Pulmonary Fibrosis Followed by Severe Pneumonia in Patients with COVID- 19 infection: A Prospective Multicentre Study

Pulmonary Fibrosis Followed by Severe Pneumonia in Patients with COVID- 19 infection: A Prospective Multicentre Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pulmonary Fibrosis Followed by Severe Pneumonia in Patients with COVID- 19 infection: A Prospective Multicentre Study Junghyun Kim, Ganghee Chae, Won-Young Kim, Chi-Ryang Chung, Young‑Jae Cho, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3868352/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Backgrounds : The management of lung complications, especially fibrosis, after coronavirus disease (COVID-19) pneumonia, is an important issue in the COVID-19 post-pandemic era. We aimed to investigate risk factors for pulmonary fibrosis development in patients with severe COVID-19 pneumonia. Methods Clinical and radiologic data were prospectively collected from 64 patients who required mechanical ventilation due to COVID-19 pneumonia and were enrolled from eight hospitals in South Korea. Fibrotic changes on chest computed tomography (CT) was evaluated by visual assessment, and extent of fibrosis (mixed disease score) was measured using automatic quantification system. Results Sixty-four patients were enrolled, and their mean age was 58.2 years (64.1% were males). On chest CT (median interval: 60 days [interquartile range, IQR; 41–78 days] from enrolment), 35 (54.7%) patients showed ≥ 3 fibrotic lesions. The most frequent fibrotic change was traction bronchiectasis (47 patients, 73.4%). Median extent of fibrosis measured by automatic quantification was 10.6% (IQR, 3.8–40.7%). In a multivariable Cox proportional hazard model, which included nine variables with a p-value of < 0.10 in an unadjusted analysis as well as age, sex, and body mass index, male sex (hazard ratio [HR], 3.01; 95% confidence interval [CI], 1.27–7.11) and higher initial sequential organ failure assessment (SOFA) score (HR, 1.18; 95% CI, 1.02–1.37) were independently associated with pulmonary fibrosis (≥ 3 fibrotic lesions). Conclusion Our data suggests that male gender and higher SOFA score at intensive care unit admission were associated with pulmonary fibrosis in patients with severe COVID-19 pneumonia requiring mechanical ventilation. pulmonary fibrosis COVID-19 pneumonia Figures Figure 1 Figure 2 INTRODUCTION Coronavirus disease (COVID-19) has resulted in a global pandemic since the first case of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was reported in Wuhan, China, in December 2019 [ 1 ]. As of June 9, 2023, 767 million confirmed cases and 6.9 million deaths have been reported globally, according to the World Health Organization (WHO) epidemiological data [ 2 ]. Clinical features of COVID-19 ranged from asymptomatic patients to those with severe complication of pneumonia requiring mechanical ventilation, which had been reported to be approximately one-fifth of hospitalized patients with COVID-19 infection [ 3 , 4 ]. Pneumonia due to COVID-19 showed various radiologic findings, including ground glass opacity (GGO) pattern and whole lung bilateral infiltration in cases of acute respiratory distress syndrome [ 5 , 6 ]. Patients who required mechanical ventilation showed more extensive pulmonary radiologic features, suggesting pulmonary sequelae in terms of pulmonary fibrosis, which cause residual respiratory symptoms, such as dyspnoea and cough for months [ 6 , 7 ]. Pulmonary fibrosis has the potential of causing permanent damage to the lungs, even after treatment [ 8 , 9 ]. To our knowledge, the mechanisms of pulmonary fibrosis are associated with activation of the pulmonary fibrosis pathway, which are related to microenvironmental insults and viral infections, resulting in direct alveolar and cytokine-related lung damages [ 10 , 11 ]. Damages caused by positive pressure ventilation may also affect pulmonary fibrosis in patients who underwent mechanical ventilation [ 12 ]. Since the COVID-19 pandemic, previous studies have reported that older age was significantly related to pulmonary fibrosis after COVID-19 infection [ 13 , 14 ]. Disease severity, such as higher initial oxygen demand [ 7 , 15 ] and some biomarkers, including inflammatory cytokines (interleukin-6 [IL-6], lactate dehydrogenase, interferon gamma, and serum Krebs von den Lungen [KL-6]), have been suggested as possible predictors for pulmonary fibrosis [ 6 , 16 , 17 ]. However, data on pulmonary fibrosis among critically ill patients requiring intensive care unit (ICU) admission with mechanical ventilation are still limited. Therefore, in this study, we aimed to evaluate the prevalence and risk factors of pulmonary fibrosis development in critically ill patients with COVID-19 pneumonia requiring mechanical ventilation. MATERIAL AND METHODS Study population This multicentre prospective study was conducted in eight centres (the Asan Medical Centre, National Medical Centre, Ulsan University Hospital, Chung-Ang University Hospital, Samsung Medical Centre, Seoul National University Bundang Hospital, Seoul National University Hospital, and Seoul Metropolitan Government-Seoul National) involved in COVID-19 infection care in the Republic of Korea. We initially enrolled 110 consecutive patients who were admitted and managed for COVID-19 pneumonia between July and December 2021. Inclusion criteria were as follows: (1) age ≥ 19 years; (2) SARS-CoV-2 infection confirmed by nasopharyngeal swab reverse transcriptase-polymerase chain reaction; (3) definite evidence of pneumonia on initial chest radiography (chest x-ray or computed tomography [CT]); (4) patients requiring mechanical ventilation; and (5) severe or critical COVID-19 infection, according to the WHO clinical classification for COVID-19 pneumonia [ 18 ]. Patients were excluded if they had a previous diagnosis of interstitial lung disease. According to the study protocol, survivors were followed up in each hospital’s outpatient department one month after discharge where chest CT scans and pulmonary function test(PFT) were performed. For patients who died before discharge or were transferred to other hospitals, the last scans performed during the hospitalization were retrieved. Of the patients enrolled, 64 patients with available follow-up CT images were finally included. The study protocol was approved by the Institutional Review Board of the eight individual centres, including the Asan Medical Centre (IRB No. 2021 − 0769). The present study was registered with the Clinical Research Informative Service (No. KCT0006312). Written informed consent was provided to individual patients or their caregivers who were unable to state their own will due to medical status. Clinical data All information were prospectively collected from each centre and recorded into a central electronic Case Report Form (iCReaT, https://icreat.nih.go.kr/icreat/ ). No identifiable data were collected. Data on demographic characteristics (age, sex, body mass index [BMI], smoking history, and underlying comorbidities) and clinical status at ICU admission (vital signs, laboratory results, Acute Physiology and Chronic Health Evaluation [APACHE] and sequential organ failure assessment [SOFA] scores, and ventilator parameters) were collected. During hospitalization, the length of hospital and ICU stay and duration of ventilator use were recorded. Data on treatment (use of continuous renal replacement therapy [CRRT], extracorporeal membrane oxygenation [ECMO], nitric oxide gas inhalation, and prone positioning and medication use, including antiviral agents and steroids) were also collected. Forced vital capacity (FVC) and diffusing capacity (DLco) by spirometry or plethysmography were measured, according to the European Respiratory Society/American Thoracic Society recommendations [ 19 , 20 ]. Radiologic assessment Fibrosis-like changes on chest CT were evaluated by visual assessment of two radiologists (B.D.N and J.H.H) who specialize in chest radiology with 7 and 26 years of experience in chest radiology, respectively. Findings compatible with fibrosis-like changes were as follows: (1) reticulation; (2) architectural distortion ± parenchymal band; (3) traction bronchiectasis/bronchiolectasis; or (4) honeycombing [ 21 ] (Fig. 1 ). Definition of pulmonary fibrosis was valid if there are ≥ 3 components of fibrosis-like changes. To minimize inter-reader variability, a consensus meeting was held for sample cases before conducting further analyses. The extent of pulmonary fibrosis was measured by computer-based analysis of high-resolution CT images using an automated quantification system for a more objective evaluation of the extent of fibrosis in patients with interstitial lung disease. Ground glass opacities (GGO), mixed disease (MD), which the summation of field of reticulation and honeycombing, consolidation (CON), or total lung density (TLD: summation of the field of GGO, MD, and CON) were scored (Fig. 2 ). Statistical analyses In this study, primary outcome was identifying the prevalence and risk factors of pulmonary fibrosis on chest CT at one month after hospital discharge. Descriptive statistics are presented as numbers (percentages) for categorical variables and mean ± standard deviation (SD) for continuous variables. We compared two groups (fibrosis and no-fibrosis groups) using the χ 2 or Fisher’s exact test for categorical variables. The Mann-Whitney U test was used for the comparison of median and interquartile range (IQR) for continuous variables between the two groups. The association between possible risk factors and pulmonary fibrosis was estimated using Cox proportional hazards analysis with crude and multivariable adjusted models. After including variables with p-values of < 0.10 in the univariate model, as well as age, sex, and BMI, adjusted hazard ratio (aHR) and 95% confidence intervals (CIs) were calculated in the multivariable analysis by enter methods after excluding selected variables showing collinearity in the model. A p-value of < 0.05 was considered statistically significant. All statistical analyses were performed using SAS statistical package (version 9.4; SAS Institute Inc., Cary, NC, USA). RESULTS Baseline characteristics In this study, 64 patients were included, with a mean age of 58.2 years, and 64.1% of the patients were males. Median follow-up duration from the index date (enrolment) to the date of the CT was 60 days (IQR, 41–78 days). Mean SOFA and APACHE scores were 4.7 (SD, 2.9) and 13.6 (SD, 9.3), respectively. In terms of treatment after the ICU admission, about 90% of the patients were administered antiviral agents (remdesivir), and all the patients were administered steroid. Thirty-four (53.1%) patients were placed in the prone position, 11 (17.2%) underwent ECMO support, followed by 6 (9.4%) who underwent CRRT. Fibrosis-like changes were identified on chest CT: ≥ 1 in 49 patients (76.6%), ≥ 2 in 48 (75.0%), and ≥ 3 in 35 (54.7%). When a fibrosis group was defined as patients with ≥ 3 fibrosis-like changes on chest CT, there was no significant difference in demographic characteristics and comorbidities between the fibrosis and no-fibrosis groups (Table 1 ). Most laboratory and clinical findings at ICU admission and treatment were also not different between the two groups (Tables 2 and 3 ). However, CRRT was performed more frequently in the no-fibrosis group compared to that performed in the fibrosis group (17.2% vs. 2.9%, p = 0.05). Table 1 Baseline characteristics of the study population (at ICU admission) Variables Total Fibrosis No-fibrosis p -value † Number of patients 64 35 29 Age, years 58.2 ± 13.7 60.5 ± 11.4 55.4 ± 15.9 0.144 Males 41 (64.1) 21 (60.0) 20 (69.0) 0.457 BMI, kg/m 2 26.3 ± 5.4 25.4 ± 3.9 27.3 ± 6.6 0.160 Smoking history Never-smoker 43 (67.2) 27 (77.1) 16 (55.2) 0.273 Ex-smoker 7 (10.9) 2 (5.7) 5 (17.2) Current smoker 7 (10.9) 3 (8.6) 4 (13.8) Missing 7 (10.9) 3 (8.6) 4 (13.8) Alcohol drinking, yes 26 (40.6) 12 (34.3) 14 (48.3) 0.256 Diabetes mellitus, yes 21 (32.8) 15 (42.9) 6 (20.7) 0.060 Hypertension, yes 25 (39.1) 15 (42.9) 10 (34.5) 0.493 Cardiovascular disease, yes 8 (12.5) 5 (14.3) 3 (10.3) 0.635 Cerebrovascular disease, yes 3 (4.7) 3 (8.6) 0 - Liver disease, yes 4 (6.3) 2 (5.7) 2 (6.9) 0.845 Kidney disease, yes 1 (1.6) 1 (2.9) 0 - Malignancy, yes 4 (6.3) 2 (5.7) 2 (6.9) 0.845 Lung diseases, yes 3 (9.4) 1 (2.9) 2 (6.9) 0.585 Asthma 2 (3.2) 1 (2.9) 1 (3.5) 0.892 TB destroyed lung 1 (1.6) 0 (-) 1 (3.5) - Charlson comorbidity index ≥ 2 8 (12.5) 6 (17.1) 2 (6.9) 0.217 Data are expressed number (%) or mean ± SD unless otherwise indicated. Table 2 Clinical and laboratory findings of the study population (at ICU admission) Variables Total Fibrosis No-fibrosis p -value Number of patients 64 35 29 Vital sign Systolic BP, mmHg 129.8 ± 27.3 129.6 ± 5.1 130.1 ± 24.7 0.944 Diastolic BP, mmHg 74.2 ± 17.2 73.7 ± 18.4 74.9 ± 15.8 0.785 Heart rate 89.6 ± 23.5 85.6 ± 25.3 94.6 ± 20.3 0.129 Respiratory rate 26.0 ± 6.0 25.4 ± 6.5 26.6 ± 5.3 0.443 Oxygen saturation, % 91.4 ± 6.4 90.8 ± 7.5 92.1 ± 5.0 0.407 P/F ratio 102.4 ± 50.7 103.9 ± 54.8 100.0 ± 44.8 0.778 Severity assessment score† SOFA score 4.7 ± 2.9 5.1 ± 3.0 4.3 ± 2.8 0.203 APACHE score 13.6 ± 9.3 15.1 ± 10.9 11.7 ± 6.1 0.139 Laboratory findings WBC 10^ 3 9,900 ± 4,460 10,740 ± 4,250 9,000 ± 4,600 0.129 Lymphocyte, % 9.0 ± 5.6 8.4 ± 4.7 10.0 ± 6.6 0.284 Neutrophil, % 85.8 ± 7.2 87.1 ± 6.4 84.1 ± 7.9 0.114 ANC /µL 8,470 ± 4,240 9,010 ± 4,040 7,800 ± 4,470 0.272 Haemoglobin, g/dL 13.2 ± 1.9 13.2 ± 2.0 13.3 ± 1.8 0.849 Platelet, 10^ 3 238.4 ± 106.9 250.4 ± 121.5 223.9 ± 85.7 0.318 AST, U/L 75.7 ± 66.3 65.2 ± 46.7 88.5 ± 83.4 0.205 ALT, U/L 58.9 ± 70.9 45.6 ± 38.6 75.1 ± 95.0 0.172 Total protein, g/dL 6.0 ± 0.7 6.0 ± 0.7 6.0 ± 0.7 0.909 Albumin, g/dL 3.1 ± 0.6 3.0 ± 0.5 3.1 ± 0.8 0.465 PT, sec 13.1 ± 1.8 13.3 ± 2.0 12.8 ± 1.3 0.261 BUN, mg/dL 21.8 ± 10.9 22.7 ± 10.0 20.7 ± 12.1 0.466 Creatinine, mg/dL 0.8 ± 0.4 0.8 ± 0.7 0.8 ± 0.7 0.857 Sodium, mmol/L 136.9 ± 5.4 136.7 ± 6.2 137.1 ± 4.4 0.738 Potassium, mmol/L 4.1 ± 0.6 4.1 ± 0.7 4.2 ± 0.6 0.919 LDH, U/L 602.0 ± 242.9 646.1 ± 283.3 554.2 ± 183.9 0.177 CRP, mg/L 11.2 ± 10.2 12.1 ± 10.6 10.1 ± 9.7 0.461 Procalcitonin, ng/mL 1.3 ± 5.5 1.0 ± 3.4 1.7 ± 7.3 0.660 Troponin, I ng/mL 0.4 ± 1.9 0.6 ± 2.7 0.1 ± 0.3 0.294 D-dimer, µg/mL 2.2 ± 3.5 2.1 ± 3.0 2.4 ± 4.0 0.842 Fibrinogen, mg/dL 535.9 ± 202.6 544.1 ± 221.5 524.1 ± 176.1 0.719 Data are expressed as mean ± standard deviation and p -values were tested by Mann-Whitney U test Table 3 Treatment of the study population (during ICU care) Variables Total Fibrosis Non-fibrosis p -value Number of patients 64 35 29 Use of Remdesivir 57 (89.1) 32 (91.4) 25 (86.2) 0.505 Duration, days 5.0 (5.0–10.0) 5.0 (5.0–10.0) 5.0 (5.0–10.0) 0.959 Use of Steroid 64 (100.0) 35 (100.0) 29 (100.0) 1.000 Duration, days 6.0 (3.0–9.5) 6.0 (3.0–9.0) 6.0 (4.0–10.0) 0.347 Equivalent dose (dexamethasone)/day 6.0 (6.0–10.6) 6.0 (6.0–11.7) 6.0 (6.0–6.0) 0.635 Total equivalent dose 42.0 (22.0–60.0) 42.0 (20.0–60.0) 42.0 (24.0–60.0) 0.787 Use of antibiotics 63 (98.4) 34 (97.1) 29 (100.0) 0.547 Use of neuromuscular blocker 47 (73.4) 26 (74.3) 21 (72.4) 0.433 Duration, days 4.0 (3.0–7.0) 4.0 (3.0–7.0) 4.0 (3.0–6.0) 0.747 Use of monoclonal antibody 20 (31.3) 8 (22.9) 12 (41.4) 0.114 Tocilizumab 17 (85.0) 7 (87.5) 10 (83.3) Regdanvimab 2 (10.0) 1 (12.5) 1 (8.3) Basiliximab 1 (5.0) 0 (-) 1 (8.3) NO inhalation 9 (14.1) 5 (14.3) 4 (13.8) 0.955 Duration, days 5.0 (3.0–8.0) 5.0 (4.0–10.0) 4.5 (2.5–7.0) 0.623 Prone position 34 (53.1) 20 (57.1) 14 (48.3) 0.479 Duration, days 3.0 (2.0–4.0) 3.0 (2.0–3.5) 3.5 (3.0–6.0) 0.104 CRRT 6 (9.4) 1 (2.9) 5 (17.2) 0.054 Duration, days 11.5 (3.0–12.0) 12.0 (12.0–12.0) 11.0 (3.0–12.0) 0.768 ECMO 11 (17.2) 5 (14.3) 6 (20.7) 0.502 Duration, days 27.5 (9.0–62.0) 62.0 (8.0–64.0) 19.0 (17.0–36.0) 0.685 Data are expressed as number of patients (%) or median (interquartile range) unless otherwise indicated. Radiological findings and lung function The most frequent findings of fibrotic change were traction bronchiectasis (n = 47, 73.4%), followed by architectural distortion with or without parenchymal band (n = 44, 68.8%) and reticulation (n = 40, 62.5%) (Table 4 ). The median extent of fibrosis (MD) was 10.6% (IQR, 3.8–40.7%), as measured by automatic quantification (Table 5 ). The median extent of fibrosis was numerically higher in the fibrosis group (12.4% vs. 4.4%, p = 0.15) than that in the no-fibrosis group without statistical significance; however, the extent of GGO was significantly higher in the fibrosis group (20.1% vs. 14.0%, p = 0.003) (Table 5 .). Table 4 Visual assessment for chest CT in the study population Variables (%) Number of patients 64 Fibrosis-like change Reticulation 40 (62.5) Architectural distortion ± parenchymal band 44 (68.8) Traction bronchiectasis/bronchiectasis 47 (73.4) Honeycombing 11 (17.2) Number of fibrosis-like change component ≥ 1 49 (76.6) Number of fibrosis-like change component ≥ 2 48 (75.0) Number of fibrosis-like change component ≥ 3 35 (54.7) Data are expressed number (%) Table 5 Automatic quantification for chest CT images and lung function in the study population Variables Total Non-fibrosis Fibrosis Number of patients 64 29 35 Automatic quantification Median (interquartile range) p -value‡ GGO, % 18.9 (13.2–24.9) 14.0 (11.1–20.1) 20.1 (16.2–26.9) 0.003 Mixed disease, % 10.6 (3.8–40.7) 4.4 (1.4–47.6) 12.4 (6.6–36.2) 0.149 Consolidation, % 0.1 (0.00.5) 0.1 (0.0–0.9) 0.1 (0.1–0.3) 0.977 TLD, % 40.5 (20.864.4) 28.5 (15.6–66.5) 42.5 (25.7–60.2) 0.373 Variables Total Non-fibrosis Fibrosis Number of patients 27 11 16 Pulmonary function test Median (interquartile range) p -value‡ FVC % predicted 74.0 (60.0–82.0) 79.0 (71.0–90.0) 70.5 (60.0–76.5) 0.108 DLco % predicted 62.0 (46.0–71.0) 69.0 (61.0–83.0) 55.0 (41.0–67.0) 0.069 Data are expressed as mean ± SD and p-values were tested by Mann-Whitney U test Pulmonary function test was performed in 27 (42.2%) of the 64 patients. The median follow-up duration from the index date to the date of the pulmonary function test was 147 days (IQR, 128–231 days). Both FVC and DLco were numerically lower in the fibrosis group than those in the no-fibrosis group without statistical significance (Table 5 ). Risk factors for post-COVID-19 pulmonary fibrosis Twelve variables (hypertension and cardiovascular disease, SOFA score, respiratory rate, serum level of white blood cells, blood urea nitrogen [BUN], and creatinine, and use of neuromuscular blockers and ECMO) that were associated with pulmonary fibrotic changes in the univariate Cox analysis with a p-value of < 0.10 ( Supplementary Table 1 ) were selected as candidate variables in the multivariable analysis, along with age, sex, and BMI. In the multivariable Cox analysis, males (aHR, 3.14; 95% CI, 1.08–9.14) and higher SOFA score (aHR, 1.18; 95% CI, 1.02–1.37) were independently associated with lung fibrosis (Table 6 ). Additionally, higher levels of serum BUN ≥ 19 had higher risk of pulmonary fibrosis (aHR, 2.69; 95% CI, 1.20–7.25, p = 0.05), with marginal significance in the final model. Table 6 Cox analysis of risk factors for pulmonary fibrosis in the study population Variables Crude HR (95% CI) p -value Adjusted HR (95% CI) ‡ p -value Age, year 1.04 (1.01–1.07) 0.024 1.01 (0.96–1.06) 0.758 Male 1.41 (0.71–2.79) 0.324 3.14 (1.08 – 9.14) 0.036 BMI, kg/m 2 0.97 (0.90–1.05) 0.487 1.02 (0.91–1.14) 0.727 Hypertension, yes 2.05 (1.00–4.18) 0.049 1.70 (0.66–4.36) 0.271 Cardiovascular disease, yes 2.72 (1.01–7.34) 0.048 2.45 (0.77–7.77) 0.129 Respiratory rate, /min † 0.93 (0.87–0.98) 0.013 SOFA score, 1-point increase 1.15 (1.04–1.27) 0.008 1.18 (1.02 – 1.37) 0.028 WBC ≥ 12000, /mm 3 2.09 (1.00–4.38) 0.051 0.91 (0.35–2.36) 0.843 BUN ≥ 19, mg/dL 2.57 (1.26–5.24) 0.008 2.69 (0.99 – 7.33) 0.053 Creatinine > 1.4, mg/dL† 2.76 (0.95–8.05) 0.098 Use of neuromuscular blocker, yes 0.29 (0.12–0.68) 0.005 0.39 (0.12–1.30) 0.124 ECMO, yes 0.41 (0.16–1.08) 0.069 0.42 (0.11–1.62) 0.206 DISCUSSION This multicentre study found risk factors, including various clinical characteristics for pulmonary fibrosis in patients with COVID-19 pneumonia requiring mechanical ventilation. About half of the patients (54.7%) had significant fibrosis, and male sex and higher SOFA scores at ICU admission were found to be risk factors for fibrotic changes. To our knowledge, this is the first study to explore pulmonary fibrosis in patients with severe COVID-19 pneumonia requiring mechanical ventilation. In this study, 54.7% of the patients had significant pulmonary fibrosis (≥ 3 fibrosis-like changes on chest CT), which was similar to previous studies [ 7 , 22 , 23 ]. The prevalence of pulmonary fibrotic changes in patients with COVID-19 following ICU admission has been reported to range between 30–70% [ 22 , 24 – 26 ]. A study by Huang et al. reported that 41 (50.6%) of 81 survivors of severe COVID-19 pneumonia had fibrosis on CT after hospital discharge [ 22 ]. In another study by Han et al., which assessed pulmonary sequalae and the risk factor for lung fibrotic-like changes in survivors of severe COVID-19 pneumonia, 40 (35.1%) of 114 patients showed fibrosis-like changes [ 24 ]. Additionally, in a prospective study by Caruso et al., they found that 72% (85 of 118) of patients with COVID-19 pneumonia had fibrosis-like changes on CT [ 26 ]. However, the definition and terminology of fibrosis, such as fibrosis-like changes, fibrotic-like changes, or fibrosis [ 25 – 27 ], has been inconsistent in previous studies. Additionally, fibrosis-like changes at baseline CT improved on follow-up CT [ 6 , 28 ], suggesting that some of the initial fibrosis may be transient. Therefore, to overcome this limitation, we used strict criteria to define fibrosis in this study. The SOFA score on the day of admission to the ICU was a predictive factor for pulmonary fibrosis in our study. In a previous study that prospectively enrolled survivors of COVID-19 pneumonia requiring oxygen supplement, the SOFA score at admission were linearly associated with the risk of fibrosis-like changes [ 29 ]. However, in this study, the risk of patients (about 40% of survivors) requiring mechanical ventilation was not explored. As a component of the SOFA score, lower PaO2/FiO2 ratio (ratio of arterial oxygen partial pressure to fractional inspired oxygen) was suggested as a predictive factor of lung fibrosis in patients with COVID-19 infection patient in previous studies [ 7 , 15 , 30 ]. The other components of the SOFA score, including acute kidney injury and liver disease, which were reported to be associated with patients with severe COVID-19 pneumonia requiring ICU care in previous studies have been reported as predictive factors for pulmonary fibrosis [ 31 , 32 ]. Additionally, previous studies reported hypertension and cardiovascular disease as risk factors associated pulmonary fibrosis development; however, no significant association was found after further adjustment for clinical and laboratory parameters [ 33 , 34 ]. In our study, cardiovascular disease was not a predictive factor for pulmonary fibrosis in the final multivariate analysis. Our findings suggested that male sex was associated with pulmonary fibrosis after COVID-19 pneumonia, which was not consistently found in previous studies. Some studies reported that male were more likely to present with COVID-19 pulmonary fibrosis [ 34 , 35 ], although others did not show any significant result [ 8 , 30 , 33 ]. However, a meta-analysis reported that males had higher mortality after COVID-19 than females [ 36 ]. Given the sex difference in the risk of idiopathic pulmonary fibrosis (IPF), in this study, the higher risk of pulmonary fibrosis after COVID-19 infection found in males seems possible, but further studies are needed to confirm the association. Higher level of serum BUN has been suggested as an indicator for poor prognosis of pneumonia [ 37 ], and was independently associated with mortality of critically ill patients, regardless of creatinine level [ 38 , 39 ]. In COVID-19 pneumonia, one retrospective cohort study reported that initial BUN and D-dimer levels were associated with mortality in COVID-19 patients with high predictive value (c-index, 0.94) [ 40 ]. However, since studies have rarely explored the association of BUN and development of pulmonary fibrosis, future longitudinal studies should be performed to find the association with pulmonary fibrosis in COVID-19 infection. This study has some limitations. First, the number of patients included in this study was relatively small, which may have limited the ability to generate statistically significant results. Nevertheless, in our study, the multivariate analysis allowed us to identify significant risk factors associated with the development of pulmonary fibrosis after COVID-19 pneumonia. Second, this study did not collect baseline CT images; thus, it did not completely exclude patients with pre-existing fibrosis. However, we excluded patients with a history of pre-existing pulmonary fibrosis by taking a detailed medical history. Third, the follow-up CTs were performed within a relatively short period of time, and the prevalence of pulmonary fibrosis at long-term follow-up may differ. Therefore, we used a stricter definition of pulmonary fibrosis in this study to reduce differences. Nevertheless, the strength of our study is that it was a multicentre prospective study, it focused on patients with severe COVID-19 requiring mechanical ventilation, and analysed fibrotic changes by both visual assessment and automated quantification of CTs. In conclusion, our findings suggest that in patients with severe COVID-19 pneumonia requiring mechanical ventilation, 54.7% have significant pulmonary fibrosis, and male sex and higher SOFA scores indicate an increased risk of pulmonary fibrosis. Declarations Acknowledgement We would like to thank Grace Hyun J. Kim PhD (David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA) for the assistance in applying and interpreting quantitative CT images for our analysis. Funding This study was supported by grants from the Basic Science Research Program (NRF-2022R1A2B5B02001602) and Bio & Medical Technology Development Program (NRF-2022M3A9E4082647) of the National Research Foundation of Korea (NRF) funded by the Ministry of Science & ICT, Republic of Korea. This study was also supported by the National Institute of Health research project (2021ER190400, 2021ER120701) and Korea Environment Industry & Technology Institute through Core Technology Development Project for Environmental Diseases Prevention and Management Program funded by the Korea Ministry of Environment (ARQ202201450001), Republic of Korea. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication. Conflicts of interest None declared. Disclosure The authors report no disclosures. Authors’ contributions JK, JWS planned this study, had access to the data, and took responsibility for the integrity of the data and data analysis. JK, JWS, and GC contributed to study concept and design, analysis, and manuscript preparation. HYY, WYK, CRC, YJC, JL, YJ, JSJ, TYP, JHH, and BDN contributed to data collection and manuscript preparation. All authors have read and agreed to the published version of the manuscript. Data availability The datasets and statistical analysis used for the current study can be repeated on a reasonable request. Ethics approval and consent to participate The study protocol was approved by the Institutional Review Board of the eight individual centres, including the Asan Medical Centre (IRB No. 2021-0769). The present study was registered with the Clinical Research Informative Service (No. KCT0006312). Written informed consent was provided to individual patients or caregivers of those who were unable to state their own will due to medical status. Consent for publication Not applicable (This study does not contain data from any individual person.) References Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, Zhao X, Huang B, Shi W, Lu R, et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N Engl J Med. 2020;382:727–33. Weekly epidemiological update on COVID-19. [ https://www.who.int/publications/m/item/weekly-epidemiological-update-on-covid-19---30-november-2022] . Gao YD, Ding M, Dong X, Zhang JJ, Kursat Azkur A, Azkur D, Gan H, Sun YL, Fu W, Li W, et al. Risk factors for severe and critically ill COVID-19 patients: A review. Allergy. 2021;76:428–55. Ma Q, Liu J, Liu Q, Kang L, Liu R, Jing W, Wu Y, Liu M. Global Percentage of Asymptomatic SARS-CoV-2 Infections Among the Tested Population and Individuals With Confirmed COVID-19 Diagnosis: A Systematic Review and Meta-analysis. JAMA Netw Open. 2021;4:e2137257. Shi H, Han X, Jiang N, Cao Y, Alwalid O, Gu J, Fan Y, Zheng C. Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect Dis. 2020;20:425–34. Zou JN, Sun L, Wang BR, Zou Y, Xu S, Ding YJ, Shen LJ, Huang WC, Jiang XJ, Chen SM. The characteristics and evolution of pulmonary fibrosis in COVID-19 patients as assessed by AI-assisted chest HRCT. PLoS ONE. 2021;16:e0248957. Nabahati M, Ebrahimpour S, Khaleghnejad Tabari R, Mehraeen R. Post-COVID-19 pulmonary fibrosis and its predictive factors: a prospective study. Egypt J Radiol Nuclear Med. 2021;52:1–7. Fernandez-Plata R, Higuera-Iglesias AL, Torres-Espindola LM, Aquino-Galvez A, Velazquez Cruz R, Camarena A, Chavez Alderete J, Romo Garcia J, Alvarado-Vasquez N, Martinez Briseno D et al. Risk of Pulmonary Fibrosis and Persistent Symptoms Post-COVID-19 in a Cohort of Outpatient Health Workers. Viruses. 2022;14. Yoon HY, Uh ST. Post-Coronavirus Disease 2019 Pulmonary Fibrosis: Wait or Needs Intervention. Tuberc Respir Dis (Seoul). 2022;85:320–31. Tran S, Ksajikian A, Overbey J, Li P, Li Y. Pathophysiology of Pulmonary Fibrosis in the Context of COVID-19 and Implications for Treatment: A Narrative Review. Cells. 2022;11. Delpino MV, Quarleri J. SARS-CoV-2 Pathogenesis: Imbalance in the Renin-Angiotensin System Favors Lung Fibrosis. Front Cell Infect Microbiol. 2020;10:340. Cabrera-Benitez NE, Parotto M, Post M, Han B, Spieth PM, Cheng WE, Valladares F, Villar J, Liu M, Sato M, et al. Mechanical stress induces lung fibrosis by epithelial-mesenchymal transition. Crit Care Med. 2012;40:510–7. Yasin R, Gomaa AAK, Ghazy T, Hassanein SA, latif Ibrahem RA, Khalifa MH. Predicting lung fibrosis in post-COVID-19 patients after discharge with follow-up chest CT findings. Egypt J Radiol Nuclear Med. 2021;52:1–13. Li X, Shen C, Wang L, Majumder S, Zhang D, Deen MJ, Li Y, Qing L, Zhang Y, Chen C. Pulmonary fibrosis and its related factors in discharged patients with new corona virus pneumonia: a cohort study. Respir Res. 2021;22:1–11. Huang C, Huang L, Wang Y, Li X, Ren L, Gu X, Kang L, Guo L, Liu M, Zhou X. 6-month consequences of COVID-19 in patients discharged from hospital: a cohort study. The Lancet. 2021;397:220–32. Naderi N, Rahimzadeh M. Krebs von den Lungen-6 (KL-6) as a clinical marker for severe COVID-19: A systematic review and meta-analyses. Virology. 2022;566:106–13. Hu ZJ, Xu J, Yin JM, Li L, Hou W, Zhang LL, Zhou Z, Yu YZ, Li HJ, Feng YM, Jin RH. Lower Circulating Interferon-Gamma Is a Risk Factor for Lung Fibrosis in COVID-19 Patients. Front Immunol. 2020;11:585647. Organization WH. Living guidance for clinical management of COVID-19. Miller MR, Hankinson J, Brusasco V, Burgos F, Casaburi R, Coates A, Crapo R, Enright P, van der Grinten CP, Gustafsson P, et al. Standardisation of spirometry. Eur Respir J. 2005;26:319–38. Graham BL, Brusasco V, Burgos F, Cooper BG, Jensen R, Kendrick A, MacIntyre NR, Thompson BR, Wanger J. 2017 ERS/ATS standards for single-breath carbon monoxide uptake in the lung. Eur Respir J. 2017;49. Lynch DASN, Travis WD, Brown KK, Colby TV, Galvin JR, Goldin JG, Hansell DM, Inoue Y, Johkoh T, Nicholson AG, Knight SL, Raoof S, Richeldi L, Ryerson CJ, Ryu JH, Wells AU. Diagnostic criteria for idiopathic pulmonary fibrosis: a Fleischner Society White Paper. Lancet Respir Med. 2018;6:138–53. Huang W, Wu Q, Chen Z, Xiong Z, Wang K, Tian J, Zhang S. The potential indicators for pulmonary fibrosis in survivors of severe COVID-19. J Infect. 2021;82:e5–e7. Rob JJ, van Gassel JLM, Bels A, Raafs T, van BC GMC, van de Poll SO, Simons, Lieke WL, van der Meer HA, Gietema, et al. High Prevalence of Pulmonary Sequelae at 3 Months after Hospital Discharge in Mechanically Ventilated Survivors of COVID-19. Am J Respir Crit Care Med. 2021;203:371–4. Han X, Fan Y, Alwalid O, Li N, Jia X, Yuan M, Li Y, Cao Y, Gu J, Wu H. Six-month follow-up chest CT findings after severe COVID-19 pneumonia. Radiology. 2021;299:E177–86. Gulati A, Lakhani P. Interstitial lung abnormalities and pulmonary fibrosis in COVID-19 patients: a short-term follow-up case series. Clin Imaging. 2021;77:180–6. Caruso D, Guido G, Zerunian M, Polidori T, Lucertini E, Pucciarelli F, Polici M, Rucci C, Bracci B, Nicolai M. Post-Acute sequelae of COVID-19 pneumonia: six-month Chest CT follow-up. Radiology. 2021;301:E396–E405. Han X, Fan Y, Alwalid O, Zhang X, Jia X, Zheng Y, Shi H. Fibrotic interstitial lung abnormalities at 1-year follow-up CT after severe COVID-19. Radiology. 2021;301:E438–40. van Raaij BFM, Stoger JL, Hinnen C, Penfornis KM, de Jong CMM, Klok FA, Roukens AHE, Veldhuijzen DS, Arbous MS, Noordam R, et al. Fibrotic-like abnormalities notably prevalent one year after hospitalization with COVID-19. Respir Med Res. 2022;82:100973. McGroder CF, Zhang D, Choudhury MA, Salvatore MM, D'Souza BM, Hoffman EA, Wei Y, Baldwin MR, Garcia CK. Pulmonary fibrosis 4 months after COVID-19 is associated with severity of illness and blood leucocyte telomere length. Thorax. 2021;76:1242–5. Lee I, Kim J, Yeo Y, Lee JY, Jeong I, Joh JS, Kim G, Chin BS, Kim Y, Kim MK et al. Prognostic Factors for Pulmonary Fibrosis Following Pneumonia in Patients with COVID-19: A Prospective Study. J Clin Med. 2022;11. Chiang KC, Imig JD, Kalantar-Zadeh K, Gupta A. Kidney in the net of acute and long-haul coronavirus disease 2019: a potential role for lipid mediators in causing renal injury and fibrosis. Curr Opin Nephrol Hypertens. 2022;31:36–46. Campos-Murguia A, Roman-Calleja BM, Toledo-Coronado IV, Gonzalez-Regueiro JA, Solis-Ortega AA, Kusulas-Delint D, Cruz-Contreras M, Cruz-Yedra N, Cubero FJ, Nevzorova YA, et al. Liver fibrosis in patients with metabolic associated fatty liver disease is a risk factor for adverse outcomes in COVID-19. Dig Liver Dis. 2021;53:525–33. Li F, Deng J, Song Y, Wu C, Yu B, Wang G, Li J, Zhong Y, Liang F. Pulmonary fibrosis in patients with COVID-19: A retrospective study. Front Cell Infect Microbiol. 2022;12:1013526. Mohammadi A, Balan I, Yadav S, Matos WF, Kharawala A, Gaddam M, Sarabia N, Koneru SC, Suddapalli SK, Marzban S. Post-COVID-19 Pulmonary Fibrosis. Cureus. 2022;14:e22770. Aul DR, Gates DJ, Draper DA, Dunleavy DA, Ruickbie DS, Meredith DH, Walters DN, van Zeller DC, Taylor DV, Bridgett DM, et al. Complications after discharge with COVID-19 infection and risk factors associated with development of post-COVID pulmonary fibrosis. Respir Med. 2021;188:106602. Li J, Huang DQ, Zou B, Yang H, Hui WZ, Rui F, Yee NTS, Liu C, Nerurkar SN, Kai JCY, et al. Epidemiology of COVID-19: A systematic review and meta-analysis of clinical characteristics, risk factors, and outcomes. J Med Virol. 2021;93:1449–58. Feng DY, Zhou YQ, Zou XL, Zhou M, Yang HL, Chen XX, Zhang TT. Elevated Blood Urea Nitrogen-to-Serum Albumin Ratio as a Factor That Negatively Affects the Mortality of Patients with Hospital-Acquired Pneumonia. Can J Infect Dis Med Microbiol. 2019;2019:1547405. Beier K, Eppanapally S, Bazick HS, Chang D, Mahadevappa K, Gibbons FK, Christopher KB. Elevation of blood urea nitrogen is predictive of long-term mortality in critically ill patients independent of normal creatinine. Crit Care Med. 2011;39:305–13. Chang CL, Sullivan GD, Karalus NC, Mills GD, McLachlan JD, Hancox RJ. Predicting early mortality in acute exacerbation of chronic obstructive pulmonary disease using the CURB65 score. Respirology. 2011;16:146–51. Cheng A, Hu L, Wang Y, Huang L, Zhao L, Zhang C, Liu X, Xu R, Liu F, Li J, et al. Diagnostic performance of initial blood urea nitrogen combined with D-dimer levels for predicting in-hospital mortality in COVID-19 patients. Int J Antimicrob Agents. 2020;56:106110. Additional Declarations No competing interests reported. Supplementary Files covid19fibrosisresressupplement.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3868352","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268626581,"identity":"f2944bee-a498-4020-86c6-24b6ba2ac794","order_by":0,"name":"Junghyun Kim","email":"","orcid":"","institution":"Hallym University Dongtan Sacred Heart Hospital, Hallym University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Junghyun","middleName":"","lastName":"Kim","suffix":""},{"id":268626582,"identity":"0b9ed9de-1efa-44dd-9cf0-ff43823c5262","order_by":1,"name":"Ganghee 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01:59:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3868352/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3868352/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50115160,"identity":"e8f7984b-3121-4c54-98bb-b5284c76bf0e","added_by":"auto","created_at":"2024-01-24 18:36:32","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":206561,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative radiologic findings of pulmonary fibrosis in study participants\u003c/p\u003e\n\u003cp\u003ea) Computed tomography showing predominantly subpleural reticulation (arrow).\u003c/p\u003e\n\u003cp\u003eb) Honeycombing is indicated by the arrow in the right lower lobe.\u003c/p\u003e\n\u003cp\u003ec) Architectural distortion are indicated by the arrow in the left lower lobe.\u003c/p\u003e\n\u003cp\u003ed) Traction bronchiectasis and parenchymal band in the right lower lobe (black arrow) and left lower lobe (white arrow).\u003c/p\u003e","description":"","filename":"covid19fibrosisresresFIG1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3868352/v1/a83e5fc7ec83b23dfd84c40a.jpg"},{"id":50115162,"identity":"3dc2dcc9-9c0b-4dc6-a6a3-7dd96ab25b0d","added_by":"auto","created_at":"2024-01-24 18:36:33","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":101792,"visible":true,"origin":"","legend":"\u003cp\u003eAutomated quantification system evaluated by computer-based analysis of high-resolution computed tomography imaging\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGGO: ground glass opacities, MD: Mixed disease, CON: consolidation\u003c/em\u003e\u003c/p\u003e","description":"","filename":"covid19fibrosisresresFIG2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3868352/v1/a26d8875aa04f810a44b1688.jpg"},{"id":50160838,"identity":"875828e8-2d49-46e1-a21c-46fb7da73a1c","added_by":"auto","created_at":"2024-01-25 13:13:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":644499,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3868352/v1/9c8537fc-3e31-48da-94fa-745c58622452.pdf"},{"id":50115161,"identity":"b07d1954-cab3-4398-be7f-98b832875e59","added_by":"auto","created_at":"2024-01-24 18:36:32","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":39043,"visible":true,"origin":"","legend":"","description":"","filename":"covid19fibrosisresressupplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-3868352/v1/a281c1a518f2eeb9feb5389c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pulmonary Fibrosis Followed by Severe Pneumonia in Patients with COVID- 19 infection: A Prospective Multicentre Study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCoronavirus disease (COVID-19) has resulted in a global pandemic since the first case of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was reported in Wuhan, China, in December 2019 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As of June 9, 2023, 767\u0026nbsp;million confirmed cases and 6.9\u0026nbsp;million deaths have been reported globally, according to the World Health Organization (WHO) epidemiological data [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Clinical features of COVID-19 ranged from asymptomatic patients to those with severe complication of pneumonia requiring mechanical ventilation, which had been reported to be approximately one-fifth of hospitalized patients with COVID-19 infection [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Pneumonia due to COVID-19 showed various radiologic findings, including ground glass opacity (GGO) pattern and whole lung bilateral infiltration in cases of acute respiratory distress syndrome [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Patients who required mechanical ventilation showed more extensive pulmonary radiologic features, suggesting pulmonary sequelae in terms of pulmonary fibrosis, which cause residual respiratory symptoms, such as dyspnoea and cough for months [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Pulmonary fibrosis has the potential of causing permanent damage to the lungs, even after treatment [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. To our knowledge, the mechanisms of pulmonary fibrosis are associated with activation of the pulmonary fibrosis pathway, which are related to microenvironmental insults and viral infections, resulting in direct alveolar and cytokine-related lung damages [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Damages caused by positive pressure ventilation may also affect pulmonary fibrosis in patients who underwent mechanical ventilation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince the COVID-19 pandemic, previous studies have reported that older age was significantly related to pulmonary fibrosis after COVID-19 infection [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Disease severity, such as higher initial oxygen demand [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and some biomarkers, including inflammatory cytokines (interleukin-6 [IL-6], lactate dehydrogenase, interferon gamma, and serum Krebs von den Lungen [KL-6]), have been suggested as possible predictors for pulmonary fibrosis [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, data on pulmonary fibrosis among critically ill patients requiring intensive care unit (ICU) admission with mechanical ventilation are still limited. Therefore, in this study, we aimed to evaluate the prevalence and risk factors of pulmonary fibrosis development in critically ill patients with COVID-19 pneumonia requiring mechanical ventilation.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003e This multicentre prospective study was conducted in eight centres (the Asan Medical Centre, National Medical Centre, Ulsan University Hospital, Chung-Ang University Hospital, Samsung Medical Centre, Seoul National University Bundang Hospital, Seoul National University Hospital, and Seoul Metropolitan Government-Seoul National) involved in COVID-19 infection care in the Republic of Korea. We initially enrolled 110 consecutive patients who were admitted and managed for COVID-19 pneumonia between July and December 2021. Inclusion criteria were as follows: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;19 years; (2) SARS-CoV-2 infection confirmed by nasopharyngeal swab reverse transcriptase-polymerase chain reaction; (3) definite evidence of pneumonia on initial chest radiography (chest x-ray or computed tomography [CT]); (4) patients requiring mechanical ventilation; and (5) severe or critical COVID-19 infection, according to the WHO clinical classification for COVID-19 pneumonia [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Patients were excluded if they had a previous diagnosis of interstitial lung disease. According to the study protocol, survivors were followed up in each hospital\u0026rsquo;s outpatient department one month after discharge where chest CT scans and pulmonary function test(PFT) were performed. For patients who died before discharge or were transferred to other hospitals, the last scans performed during the hospitalization were retrieved. Of the patients enrolled, 64 patients with available follow-up CT images were finally included.\u003c/p\u003e \u003cp\u003eThe study protocol was approved by the Institutional Review Board of the eight individual centres, including the Asan Medical Centre (IRB No. 2021\u0026thinsp;\u0026minus;\u0026thinsp;0769). The present study was registered with the Clinical Research Informative Service (No. KCT0006312). Written informed consent was provided to individual patients or their caregivers who were unable to state their own will due to medical status.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eClinical data\u003c/h2\u003e \u003cp\u003eAll information were prospectively collected from each centre and recorded into a central electronic Case Report Form (iCReaT, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://icreat.nih.go.kr/icreat/\u003c/span\u003e\u003cspan address=\"https://icreat.nih.go.kr/icreat/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). No identifiable data were collected. Data on demographic characteristics (age, sex, body mass index [BMI], smoking history, and underlying comorbidities) and clinical status at ICU admission (vital signs, laboratory results, Acute Physiology and Chronic Health Evaluation [APACHE] and sequential organ failure assessment [SOFA] scores, and ventilator parameters) were collected. During hospitalization, the length of hospital and ICU stay and duration of ventilator use were recorded. Data on treatment (use of continuous renal replacement therapy [CRRT], extracorporeal membrane oxygenation [ECMO], nitric oxide gas inhalation, and prone positioning and medication use, including antiviral agents and steroids) were also collected. Forced vital capacity (FVC) and diffusing capacity (DLco) by spirometry or plethysmography were measured, according to the European Respiratory Society/American Thoracic Society recommendations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eRadiologic assessment\u003c/h2\u003e \u003cp\u003eFibrosis-like changes on chest CT were evaluated by visual assessment of two radiologists (B.D.N and J.H.H) who specialize in chest radiology with 7 and 26 years of experience in chest radiology, respectively. Findings compatible with fibrosis-like changes were as follows: (1) reticulation; (2) architectural distortion\u0026thinsp;\u0026plusmn;\u0026thinsp;parenchymal band; (3) traction bronchiectasis/bronchiolectasis; or (4) honeycombing [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Definition of pulmonary fibrosis was valid if there are \u0026ge;\u0026thinsp;3 components of fibrosis-like changes. To minimize inter-reader variability, a consensus meeting was held for sample cases before conducting further analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe extent of pulmonary fibrosis was measured by computer-based analysis of high-resolution CT images using an automated quantification system for a more objective evaluation of the extent of fibrosis in patients with interstitial lung disease. Ground glass opacities (GGO), mixed disease (MD), which the summation of field of reticulation and honeycombing, consolidation (CON), or total lung density (TLD: summation of the field of GGO, MD, and CON) were scored (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eIn this study, primary outcome was identifying the prevalence and risk factors of pulmonary fibrosis on chest CT at one month after hospital discharge. Descriptive statistics are presented as numbers (percentages) for categorical variables and mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for continuous variables. We compared two groups (fibrosis and no-fibrosis groups) using the χ\u003csup\u003e2\u003c/sup\u003e or Fisher\u0026rsquo;s exact test for categorical variables. The Mann-Whitney U test was used for the comparison of median and interquartile range (IQR) for continuous variables between the two groups. The association between possible risk factors and pulmonary fibrosis was estimated using Cox proportional hazards analysis with crude and multivariable adjusted models. After including variables with p-values of \u0026lt;\u0026thinsp;0.10 in the univariate model, as well as age, sex, and BMI, adjusted hazard ratio (aHR) and 95% confidence intervals (CIs) were calculated in the multivariable analysis by enter methods after excluding selected variables showing collinearity in the model. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant. All statistical analyses were performed using SAS statistical package (version 9.4; SAS Institute Inc., Cary, NC, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eIn this study, 64 patients were included, with a mean age of 58.2 years, and 64.1% of the patients were males. Median follow-up duration from the index date (enrolment) to the date of the CT was 60 days (IQR, 41\u0026ndash;78 days). Mean SOFA and APACHE scores were 4.7 (SD, 2.9) and 13.6 (SD, 9.3), respectively. In terms of treatment after the ICU admission, about 90% of the patients were administered antiviral agents (remdesivir), and all the patients were administered steroid. Thirty-four (53.1%) patients were placed in the prone position, 11 (17.2%) underwent ECMO support, followed by 6 (9.4%) who underwent CRRT.\u003c/p\u003e \u003cp\u003eFibrosis-like changes were identified on chest CT: \u0026ge; 1 in 49 patients (76.6%), \u0026ge; 2 in 48 (75.0%), and \u0026ge;\u0026thinsp;3 in 35 (54.7%). When a fibrosis group was defined as patients with \u0026ge;\u0026thinsp;3 fibrosis-like changes on chest CT, there was no significant difference in demographic characteristics and comorbidities between the fibrosis and no-fibrosis groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Most laboratory and clinical findings at ICU admission and treatment were also not different between the two groups (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, CRRT was performed more frequently in the no-fibrosis group compared to that performed in the fibrosis group (17.2% vs. 2.9%, p\u0026thinsp;=\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the study population (at ICU admission)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFibrosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo-fibrosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.4\u0026thinsp;\u0026plusmn;\u0026thinsp;15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (64.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (69.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (67.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (77.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (55.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEx-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol drinking, yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus, yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (32.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular disease, yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular disease, yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver disease, yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney disease, yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy, yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung diseases, yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTB destroyed lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharlson comorbidity index\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are expressed number (%) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD unless otherwise indicated.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical and laboratory findings of the study population (at ICU admission)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eFibrosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo-fibrosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVital sign\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129.8\u0026thinsp;\u0026plusmn;\u0026thinsp;27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e129.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130.1\u0026thinsp;\u0026plusmn;\u0026thinsp;24.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.2\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e73.7\u0026thinsp;\u0026plusmn;\u0026thinsp;18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.9\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.6\u0026thinsp;\u0026plusmn;\u0026thinsp;23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e85.6\u0026thinsp;\u0026plusmn;\u0026thinsp;25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.6\u0026thinsp;\u0026plusmn;\u0026thinsp;20.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e25.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxygen saturation, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e90.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP/F ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102.4\u0026thinsp;\u0026plusmn;\u0026thinsp;50.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e103.9\u0026thinsp;\u0026plusmn;\u0026thinsp;54.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100.0\u0026thinsp;\u0026plusmn;\u0026thinsp;44.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeverity assessment score\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e15.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC 10^\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,900\u0026thinsp;\u0026plusmn;\u0026thinsp;4,460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e10,740\u0026thinsp;\u0026plusmn;\u0026thinsp;4,250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,000\u0026thinsp;\u0026plusmn;\u0026thinsp;4,600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e87.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANC /\u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8,470\u0026thinsp;\u0026plusmn;\u0026thinsp;4,240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e9,010\u0026thinsp;\u0026plusmn;\u0026thinsp;4,040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7,800\u0026thinsp;\u0026plusmn;\u0026thinsp;4,470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaemoglobin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e13.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet, 10^\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e238.4\u0026thinsp;\u0026plusmn;\u0026thinsp;106.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e250.4\u0026thinsp;\u0026plusmn;\u0026thinsp;121.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e223.9\u0026thinsp;\u0026plusmn;\u0026thinsp;85.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.7\u0026thinsp;\u0026plusmn;\u0026thinsp;66.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e65.2\u0026thinsp;\u0026plusmn;\u0026thinsp;46.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.5\u0026thinsp;\u0026plusmn;\u0026thinsp;83.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.9\u0026thinsp;\u0026plusmn;\u0026thinsp;70.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e45.6\u0026thinsp;\u0026plusmn;\u0026thinsp;38.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.1\u0026thinsp;\u0026plusmn;\u0026thinsp;95.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal protein, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT, sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e13.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e22.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e136.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e137.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e602.0\u0026thinsp;\u0026plusmn;\u0026thinsp;242.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e646.1\u0026thinsp;\u0026plusmn;\u0026thinsp;283.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e554.2\u0026thinsp;\u0026plusmn;\u0026thinsp;183.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e12.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcalcitonin, ng/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTroponin, I ng/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-dimer, \u0026micro;g/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibrinogen, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e535.9\u0026thinsp;\u0026plusmn;\u0026thinsp;202.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e544.1\u0026thinsp;\u0026plusmn;\u0026thinsp;221.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e524.1\u0026thinsp;\u0026plusmn;\u0026thinsp;176.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eData are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and \u003cem\u003ep\u003c/em\u003e-values were tested by Mann-Whitney U test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTreatment of the study population (during ICU care)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFibrosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-fibrosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of Remdesivir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (89.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (91.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (86.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration, days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0 (5.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (5.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0 (5.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of Steroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration, days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.0 (3.0\u0026ndash;9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (3.0\u0026ndash;9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0 (4.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquivalent dose (dexamethasone)/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.0 (6.0\u0026ndash;10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (6.0\u0026ndash;11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0 (6.0\u0026ndash;6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal equivalent dose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.0 (22.0\u0026ndash;60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.0 (20.0\u0026ndash;60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.0 (24.0\u0026ndash;60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of antibiotics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (98.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (97.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of neuromuscular blocker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (73.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (74.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (72.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration, days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.0 (3.0\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0 (3.0\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0 (3.0\u0026ndash;6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of monoclonal antibody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (41.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTocilizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (85.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (87.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegdanvimab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasiliximab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO inhalation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration, days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0 (3.0\u0026ndash;8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (4.0\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5 (2.5\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProne position\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (53.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (57.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration, days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0 (2.0\u0026ndash;4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0 (2.0\u0026ndash;3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5 (3.0\u0026ndash;6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration, days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.5 (3.0\u0026ndash;12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.0 (12.0\u0026ndash;12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.0 (3.0\u0026ndash;12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECMO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration, days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.5 (9.0\u0026ndash;62.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.0 (8.0\u0026ndash;64.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.0 (17.0\u0026ndash;36.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are expressed as number of patients (%) or median (interquartile range) unless otherwise indicated.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eRadiological findings and lung function\u003c/h2\u003e \u003cp\u003eThe most frequent findings of fibrotic change were traction bronchiectasis (n\u0026thinsp;=\u0026thinsp;47, 73.4%), followed by architectural distortion with or without parenchymal band (n\u0026thinsp;=\u0026thinsp;44, 68.8%) and reticulation (n\u0026thinsp;=\u0026thinsp;40, 62.5%) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The median extent of fibrosis (MD) was 10.6% (IQR, 3.8\u0026ndash;40.7%), as measured by automatic quantification (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The median extent of fibrosis was numerically higher in the fibrosis group (12.4% vs. 4.4%, p\u0026thinsp;=\u0026thinsp;0.15) than that in the no-fibrosis group without statistical significance; however, the extent of GGO was significantly higher in the fibrosis group (20.1% vs. 14.0%, p\u0026thinsp;=\u0026thinsp;0.003) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVisual assessment for chest CT in the study population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibrosis-like change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReticulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (62.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArchitectural distortion\u0026thinsp;\u0026plusmn;\u0026thinsp;parenchymal band\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (68.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraction bronchiectasis/bronchiectasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (73.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHoneycombing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (17.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of fibrosis-like change component\u0026thinsp;\u0026ge;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (76.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of fibrosis-like change component\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (75.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of fibrosis-like change component\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (54.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eData are expressed number (%)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAutomatic quantification for chest CT images and lung function in the study population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-fibrosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFibrosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutomatic quantification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMedian (interquartile range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGO, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.9 (13.2\u0026ndash;24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.0 (11.1\u0026ndash;20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.1 (16.2\u0026ndash;26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed disease, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.6 (3.8\u0026ndash;40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4 (1.4\u0026ndash;47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.4 (6.6\u0026ndash;36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsolidation, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1 (0.00.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1 (0.0\u0026ndash;0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 (0.1\u0026ndash;0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLD, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.5 (20.864.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.5 (15.6\u0026ndash;66.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.5 (25.7\u0026ndash;60.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-fibrosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFibrosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary function test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMedian (interquartile range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFVC % predicted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.0 (60.0\u0026ndash;82.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.0 (71.0\u0026ndash;90.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.5 (60.0\u0026ndash;76.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLco % predicted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.0 (46.0\u0026ndash;71.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.0 (61.0\u0026ndash;83.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.0 (41.0\u0026ndash;67.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and p-values were tested by Mann-Whitney U test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePulmonary function test was performed in 27 (42.2%) of the 64 patients. The median follow-up duration from the index date to the date of the pulmonary function test was 147 days (IQR, 128\u0026ndash;231 days). Both FVC and DLco were numerically lower in the fibrosis group than those in the no-fibrosis group without statistical significance (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eRisk factors for post-COVID-19 pulmonary fibrosis\u003c/h2\u003e \u003cp\u003eTwelve variables (hypertension and cardiovascular disease, SOFA score, respiratory rate, serum level of white blood cells, blood urea nitrogen [BUN], and creatinine, and use of neuromuscular blockers and ECMO) that were associated with pulmonary fibrotic changes in the univariate Cox analysis with a p-value of \u0026lt;\u0026thinsp;0.10 (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e) were selected as candidate variables in the multivariable analysis, along with age, sex, and BMI.\u003c/p\u003e \u003cp\u003eIn the multivariable Cox analysis, males (aHR, 3.14; 95% CI, 1.08\u0026ndash;9.14) and higher SOFA score (aHR, 1.18; 95% CI, 1.02\u0026ndash;1.37) were independently associated with lung fibrosis (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Additionally, higher levels of serum BUN\u0026thinsp;\u0026ge;\u0026thinsp;19 had higher risk of pulmonary fibrosis (aHR, 2.69; 95% CI, 1.20\u0026ndash;7.25, p\u0026thinsp;=\u0026thinsp;0.05), with marginal significance in the final model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCox analysis of risk factors for pulmonary fibrosis in the study population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude HR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted HR (95% CI) \u0026Dagger;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 (1.01\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.96\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41 (0.71\u0026ndash;2.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.14 (1.08\u003c/b\u003e\u0026ndash;\u003cb\u003e9.14)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.90\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.91\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.05 (1.00\u0026ndash;4.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.70 (0.66\u0026ndash;4.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular disease, yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.72 (1.01\u0026ndash;7.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.45 (0.77\u0026ndash;7.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory rate, /min \u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.87\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA score, 1-point increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15 (1.04\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.18 (1.02\u003c/b\u003e\u0026ndash;\u003cb\u003e1.37)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u0026thinsp;\u0026ge;\u0026thinsp;12000, /mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.09 (1.00\u0026ndash;4.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91 (0.35\u0026ndash;2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN\u0026thinsp;\u0026ge;\u0026thinsp;19, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.57 (1.26\u0026ndash;5.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.69 (0.99\u003c/b\u003e\u0026ndash;\u003cb\u003e7.33)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.053\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine\u0026thinsp;\u0026gt;\u0026thinsp;1.4, mg/dL\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.76 (0.95\u0026ndash;8.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of neuromuscular blocker, yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.29 (0.12\u0026ndash;0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39 (0.12\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECMO, yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.41 (0.16\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42 (0.11\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis multicentre study found risk factors, including various clinical characteristics for pulmonary fibrosis in patients with COVID-19 pneumonia requiring mechanical ventilation. About half of the patients (54.7%) had significant fibrosis, and male sex and higher SOFA scores at ICU admission were found to be risk factors for fibrotic changes. To our knowledge, this is the first study to explore pulmonary fibrosis in patients with severe COVID-19 pneumonia requiring mechanical ventilation.\u003c/p\u003e \u003cp\u003eIn this study, 54.7% of the patients had significant pulmonary fibrosis (\u0026ge;\u0026thinsp;3 fibrosis-like changes on chest CT), which was similar to previous studies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The prevalence of pulmonary fibrotic changes in patients with COVID-19 following ICU admission has been reported to range between 30\u0026ndash;70% [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. A study by Huang et al. reported that 41 (50.6%) of 81 survivors of severe COVID-19 pneumonia had fibrosis on CT after hospital discharge [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In another study by Han et al., which assessed pulmonary sequalae and the risk factor for lung fibrotic-like changes in survivors of severe COVID-19 pneumonia, 40 (35.1%) of 114 patients showed fibrosis-like changes [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additionally, in a prospective study by Caruso et al., they found that 72% (85 of 118) of patients with COVID-19 pneumonia had fibrosis-like changes on CT [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, the definition and terminology of fibrosis, such as fibrosis-like changes, fibrotic-like changes, or fibrosis [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], has been inconsistent in previous studies. Additionally, fibrosis-like changes at baseline CT improved on follow-up CT [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], suggesting that some of the initial fibrosis may be transient. Therefore, to overcome this limitation, we used strict criteria to define fibrosis in this study.\u003c/p\u003e \u003cp\u003eThe SOFA score on the day of admission to the ICU was a predictive factor for pulmonary fibrosis in our study. In a previous study that prospectively enrolled survivors of COVID-19 pneumonia requiring oxygen supplement, the SOFA score at admission were linearly associated with the risk of fibrosis-like changes [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, in this study, the risk of patients (about 40% of survivors) requiring mechanical ventilation was not explored. As a component of the SOFA score, lower PaO2/FiO2 ratio (ratio of arterial oxygen partial pressure to fractional inspired oxygen) was suggested as a predictive factor of lung fibrosis in patients with COVID-19 infection patient in previous studies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The other components of the SOFA score, including acute kidney injury and liver disease, which were reported to be associated with patients with severe COVID-19 pneumonia requiring ICU care in previous studies have been reported as predictive factors for pulmonary fibrosis [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Additionally, previous studies reported hypertension and cardiovascular disease as risk factors associated pulmonary fibrosis development; however, no significant association was found after further adjustment for clinical and laboratory parameters [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In our study, cardiovascular disease was not a predictive factor for pulmonary fibrosis in the final multivariate analysis.\u003c/p\u003e \u003cp\u003eOur findings suggested that male sex was associated with pulmonary fibrosis after COVID-19 pneumonia, which was not consistently found in previous studies. Some studies reported that male were more likely to present with COVID-19 pulmonary fibrosis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], although others did not show any significant result [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, a meta-analysis reported that males had higher mortality after COVID-19 than females [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Given the sex difference in the risk of idiopathic pulmonary fibrosis (IPF), in this study, the higher risk of pulmonary fibrosis after COVID-19 infection found in males seems possible, but further studies are needed to confirm the association.\u003c/p\u003e \u003cp\u003eHigher level of serum BUN has been suggested as an indicator for poor prognosis of pneumonia [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and was independently associated with mortality of critically ill patients, regardless of creatinine level [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In COVID-19 pneumonia, one retrospective cohort study reported that initial BUN and D-dimer levels were associated with mortality in COVID-19 patients with high predictive value (c-index, 0.94) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. However, since studies have rarely explored the association of BUN and development of pulmonary fibrosis, future longitudinal studies should be performed to find the association with pulmonary fibrosis in COVID-19 infection.\u003c/p\u003e \u003cp\u003eThis study has some limitations. First, the number of patients included in this study was relatively small, which may have limited the ability to generate statistically significant results. Nevertheless, in our study, the multivariate analysis allowed us to identify significant risk factors associated with the development of pulmonary fibrosis after COVID-19 pneumonia. Second, this study did not collect baseline CT images; thus, it did not completely exclude patients with pre-existing fibrosis. However, we excluded patients with a history of pre-existing pulmonary fibrosis by taking a detailed medical history. Third, the follow-up CTs were performed within a relatively short period of time, and the prevalence of pulmonary fibrosis at long-term follow-up may differ. Therefore, we used a stricter definition of pulmonary fibrosis in this study to reduce differences. Nevertheless, the strength of our study is that it was a multicentre prospective study, it focused on patients with severe COVID-19 requiring mechanical ventilation, and analysed fibrotic changes by both visual assessment and automated quantification of CTs.\u003c/p\u003e \u003cp\u003eIn conclusion, our findings suggest that in patients with severe COVID-19 pneumonia requiring mechanical ventilation, 54.7% have significant pulmonary fibrosis, and male sex and higher SOFA scores indicate an increased risk of pulmonary fibrosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Grace Hyun J. Kim PhD (David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA) for the assistance in applying and interpreting quantitative CT images for our analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the Basic Science Research Program (NRF-2022R1A2B5B02001602) and Bio \u0026amp; Medical Technology Development Program (NRF-2022M3A9E4082647) of the National Research Foundation of Korea (NRF) funded by the Ministry of Science \u0026amp; ICT, Republic of Korea. This study was also supported by the National Institute of Health research project (2021ER190400, 2021ER120701) and Korea Environment Industry \u0026amp; Technology Institute through Core Technology Development Project for Environmental Diseases Prevention and Management Program funded by the Korea Ministry of Environment (ARQ202201450001), Republic of Korea. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no disclosures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJK, JWS planned this study, had access to the data, and took responsibility for the integrity of the data and data analysis. JK, JWS, and GC contributed to study concept and design, analysis, and manuscript preparation. HYY, WYK, CRC, YJC, JL, YJ, JSJ, TYP, JHH, and BDN contributed to data collection and manuscript preparation. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets and statistical analysis used for the current study can be repeated on a reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Institutional Review Board of the eight\u0026nbsp;individual\u0026nbsp;centres,\u0026nbsp;including the Asan Medical Centre\u0026nbsp;(IRB No. 2021-0769). The present study was registered with the Clinical Research Informative Service (No. KCT0006312). Written informed consent was provided to individual patients or caregivers of those who were unable to state their own will due to medical status.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable (This study does not contain data from any individual person.)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhu N, Zhang D, Wang W, Li X, Yang B, Song J, Zhao X, Huang B, Shi W, Lu R, et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N Engl J Med. 2020;382:727\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeekly epidemiological update on COVID-19. [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/m/item/weekly-epidemiological-update-on-covid-19---30-november-2022]\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/m/item/weekly-epidemiological-update-on-covid-19---30-november-2022]\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao YD, Ding M, Dong X, Zhang JJ, Kursat Azkur A, Azkur D, Gan H, Sun YL, Fu W, Li W, et al. Risk factors for severe and critically ill COVID-19 patients: A review. Allergy. 2021;76:428\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa Q, Liu J, Liu Q, Kang L, Liu R, Jing W, Wu Y, Liu M. Global Percentage of Asymptomatic SARS-CoV-2 Infections Among the Tested Population and Individuals With Confirmed COVID-19 Diagnosis: A Systematic Review and Meta-analysis. JAMA Netw Open. 2021;4:e2137257.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi H, Han X, Jiang N, Cao Y, Alwalid O, Gu J, Fan Y, Zheng C. Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect Dis. 2020;20:425\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou JN, Sun L, Wang BR, Zou Y, Xu S, Ding YJ, Shen LJ, Huang WC, Jiang XJ, Chen SM. The characteristics and evolution of pulmonary fibrosis in COVID-19 patients as assessed by AI-assisted chest HRCT. PLoS ONE. 2021;16:e0248957.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNabahati M, Ebrahimpour S, Khaleghnejad Tabari R, Mehraeen R. Post-COVID-19 pulmonary fibrosis and its predictive factors: a prospective study. Egypt J Radiol Nuclear Med. 2021;52:1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFernandez-Plata R, Higuera-Iglesias AL, Torres-Espindola LM, Aquino-Galvez A, Velazquez Cruz R, Camarena A, Chavez Alderete J, Romo Garcia J, Alvarado-Vasquez N, Martinez Briseno D et al. Risk of Pulmonary Fibrosis and Persistent Symptoms Post-COVID-19 in a Cohort of Outpatient Health Workers. Viruses. 2022;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoon HY, Uh ST. Post-Coronavirus Disease 2019 Pulmonary Fibrosis: Wait or Needs Intervention. Tuberc Respir Dis (Seoul). 2022;85:320\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTran S, Ksajikian A, Overbey J, Li P, Li Y. Pathophysiology of Pulmonary Fibrosis in the Context of COVID-19 and Implications for Treatment: A Narrative Review. Cells. 2022;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelpino MV, Quarleri J. SARS-CoV-2 Pathogenesis: Imbalance in the Renin-Angiotensin System Favors Lung Fibrosis. Front Cell Infect Microbiol. 2020;10:340.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCabrera-Benitez NE, Parotto M, Post M, Han B, Spieth PM, Cheng WE, Valladares F, Villar J, Liu M, Sato M, et al. Mechanical stress induces lung fibrosis by epithelial-mesenchymal transition. Crit Care Med. 2012;40:510\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYasin R, Gomaa AAK, Ghazy T, Hassanein SA, latif Ibrahem RA, Khalifa MH. Predicting lung fibrosis in post-COVID-19 patients after discharge with follow-up chest CT findings. Egypt J Radiol Nuclear Med. 2021;52:1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Shen C, Wang L, Majumder S, Zhang D, Deen MJ, Li Y, Qing L, Zhang Y, Chen C. Pulmonary fibrosis and its related factors in discharged patients with new corona virus pneumonia: a cohort study. Respir Res. 2021;22:1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang C, Huang L, Wang Y, Li X, Ren L, Gu X, Kang L, Guo L, Liu M, Zhou X. 6-month consequences of COVID-19 in patients discharged from hospital: a cohort study. The Lancet. 2021;397:220\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaderi N, Rahimzadeh M. Krebs von den Lungen-6 (KL-6) as a clinical marker for severe COVID-19: A systematic review and meta-analyses. Virology. 2022;566:106\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu ZJ, Xu J, Yin JM, Li L, Hou W, Zhang LL, Zhou Z, Yu YZ, Li HJ, Feng YM, Jin RH. Lower Circulating Interferon-Gamma Is a Risk Factor for Lung Fibrosis in COVID-19 Patients. Front Immunol. 2020;11:585647.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganization WH. \u003cem\u003eLiving guidance for clinical management of COVID-19.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiller MR, Hankinson J, Brusasco V, Burgos F, Casaburi R, Coates A, Crapo R, Enright P, van der Grinten CP, Gustafsson P, et al. Standardisation of spirometry. Eur Respir J. 2005;26:319\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGraham BL, Brusasco V, Burgos F, Cooper BG, Jensen R, Kendrick A, MacIntyre NR, Thompson BR, Wanger J. 2017 ERS/ATS standards for single-breath carbon monoxide uptake in the lung. Eur Respir J. 2017;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLynch DASN, Travis WD, Brown KK, Colby TV, Galvin JR, Goldin JG, Hansell DM, Inoue Y, Johkoh T, Nicholson AG, Knight SL, Raoof S, Richeldi L, Ryerson CJ, Ryu JH, Wells AU. Diagnostic criteria for idiopathic pulmonary fibrosis: a Fleischner Society White Paper. Lancet Respir Med. 2018;6:138\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang W, Wu Q, Chen Z, Xiong Z, Wang K, Tian J, Zhang S. The potential indicators for pulmonary fibrosis in survivors of severe COVID-19. J Infect. 2021;82:e5\u0026ndash;e7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRob JJ, van Gassel JLM, Bels A, Raafs T, van BC GMC, van de Poll SO, Simons, Lieke WL, van der Meer HA, Gietema, et al. High Prevalence of Pulmonary Sequelae at 3 Months after Hospital Discharge in Mechanically Ventilated Survivors of COVID-19. Am J Respir Crit Care Med. 2021;203:371\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan X, Fan Y, Alwalid O, Li N, Jia X, Yuan M, Li Y, Cao Y, Gu J, Wu H. Six-month follow-up chest CT findings after severe COVID-19 pneumonia. Radiology. 2021;299:E177\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGulati A, Lakhani P. Interstitial lung abnormalities and pulmonary fibrosis in COVID-19 patients: a short-term follow-up case series. Clin Imaging. 2021;77:180\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaruso D, Guido G, Zerunian M, Polidori T, Lucertini E, Pucciarelli F, Polici M, Rucci C, Bracci B, Nicolai M. Post-Acute sequelae of COVID-19 pneumonia: six-month Chest CT follow-up. Radiology. 2021;301:E396\u0026ndash;E405.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan X, Fan Y, Alwalid O, Zhang X, Jia X, Zheng Y, Shi H. Fibrotic interstitial lung abnormalities at 1-year follow-up CT after severe COVID-19. Radiology. 2021;301:E438\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Raaij BFM, Stoger JL, Hinnen C, Penfornis KM, de Jong CMM, Klok FA, Roukens AHE, Veldhuijzen DS, Arbous MS, Noordam R, et al. Fibrotic-like abnormalities notably prevalent one year after hospitalization with COVID-19. Respir Med Res. 2022;82:100973.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcGroder CF, Zhang D, Choudhury MA, Salvatore MM, D'Souza BM, Hoffman EA, Wei Y, Baldwin MR, Garcia CK. Pulmonary fibrosis 4 months after COVID-19 is associated with severity of illness and blood leucocyte telomere length. Thorax. 2021;76:1242\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee I, Kim J, Yeo Y, Lee JY, Jeong I, Joh JS, Kim G, Chin BS, Kim Y, Kim MK et al. Prognostic Factors for Pulmonary Fibrosis Following Pneumonia in Patients with COVID-19: A Prospective Study. J Clin Med. 2022;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiang KC, Imig JD, Kalantar-Zadeh K, Gupta A. Kidney in the net of acute and long-haul coronavirus disease 2019: a potential role for lipid mediators in causing renal injury and fibrosis. Curr Opin Nephrol Hypertens. 2022;31:36\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampos-Murguia A, Roman-Calleja BM, Toledo-Coronado IV, Gonzalez-Regueiro JA, Solis-Ortega AA, Kusulas-Delint D, Cruz-Contreras M, Cruz-Yedra N, Cubero FJ, Nevzorova YA, et al. Liver fibrosis in patients with metabolic associated fatty liver disease is a risk factor for adverse outcomes in COVID-19. Dig Liver Dis. 2021;53:525\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi F, Deng J, Song Y, Wu C, Yu B, Wang G, Li J, Zhong Y, Liang F. Pulmonary fibrosis in patients with COVID-19: A retrospective study. Front Cell Infect Microbiol. 2022;12:1013526.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohammadi A, Balan I, Yadav S, Matos WF, Kharawala A, Gaddam M, Sarabia N, Koneru SC, Suddapalli SK, Marzban S. Post-COVID-19 Pulmonary Fibrosis. Cureus. 2022;14:e22770.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAul DR, Gates DJ, Draper DA, Dunleavy DA, Ruickbie DS, Meredith DH, Walters DN, van Zeller DC, Taylor DV, Bridgett DM, et al. Complications after discharge with COVID-19 infection and risk factors associated with development of post-COVID pulmonary fibrosis. Respir Med. 2021;188:106602.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Huang DQ, Zou B, Yang H, Hui WZ, Rui F, Yee NTS, Liu C, Nerurkar SN, Kai JCY, et al. Epidemiology of COVID-19: A systematic review and meta-analysis of clinical characteristics, risk factors, and outcomes. J Med Virol. 2021;93:1449\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng DY, Zhou YQ, Zou XL, Zhou M, Yang HL, Chen XX, Zhang TT. Elevated Blood Urea Nitrogen-to-Serum Albumin Ratio as a Factor That Negatively Affects the Mortality of Patients with Hospital-Acquired Pneumonia. Can J Infect Dis Med Microbiol. 2019;2019:1547405.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeier K, Eppanapally S, Bazick HS, Chang D, Mahadevappa K, Gibbons FK, Christopher KB. Elevation of blood urea nitrogen is predictive of long-term mortality in critically ill patients independent of normal creatinine. Crit Care Med. 2011;39:305\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang CL, Sullivan GD, Karalus NC, Mills GD, McLachlan JD, Hancox RJ. Predicting early mortality in acute exacerbation of chronic obstructive pulmonary disease using the CURB65 score. Respirology. 2011;16:146\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng A, Hu L, Wang Y, Huang L, Zhao L, Zhang C, Liu X, Xu R, Liu F, Li J, et al. Diagnostic performance of initial blood urea nitrogen combined with D-dimer levels for predicting in-hospital mortality in COVID-19 patients. Int J Antimicrob Agents. 2020;56:106110.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"pulmonary fibrosis, COVID-19, pneumonia","lastPublishedDoi":"10.21203/rs.3.rs-3868352/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3868352/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackgrounds\u003c/h2\u003e \u003cp\u003e: The management of lung complications, especially fibrosis, after coronavirus disease (COVID-19) pneumonia, is an important issue in the COVID-19 post-pandemic era. We aimed to investigate risk factors for pulmonary fibrosis development in patients with severe COVID-19 pneumonia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eClinical and radiologic data were prospectively collected from 64 patients who required mechanical ventilation due to COVID-19 pneumonia and were enrolled from eight hospitals in South Korea. Fibrotic changes on chest computed tomography (CT) was evaluated by visual assessment, and extent of fibrosis (mixed disease score) was measured using automatic quantification system.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSixty-four patients were enrolled, and their mean age was 58.2 years (64.1% were males). On chest CT (median interval: 60 days [interquartile range, IQR; 41\u0026ndash;78 days] from enrolment), 35 (54.7%) patients showed\u0026thinsp;\u0026ge;\u0026thinsp;3 fibrotic lesions. The most frequent fibrotic change was traction bronchiectasis (47 patients, 73.4%). Median extent of fibrosis measured by automatic quantification was 10.6% (IQR, 3.8\u0026ndash;40.7%). In a multivariable Cox proportional hazard model, which included nine variables with a p-value of \u0026lt;\u0026thinsp;0.10 in an unadjusted analysis as well as age, sex, and body mass index, male sex (hazard ratio [HR], 3.01; 95% confidence interval [CI], 1.27\u0026ndash;7.11) and higher initial sequential organ failure assessment (SOFA) score (HR, 1.18; 95% CI, 1.02\u0026ndash;1.37) were independently associated with pulmonary fibrosis (\u0026ge;\u0026thinsp;3 fibrotic lesions).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur data suggests that male gender and higher SOFA score at intensive care unit admission were associated with pulmonary fibrosis in patients with severe COVID-19 pneumonia requiring mechanical ventilation.\u003c/p\u003e","manuscriptTitle":"Pulmonary Fibrosis Followed by Severe Pneumonia in Patients with COVID- 19 infection: A Prospective Multicentre Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-24 18:36:27","doi":"10.21203/rs.3.rs-3868352/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"082abc15-59e6-4a48-b34c-0bf0e8b97130","owner":[],"postedDate":"January 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-25T13:05:38+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-24 18:36:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3868352","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3868352","identity":"rs-3868352","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}