Abstract
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 < 0.05). Remarkable evolution was observed in Pattern 0–2 and Pattern 3–4 within 3 and 2 weeks after symptom-onset, respectively; most of patterns remained thereafter. After controlling for age, CT pattern significantly correlated with adverse outcomes [Pattern 4 vs. Pattern 0–3 [reference]; hazard-ratio [95% CI], 18.90 [1.91–186.60], P = 0.012]. CT pattern [Pattern 3–4 vs. Pattern 0–2 [reference]; 0.26 [0.08–0.88], P = 0.030] and C-reactive protein [>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.
Highlights
Since the latter part of December of 2019, an outbreak of respiratory disease caused by severe acute respiratory syndromecoronavirus-2 (SARS-CoV-2) has become a pandemic [1]
After controlling for age, computed tomography (CT) pattern significantly correlated with adverse outcomes [Pattern 4 vs. Pattern 0–3 [reference]; hazard-ratio [95% CI], 18.90 [1.91–186.60], P = 0.012]
CT pattern categorization allied with clinical characteristics within 2 weeks after symptom onset would facilitate early prognostic stratification in COVID19 pneumonia
Summary
Since the latter part of December of 2019, an outbreak of respiratory disease caused by severe acute respiratory syndromecoronavirus-2 (SARS-CoV-2) has become a pandemic [1]. Despite the fact that more than 80% of infected patients manifest with only mild clinical symptoms [3], early identifying the risks of an adverse outcome remains the key to optimize management and improve survival. Previous studies found that advanced age and presence of comorbidity (e.g., cardiovascular disease or hypertension) were risk factors associated with an adverse outcome such as admission to intensive care unit (ICU), need for mechanical ventilation, or death [7, 8]. The identification of early prognostic signs of COVID-19 remains of urgent importance due to the diversity in clinical and imaging findings as well as the severity and rapid progression of disease. 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
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