A Pattern Categorization of CT Findings to Predict Outcome of 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..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:8 |
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| Enthalten in: |
Frontiers in public health - 8(2020) vom: 07., Seite 567672 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Jin, Chao [VerfasserIn] |
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| Links: |
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| Themen: |
CT pattern |
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| Anmerkungen: |
Date Completed 14.05.2021 Date Revised 29.03.2024 published: Electronic-eCollection Citation Status MEDLINE |
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| doi: |
10.3389/fpubh.2020.567672 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM316421227 |
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| 520 | |a Copyright © 2020 Jin, Tian, Wang, Wu, Zhao, Liang, Liu, Jian, Li, Wang, Li, Zhou, Cai, Liu, Li, Li, Liang, Zhou, Wang, Ren and Yang. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a CT pattern | |
| 650 | 4 | |a clinical outcome | |
| 650 | 4 | |a computed tomography | |
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| 650 | 4 | |a pulmonary sequelae | |
| 700 | 1 | |a Tian, Cong |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yan |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Carol C |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Huifang |e verfasserin |4 aut | |
| 700 | 1 | |a Liang, Ting |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Zhe |e verfasserin |4 aut | |
| 700 | 1 | |a Jian, Zhijie |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Runqing |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Zekun |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Fen |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Jie |e verfasserin |4 aut | |
| 700 | 1 | |a Cai, Shubo |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Yang |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Hao |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Zhongyi |e verfasserin |4 aut | |
| 700 | 1 | |a Liang, Yukun |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Heping |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xibin |e verfasserin |4 aut | |
| 700 | 1 | |a Ren, Zhuanqin |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Jian |e verfasserin |4 aut | |
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