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Association between Initial Chest CT or Clinical Features and Clinical Course in Patients with Coronavirus Disease 2019 Pneumonia

Zhe Liu, Chao Jin, Carol C. Wu, Ting Liang, Huifang Zhao, Yan Wang, Zekun Wang, Fen Li, Jie Zhou, Shubo Cai, Lingxia Zeng, Jian Yang

2020Korean Journal of Radiology65 citationsDOIOpen Access PDF

Abstract

OBJECTIVE: To identify the initial chest computed tomography (CT) findings and clinical characteristics associated with the course of coronavirus disease 2019 (COVID-19) pneumonia. MATERIALS AND METHODS: Baseline CT scans and clinical and laboratory data of 72 patients admitted with COVID-19 pneumonia (39 men, 46.2 ± 15.9 years) were retrospectively analyzed. Baseline CT findings including lobar distribution, presence of ground glass opacities, consolidation, linear opacities, and lung severity score were evaluated. The outcome event was recovery with hospital discharge. The time from symptom onset to discharge or the end of follow-up (for those remained hospitalized) was recorded. Data were censored in events such as death or discharge without recovery. Multivariable Cox proportional hazard regression was used to explore the association between initial CT, clinical or laboratory findings, and discharge with recovery, whereby hazard ratio (HR) values < 1 indicated a lower rate of discharge at four weeks and longer time until discharge. RESULTS: = 0.008) were two significant independent factors that influenced recovery and discharge. CONCLUSION: Lung severity score > 4 and reduced lymphocyte count at initial evaluation were independently associated with a significantly lower rate of recovery and discharge and extended hospitalization in patients admitted for COVID-19 pneumonia.

Topics & Concepts

MedicinePneumoniaCoronavirus disease 2019 (COVID-19)DiseaseRadiologyCoronavirus2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Organizing pneumoniaInternal medicinePathologyLungInfectious disease (medical specialty)OutbreakCOVID-19 diagnosis using AIUltrasound in Clinical ApplicationsRadiomics and Machine Learning in Medical Imaging