CT as additional diagnosis or screening tool of COVID-19 pneumonia. Basically, bilateral GGOs, consolidative pulmonary opacities, as well as the prominent subpleural distribution are regarded as classical features in chest CT images of patients diagnosed with COVID-19 pneumonia, which are similar to those reported with SARS-CoV and MERS-CoV [9][10][11][12][13][14][15][16][17][18][19]. In parallel with these findings, our study also demonstrated higher incidence of GGOs and consolidative opacities in the CT images from COVID pneumonia patients.…”

“…We used a commercially available deep learning algorithm (Deepwise & League of PhD Technology Co. Ltd.) [16] which was previously trained and validated in 19,291 CT scans from 14,435 patients collected from seven hospitals in China (mean age 40.9 ± 0.9; 51% male, 49% female) with the inclusion criteria of (1) CT images with slice thickness ≤ 2 mm and (2) patients diagnosed as pneumonia or healthy participants, and the exclusion criteria of (1) patients had history of pulmonary surgery; (2) CT images diagnosed as infection but not pneumonia, such as pulmonary tuberculosis; and (3) CT images with poor quality, e.g., heavy breathing artifacts and metal artifacts. Among all the 14,435 collected patients, 2154 patients were diagnosed as COVID-19 by pathogenic test, while 5874 patients were diagnosed as other pulmonary pneumonia (bacterial pneumonia, fungal pneumonia, and other viral pneumonia).…”

Lower Circulating Interferon-Gamma Is a Risk Factor for Lung Fibrosis in COVID-19 Patients

“…Thereafter, the index that evaluated the severity of pneumonia was computed by the AI system (Dr. Wise@Pneumonia, version 1.0, Beijing Deepwise & League of Ph.D. Technology Co., Ltd., China), which had been proved to be effective in the analysis of CT images from COVID-19 patients ( 13 , 14 ). As shown in Figure 2 , there were three major modules in this system.…”