Prognostic Implications of CT Feature Analysis in Patients with COVID-19: a Nationwide Cohort Study

Journal of Korean Medical Science 2021³â 36±Ç 8È£ p.51 ~ p.51

Á¤¿¬ÁÖ(Jeong Yeon-Joo) - Pusan National University Hospital Department of Radiology
³²º¸´Ù(Nam Bo-Da) - Soonchunhyang University College of Medicine Soonchunhyang University Seoul Hospital Department of Radiology
À¯Áø¿µ(Yoo Jin-Young) - Chungbuk National University Hospital Department of Radiology
±è°ÇÀÏ(Kim Kun-Il) - Pusan National University Yangsan Hospital Department of Radiology
°­Èñ(Kang Hee) - Kosin University Gospel Hospital Department of Radiology
ȲÁ¤È­(Hwang Jung-Hwa) - Soonchunhyang University College of Medicine Soonchunhyang University Seoul Hospital Department of Radiology
±èÀ±Çö(Kim Yun-Hyeon) - Chonnam National University Hospital Department of Radiology
ÀÌ°æ¼ö(Lee Kyung-Soo) - Sungkyunkwan University School of Medicine Samsung Medical Center Department of Radiology

Abstract

Background: Few studies have classified chest computed tomography (CT) findings of coronavirus disease 2019 (COVID-19) and analyzed their correlations with prognosis. The present study aimed to evaluate retrospectively the clinical and chest CT findings of COVID-19 and to analyze CT findings and determine their relationships with clinical severity.

Methods: Chest CT and clinical features of 271 COVID-19 patients were assessed. The presence of CT findings and distribution of parenchymal abnormalities were evaluated, and CT patterns were classified as bronchopneumonia, organizing pneumonia (OP), or diffuse alveolar damage (DAD). Total extents were assessed using a visual scoring system and artificial intelligence software. Patients were allocated to two groups based on clinical outcomes, that is, to a severe group (requiring O2 therapy or mechanical ventilation, n = 55) or a mild group (not requiring O2 therapy or mechanical ventilation, n = 216). Clinical and CT features of these two groups were compared and univariate and multivariate logistic regression analyses were performed to identify independent prognostic factors.

Results: Age, lymphocyte count, levels of C-reactive protein, and procalcitonin were significantly different in the two groups. Forty-five of the 271 patients had normal chest CT findings. The most common CT findings among the remaining 226 patients were ground-glass opacity (98%), followed by consolidation (53%). CT findings were classified as OP (93%), DAD (4%), or bronchopneumonia (3%) and all nine patients with DAD pattern were included in the severe group. Uivariate and multivariate analyses showed an elevated procalcitonin (odds ratio [OR], 2.521; 95% confidence interval [CI], 1.001?6.303, P = 0.048), and higher visual CT scores (OR, 1.137; 95% CI, 1.042?1.236; P = 0.003) or higher total extent by AI measurement (OR, 1.048; 95% CI, 1.020?1.076; P < 0.001) were significantly associated with a severe clinical course.

Conclusion: CT findings of COVID-19 pneumonia can be classified into OP, DAD, or bronchopneumonia patterns and all patients with DAD pattern were included in severe group. Elevated inflammatory markers and higher CT scores were found to be significant predictors of poor prognosis in patients with COVID-19 pneumonia.

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Chest, Coronavirus, COVID-19, Tomography, X-Ray Computed, Pneumonia, Prognosis
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The main results of this study are that GGO, consolidation, interstitial thickening, a crazy-paving appearance, mediastinal and hilar lymph node enlargement, visual CT scores and total extent by automatic measurement of lung parenchymal abnormalities, and the presence of a DAD pattern were significantly more prevalent in the severe clinical outcome group rather than in the mild group.; the most common CT findings observed for COVID-19 pneumonia were GGO, consolidation, intralobular interstitial thickening, and a crazy-paving appearance with a bilateral, lower lung and peripheral and peribronchovascular predominance exhibiting patterns of bronchopneumonia, OP, or DAD. Older age and inflammatory marker elevations at an early stage were found to risk factors of a poor clinical outcome.
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