a pattern categorization of ct findings to predict outcome of covid 19 pneumonia CORD-Papers (Version 1)

Title: A pattern categorization of CT findings to predict outcome of COVID-19 pneumonia
Abstract: Purpose 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 165 patients with COVID-19 (91 men 4-89 years) underwent chest CT were retrospectively enrolled. CT findings were categorized as Pattern0 (negative) Pattern1 (bronchopneumonia) Pattern2 (organizing pneumonia) Pattern3 (progressive organizing pneumonia) and Pattern4 (diffuse alveolar damage). 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 Pattern0-2 and Pattern3-4 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 (Pattern4 vs. Pattern0-3 [reference]; hazard-ratio[95%CI] 18.90[1.91-186.60] P=0.012). CT pattern (Pattern3-4 vs. Pattern0-2 [reference]; 0.26[0.08-0.88] P=0.030) and C-reactive protein (>10 vs. []10mg/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.
Published: 2020-05-25
DOI: 10.1101/2020.05.19.20107409
DOI_URL: http://doi.org/10.1101/2020.05.19.20107409
Author Name: Jin C
Author link: https://covid19-data.nist.gov/pid/rest/local/author/jin_c
Author Name: Wang Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wang_y
Author Name: Wu C C
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wu_c_c
Author Name: Zhao H
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhao_h
Author Name: Liang T
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liang_t
Author Name: Liu Z
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_z
Author Name: Jian Z
Author link: https://covid19-data.nist.gov/pid/rest/local/author/jian_z
Author Name: Li R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/li_r
Author Name: Wang Z
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wang_z
Author Name: Li F
Author link: https://covid19-data.nist.gov/pid/rest/local/author/li_f
Author Name: Zhou J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhou_j
Author Name: Cai S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/cai_s
Author Name: Liu Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_y
Author Name: Li H
Author link: https://covid19-data.nist.gov/pid/rest/local/author/li_h
Author Name: Li Z
Author link: https://covid19-data.nist.gov/pid/rest/local/author/li_z
Author Name: Liang Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liang_y
Author Name: Zhou H
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhou_h
Author Name: Wang X
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wang_x
Author Name: Ren Z
Author link: https://covid19-data.nist.gov/pid/rest/local/author/ren_z
Author Name: Yang J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/yang_j
sha: c8f9bbfb7753a226a511eaee4063d0bbd7b4a769
license: medrxiv
source_x: MedRxiv; WHO
source_x_url: https://www.who.int/
url: https://doi.org/10.1101/2020.05.19.20107409 http://medrxiv.org/cgi/content/short/2020.05.19.20107409v1?rss=1
has_full_text: TRUE
Keywords Extracted from Text Content: medRxiv preprint alveolar Pattern0-2 Pattern3-4 Pattern4 Pattern0-3 C-reactive 18.90[1.91-186.60 COVID-19 coronavirus men Pattern0 pattern-categories patients Pattern2 pulmonary Pattern1 Pattern3 hazard-ratio[95%CI Hanzhong lung lymphadenopathy medRxiv preprint COVID-19 [16] alveolar OP-like pleural Hubei province Patients bronchial creatine tube 2-8 DAD [16,24]. cardiovascular medRxiv preprint healthcare lesions upper thoracic cellular infiltrates C-reactive People's Republic 9-29 [7] [8] [9] COVID-19 [10]. SARS-CoV-2 7-39 chest CT neutrophil medRxiv oxygen fibrin COVID-19 P<0.01 pulmonary CT China1 coronavirus Shaanxi province D-dimer patient Axial CT Xi'an H1N1 lobe USA GGO Lung lobar patients lungs pulmonary lobar 19.1.7 [3] [4] [5] [6] P<0.017 barotrauma IL medRxiv preprint Tables Table 1 DAD troponin I H1N1 pneumonia Axial [7-9 lymphocyte Wuhan ( lung lobes lobes bronchovascular bundles https://doi.org/10.1101/2020.05.19.20107409 doi COVID-19 patients hyaline membrane interlobular septal Fisher's intraalveolar edema
Extracted Text Content in Record: First 5000 Characters:medRxiv preprint Purpose As global healthcare system is overwhelmed by novel coronavirus disease , 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 165 patients with COVID-19 (91 men, 4-89 years) underwent chest CT were retrospectively enrolled. CT findings were categorized as Pattern0 (negative), Pattern1 (bronchopneumonia), Pattern2 (organizing pneumonia), Pattern3 (progressive organizing pneumonia) and Pattern4 (diffuse alveolar damage). 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. 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 Pattern0-2 and Pattern3-4 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 (Pattern4 vs. Pattern0-3 [reference]; hazard-ratio[95%CI], 18.90[1.91-186.60], P=0.012). CT pattern (Pattern3-4 vs. Pattern0-2 [reference]; 0.26[0.08-0.88], P=0.030) and C-reactive protein (>10 All rights reserved. No reuse allowed without permission. : medRxiv preprint vs. ≤10mg/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. Since the latter part of December of 2019, an outbreak of respiratory disease caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has become a pandemic [1] . As of May 4, 2020, 3,356,205 laboratory-confirmed cases and 238,730 deaths have been reported [2] . Numerous studies have revealed the epidemiological, clinical and radiological characteristics of the novel coronavirus disease (COVID-19) [3] [4] [5] [6] . 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] . In addition, some laboratory indicators e.g. elevated hypersensitive troponin I, leukocytosis, neutrophilia, lymphopenia and elevated D-dimer were found to be linked with unfavorable All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 25, 2020. . https://doi.org/10.1101/2020.05.19.20107409 doi: medRxiv preprint clinical outcomes [7] [8] [9] . Presence of consolidation on computed tomography (CT) was also considered to be predictive of poor outcome in COVID-19 [10]. Despite the above, 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. It is recognized that CT plays a central role in diagnosis and management of COVID-19 pneumonia [11] [12] [13] . Reported CT findings of COVID-19 pneumonia included the ground glass opacities (GGO), consolidation, septal thickening mainly along the subpleural lungs or bronchovascular bundles or diffusely in the entire lungs [14] . These are highly suggestive of lung organization response to injury from COVID-19 pneumonia, similar to radiological findings in the diffuse alveolar damage (DAD) and organizing pneumonia (OP) [15] . Pathological studies also observed DAD in patients who succumbed to COVID-19 [16] . Previous studies have demonstrated a decreased survival rate of 35-50% in DAD, while most patients with OP had better prognosis [15] . In this regard, a pattern categorization of COVID-19 pneumonia, i.e. DAD and OP patterns may help the prognostic stratification. Based on the prior study regarding influenza A (H1N1) pneumonia [17] , Lee also suggested a pattern categorization of COVID-19, i.e. bronchopneumonia, OP and DAD [18] . A rapid progression of OP-like injury
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