Usage of Compromised Lung Volume in Monitoring Steroid Therapy on Severe COVID-19

Usage of Compromised Lung Volume in Monitoring Steroid Therapy on Severe COVID-19 | 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 Usage of Compromised Lung Volume in Monitoring Steroid Therapy on Severe COVID-19 Ying Su, Ze-song Qiu, Jun Chen, Min-jie Ju, Guo-guang Ma, Jin-wei He, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-698051/v2 This work is licensed under a CC BY 4.0 License Status: Under Review Version 2 posted 7 You are reading this latest preprint version Show more versions Abstract Background Quantitative computed tomography (QCT) analysis may serve as a tool for assessing the severity of coronavirus disease 2019 (COVID-19)and for monitoringits progress. The present study aimed to assess the association between steroid therapy and quantitative CT parameters in a longitudinal cohort with COVID-19. Methods Between February 7 and February 17, 2020, 300 chest CT scans from 72 patients with severe COVID-19 were retrospectively collected and classified into five stages according to the interval between hospital admission and follow-up CT scans: Stage 1 (at admission); Stage 2 (3–7 days); Stage 3 (8–14 days); Stage 4 (15–21 days); and Stage 5 (22–31 days). QCT was performed using a threshold-based quantitative analysis to segment the lungaccording to different Hounsfield unit (HU) intervals. The primary outcomeswerechanges in percentage of compromised lung volume (%CL, –500 to 100 HU) at different stages. Multivariate Generalized Estimating Equations were performed after adjusting for potential confounders. Results Of 72 patients, 31 patients (43.1%) received steroid therapy. Steroid therapy was associated with a decrease in %CL (-3.27% [95%CI, -5.86 to -0.68, P = 0.01]) after adjusting for duration and baseline %CL. Associations between steroid therapy and changes in %CL varied between different stages or baseline %CL (all interactions, P <0.01). Steroid therapy was associated with decrease in %CL after stage 3 (all P <0.05), but not at stage 2. Similarly, steroid therapy was associated with a more significant decrease in %CL in the high CL group ( P <0.05), but not inthe low CL group. Conclusions Steroid administration was independently associated with a decrease in %CL, with interaction by duration or disease severity in a longitudinal cohort. The quantitative CT parameters, particularly compromised lung volume, may provide a useful tool to monitor COVID-19 progression during the treatment process. Trial registration : Clinicaltrials.gov, NCT04953247. Registered July 7, 2021, https://clinicaltrials.gov/ct2/show/NCT04953247 Pulmonology Steroid Quantitative computed tomography Compromised lung volume COVID-19 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Currently, the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has spread across the world[ 1 ]. As of April 27, 2021, 147,377,159 people have been diagnosed with COVID-19, including 3,112,041 deaths, according to the international World Health Organization (WHO) dashboard. Among various therapies for COVID-19, corticosteroid therapy has been proven to be effective for critical cases. The RECOVERY trial first reported the effectiveness of corticosteroid therapy in reducing mortality in patients with COVID-19 receiving oxygen supplementation or invasive mechanical ventilation[ 2 ]. The WHO REACT meta-analysis that included data from seven randomized clinical trials (RCTs) also demonstrated that the use of systemic corticosteroids was associated with lower 28-day all-cause mortality in critically ill patients with COVID-19[ 3 ]. Although corticosteroid therapy represents a milestone in the management of COVID-19, many questions remain unanswered[ 4 ]. The optimal type of corticosteroids, timing of initiation, dose, mode of administration, duration, and dose tapering are still unclear. An approach to resolve these issues is to develop accurate tools to assess or monitor the progression of COVID-19 during the corticosteroid therapy process. Chest computed tomography (CT) plays an important role in screening, diagnosing, and evaluating longitudinally patients with COVID-19[ 5 , 6 ]. Visual assessment of pulmonary lesions on chest CT scans has been proven to be valuable in predicting outcome and assessing progression[ 7 , 8 ]. However, inter- or intra-observer variation in traditional visual assessment of chest CT images increases the challenge of therapeutic assessment[ 9 , 10 ]. Quantitative computed tomography (QCT) analyses have recently been widely used to evaluate various pulmonary diseases[ 11 ]. QCT enable to extract quantitative data from medical images and increases the reproducibility of evaluation, thus, serving as a potential tool for monitoring disease progression and treatment response[ 12 ]. This may be particularly valuable in circumstance of minor effect as new therapies are developed and evaluated[ 7 ] . We hypothesize that QCT can serve as a tool for monitoring steroid treatment response during the course of disease. Our preliminary studies have confirmed the value of QCT in monitoring the progression and clinical decision-making in patients with COVID-19 [ 13 , 14 ]. However, the effect of steroids on quantitative chest CT parameters during the treatment process remains unknown. In this retrospectively study, we aimed to assess the association between steroid administration and QCT variables in a longitudinal cohort with COVID-19. Methods Study design and participants The present study was approved by the Ethics Committee of Renmin Hospital of Wuhan University (WDRY2020-K048) and was performed in accordance with the Declaration of Helsinki. Written informed consent was waived by the Ethics Committee in the setting of COVID-19 crisis in Wuhan. From February 7, 2020 to February 17, 2020, consecutive patients with confirmed COVID-19 admitted to the east campus of Renmin Hospital of Wuhan University were screened. The diagnosis of COVID-19 was based on the detection of SARS-CoV-2 nucleic acid by a real-time RT-PCR assay. The inclusion criteria were as follows: (1) age ≥ 18 years and (2) patients with severe or critical COVID-19. Exclusion criteria included (1) hematological or solid malignancies, (2) patients with less than two CT scans during hospital stay, and (3) systemic corticosteroid or immunosuppressive therapy in the previous 6 weeks. The severity of COVID-19 was defined according to WHO interim guidance or the sixth edition of the Chinese national guidelines on the diagnosis and treatment for COVID-19[ 15 , 16 ]. Patients were considered to have a severe infection if they met any of the following conditions: respiratory distress and a respiratory rate of > 30 times/min; oxygen saturation on room air at rest < 93%; and partial arterial oxygen pressure (PaO 2 )/fraction of inspiration oxygen (FiO 2 ) ≤ 300 mmHg. Patients were considered to be in a critical state if they met any of the following conditions: respiratory failure requiring mechanical ventilation, shock, and dysfunction of other organs requiring ICU management. Data Collection Data regarding baseline demographic features, co-morbidities, and clinical characteristics on hospital arrival, including symptoms, vital signs, and interventions during hospital stay, were retrospectively collected by two trained reviewers. Because there was no consensus on the use of steroids in the early stage of the COVID-19 pandemic, all steroid therapies were initiated at the time of admission at the discretion of attending physicians on the basis of clinical symptoms and CT images. According to our previous experience[ 17 – 21 ], intravenous methylprednisolone at a dose of 1.0-1.5 mg/kg every 12 h was initiated for 5 days or until oxygen saturation improved, followed by gradual tapering by 0.5 mg/kg every 3–5 days. Other therapeutic interventions such as the use of antibiotics, ventilation, laboratory testing, and hemodynamic management were performed following the sixth edition of the Guidelines on the Diagnosis and Treatment of COVID-19 published by the National Health Commission of China. Ct Protocol All patients underwent CT examination at hospital admission. Serial chest CT scans were performed during hospital stay. All CT scans were performed within a single inspiratory phase on a commercial multidetector CT scanner (GE Optima CT680). To minimize motion artifacts, CT images were acquired during a single breath-hold. Standard lung algorithm settings were used as follows: 120 kV and automatic tube current (180–400 mA); iterative reconstruction technique; detector, 64 mm; rotation time, 0.35 s; section thickness, 5 mm; collimation, 0.625 mm; pitch, 1.5; matrix, 512 × 512. Based on the time interval between hospital admission and CT scan, we designated five stages in this study: stage 1 (baseline CT scans, in which CT scans were acquired at hospital admission); stage 2 (CT scans acquired > 3 to 7 days after admission); stage 3 (CT scans acquired > 8 to 14 days after admission); stage 4 (CT scans acquired > 15 to 21 days after admission); and stage 5 (CT scans acquired > 22 to 31 days after admission). Ct Image Analysis For patient lung parenchyma segmentation, we performed a volumetric analysis in 3D Slicer ( http://www.slicer.org ) via the Lung CT Analyzer project ( https://github.com/rbumm/SlicerLungCTAnalyzer/) "[ 22 ]. We extracted right and left lung segments from individual CT slices. For unsatisfactory lung segmentation, we refined the lung contours with the manual segmentation tool implemented in ITK-SNAP ( www.itksnap.org )[ 23 ]. The trachea was excluded from the lung segmentation, while segmental arteries and bronchi were included. We then further divided the lung region into four components and computed the volume of each lung component, which were considered as the percentages of the total volume. According to different Hounsfield unit (HU) intervals in the quantitative chest CT scan, we divided each lung into nonaerated lung volume (%NNL, 100 to − 100 HU), poorly aerated lung volume (%PAL, − 101 to − 500 HU), normally aerated lung volume (%NAL, − 501 to − 900 HU), and hyperinflated lung volume (%HI, − 901 to − 1000 HU) regions [ 24 , 25 ]. The additional “compromised lung” volume (%CL) was considered as the sum of %PAL and %NNL (− 500 to 100 HU). The authors (Ze-song Qiu, Jin-wei He, and Yu-yao Zhang) who performed quantitative CT analysis were blinded to clinical characteristics and outcomes. Outcome Assessment To monitor COVID-19 progression during the treatment process, we chose changes in the percentage of compromised lung volume (Δ%CL) at different stages (Δ%CL = %CL at different stages-baseline %CL) as the primary outcome. The negative value of Δ%CL thus reflected clinical improvement. The secondary outcomes were changes in the percentage of NNL (Δ%NNL = NNL at different stages-baseline NNL), PAL (Δ%PAL = %PAL at different stages།baseline %PAL), NAL (Δ%NAL = %NAL at different stages།baseline %NAL), and HI (Δ%HI = %HI at different stages།baseline %HI) at different stages. Under these circumstances, clinical improvement was reflected by the negative value of Δ%NNL and Δ%PAL, and the positive value of Δ%NAL. Statistical analysis Categorical variables were expressed as numbers and percentages. The normality of the distribution for continuous variables was assessed by the Kolmogorov–Smirnov test. Continuous variables were expressed as the mean and standard deviation (SD) or median and interquartile range [IQR]. The chi-square test or Fisher’s exact test was used for categorical variables, while the Student’s t test or Mann–Whitney U test was used for continuous variables, as appropriate. For serial quantitative CT parameters, we tested differences between two groups over time by using repeated-measures analysis of variance (ANOVA) with no imputation for missing values. The Šidák multiple comparisons correction was used to compare each stage against the baseline (Stage 1). To longitudinally assess the association between steroid administration and changes in the percentage of quantitative CT parameters, generalized estimating equations (GEE) were used so that correlations between repeat CT quantitative parameters for an individual patient could be considered. The outcome variables Δ%CL, Δ%NNL, Δ%PAL, Δ%NAL, and Δ%HI were set as time-dependent variables because they were measured at each examination visit. Associations between steroid use and Δ%CL, Δ%NNL, Δ%PAL, Δ%NAL, and Δ%HI at different stages were assessed using GEE with correlations between repeated measures on participants modeled using an exchangeable structure to implement linear regression models. Covariates included in the GEE models were first screened by univariate GEE analysis, and those covariates with P < 0.10 were introduced in multivariate GEE models. These models were finally adjusted for duration and baseline quantitative CT parameters. Interactions between steroid use and duration (steroid use*duration) or between steroid use and baseline quantitative CT parameters (steroid use*baseline quantitative CT parameters) were tested by multivariate GEE analysis. Subsequent analyses assessed the associations between steroid use and changes in the percentage of quantitative CT parameters at different stages by linear regression models. We also assessed the associations between steroid use and changes in the percentage of quantitative CT parameters in relation to different baseline quantitative CT parameters. Patients were classified into high ( ≥ median level) and low groups (< median level) according to baseline quantitative CT parameters. The β coefficients and 95% confidence intervals (CI) were calculated. All statistical analyses were performed with SPSS software package, version 15.0 (SPSS Inc., Chicago, IL, USA), and two-sided P values of less than 0.05 were considered to be statistically significant. Results Patient characteristics Between February 7 and February 17, 2020, a total of 83 patients with severe or critical COVID-19 were screened for inclusion. Of these patients, 11 patients were excluded, including eight critical COVID-19 patients with less than two serial CT scans during hospital stay, two patients receiving systemic corticosteroids or immunosuppressive therapy in the previous 6 weeks, and one patient with solid malignancy. Within the study period, 72 patients with severe COVID-19 were finally included in this study (Fig. 1 ). Thirty-one patients (43.1%) received steroid therapy. Table 1 presents the baseline characteristics of the patients. The median age of the 72 patients was 63 years [IQR 49 to 69 years], and 51.4% of the patients were male. Fever was the most common symptom (90.3%), followed by fatigue (87.5%), cough (73.6%), and dyspnea (55.6%). The baseline characteristics between patients receiving steroid therapy and those not receiving steroid therapy were similar (Table 1 ). The vital signs and the PaO 2 /FiO 2 ratio on admission were also similar between the two groups. Table 1 Clinical characteristics of patients with severe COVID-19 Entire cohort Steroid group Standard care group P value Number of patients 72 31 41 Age (years) 63[49,69] 65[55,72] 62[48,69] 0.31 Gender (male), n (%) 37(51.4) 14(45.2) 23(56.1) 0.48 Smoking history, n (%) 9(12.5) 4(12.9) 5(12.2) 1.00 Comorbidities Hypertension, n (%) 22(30.6) 11(35.5) 11(26.8) 0.45 Diabetes mellitus, n (%) 16(22.2) 7(22.6) 9(22.0) 1.00 CAD, n (%) 4(5.6) 2(6.5) 2(4.9) 1.00 COPD, n (%) 1(1.4) 0(0) 1(2.4) 1.00 Cerebrovascular disease, n (%) 1(1.4) 1(3.2) 0(0) 1.00 Chronic renal disease, n (%) 2(2.8) 1(3.2) 1(2.4) 1.00 Signs and symptoms Fever, n (%) 65(90.3) 30(96.8) 35(85.4) 0.23 Cough, n (%) 53(73.6) 24(77.4) 29(70.7) 0.60 Sputum production, n (%) 10(13.9) 4(12.9) 6(14.6) 1.00 Fatigue, n (%) 63(87.5) 28(90.3) 35(85.4) 0.72 Headache, n (%) 4(5.6) 1(3.2) 3(7.3) 0.63 Dyspnea, n (%) 40(55.6) 20(64.5) 20(48.8) 0.23 Nausea or vomiting, n (%) 13(18.1) 6(19.4) 7(17.1) 1.00 Diarrhea, n (%) 15(20.8) 7(22.6) 8(19.5) 0.77 Anorexia, n (%) 4(5.6) 3(9.7) 1(2.4) 0.31 Myalgia or arthralgia, n (%) 7(9.7) 4(12.9) 3(7.3) 0.45 Onset of symptom to first CT scan (days) 14[ 11 , 17 ] 14[ 11 , 19 ] 13[ 11 , 16 ] 0.16 Vital signs at hospital admission Altered mental status, n (%) 2(2.8) 2(6.5) 0(0) 0.18 Heart rate (beats/minute) 88[78,102] 85[77,105] 90[81,100] 0.83 Respiratory rate (breaths/minute) 24[ 23 , 31 ] 25[ 24 , 32 ] 24[ 23 , 29 ] 0.20 Systolic blood pressure (mm Hg) 132[122,145] 132[123,147] 131[120,144] 0.44 Diastolic blood pressure (mm Hg) 78[71,83] 79[72,83] 76[70,84] 0.54 Respiratory status assessment PaO 2 on admission (mmHg) 67[61,86] 66[59,84] 68[63,87] 0.49 PaCO 2 on admission (mmHg) 39[ 34 , 42 ] 39[ 33 , 41 ] 40[ 36 , 43 ] 0.36 PaO 2 /FiO 2 on admission (mmHg) 256[226,277] 246[195,279] 258[228,277] 0.40 Respiratory support High flow nasal oxygen, n (%) 14(19.4) 11(35.5) 3(7.3) < 0.01 Non-invasive mechanical ventilation, n (%) 2(2.8) 2(6.5) 0(0) 0.18 Invasive mechanical ventilation, n (%) 1(1.4) 1(3.2) 0(0) 0.43 Renal replacement therapy, n (%) 1(1.4) 0(0) 1(2.4) 1.00 Hospital mortality, n (%) 0(0) 0(0) 0(0) - Duration of viral shedding after COVID-19 onset (days) 25[ 18 , 31 ] 27[ 22 , 34 ] 23[ 17 , 29 ] 0.03 Hospital length of stay (days) 33[ 27 , 39 ] 36[ 31 , 42 ] 29[ 24 , 36 ] < 0.01 Data are expressed as the median with interquartile range (IQR) in square brackets for non-normally distributed data. Continuous variables are shown as the mean ± SD or median [IQR], as appropriate. Categorical variables are shown as number (%). COVID-19, coronavirus disease 2019; CAD, coronary artery disease; COPD, chronic obstructive pulmonary disease; FiO 2 , fraction of inspired oxygen; PaO 2 , partial pressure of oxygen. During hospital stay, more patients in the steroid therapy group were treated with high-flow nasal oxygen than standard care group (35.5% vs. 7.3%, P < 0.01). However, the proportion of invasive or noninvasive ventilation between the two groups was comparable. All patients survived to discharge from hospital in this study. The length of hospital stay was higher in patients receiving steroid therapy than in those not receiving steroid therapy (36 vs. 29 days, P < 0.01). Quantitative CT parameters over time in the steroid therapy group and the standard care group Three hundred chest CT scans were longitudinally collected from 72 patients with severe COVID-19. All CT scans were classified into five stages according to the interval between hospital admission and follow-up CT scans: Stage 1 (T1, at admission); Stage 2 (T2, 3–7 days); Stage 3 (T3, 8–14 days); Stage 4 (T4, 15–21 days); and Stage 5 (T5, 22–31 days). The quantitative percentages and volumes of compromised lung (CL), nonaerated lung (NNL), poorly aerated lung (PAL), normally aerated lung (NAL), and hyperinflated lung (HI) were extracted from CT scans according to the recommended protocols (Fig. 2 and Table 2 ). Quantitative CT parameters over time in the steroid therapy group and the standard care group are shown in Fig. 3 . At baseline (Stage 1), the percentages of CL, NNL, PAL, and HI were higher in the steroid therapy group than in the standard care group (all P < 0.05; Table 2 ). The percentages of %CL, %PAL, and %HI were significantly decreased in the steroid therapy group as compared to those in the standard care group during the follow-up period (all P 0.05 by repeated-measures ANOVA; Fig. 3 B and 3 D). Examples of quantitative lung CT analysis for patients with severe COVID-19 receiving steroid therapy and with no steroid therapy were presented in Fig. 4 and Fig. 5 , respectively. Table 2 Quantitative chest CT parameters (%) during the follow-up period Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 Quantitative chest CT parameters (%) Entire cohort(n = 72) Steroid group(n = 31) Standard care group(n = 41) Entire cohort(n = 62) Steroid group(n = 27) Standard care group(n = 35) Entire cohort(n = 65) Steroid group(n = 29) Standard care group(n = 36) Entire cohort(n = 56) Steroid group(n = 29) Standard care group(n = 27) Entire cohort(n = 45) Steroid group(n = 25) Standard care group(n = 20) %CL 23.90[19.83,36.11] 36.45[25.12,48.53] * 22.29[18.81,24.36] 22.38[19.89,29.54] 28.35[22.47,40.52] * 20.57[18.63,24.13] 21.92[18.88,31.81] 24.95[20.90,34.29] * 19.30[17.91,26.73] 21.44[18.18,27.84] 23.91[19.80,29.57] * 19.61[17.73,26.50] 22.49[18.44,30.80] 22.49[18.56,30.80] 22.65[17.85,30.37] %NNL 18.02[15.91,21.35] 20.22[17.72,24.37] * 17.05[15.71,18.53] 17.26[16.01,20.15] 18.93[17.19,23.28] * 16.52[15.60,18.03] 17.03[15.62,20.75] 17.35[16.35,22.23] 16.20[15.50,20.13] 16.72[15.52,19.40] 16.56[15.78,20.92] 16.88[15.28,18.94] 17.10[15.35,22.47] 17.10[15.40,22.53] 17.00[15.31,22.08] %PAL 5.92[3.81,13.59] 13.73[7.06,20.00] * 4.18[2.97,5.97] 5.10[3.47,8.74] 8.98[5.88,17.93] * 3.63[2.78,5.67] 4.67[2.87,9.79] 7.05[4.57,14.42] * 3.24[2.31,6.38] 4.28[2.63,8.49] 4.92[3.83,10.69] * 2.97[2.20,7.46] 5.41[2.66,8.15] 5.41[2.95,8.38] 5.16[2.37,7.12] %NAL 59.23[50.79,67.26] 57.20[46.68,66.59] 61.69[53.69,68.51] 61.31[55.10,70.01] 62.25[53.28,70.09] 60.78[55.65,69.75] 58.72[51.95,67.24] 61.96[54.71,71.88] * 57.59[49.00,62.34] 58.46[53.41,68.98] 62.01[55.39,70.18] * 55.64[45.57,62.51] 60.32[53.37,66.56] 61.72[56.39,68.62] * 56.25[44.75,62.06] %HI 7.62[2.12,19.40] 2.39[1.40,7.72] * 14.11[6.90,23.29] 7.93[3.44,19.27] 4.65[2.13,7.23] * 17.58[7.17,25.27] 10.57[3.50,22.23] 5.87[2.57,11.79] * 20.12[5.72,30.17] 12.81[5.64,25.04] 9.42[4.46,14.46] * 21.78[8.42,34.32] 13.21[3.45,23.48] 6.38[2.22,17.04] * * 19.90[4.28,33.03] Data are expressed as the median with interquartile range in square brackets for non-normally distributed data. The Kruskal–Wallis analysis of variance was used for non-normally distributed data comparison. %CL, percentage of compromised lung volume, considered as the sum of %PAL and %NNL; %NNL, percentage of nonaerated lung volume; %PAL, percentage of poorly aerated lung volume; %NAL, percentage of normally aerated lung volume; %HI, percentage of hyperinflated lung volume. * denotes P < 0.05 between two groups at each stage. Associations between steroid administration and changes in quantitative chest CT parameters during the follow-up period Main Effect Associations GEE models were used to assess the effect of steroid on changes in quantitative chest CT parameters during the follow-up period. The main effect analyses, without interactions in the GEE model, are shown in Table 3 . Across the entire cohort, univariate GEE analysis revealed that steroid administration was associated with decrease in %CL (-7.44% [95% CI, -10.01 to -4.87, P < 0.001]) and increase in %NAL (7.46% [95% CI, 4.03 to 10.88, P < 0.001]). After adjusting for duration and baseline quantitative CT parameters (%), steroid administration was still associated with decrease in %CL (-3.27% [95% CI, -5.86 to -0.68, P = 0.01]) and increase in %NAL (6.17% [95% CI, 3.51 to 8.83, P 0.1). Table 3 Associations between steroid administration and changes in quantitative chest CT parameters (%) in the entire cohort Outcome Unadjusted Adjusted Changes in quantitative CT parameters (%) Coefficient 95% CI P value Coefficient 95% CI P value Changes in CL a -7.44 -10.01 to -4.87 < 0.01 -3.27 -5.86 to -0.68 0.01 Changes in NNL b -1.75 -3.01 to -0.48 < 0.01 -0.68 -1.61 to 0.24 0.15 Changes in PAL c -5.71 -7.66 to -3.77 < 0.01 -1.44 -3.46 to 0.57 0.16 Changes in NAL d 7.46 4.03 to 10.88 < 0.01 6.17 3.51 to 8.83 < 0.01 Changes in HI e 0.06 -2.81 to 2.92 0.97 -1.86 -4.73to 1.01 0.20 Analyses using generalized estimating equations to implement linear regression models. Coefficients represent differences in changes in quantitative chest CT parameters associated with steroid administration. All analyses were adjusted for duration and baseline quantitative chest CT parameters. a Model adjusted for duration and baseline %CL; b Model adjusted for duration and baseline %NNL; c Model adjusted for duration and baseline %PAL; d Model adjusted for duration and baseline %NAL; e Model adjusted for duration and baseline %HI. %CL, percentage of compromised lung volume, calculated as the sum of %PAL and %NNL; %NNL, percentage of nonaerated lung volume; %PAL, percentage of poorly aerated lung volume; %NAL, percentage of normally aerated lung volume; %HI, percentage of hyperinflated lung volume. Interaction By Duration Statistically significant interactions were observed between steroid administration and duration for quantitative chest CT parameters (all interactions, P < 0.05). Therefore, we analyzed the association between steroid administration and change in quantitative chest CT parameters at each stage separately (Table 4 ). At stage 2, steroid administration was not associated with changes in %CL (-2.02%, [95% CI, -4.55 to 0.51], P = 0.12). However, after stage 3, steroid administration was significantly associated with decreases in %CL. Steroid administration was associated with a 3.73% decrease in %CL ([95% CI, -7.18 to -0.29], P = 0.03) at stage 3, a 3.95% decrease in %CL ([95% CI, -7.57 to -0.33], P = 0.03) at stage 4, and a 5.01% decrease in %CL ([95% CI, -8.95 to -1.08], P = 0.01) at stage 5. Steroid administration at each stage was significantly associated with changes in %NAL. Steroid administration was associated with a 3.65% increase in %NAL ([95% CI, 0.68 to 6.63], P = 0.02) at stage 2, a 8.23% increase in %NAL ([95% CI, 4.50 to 11.96], P = 0.000) at stage 3, a 7.82% increase in %NAL ([95% CI, -3.41 to 12.23], P = 0.001) at stage 4, and a 9.59% increase in %NAL ([95% CI, 4.65 to 14.53], P = 0.000) at stage 5. No associations were observed between steroid administration and changes in %NNL or %HI after analyzing each stage separately. Steroid administration was also not associated with changes in %PAL at all stages, except stage 5. Table 4 Associations between steroid administration and changes in quantitative CT parameters (%) at different stages Outcome Stage 2 Stage 3 Stage 4 Stage 5 Changes in quantitative CT parameters (%) Coefficient 95% CI P value Coefficient 95% CI P value Coefficient 95% CI P value Coefficient 95% CI P value Changes in CL a -2.02 -4.55 to 0.51 0.12 -3.73 -7.18 to -0.29 0.03 -3.95 -7.57 to -0.33 0.03 -5.01 -8.95 to -1.08 0.01 Changes in NNL b -0.23 -1.49 to 1.02 0.71 -0.83 -2.34 to 0.68 0.28 -1.15 -2.99 to 0.69 0.22 -1.07 -3.06 to 0.92 0.28 Changes in PAL c -1.01 -2.74 to 0.72 0.25 -2.31 -4.71 to 0.08 0.06 -1.85 -4.10 to 0.40 0.11 -2.99 -5.24 to-0.75 0.01 Changes in NAL d 3.65 0.68 to 6.63 0.02 8.23 4.50 to 11.96 < 0.01 7.82 3.41 to 12.23 < 0.01 9.59 4.65 to 14.53 < 0.01 Changes in HI e -2.54 -5.96 to 0.88 0.14 -2.73 -6.91 to 1.45 0.20 -1.77 -7.01 to 3.47 0.50 -3.27 -8.67to 2.12 0.23 Analyses were performed using linear regression models adjusted for baseline quantitative chest CT parameters. Coefficients represent differences in changes in quantitative CT parameters associated with steroid administration. a Model adjusted for duration and baseline %CL; b Model adjusted for duration and baseline %NNL; c Model adjusted for duration and baseline %PAL; d Model adjusted for duration and baseline %NAL; e Model adjusted for duration and baseline %HI. %CL, percentage of compromised lung volume, calculated as the sum of %PAL and %NNL; %NNL, percentage of nonaerated lung volume; %PAL, percentage of poorly aerated lung volume; %NAL, percentage of normally aerated lung volume; %HI, percentage of hyperinflated lung volume. Interaction By Disease Severity Associations between steroid administration and changes in %CL, %NNL, %PAL, %NAL, or %HI during the follow-up period varied between different baseline quantitative CT parameters (all interactions, P median level) and low groups (< median level) according to baseline quantitative CT parameters. On the basis of the analysis of different quantitative parameters separately, the associations between steroid administration and changes in quantitative CT parameters are shown in Table 5 . Steroid administration was associated with more significant decrease in %CL in the high CL group (-9.67%, [95% CI, -13.12 to -6.22], P < 0.001), but there was no significant association between steroid administration and %CL decrease in the low CL group (0.81%, [95% CI, -1.72 to 3.34], P = 0.53). Similarly, steroid administration was associated with a greater decrease in NNL or PAL in the high NNL or PAL group, but the association was not significantly different in the low NNL or PAL group. For NAL, steroid administration was associated with a more significant increase in NAL in both high NAL group (5.92%, [95% CI, 1.88 to 9.95], P = 0.004) and low NAL group (7.28%, [95% CI, 2.59 to 11.97], P = 0.002). Steroid administration was, however, not associated with changes in HI in both high and low HI groups (all P > 0.1). Table 5 Associations between steroid administration and changes in quantitative chest CT parameters (%) according to baseline parameters. Outcome Coefficient 95% CI P value Changes in CL (%) High CL group (≥ 23.90) N = 144 -9.67 -13.12 to -6.22 < 0.01 Low CL group (< 23.90) N = 144 0.81 -1.72 to 3.34 0.53 Changes in NNL (%) High NNL group (≥ 18.02) N = 144 -1.88 -3.63 to -0.12 0.04 Low NNL group (< 18.02) N = 144 -0.17 -1.20 to 0.86 0.75 Changes in PAL (%) High PAL group (≥ 5.92) N = 144 -6.74 -9.29 to -4.19 < 0.01 Low PAL group (< 5.92) N = 144 0.96 -1.00 to 2.92 0.34 Changes in NAL (%) High NAL group (≥ 59.23) N = 144 5.92 1.88 to 9.95 < 0.01 Low NAL group (< 59.23) N = 144 7.28 2.59 to 11.97 < 0.01 Changes in HI (%) High HI group (≥ 7.62) N = 144 -1.84 -8.12 to 4.44 0.57 Low HI group (< 7.62) N = 144 -0.35 -3.73 to 3.02 0.84 Analyses using generalized estimating equations to implement linear regression models. Coefficients represent the differences in changes in quantitative CT parameters associated with steroid administration. All analyses were adjusted for duration. %CL, percentage of compromised lung volume, calculated as the sum of %PAL and %NNL; %NNL, percentage of nonaerated lung volume; %PAL, percentage of poorly aerated lung volume; %NAL, percentage of normally aerated lung volume; %HI, percentage of hyperinflated lung volume. Discussion To the best of our knowledge, the present study is the first to assess the association between steroid administration and quantitative CT variables in patients with COVID-19. Our results showed that steroid administration was independently associated with decreases in compromised lung volume (%CL, – 500 to 100 HU) with a significant interaction by duration or disease severity in a longitudinal cohort. Chest CT plays an important role in differentiating, diagnosing, and monitoring pulmonary disease progression[ 13 ]. Chest CT has been included as an important modality for COVID-19 diagnosis and management in the sixth edition of Chinese national guidelines on the diagnosis and treatment for COVID-19[ 16 ]. Several QCT analysis approaches have been developed recently for quantifying a range of lesions. The visual or semi-quantitative analysis methods (i.e., “CT severity score”) were used for assessing disease severity [ 26 , 27 ]. However, because of large inter- and intra-observer variability, visual or semi-quantitative assessment of CT findings cannot accurately and quantitatively monitor disease progression and treatment response [ 9 ]. In our preliminary study, we found that quantitative parameters of QCT may serve as a useful endpoint in evaluating treatment efficacy in patients with COVID-19[ 13 ]. Further, we found that patients with severe COVID-19 receiving steroid therapy showed a significant different recovery pattern compared to those not receiving steroid therapy using a deep learning method[ 14 ]. However, due to the black-box brought by deep networks, we were not able to further interpret the difference we have observed. In the present study, we performed a computer-aided quantitative lung lesion extraction analysis to assess lung lesions. The software (3D-slicer) we used for lung segmentation has been specifically updated recently to support the urgent need of segmenting compromised lung parenchyma in COVID-19 pandemic ( https://github.com/rbumm/SlicerLungCTAnalyzer/ ). In the present study, threshold-based quantitative analyses were conducted for the aerated-condition segmentation process, which provided standardized and reproducible evaluation for lung lesions[ 12 ]. This method has been confirmed to predict the need for oxygenation support and intubation in patients with COVID-19[ 25 ]. Compromised lung volume, including NNL and PAL, is significantly correlated with respiratory dysfunction and can accurately predict poor outcomes in patients with COVID-19[ 25 , 28 ]. Hence, we chose changes in percentage of compromised lung volume (Δ%CL) at different stages as the primary outcome to evaluate response to steroid therapy. In this study, the steroid therapy group exhibited a higher baseline compromised lung volume than the standard care group, although vital signs and the PaO 2 /FiO 2 ratio at the start of therapy were comparable in both groups. This indicated that steroids were used in patients who were relatively critically ill. Patients receiving steroid therapy showed a significant decrease in compromised lung volume as compared to those without steroid therapy, which indicates a faster resolution rate in the steroid therapy group. The association between steroid administration and decrease in %CL varied between different stages. In stage 2 (within 1 week of admission), steroid therapy did not show an association with decrease in %CL. However, after 1 week of admission (stages 3–5), steroid therapy showed a significant association with decrease in %CL. This indicated that steroid therapy exerted an effect of long-term radiographic improvement, rather than an early short-term effect. These findings were consistent with a previous study, in which steroid therapy was found to decrease late treatment failure in patients with severe community-acquired pneumonia and high inflammatory response, primarily due to decrease in radiographic progression[ 29 ]. Patients with severe COVID-19 usually rapidly progress to acute respiratory distress syndrome (ARDS) at an early stage, which is mainly related to dysregulated immune response [ 30 – 32 ]. Theoretically, with several anti-inflammatory and immunomodulatory properties, corticosteroids could prevent an excessive immune response and might also prevent the progression of COVID-19 [ 33 , 34 ]. Pooled results from recent RCTs in patients with severe or critical COVID-19 showed a significant reduction in mortality following the use of systemic corticosteroids[ 3 , 35 , 36 ]. However, the use of corticosteroid therapy is highly controversial for patients with severe pneumonia, including SARS, Middle East respiratory syndrome (MERS), influenza, and community-acquired pneumonia [ 37 – 40 ]. It can be inferred that the discrepancy may be attributed to heterogeneity in the type of corticosteroids administered, timing of therapy initiation, dose, medical conditions, and disease severity. In the present study, the association between steroid administration and decreases in %CL varied with disease severity. Steroid therapy was associated with a decrease in %CL in the high CL group, but not in the low CL group. This finding was supported by recent studies[ 2 , 41 , 42 ], in which corticosteroid therapy led to different clinical outcomes according to severity of illness. We speculated that the absence of benefit in the low CL group may be partially explained by the delayed viral clearance[ 43 – 45 ]. In the present study, patients in the low CL group receiving steroids had relatively longer viral shedding duration than those who were not receiving steroids, although the difference was not statistically significant. Although recent WHO guidelines recommend steroid therapy for patients with severe COVID-19[ 15 ], our study showed that steroid therapy may not be appropriate for all those patients. Further studies may be needed to clarify the optimal indication of steroid therapy. The present study has several limitations. First, the sample size in this longitudinal study was relatively small, which might restrict its statistical power. Second, our findings cannot be generalized to all patients with COVID-19 as most critical patients with less than two serial CT scans were excluded from this cohort. Moreover, selective biases in the type of corticosteroids administered, dose, duration, and dose tapering might affect the efficacy of corticosteroids in patients with severe COVID-19. Third, adverse outcomes related to steroid therapy, such as opportunistic infections, hyperglycemia, and neuromyopathy, were not assessed in this study. Fourth, as arteries and bronchi were not excluded from lung segmentation, these interstitial structures may partially fall in the same threshold as the nonaerated lung, which might make the quantification of compromised lung volume inaccurate. All patients with COVID-19 in our study had severe infection, and therefore, the effect of additional volume for lung interstitial structure was very limited. However, because the same method was used for all patients, the potential inaccuracy in the estimation of compromised lung volume was counterbalanced to some extent. Conclusions Steroid administration was independently associated with decrease in compromised lung volume (%CL, − 500 to 100 HU) with significant interaction by duration or disease severity in a longitudinal cohort. The QCT parameters, particularly compromised lung volume, may provide clinicians with an accurate tool to assess or monitor the progression of COVID-19 during the treatment process. Future large-scale prospective studies are warranted to validate our findings. Abbreviations CL: compromised lung CI: confidence interval COVID-19: coronavirus disease 2019 FiO 2 : fraction of inspiration oxygen GEE: generalized estimating equations HI: hyperinflated lung HU: Hounsfield unit IQR: interquartile range NAL: normally aerated lung NNL: nonaerated lung PAL: poorly aerated lung PaO 2 : partial arterial oxygen pressure QCT: quantitative computed tomography SD: standard deviation Declarations Ethics approval and consent to participate: The present study was approved by the Ethics Committee of Renmin Hospital of Wuhan University (WDRY2020-K048) and was performed in accordance with the Declaration of Helsinki. Written informed consent waswaived by the Ethics Committee in the setting of COVID-19 crisis in Wuhan. Consent for publication: Not applicable Availability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors have no conflict of interests to declare. Funding: This article was supported by grants from Natural Science Foundation of Shanghai (20ZR1411100 and 21ZR1412900), Science and Technology Commission of Shanghai Municipality (20DZ2261200), Program of Shanghai Academic/Technology Research Leader (20XD1421000), National Natural Science Foundation of China (82070085 and 82072131), Construction program of key but weak disciplines of shanghai health commission (2019ZB0105), Clinical Research Funds of Zhongshan Hospital (2020ZSLC38 and 2020ZSLC27), Smart Medical Care of Zhongshan Hospital (2020ZHZS01). Authors' contributions: YSparticipated in its design and coordination and drafted the manuscript.ZQ performed quantitative computed tomography analysis and helped to draft the manuscript.JC participated in its design and coordination and helped to draft the manuscript.MJ helped tocollect data and performthe statistical analysis.GM helped tocollect data. JH helped to perform quantitative computed tomography analysis. SYhelped tocollect data. KLhelped tocollect data. FL helped to draft the manuscript.GT conceived of the study and participated in its design and performed the statistical analysis.YZ participated in its design and helped to perform quantitative computed tomography analysis.ZL conceived of the study and participated in its design and coordination and helped to draft the manuscript.All authors read and approved the final manuscript. Acknowledgements:We thankall the staff members of Fudan Zhongshan National Medical Teamwho are on the front line of caring for patients in Wuhan. References Shang Y, Pan C, Yang X, Zhong M, Shang X, Wu Z, Yu Z, Zhang W, Zhong Q, Zheng X et al : Management of critically ill patients with COVID-19 in ICU: statement from front-line intensive care experts in Wuhan, China. ANN INTENSIVE CARE 2020, 10(1):73. 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Cite Share Download PDF Status: Under Review Version 2 posted Editorial decision: Major revision 11 Mar, 2022 Reviews received at journal 08 Feb, 2022 Reviewers invited by journal 13 Sep, 2021 Editor assigned by journal 12 Aug, 2021 First submitted to journal 12 Aug, 2021 Submission checks completed at journal 11 Aug, 2021 Editor invited by journal 11 Aug, 2021 You are reading this latest preprint version Show more versions 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-698051","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[{"code":1,"date":"2021-07-15 15:43:20","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}}],"articleType":"Research","associatedPublications":[],"authors":[{"id":53199967,"identity":"fcc36439-cfce-43fc-8166-de8e8185f9fa","order_by":0,"name":"Ying Su","email":"","orcid":"","institution":"Zhongshan Hospital Fudan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Su","suffix":""},{"id":53199968,"identity":"4c5432cf-f37c-4ed9-847d-08d66352c57e","order_by":1,"name":"Ze-song Qiu","email":"","orcid":"","institution":"ShanghaiTech University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ze-song","middleName":"","lastName":"Qiu","suffix":""},{"id":53199969,"identity":"7411fc09-a82a-4dc7-a2b1-8af307eb31a0","order_by":2,"name":"Jun Chen","email":"","orcid":"","institution":"Wuhan University Renmin Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Chen","suffix":""},{"id":53199970,"identity":"de240466-e382-4435-8810-36b173509513","order_by":3,"name":"Min-jie Ju","email":"","orcid":"","institution":"Zhongshan Hospital Fudan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Min-jie","middleName":"","lastName":"Ju","suffix":""},{"id":53199971,"identity":"75c2dc95-cbf9-443b-91e5-f9bd2f4278ec","order_by":4,"name":"Guo-guang Ma","email":"","orcid":"","institution":"Zhongshan Hospital Fudan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guo-guang","middleName":"","lastName":"Ma","suffix":""},{"id":53199972,"identity":"95cd2f3a-f503-4a7b-afd5-ea521269ffa1","order_by":5,"name":"Jin-wei He","email":"","orcid":"","institution":"ShanghaiTech University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jin-wei","middleName":"","lastName":"He","suffix":""},{"id":53199973,"identity":"cb6b26dd-193c-4b23-8f62-68bc220a5208","order_by":6,"name":"Shen-ji Yu","email":"","orcid":"","institution":"Zhongshan Hospital Fudan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shen-ji","middleName":"","lastName":"Yu","suffix":""},{"id":53199974,"identity":"f8378eb5-70f0-4ecb-b047-648d0ebb2c76","order_by":7,"name":"Kai Liu","email":"","orcid":"","institution":"Zhongshan Hospital Fudan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Liu","suffix":""},{"id":53199975,"identity":"bf5d21d2-7fc2-4750-8c08-81179fbde372","order_by":8,"name":"Fleming Y. 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M.","lastName":"Lure","suffix":""},{"id":53199976,"identity":"b71ad90c-c886-4a22-a649-f01f4e6392b1","order_by":9,"name":"Guo-wei Tu","email":"","orcid":"","institution":"Zhongshan Hospital Fudan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guo-wei","middleName":"","lastName":"Tu","suffix":""},{"id":53199977,"identity":"90309130-e818-4365-85f9-957ae7deca48","order_by":10,"name":"Yu-yao Zhang","email":"","orcid":"","institution":"ShanghaiTech University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yu-yao","middleName":"","lastName":"Zhang","suffix":""},{"id":53199978,"identity":"75d1e798-87df-42c7-b1f4-71fa972baa31","order_by":11,"name":"Zhe Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIie2QsQrCMBBArwjpkto1othfMDi4FP2VlIIu+g8RIaurTv6Co24pXfsBDg52cXYSBEEvKi6S6uiQN1wuRx6XOwCH4w+pm+BJaONBTE4BatJUrJCX0n0r1Fx/URL5yuG7wlLtXTbxaDXX5HC67lsDP5NYgahnVYYCGsVwMl0Iny/VkVKayFpQAN9aOhE27gBX+WTGwnMzkDnOgoqnQHR0hZKo24iEmjTpFZWwxI99UzKlBQWBCkGF4SqCKoUeRTZVKV8wQRqPWXalzAPF+NqihH6alRfVjyLcGMONDfz5oxJHti4G/ZFjZPb3FbrD4XA4ntwBH7lT+cxR4mQAAAAASUVORK5CYII=","orcid":"","institution":"Zhongshan Hospital Fudan University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Zhe","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2021-07-08 10:38:14","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-698051/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-698051/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":13874309,"identity":"1304540a-ee9b-4b94-9fd3-1de37599c909","added_by":"auto","created_at":"2021-09-22 15:47:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":237837,"visible":true,"origin":"","legend":"The enrollment of patients.\nCOVID-19, coronavirus disease 2019;CT,computed tomography.\n","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-698051/v2/03271fdd4a5eebc6188acb11.png"},{"id":13873726,"identity":"0ef0d3e6-99c6-4935-bc3f-dc9ebd39f249","added_by":"auto","created_at":"2021-09-22 15:44:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1444625,"visible":true,"origin":"","legend":"Quantitative lung CT analysis of a 55-year-old male with severe COVID-19 at admission. (A) Chest CT scan showing a bilateral mixed pattern (ground-glass opacities, interstitial thickening, and consolidation). (B) Illustration of automated lung segmentation. Yellow areas represent hyperinflated regions (%HI, −901 to −1000 HU); blue areas indicate normally aerated regions (%NAL, −501 to −900 HU); green areas represent poorly aerated regions (%PAL, −101 to −500 HU); red areas indicate nonaerated regions (%NNL, 100 to −100 HU). The compromised lung volume was calculated as the sum of NNL and PAL. (C) 3D volumetric representation of the bilateral lungs. (D) Comparison among different quantitative CT parameters. The patient had 30.53% of compromised lung volume (sum of 17.72% NNL and 12.81% PAL), 67.12% NAL, and 2.35% HI.","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-698051/v2/2eba3ed5b8369f06bbfe1199.png"},{"id":13873722,"identity":"5765e0cb-0d65-4b92-ab23-9d28209bdd1b","added_by":"auto","created_at":"2021-09-22 15:44:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":241794,"visible":true,"origin":"","legend":"Quantitative CT parameters over time in the steroid therapy group and the standard care group.\n(A) %CL, compromised lung volume, considered as the sum of %PAL and %NNL; P\u003c 0.001 for change over time; P = 0.01 for between-group difference. (B) %NNL, percentage of nonaerated lung volume; P = 0.08 for change over time; P = 0.09 for between-group difference. (C) %PAL, percentage of poorly aerated lung volume; P\u003c 0.001 for change over time; P = 0.006 for between-group difference. (D) %NAL, percentage of normally aerated lung volume; P = 0.04 for change over time; P = 0.13 for between-group difference. (E) %HI, percentage of hyperinflated lung volume; P\u003c 0.001 for change over time; P = 0.002 for between-group difference. P values for between-group difference were calculated by repeated-measures analysis of variance (ANOVA). The trends over time in quantitative CT parameters were also assessed using repeated-measures ANOVA. SIDAK multiple comparisons correction was used to compare each stage against the baseline (T1). * denotes a significant difference between two groups at each stage. # denotes a significant difference in the steroid therapy group when comparing each stage against the baseline (T1). † denotes a significant difference in the standard care group when comparing each stage against the baseline (T1). All CT scans were classified into five stages according to the interval between hospital admission and follow-up CT scans: Stage 1 (T1, at admission); Stage 2 (T2, 3–7 days); Stage 3 (T3, 8–14 days); Stage 4 (T4, 15–21 days); and Stage 5 (T5, 22–31 days).","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-698051/v2/817fb8b73b126af507c2cece.png"},{"id":13873725,"identity":"0beee51a-f05b-4ecb-be0b-6392301892fa","added_by":"auto","created_at":"2021-09-22 15:44:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2121445,"visible":true,"origin":"","legend":"Example of quantitative lung CT analysis for a patient with severe COVID-19 receiving steroid therapy.\nA 69-year-old female complained of fever for 19 days accompanied with dyspnea and fatigue. After admission to hospital, she received high-flow nasal cannula oxygen therapy, arbidol, and steroid therapy. At admission, intravenous methylprednisolone was initiated with 40 mg every 12 h (1.31 mg/kg/d) for 5 days, followed by gradual tapering by 0.5 mg/kg every 5 days. Methylprednisolone was withdrawn at hospital day 15.Chest CT scans were performed at admission (A), day 3 (B), day 9 (C), day 16 (D), and day 22 (E).Chest CT scans (a), illustration of automated lung segmentation (b), 3D volumetric representation of the bilateral lungs (c), and comparison among different quantitative CT parameters (d) are shown in Fig. 3 at each stage. Yellow areas represent hyperinflated regions (%HI, −901 to −1000 HU); blue areas indicate normally aerated regions (%NAL, −501 to −900 HU); green areas represent poorly aerated regions (%PAL, −101 to −500 HU); red areas indicate nonaerated regions (%NNL, 100 to −100 HU). The compromised lung volume was calculated as the sum of NNL and PAL. During the treatment process, the compromised lung volume decreased significantly over time from 39.01% (A-d) at admission to 27.49% (E-d) at stage 5.","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-698051/v2/fe81a1b28e7c119044a876ba.png"},{"id":13875254,"identity":"60c286df-c3fa-4532-a0e9-6e1589894950","added_by":"auto","created_at":"2021-09-22 15:50:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2344100,"visible":true,"origin":"","legend":"Example of quantitative lung CT analysis for a patient with severe COVID-19 with no steroid therapy.\nA 57-year-old male complained of fever for 12 days accompanied with dyspnea. After admission to hospital, he received high-flow nasal cannula oxygen therapy and arbidol, but without steroid therapy. Chest CT scans were performed at admission (A), day 5 (B), day 13 (C), day 19 (D), and day 21 (E).Chest CT scans (a), illustration of automated lung segmentation (b), 3D volumetric representation of the bilateral lungs (c), and comparison among different quantitative CT parameters (d) are shown in Fig. 4 at each time point. Yellow areas represent hyperinflated regions (%HI, −901 to −1000 HU); blue areas indicate normally aerated regions (%NAL, −501 to −900 HU); green areas represent poorly aerated regions (%PAL, −101 to −500 HU); red areas indicate nonaerated regions (%NNL, 100 to −100 HU). The compromised lung volume was calculated as the sum of NNL and PAL. During the treatment process, the compromised lung volume showed no significant change over time from 25.6% (A-d) at admission to 23.14% (E-d) at stage 5.","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-698051/v2/f53fe4e0f3eac8412b23a202.png"},{"id":13875285,"identity":"c8f24e81-2633-4e1d-b612-f7405e1d2be9","added_by":"auto","created_at":"2021-09-22 15:51:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3324133,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-698051/v2/9de51068-3cd2-4870-ade2-ab6167a3698c.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eUsage of Compromised Lung Volume in Monitoring Steroid Therapy on Severe COVID-19\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eCurrently, the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has spread across the world[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As of April 27, 2021, 147,377,159 people have been diagnosed with COVID-19, including 3,112,041 deaths, according to the international World Health Organization (WHO) dashboard. Among various therapies for COVID-19, corticosteroid therapy has been proven to be effective for critical cases. The RECOVERY trial first reported the effectiveness of corticosteroid therapy in reducing mortality in patients with COVID-19 receiving oxygen supplementation or invasive mechanical ventilation[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The WHO REACT meta-analysis that included data from seven randomized clinical trials (RCTs) also demonstrated that the use of systemic corticosteroids was associated with lower 28-day all-cause mortality in critically ill patients with COVID-19[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although corticosteroid therapy represents a milestone in the management of COVID-19, many questions remain unanswered[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The optimal type of corticosteroids, timing of initiation, dose, mode of administration, duration, and dose tapering are still unclear. An approach to resolve these issues is to develop accurate tools to assess or monitor the progression of COVID-19 during the corticosteroid therapy process.\u003c/p\u003e \u003cp\u003eChest computed tomography (CT) plays an important role in screening, diagnosing, and evaluating longitudinally patients with COVID-19[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Visual assessment of pulmonary lesions on chest CT scans has been proven to be valuable in predicting outcome and assessing progression[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, inter- or intra-observer variation in traditional visual assessment of chest CT images increases the challenge of therapeutic assessment[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Quantitative computed tomography (QCT) analyses have recently been widely used to evaluate various pulmonary diseases[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. QCT enable to extract quantitative data from medical images and increases the reproducibility of evaluation, thus, serving as a potential tool for monitoring disease progression and treatment response[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This may be particularly valuable in circumstance of minor effect as new therapies are developed and evaluated[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003eWe hypothesize that QCT can serve as a tool for monitoring steroid treatment response during the course of disease. Our preliminary studies have confirmed the value of QCT in monitoring the progression and clinical decision-making in patients with COVID-19 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the effect of steroids on quantitative chest CT parameters during the treatment process remains unknown. In this retrospectively study, we aimed to assess the association between steroid administration and QCT variables in a longitudinal cohort with COVID-19.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003e The present study was approved by the Ethics Committee of Renmin Hospital of Wuhan University (WDRY2020-K048) and was performed in accordance with the Declaration of Helsinki. Written informed consent was waived by the Ethics Committee in the setting of COVID-19 crisis in Wuhan.\u003c/p\u003e \u003cp\u003eFrom February 7, 2020 to February 17, 2020, consecutive patients with confirmed COVID-19 admitted to the east campus of Renmin Hospital of Wuhan University were screened. The diagnosis of COVID-19 was based on the detection of SARS-CoV-2 nucleic acid by a real-time RT-PCR assay.\u003c/p\u003e \u003cp\u003eThe inclusion criteria were as follows: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years and (2) patients with severe or critical COVID-19. Exclusion criteria included (1) hematological or solid malignancies, (2) patients with less than two CT scans during hospital stay, and (3) systemic corticosteroid or immunosuppressive therapy in the previous 6 weeks. The severity of COVID-19 was defined according to WHO interim guidance or the sixth edition of the Chinese national guidelines on the diagnosis and treatment for COVID-19[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Patients were considered to have a severe infection if they met any of the following conditions: respiratory distress and a respiratory rate of \u0026gt;\u0026thinsp;30 times/min; oxygen saturation on room air at rest\u0026thinsp;\u0026lt;\u0026thinsp;93%; and partial arterial oxygen pressure (PaO\u003csub\u003e2\u003c/sub\u003e)/fraction of inspiration oxygen (FiO\u003csub\u003e2\u003c/sub\u003e)\u0026thinsp;\u0026le;\u0026thinsp;300 mmHg. Patients were considered to be in a critical state if they met any of the following conditions: respiratory failure requiring mechanical ventilation, shock, and dysfunction of other organs requiring ICU management.\u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003eData Collection\u003c/h2\u003e\n\u003cp\u003e Data regarding baseline demographic features, co-morbidities, and clinical characteristics on hospital arrival, including symptoms, vital signs, and interventions during hospital stay, were retrospectively collected by two trained reviewers.\u003c/p\u003e \u003cp\u003eBecause there was no consensus on the use of steroids in the early stage of the COVID-19 pandemic, all steroid therapies were initiated at the time of admission at the discretion of attending physicians on the basis of clinical symptoms and CT images. According to our previous experience[\u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], intravenous methylprednisolone at a dose of 1.0-1.5 mg/kg every 12 h was initiated for 5 days or until oxygen saturation improved, followed by gradual tapering by 0.5 mg/kg every 3\u0026ndash;5 days.\u003c/p\u003e \u003cp\u003eOther therapeutic interventions such as the use of antibiotics, ventilation, laboratory testing, and hemodynamic management were performed following the sixth edition of the Guidelines on the Diagnosis and Treatment of COVID-19 published by the National Health Commission of China.\u003c/p\u003e\n\u003ch2\u003eCt Protocol\u003c/h2\u003e\n\u003cp\u003eAll patients underwent CT examination at hospital admission. Serial chest CT scans were performed during hospital stay. All CT scans were performed within a single inspiratory phase on a commercial multidetector CT scanner (GE Optima CT680). To minimize motion artifacts, CT images were acquired during a single breath-hold. Standard lung algorithm settings were used as follows: 120 kV and automatic tube current (180\u0026ndash;400 mA); iterative reconstruction technique; detector, 64 mm; rotation time, 0.35 s; section thickness, 5 mm; collimation, 0.625 mm; pitch, 1.5; matrix, 512 \u0026times; 512. Based on the time interval between hospital admission and CT scan, we designated five stages in this study: stage 1 (baseline CT scans, in which CT scans were acquired at hospital admission); stage 2 (CT scans acquired\u0026thinsp;\u0026gt;\u0026thinsp;3 to 7 days after admission); stage 3 (CT scans acquired\u0026thinsp;\u0026gt;\u0026thinsp;8 to 14 days after admission); stage 4 (CT scans acquired\u0026thinsp;\u0026gt;\u0026thinsp;15 to 21 days after admission); and stage 5 (CT scans acquired\u0026thinsp;\u0026gt;\u0026thinsp;22 to 31 days after admission).\u003c/p\u003e\n\u003ch2\u003eCt Image Analysis\u003c/h2\u003e\n\u003cp\u003eFor patient lung parenchyma segmentation, we performed a volumetric analysis in 3D Slicer (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.slicer.org\u003c/span\u003e\u003c/span\u003e) via the Lung CT Analyzer project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/rbumm/SlicerLungCTAnalyzer/)\u003c/span\u003e\u003c/span\u003e\"[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. We extracted right and left lung segments from individual CT slices. For unsatisfactory lung segmentation, we refined the lung contours with the manual segmentation tool implemented in ITK-SNAP (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://clinicaltrials.gov/ct2/show/NCT04953247\" target=\"_blank\"\u003ewww.itksnap.org\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The trachea was excluded from the lung segmentation, while segmental arteries and bronchi were included. We then further divided the lung region into four components and computed the volume of each lung component, which were considered as the percentages of the total volume. According to different Hounsfield unit (HU) intervals in the quantitative chest CT scan, we divided each lung into nonaerated lung volume (%NNL, 100 to \u0026minus;\u0026thinsp;100 HU), poorly aerated lung volume (%PAL, \u0026minus;\u0026thinsp;101 to \u0026minus;\u0026thinsp;500 HU), normally aerated lung volume (%NAL, \u0026minus;\u0026thinsp;501 to \u0026minus;\u0026thinsp;900 HU), and hyperinflated lung volume (%HI, \u0026minus;\u0026thinsp;901 to \u0026minus;\u0026thinsp;1000 HU) regions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The additional \u0026ldquo;compromised lung\u0026rdquo; volume (%CL) was considered as the sum of %PAL and %NNL (\u0026minus;\u0026thinsp;500 to 100 HU). The authors (Ze-song Qiu, Jin-wei He, and Yu-yao Zhang) who performed quantitative CT analysis were blinded to clinical characteristics and outcomes.\u003c/p\u003e\n\u003ch2\u003eOutcome Assessment\u003c/h2\u003e\n\u003cp\u003eTo monitor COVID-19 progression during the treatment process, we chose changes in the percentage of compromised lung volume (Δ%CL) at different stages (Δ%CL = %CL at different stages-baseline %CL) as the primary outcome. The negative value of Δ%CL thus reflected clinical improvement.\u003c/p\u003e \u003cp\u003eThe secondary outcomes were changes in the percentage of NNL (Δ%NNL\u0026thinsp;=\u0026thinsp;NNL at different stages-baseline NNL), PAL (Δ%PAL = %PAL at different stages།baseline %PAL), NAL (Δ%NAL = %NAL at different stages།baseline %NAL), and HI (Δ%HI = %HI at different stages།baseline %HI) at different stages. Under these circumstances, clinical improvement was reflected by the negative value of Δ%NNL and Δ%PAL, and the positive value of Δ%NAL.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eCategorical variables were expressed as numbers and percentages. The normality of the distribution for continuous variables was assessed by the Kolmogorov\u0026ndash;Smirnov test. Continuous variables were expressed as the mean and standard deviation (SD) or median and interquartile range [IQR]. The chi-square test or Fisher\u0026rsquo;s exact test was used for categorical variables, while the Student\u0026rsquo;s t test or Mann\u0026ndash;Whitney U test was used for continuous variables, as appropriate. For serial quantitative CT parameters, we tested differences between two groups over time by using repeated-measures analysis of variance (ANOVA) with no imputation for missing values. The Šid\u0026aacute;k multiple comparisons correction was used to compare each stage against the baseline (Stage 1).\u003c/p\u003e \u003cp\u003eTo longitudinally assess the association between steroid administration and changes in the percentage of quantitative CT parameters, generalized estimating equations (GEE) were used so that correlations between repeat CT quantitative parameters for an individual patient could be considered. The outcome variables Δ%CL, Δ%NNL, Δ%PAL, Δ%NAL, and Δ%HI were set as time-dependent variables because they were measured at each examination visit. Associations between steroid use and Δ%CL, Δ%NNL, Δ%PAL, Δ%NAL, and Δ%HI at different stages were assessed using GEE with correlations between repeated measures on participants modeled using an exchangeable structure to implement linear regression models. Covariates included in the GEE models were first screened by univariate GEE analysis, and those covariates with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10 were introduced in multivariate GEE models. These models were finally adjusted for duration and baseline quantitative CT parameters. Interactions between steroid use and duration (steroid use*duration) or between steroid use and baseline quantitative CT parameters (steroid use*baseline quantitative CT parameters) were tested by multivariate GEE analysis. Subsequent analyses assessed the associations between steroid use and changes in the percentage of quantitative CT parameters at different stages by linear regression models. We also assessed the associations between steroid use and changes in the percentage of quantitative CT parameters in relation to different baseline quantitative CT parameters. Patients were classified into high (\u003cb\u003e\u0026ge;\u003c/b\u003e\u0026thinsp;median level) and low groups (\u0026lt;\u0026thinsp;median level) according to baseline quantitative CT parameters. The β coefficients and 95% confidence intervals (CI) were calculated. All statistical analyses were performed with SPSS software package, version 15.0 (SPSS Inc., Chicago, IL, USA), and two-sided \u003cem\u003eP\u003c/em\u003e values of less than 0.05 were considered to be statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eBetween February 7 and February 17, 2020, a total of 83 patients with severe or critical COVID-19 were screened for inclusion. Of these patients, 11 patients were excluded, including eight critical COVID-19 patients with less than two serial CT scans during hospital stay, two patients receiving systemic corticosteroids or immunosuppressive therapy in the previous 6 weeks, and one patient with solid malignancy. Within the study period, 72 patients with severe COVID-19 were finally included in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThirty-one patients (43.1%) received steroid therapy. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline characteristics of the patients. The median age of the 72 patients was 63 years [IQR 49 to 69 years], and 51.4% of the patients were male. Fever was the most common symptom (90.3%), followed by fatigue (87.5%), cough (73.6%), and dyspnea (55.6%). The baseline characteristics between patients receiving steroid therapy and those not receiving steroid therapy were similar (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The vital signs and the PaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e ratio on admission were also similar between the two groups.\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\u003eClinical characteristics of patients with severe COVID-19\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEntire cohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSteroid group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard care group\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\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\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\u003e63[49,69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65[55,72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62[48,69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (male), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37(51.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(56.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities\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\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(30.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic renal disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSigns and symptoms\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\u003eFever, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65(90.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(96.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35(85.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCough, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53(73.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(77.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(70.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSputum production, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatigue, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63(87.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(90.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35(85.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyspnea, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(64.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(48.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNausea or vomiting, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiarrhea, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnorexia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyalgia or arthralgia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnset of symptom to first CT scan (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVital signs at hospital admission\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\u003eAltered mental status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate (beats/minute)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88[78,102]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85[77,105]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90[81,100]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory rate (breaths/minute)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132[122,145]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132[123,147]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131[120,144]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure (mm Hg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78[71,83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79[72,83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76[70,84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory status assessment\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\u003ePaO\u003csub\u003e2\u003c/sub\u003e on admission (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67[61,86]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66[59,84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68[63,87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaCO\u003csub\u003e2\u003c/sub\u003e on admission (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e on admission (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e256[226,277]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e246[195,279]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e258[228,277]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory support\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\u003eHigh flow nasal oxygen, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-invasive mechanical ventilation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvasive mechanical ventilation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal replacement therapy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital mortality, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\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\u003eDuration of viral shedding after COVID-19 onset (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital length of stay (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\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 the median with interquartile range (IQR) in square brackets for non-normally distributed data. Continuous variables are shown as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median [IQR], as appropriate. Categorical variables are shown as number (%). COVID-19, coronavirus disease 2019; CAD, coronary artery disease; COPD, chronic obstructive pulmonary disease; FiO\u003csub\u003e2\u003c/sub\u003e, fraction of inspired oxygen; PaO\u003csub\u003e2\u003c/sub\u003e, partial pressure of oxygen.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDuring hospital stay, more patients in the steroid therapy group were treated with high-flow nasal oxygen than standard care group (35.5% vs. 7.3%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). However, the proportion of invasive or noninvasive ventilation between the two groups was comparable. All patients survived to discharge from hospital in this study. The length of hospital stay was higher in patients receiving steroid therapy than in those not receiving steroid therapy (36 vs. 29 days, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003cb\u003eQuantitative CT parameters over time in the steroid therapy group and the standard care group\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThree hundred chest CT scans were longitudinally collected from 72 patients with severe COVID-19. All CT scans were classified into five stages according to the interval between hospital admission and follow-up CT scans: Stage 1 (T1, at admission); Stage 2 (T2, 3\u0026ndash;7 days); Stage 3 (T3, 8\u0026ndash;14 days); Stage 4 (T4, 15\u0026ndash;21 days); and Stage 5 (T5, 22\u0026ndash;31 days).\u003c/p\u003e \u003cp\u003eThe quantitative percentages and volumes of compromised lung (CL), nonaerated lung (NNL), poorly aerated lung (PAL), normally aerated lung (NAL), and hyperinflated lung (HI) were extracted from CT scans according to the recommended protocols (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Quantitative CT parameters over time in the steroid therapy group and the standard care group are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. At baseline (Stage 1), the percentages of CL, NNL, PAL, and HI were higher in the steroid therapy group than in the standard care group (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The percentages of %CL, %PAL, and %HI were significantly decreased in the steroid therapy group as compared to those in the standard care group during the follow-up period (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 by repeated-measures ANOVA; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE), whereas the percentage of %NNL and %NAL did not significantly differ between the groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 by repeated-measures ANOVA; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Examples of quantitative lung CT analysis for patients with severe COVID-19 receiving steroid therapy and with no steroid therapy were presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, respectively.\u003c/p\u003e \u003cp\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\u003eQuantitative chest CT parameters (%) during the follow-up period\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"16\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eStage 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eStage 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eStage 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eStage 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003eStage 5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuantitative chest CT parameters (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEntire cohort(n\u0026thinsp;=\u0026thinsp;72)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSteroid group(n\u0026thinsp;=\u0026thinsp;31)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eStandard care group(n\u0026thinsp;=\u0026thinsp;41)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eEntire cohort(n\u0026thinsp;=\u0026thinsp;62)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eSteroid group(n\u0026thinsp;=\u0026thinsp;27)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eStandard care group(n\u0026thinsp;=\u0026thinsp;35)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eEntire cohort(n\u0026thinsp;=\u0026thinsp;65)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eSteroid group(n\u0026thinsp;=\u0026thinsp;29)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eStandard care group(n\u0026thinsp;=\u0026thinsp;36)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eEntire cohort(n\u0026thinsp;=\u0026thinsp;56)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003eSteroid group(n\u0026thinsp;=\u0026thinsp;29)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003eStandard care group(n\u0026thinsp;=\u0026thinsp;27)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003eEntire cohort(n\u0026thinsp;=\u0026thinsp;45)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003eSteroid group(n\u0026thinsp;=\u0026thinsp;25)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003eStandard care group(n\u0026thinsp;=\u0026thinsp;20)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%CL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.90[19.83,36.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.45[25.12,48.53] \u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.29[18.81,24.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.38[19.89,29.54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.35[22.47,40.52] \u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.57[18.63,24.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21.92[18.88,31.81]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24.95[20.90,34.29] \u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19.30[17.91,26.73]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e21.44[18.18,27.84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e23.91[19.80,29.57] \u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e19.61[17.73,26.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e22.49[18.44,30.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e22.49[18.56,30.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e22.65[17.85,30.37]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%NNL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.02[15.91,21.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.22[17.72,24.37] \u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.05[15.71,18.53]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.26[16.01,20.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.93[17.19,23.28] \u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.52[15.60,18.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.03[15.62,20.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.35[16.35,22.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.20[15.50,20.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e16.72[15.52,19.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e16.56[15.78,20.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e16.88[15.28,18.94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e17.10[15.35,22.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e17.10[15.40,22.53]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e17.00[15.31,22.08]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%PAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.92[3.81,13.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.73[7.06,20.00] \u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.18[2.97,5.97]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.10[3.47,8.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.98[5.88,17.93] \u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.63[2.78,5.67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.67[2.87,9.79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.05[4.57,14.42] \u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.24[2.31,6.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.28[2.63,8.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.92[3.83,10.69] \u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.97[2.20,7.46]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5.41[2.66,8.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e5.41[2.95,8.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e5.16[2.37,7.12]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%NAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.23[50.79,67.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.20[46.68,66.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.69[53.69,68.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.31[55.10,70.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.25[53.28,70.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60.78[55.65,69.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58.72[51.95,67.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e61.96[54.71,71.88] \u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e57.59[49.00,62.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e58.46[53.41,68.98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e62.01[55.39,70.18] \u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e55.64[45.57,62.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e60.32[53.37,66.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e61.72[56.39,68.62] \u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e56.25[44.75,62.06]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%HI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.62[2.12,19.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.39[1.40,7.72] \u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.11[6.90,23.29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.93[3.44,19.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.65[2.13,7.23] \u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.58[7.17,25.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.57[3.50,22.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.87[2.57,11.79] \u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20.12[5.72,30.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e12.81[5.64,25.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e9.42[4.46,14.46] \u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e21.78[8.42,34.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e13.21[3.45,23.48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e6.38[2.22,17.04] \u003csup\u003e*\u003c/sup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e19.90[4.28,33.03]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"16\"\u003eData are expressed as the median with interquartile range in square brackets for non-normally distributed data. The Kruskal\u0026ndash;Wallis analysis of variance was used for non-normally distributed data comparison. %CL, percentage of compromised lung volume, considered as the sum of %PAL and %NNL; %NNL, percentage of nonaerated lung volume; %PAL, percentage of poorly aerated lung volume; %NAL, percentage of normally aerated lung volume; %HI, percentage of hyperinflated lung volume. \u003cb\u003e*\u003c/b\u003e denotes \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 between two groups at each stage.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociations between steroid administration and changes in quantitative chest CT parameters during the follow-up period\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003eMain Effect Associations\u003c/h2\u003e\n\u003cp\u003eGEE models were used to assess the effect of steroid on changes in quantitative chest CT parameters during the follow-up period. The main effect analyses, without interactions in the GEE model, are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Across the entire cohort, univariate GEE analysis revealed that steroid administration was associated with decrease in %CL (-7.44% [95% CI, -10.01 to -4.87, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001]) and increase in %NAL (7.46% [95% CI, 4.03 to 10.88, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001]). After adjusting for duration and baseline quantitative CT parameters (%), steroid administration was still associated with decrease in %CL (-3.27% [95% CI, -5.86 to -0.68, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01]) and increase in %NAL (6.17% [95% CI, 3.51 to 8.83, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001]). There were no significant associations between steroid administration and changes in %NNL, %PAL, and %HI (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.1).\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\u003eAssociations between steroid administration and changes in quantitative chest CT parameters (%) in the entire cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChanges in quantitative CT parameters (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCoefficient\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eP\u003c/span\u003e \u003cb\u003evalue\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eCoefficient\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eP\u003c/span\u003e \u003cb\u003evalue\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChanges in CL\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-10.01 to -4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.86 to -0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChanges in NNL\u003c/b\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.01 to -0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.61 to 0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChanges in PAL\u003c/b\u003e\u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7.66 to -3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.46 to 0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChanges in NAL\u003c/b\u003e\u003csup\u003e\u003cb\u003ed\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.03 to 10.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.51 to 8.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChanges in HI\u003c/b\u003e\u003csup\u003e\u003cb\u003ee\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.81 to 2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.73to 1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAnalyses using generalized estimating equations to implement linear regression models. Coefficients represent differences in changes in quantitative chest CT parameters associated with steroid administration. All analyses were adjusted for duration and baseline quantitative chest CT parameters. \u003csup\u003ea\u003c/sup\u003e Model adjusted for duration and baseline %CL; \u003csup\u003eb\u003c/sup\u003e Model adjusted for duration and baseline %NNL; \u003csup\u003ec\u003c/sup\u003e Model adjusted for duration and baseline %PAL; \u003csup\u003ed\u003c/sup\u003e Model adjusted for duration and baseline %NAL; \u003csup\u003ee\u003c/sup\u003e Model adjusted for duration and baseline %HI. %CL, percentage of compromised lung volume, calculated as the sum of %PAL and %NNL; %NNL, percentage of nonaerated lung volume; %PAL, percentage of poorly aerated lung volume; %NAL, percentage of normally aerated lung volume; %HI, percentage of hyperinflated lung volume.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch2\u003eInteraction By Duration\u003c/h2\u003e\n\u003cp\u003eStatistically significant interactions were observed between steroid administration and duration for quantitative chest CT parameters (all interactions, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Therefore, we analyzed the association between steroid administration and change in quantitative chest CT parameters at each stage separately (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). At stage 2, steroid administration was not associated with changes in %CL (-2.02%, [95% CI, -4.55 to 0.51], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.12). However, after stage 3, steroid administration was significantly associated with decreases in %CL. Steroid administration was associated with a 3.73% decrease in %CL ([95% CI, -7.18 to -0.29], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) at stage 3, a 3.95% decrease in %CL ([95% CI, -7.57 to -0.33], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) at stage 4, and a 5.01% decrease in %CL ([95% CI, -8.95 to -1.08], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) at stage 5. Steroid administration at each stage was significantly associated with changes in %NAL. Steroid administration was associated with a 3.65% increase in %NAL ([95% CI, 0.68 to 6.63], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) at stage 2, a 8.23% increase in %NAL ([95% CI, 4.50 to 11.96], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000) at stage 3, a 7.82% increase in %NAL ([95% CI, -3.41 to 12.23], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) at stage 4, and a 9.59% increase in %NAL ([95% CI, 4.65 to 14.53], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000) at stage 5. No associations were observed between steroid administration and changes in %NNL or %HI after analyzing each stage separately. Steroid administration was also not associated with changes in %PAL at all stages, except stage 5.\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\u003eAssociations between steroid administration and changes in quantitative CT parameters (%) at different stages\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eStage 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eStage 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eStage 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eStage 5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChanges in quantitative CT parameters (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCoefficient\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eP\u003c/span\u003e \u003cb\u003evalue\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eCoefficient\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eP\u003c/span\u003e \u003cb\u003evalue\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eCoefficient\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eP\u003c/span\u003e \u003cb\u003evalue\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eCoefficient\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eP\u003c/span\u003e \u003cb\u003evalue\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChanges in CL\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.55 to 0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-7.18 to -0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-3.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-7.57 to -0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-8.95 to -1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChanges in NNL\u003c/b\u003e \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.49 to 1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.34 to 0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-2.99 to 0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-3.06 to 0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChanges in PAL\u003c/b\u003e \u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.74 to 0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.71 to 0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-4.10 to 0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-5.24 to-0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChanges in NAL\u003c/b\u003e \u003csup\u003e\u003cb\u003ed\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68 to 6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.50 to 11.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.41 to 12.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.65 to 14.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChanges in HI\u003c/b\u003e \u003csup\u003e\u003cb\u003ee\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.96 to 0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.91 to 1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-7.01 to 3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-8.67to 2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eAnalyses were performed using linear regression models adjusted for baseline quantitative chest CT parameters. Coefficients represent differences in changes in quantitative CT parameters associated with steroid administration. \u003csup\u003ea\u003c/sup\u003e Model adjusted for duration and baseline %CL; \u003csup\u003eb\u003c/sup\u003e Model adjusted for duration and baseline %NNL; \u003csup\u003ec\u003c/sup\u003e Model adjusted for duration and baseline %PAL; \u003csup\u003ed\u003c/sup\u003e Model adjusted for duration and baseline %NAL; \u003csup\u003ee\u003c/sup\u003e Model adjusted for duration and baseline %HI. %CL, percentage of compromised lung volume, calculated as the sum of %PAL and %NNL; %NNL, percentage of nonaerated lung volume; %PAL, percentage of poorly aerated lung volume; %NAL, percentage of normally aerated lung volume; %HI, percentage of hyperinflated lung volume.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch2\u003eInteraction By Disease Severity\u003c/h2\u003e\n\u003cp\u003eAssociations between steroid administration and changes in %CL, %NNL, %PAL, %NAL, or %HI during the follow-up period varied between different baseline quantitative CT parameters (all interactions, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). All patients were classified into high (\u0026gt;\u0026thinsp;median level) and low groups (\u0026lt;\u0026thinsp;median level) according to baseline quantitative CT parameters. On the basis of the analysis of different quantitative parameters separately, the associations between steroid administration and changes in quantitative CT parameters are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Steroid administration was associated with more significant decrease in %CL in the high CL group (-9.67%, [95% CI, -13.12 to -6.22], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but there was no significant association between steroid administration and %CL decrease in the low CL group (0.81%, [95% CI, -1.72 to 3.34], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.53). Similarly, steroid administration was associated with a greater decrease in NNL or PAL in the high NNL or PAL group, but the association was not significantly different in the low NNL or PAL group. For NAL, steroid administration was associated with a more significant increase in NAL in both high NAL group (5.92%, [95% CI, 1.88 to 9.95], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) and low NAL group (7.28%, [95% CI, 2.59 to 11.97], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Steroid administration was, however, not associated with changes in HI in both high and low HI groups (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.1).\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\u003eAssociations between steroid administration and changes in quantitative chest CT parameters (%) according to baseline parameters.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\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\u003e\u003cb\u003eChanges in CL (%)\u003c/b\u003e\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\u003eHigh CL group (\u0026ge;\u0026thinsp;23.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-9.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-13.12 to -6.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow CL group (\u0026lt;\u0026thinsp;23.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.72 to 3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChanges in NNL (%)\u003c/b\u003e\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\u003eHigh NNL group (\u0026ge;\u0026thinsp;18.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.63 to -0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow NNL group (\u0026lt;\u0026thinsp;18.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.20 to 0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChanges in PAL (%)\u003c/b\u003e\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\u003eHigh PAL group (\u0026ge;\u0026thinsp;5.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.29 to -4.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow PAL group (\u0026lt;\u0026thinsp;5.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.00 to 2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChanges in NAL (%)\u003c/b\u003e\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\u003eHigh NAL group (\u0026ge;\u0026thinsp;59.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.88 to 9.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow NAL group (\u0026lt;\u0026thinsp;59.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.59 to 11.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChanges in HI (%)\u003c/b\u003e\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\u003eHigh HI group (\u0026ge;\u0026thinsp;7.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.12 to 4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow HI group (\u0026lt;\u0026thinsp;7.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.73 to 3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAnalyses using generalized estimating equations to implement linear regression models. Coefficients represent the differences in changes in quantitative CT parameters associated with steroid administration. All analyses were adjusted for duration. %CL, percentage of compromised lung volume, calculated as the sum of %PAL and %NNL; %NNL, percentage of nonaerated lung volume; %PAL, percentage of poorly aerated lung volume; %NAL, percentage of normally aerated lung volume; %HI, percentage of hyperinflated lung volume.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, the present study is the first to assess the association between steroid administration and quantitative CT variables in patients with COVID-19. Our results showed that steroid administration was independently associated with decreases in compromised lung volume (%CL, \u0026ndash; 500 to 100 HU) with a significant interaction by duration or disease severity in a longitudinal cohort.\u003c/p\u003e \u003cp\u003eChest CT plays an important role in differentiating, diagnosing, and monitoring pulmonary disease progression[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Chest CT has been included as an important modality for COVID-19 diagnosis and management in the sixth edition of Chinese national guidelines on the diagnosis and treatment for COVID-19[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Several QCT analysis approaches have been developed recently for quantifying a range of lesions. The visual or semi-quantitative analysis methods (i.e., \u0026ldquo;CT severity score\u0026rdquo;) were used for assessing disease severity [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, because of large inter- and intra-observer variability, visual or semi-quantitative assessment of CT findings cannot accurately and quantitatively monitor disease progression and treatment response [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In our preliminary study, we found that quantitative parameters of QCT may serve as a useful endpoint in evaluating treatment efficacy in patients with COVID-19[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Further, we found that patients with severe COVID-19 receiving steroid therapy showed a significant different recovery pattern compared to those not receiving steroid therapy using a deep learning method[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, due to the black-box brought by deep networks, we were not able to further interpret the difference we have observed. In the present study, we performed a computer-aided quantitative lung lesion extraction analysis to assess lung lesions. The software (3D-slicer) we used for lung segmentation has been specifically updated recently to support the urgent need of segmenting compromised lung parenchyma in COVID-19 pandemic (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/rbumm/SlicerLungCTAnalyzer/\u003c/span\u003e\u003c/span\u003e). In the present study, threshold-based quantitative analyses were conducted for the aerated-condition segmentation process, which provided standardized and reproducible evaluation for lung lesions[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This method has been confirmed to predict the need for oxygenation support and intubation in patients with COVID-19[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Compromised lung volume, including NNL and PAL, is significantly correlated with respiratory dysfunction and can accurately predict poor outcomes in patients with COVID-19[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Hence, we chose changes in percentage of compromised lung volume (Δ%CL) at different stages as the primary outcome to evaluate response to steroid therapy.\u003c/p\u003e \u003cp\u003eIn this study, the steroid therapy group exhibited a higher baseline compromised lung volume than the standard care group, although vital signs and the PaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e ratio at the start of therapy were comparable in both groups. This indicated that steroids were used in patients who were relatively critically ill. Patients receiving steroid therapy showed a significant decrease in compromised lung volume as compared to those without steroid therapy, which indicates a faster resolution rate in the steroid therapy group.\u003c/p\u003e \u003cp\u003eThe association between steroid administration and decrease in %CL varied between different stages. In stage 2 (within 1 week of admission), steroid therapy did not show an association with decrease in %CL. However, after 1 week of admission (stages 3\u0026ndash;5), steroid therapy showed a significant association with decrease in %CL. This indicated that steroid therapy exerted an effect of long-term radiographic improvement, rather than an early short-term effect. These findings were consistent with a previous study, in which steroid therapy was found to decrease late treatment failure in patients with severe community-acquired pneumonia and high inflammatory response, primarily due to decrease in radiographic progression[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePatients with severe COVID-19 usually rapidly progress to acute respiratory distress syndrome (ARDS) at an early stage, which is mainly related to dysregulated immune response [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Theoretically, with several anti-inflammatory and immunomodulatory properties, corticosteroids could prevent an excessive immune response and might also prevent the progression of COVID-19 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Pooled results from recent RCTs in patients with severe or critical COVID-19 showed a significant reduction in mortality following the use of systemic corticosteroids[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, the use of corticosteroid therapy is highly controversial for patients with severe pneumonia, including SARS, Middle East respiratory syndrome (MERS), influenza, and community-acquired pneumonia [\u003cspan additionalcitationids=\"CR38 CR39\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. It can be inferred that the discrepancy may be attributed to heterogeneity in the type of corticosteroids administered, timing of therapy initiation, dose, medical conditions, and disease severity.\u003c/p\u003e \u003cp\u003eIn the present study, the association between steroid administration and decreases in %CL varied with disease severity. Steroid therapy was associated with a decrease in %CL in the high CL group, but not in the low CL group. This finding was supported by recent studies[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], in which corticosteroid therapy led to different clinical outcomes according to severity of illness. We speculated that the absence of benefit in the low CL group may be partially explained by the delayed viral clearance[\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In the present study, patients in the low CL group receiving steroids had relatively longer viral shedding duration than those who were not receiving steroids, although the difference was not statistically significant. Although recent WHO guidelines recommend steroid therapy for patients with severe COVID-19[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], our study showed that steroid therapy may not be appropriate for all those patients. Further studies may be needed to clarify the optimal indication of steroid therapy.\u003c/p\u003e \u003cp\u003eThe present study has several limitations. First, the sample size in this longitudinal study was relatively small, which might restrict its statistical power. Second, our findings cannot be generalized to all patients with COVID-19 as most critical patients with less than two serial CT scans were excluded from this cohort. Moreover, selective biases in the type of corticosteroids administered, dose, duration, and dose tapering might affect the efficacy of corticosteroids in patients with severe COVID-19. Third, adverse outcomes related to steroid therapy, such as opportunistic infections, hyperglycemia, and neuromyopathy, were not assessed in this study. Fourth, as arteries and bronchi were not excluded from lung segmentation, these interstitial structures may partially fall in the same threshold as the nonaerated lung, which might make the quantification of compromised lung volume inaccurate. All patients with COVID-19 in our study had severe infection, and therefore, the effect of additional volume for lung interstitial structure was very limited. However, because the same method was used for all patients, the potential inaccuracy in the estimation of compromised lung volume was counterbalanced to some extent.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eSteroid administration was independently associated with decrease in compromised lung volume (%CL, \u0026minus;\u0026thinsp;500 to 100 HU) with significant interaction by duration or disease severity in a longitudinal cohort. The QCT parameters, particularly compromised lung volume, may provide clinicians with an accurate tool to assess or monitor the progression of COVID-19 during the treatment process. Future large-scale prospective studies are warranted to validate our findings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCL: compromised lung\u003c/p\u003e\n\u003cp\u003eCI: confidence interval\u003c/p\u003e\n\u003cp\u003eCOVID-19: coronavirus disease 2019\u003c/p\u003e\n\u003cp\u003eFiO\u003csub\u003e2\u003c/sub\u003e: fraction of inspiration oxygen\u003c/p\u003e\n\u003cp\u003eGEE: generalized estimating equations\u003c/p\u003e\n\u003cp\u003eHI: hyperinflated lung\u003c/p\u003e\n\u003cp\u003eHU: Hounsfield unit\u003c/p\u003e\n\u003cp\u003eIQR: interquartile range\u003c/p\u003e\n\u003cp\u003eNAL: normally aerated lung\u003c/p\u003e\n\u003cp\u003eNNL: nonaerated lung\u003c/p\u003e\n\u003cp\u003ePAL: poorly aerated lung\u003c/p\u003e\n\u003cp\u003ePaO\u003csub\u003e2\u003c/sub\u003e: partial arterial oxygen pressure\u003c/p\u003e\n\u003cp\u003eQCT: quantitative computed tomography\u003c/p\u003e\n\u003cp\u003eSD: standard deviation\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate: The present study was approved by the Ethics Committee of Renmin Hospital of Wuhan University (WDRY2020-K048) and was performed in accordance with the Declaration of Helsinki. Written informed consent waswaived by the Ethics Committee in the setting of COVID-19 crisis in Wuhan.\u003c/p\u003e\n\u003cp\u003eConsent for publication: Not applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests: The authors have no conflict of interests to declare.\u003c/p\u003e\n\u003cp\u003eFunding:\u003c/p\u003e\n\u003cp\u003eThis article was supported by grants from Natural Science Foundation of Shanghai (20ZR1411100 and 21ZR1412900), Science and Technology Commission of Shanghai Municipality (20DZ2261200), Program of Shanghai Academic/Technology Research Leader (20XD1421000), National Natural Science Foundation of China (82070085 and 82072131), Construction program of key but weak disciplines of shanghai health commission (2019ZB0105), Clinical Research Funds of Zhongshan Hospital (2020ZSLC38 and 2020ZSLC27), Smart Medical Care of Zhongshan Hospital (2020ZHZS01).\u003c/p\u003e\n\u003cp\u003eAuthors' contributions:\u003c/p\u003e\n\u003cp\u003eYSparticipated in its design and coordination and drafted the manuscript.ZQ performed quantitative computed tomography analysis and helped to draft the manuscript.JC participated in its design and coordination and helped to draft the manuscript.MJ helped tocollect data and performthe statistical analysis.GM helped tocollect data. JH helped to perform quantitative computed tomography analysis. SYhelped tocollect data. KLhelped tocollect data. FL helped to draft the manuscript.GT conceived of the study and participated in its design and performed the statistical analysis.YZ participated in its design and helped to perform quantitative computed tomography analysis.ZL conceived of the study and participated in its design and coordination and helped to draft the manuscript.All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements:We thankall the staff members of Fudan Zhongshan National Medical Teamwho are on the front line of caring for patients in Wuhan.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eShang Y, Pan C, Yang X, Zhong M, Shang X, Wu Z, Yu Z, Zhang W, Zhong Q, Zheng X\u003cem\u003e et al\u003c/em\u003e: Management of critically ill patients with COVID-19 in ICU: statement from front-line intensive care experts in Wuhan, China. 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RESPIRATION 2021, 100(2):116-26.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"respiratory-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rere","sideBox":"Learn more about [Respiratory Research](http://respiratory-research.biomedcentral.com/)","snPcode":"12931","submissionUrl":"https://submission.nature.com/new-submission/12931/3","title":"Respiratory Research","twitterHandle":"@RespiratoryBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Steroid, Quantitative computed tomography, Compromised lung volume, COVID-19","lastPublishedDoi":"10.21203/rs.3.rs-698051/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-698051/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eQuantitative computed tomography (QCT) analysis may serve as a tool for assessing the severity of coronavirus disease 2019 (COVID-19)and for monitoringits progress. The present study aimed to assess the association between steroid therapy and quantitative CT parameters in a longitudinal cohort with COVID-19.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eBetween February 7 and February 17, 2020, 300 chest CT scans from 72 patients with severe COVID-19 were retrospectively collected and classified into five stages according to the interval between hospital admission and follow-up CT scans: Stage 1 (at admission); Stage 2 (3–7 days); Stage 3 (8–14 days); Stage 4 (15–21 days); and Stage 5 (22–31 days). QCT was performed using a threshold-based quantitative analysis to segment the lungaccording to different Hounsfield unit (HU) intervals. The primary outcomeswerechanges in percentage of compromised lung volume (%CL, –500 to 100 HU) at different stages. Multivariate Generalized Estimating Equations were performed after adjusting for potential confounders.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eOf 72 patients, 31 patients (43.1%) received steroid therapy. Steroid therapy was associated with a decrease in %CL (-3.27% [95%CI, -5.86 to -0.68,\u003cem\u003eP \u003c/em\u003e= 0.01]) after adjusting for duration and baseline %CL. Associations between steroid therapy and changes in %CL varied between different stages or baseline %CL (all interactions,\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01). Steroid therapy was associated with decrease in %CL after stage 3 (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), but not at stage 2. Similarly, steroid therapy was associated with a more significant decrease in %CL in the high CL group (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), but not inthe low CL group.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eSteroid administration was independently associated with a decrease in %CL, with interaction by duration or disease severity in a longitudinal cohort. The quantitative CT parameters, particularly compromised lung volume, may provide a useful tool to monitor COVID-19 progression during the treatment process. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTrial registration\u003c/strong\u003e: Clinicaltrials.gov, NCT04953247. Registered July 7, 2021, https://clinicaltrials.gov/ct2/show/NCT04953247\u003c/p\u003e","manuscriptTitle":"Usage of Compromised Lung Volume in Monitoring Steroid Therapy on Severe COVID-19","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2021-09-22 15:44:52","doi":"10.21203/rs.3.rs-698051/v2","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-03-11T09:33:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-02-08T19:51:30+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-09-13T15:01:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-08-13T01:09:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Respiratory Research","date":"2021-08-12T11:23:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-08-11T23:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-08-11T23:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"respiratory-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rere","sideBox":"Learn more about [Respiratory Research](http://respiratory-research.biomedcentral.com/)","snPcode":"12931","submissionUrl":"https://submission.nature.com/new-submission/12931/3","title":"Respiratory Research","twitterHandle":"@RespiratoryBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"41001b8a-b169-46dc-a986-be22dddffd93","owner":[],"postedDate":"September 22nd, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":7350116,"name":"Pulmonology"}],"tags":[],"updatedAt":"2022-04-14T06:35:13+00:00","versionOfRecord":[],"versionCreatedAt":"2021-09-22 15:44:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-698051","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-698051","identity":"rs-698051","version":["v2"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}