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?:abstract
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Background: As global healthcare system is overwhelmed by novel coronavirus disease (COVID-19), early identification of risks of adverse outcomes becomes the key to optimize management and improve survival This study aimed to provide a CT-based pattern categorization to predict outcome of COVID-19 pneumonia Methods: One hundred and sixty-five patients with COVID-19 (91 men, 4–89 years) underwent chest CT were retrospectively enrolled CT findings were categorized as Pattern 0 (negative), Pattern 1 (bronchopneumonia pattern), Pattern 2 (organizing pneumonia pattern), Pattern 3 (progressive organizing pneumonia pattern), and Pattern 4 (diffuse alveolar damage pattern) Clinical findings were compared across different categories Time-dependent progression of CT patterns and correlations with clinical outcomes, i e „ discharge or adverse outcome (admission to ICU, requiring mechanical ventilation, or death), with pulmonary sequelae (complete absorption or residuals) on CT after discharge were analyzed Results: Of 94 patients with outcome, 81 (86 2%) were discharged, 3 (3 2%) were admitted to ICU, 4 (4 3%) required mechanical ventilation, 6 (6 4%) died 31 (38 3%) had complete absorption at median day 37 after symptom onset Significant differences between pattern-categories were found in age, disease severity, comorbidity and laboratory results (all P 10 vs ≤ 10 mg/L [reference];0 31 [0 13–0 72], P = 0 006] were risk factors associated with pulmonary residuals Conclusion: CT pattern categorization allied with clinical characteristics within 2 weeks after symptom onset would facilitate early prognostic stratification in COVID-19 pneumonia © Copyright © 2020 Jin, Tian, Wang, Wu, Zhao, Liang, Liu, Jian, Li, Wang, Li, Zhou, Cai, Liu, Li, Li, Liang, Zhou, Wang, Ren and Yang
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