A Retrospective Cohort Study on the Effect of Prior Corticosteroid Use on COVID-19 Severity and Long-Term Outcomes in COPD Patients

A Retrospective Cohort Study on the Effect of Prior Corticosteroid Use on COVID-19 Severity and Long-Term Outcomes in COPD Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Retrospective Cohort Study on the Effect of Prior Corticosteroid Use on COVID-19 Severity and Long-Term Outcomes in COPD Patients Talha Zaman, Muhammad Arslan Ghaffar, Chengshui Chen, ChaoLei Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9661542/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The impact of prior corticosteroid use on coronavirus disease 2019 (COVID-19) outcomes in patients with chronic obstructive pulmonary disease (COPD) remains uncertain. Methods We conducted a retrospective cohort study of 166 hospitalized COPD patients with confirmed COVID-19, comparing those with prior corticosteroid use (n = 82) and without (n = 84). Clinical outcomes included severity of airflow limitation, oxygen therapy requirements, intensive care unit (ICU) admission, and one-year survival. Uni- and multivariable regression analyses were performed to adjust for potential confounders. Results Corticosteroid users were more likely to have severe or very severe obstructive dysfunction (46.3% vs. 16.7%; p = 0.001). Prior corticosteroid therapy was associated with reduced odds of requiring advanced oxygen therapy (OR = 0.54, 95% CI: 0.30–0.98; p = 0.044; aOR = 0.65, 95% CI: 0.34–1.25; p = 0.197), lower risk of ICU admission (OR = 0.61, 95% CI: 0.29–1.24; p = 0.174; aOR = 0.56, 95% CI: 0.25–1.23; p = 0.155), and a trend toward improved one-year survival (OR = 1.45, 95% CI: 0.60–3.58; p = 0.415; aOR = 1.45, 95% CI: 0.55–3.91; p = 0.453). Patients with pre-existing respiratory disease showed a tendency toward lower survival (OR = 0.36, 95% CI: 0.12–1.14; p = 0.076). In a subgroup of 26 patients who received other corticosteroids, no statistically significant associations were observed for any of the studied outcomes is likely due to the very limited sample size of this subgroup and the consequently wide confidence intervals.. The adjusted odds ratios were 1.13 (95% CI: 0.35–3.28; p = 0.846) for ICU admission, 0.97 (95% CI: 0.38–2.50; p = 0.950) for oxygen-therapy severity, and 2.30 (95% CI: 0.49–16.99; p = 0.339) for one-year survival. The wide confidence intervals reflect the limited sample size, and results should therefore be interpreted with caution. Conclusion Prior corticosteroid use in COPD patients hospitalized with COVID-19 was not associated with worse outcomes and showed non-significant trends toward potential protective benefits, including reduced oxygen therapy requirements and a trend toward improved survival. Observed heterogeneity across studies likely reflects differences in design, patient populations, and underlying disease severity. Larger prospective, multicenter studies are needed to clarify corticosteroids’ role in COVID-19 outcomes among COPD patients. Pulmonology Infectious Diseases Clinical Pharmacology Immunology Internal Medicine Corticosteroids Chronic Obstructive Pulmonary Disease COVID-19 Clinical Outcomes Retrospective Cohort Study Figures Figure 1 Plain Language Summary People living with chronic obstructive pulmonary disease (COPD) are at higher risk of severe illness when they develop COVID-19. Many people with COPD regularly use corticosteroids to control breathing symptoms and reduce flare-ups. However, there has been concern that using these medicines before getting COVID-19 might worsen infection outcomes by weakening the immune system. This uncertainty led us to carry out this study. We reviewed the medical records of 166 people with COPD who were hospitalized with COVID-19. About half of these patients had used corticosteroids regularly in the year before they were admitted to hospital, while the others had not. We compared the two groups to see whether previous corticosteroid use was linked to the need for higher levels of oxygen, admission to intensive care, or survival one year after infection. We also took into account other health conditions that could affect outcomes. We found that people who had used corticosteroids before hospitalization did not have worse outcomes. In fact, they showed a tendency toward needing less intensive oxygen support and had similar or slightly better survival over one year, although these differences were not statistically certain. These findings suggest that continuing necessary corticosteroid treatment for COPD does not appear to increase the risk of poor outcomes from COVID-19. Larger studies are needed to confirm these results and to better understand which patients may benefit the most. Introduction The emergence of SARS-CoV-2 in late 2019 rapidly evolved into a global pandemic, posing a major public health threat worldwide.( 1 , 2 ) COVID-19 presents with a wide clinical spectrum, ranging from asymptomatic infection to severe pneumonia and acute respiratory distress syndrome (ARDS), which can lead to death.( 3 , 4 ) Viral entry is mediated by the angiotensin-converting enzyme 2 (ACE2) receptor, which is highly expressed in type II alveolar, bronchial, and tracheal epithelial cells, rendering the respiratory tract a principal target for infection.( 5 ) The expression level of ACE2 influences susceptibility and disease severity and can be modified by environmental and clinical factors such as smoking.( 6 , 7 ) Chronic obstructive pulmonary disease (COPD), commonly associated with smoking, has been linked to upregulated ACE2 expression and heightened COVID-19 vulnerability.( 8 ) Inhaled corticosteroids (ICS) are a cornerstone in COPD management and have been shown experimentally to downregulate the expression of viral entry–related genes, potentially influencing SARS-CoV-2 infection dynamics.( 9 , 10 ) However, clinical evidence remains inconsistent. Early reports suggested that high-dose ICS use might increase COVID-19–related mortality in COPD,( 9 ) while large-scale studies indicated that systemic corticosteroids such as dexamethasone reduced mortality among hospitalized and critically ill COVID-19 patients.( 11 , 12 ) These findings prompted global guidelines, including from the WHO and NIH, to recommend corticosteroids for severe or critical COVID-19 cases but to avoid routine use in mild infections.( 13 ) Despite the well-documented role of systemic corticosteroids in acute COVID-19, the effects of pre-existing ICS therapy in COPD patients remain uncertain. Some studies have suggested a neutral or even protective association between regular ICS use and COVID-19 outcomes.( 14 , 15 ) A Swedish population study and a Korean national cohort both found that ongoing ICS exposure did not increase COVID-19 susceptibility or worsen clinical outcomes in COPD or asthma, though results varied depending on disease severity.( 14 , 15 ) Corticosteroids exert potent anti-inflammatory and immunosuppressive effects.( 16 , 17 ) While these actions alleviate airway inflammation, they may also dampen immune defenses against viral pathogens. Severe COVID-19 is characterized by excessive cytokine release, particularly interleukin-6 (IL-6), which correlates strongly with disease severity and mortality.( 18 , 19 ) ICS may theoretically modulate this cytokine surge but could also alter baseline immune function in COPD patients. Corticosteroid-induced T-cell apoptosis and suppression of CD4 + and CD8 + activity can impair viral clearance and prolong infection.( 20 , 21 ) Moreover, systemic inflammation in severe COVID-19 can cause multi-organ injury, affecting the liver, kidneys, and cardiovascular system.( 22 , 23 ) The intersection of COPD-related inflammation, ICS-induced immunomodulation, and COVID-19 cytokine dysregulation therefore remains clinically relevant but poorly understood. Given these uncertainties, it is essential to clarify whether prior ICS exposure influences COVID-19 severity and recovery in COPD patients. This study aimed to evaluate the association between pre-admission corticosteroid use including inhaled and systemic forms and both in-hospital outcomes and long-term survival among COPD patients hospitalized with COVID-19. Methodology Study Design and Setting This retrospective cohort study was conducted at the First Affiliated Hospital of Wenzhou Medical University between December 2022 and February 2025. The objective was to evaluate the association between prior corticosteroid use and clinical, laboratory, and survival outcomes among hospitalized patients with chronic obstructive pulmonary disease (COPD) and confirmed COVID-19. Study Population A total of 201 patients were screened, and 166 with complete records were included in the final analysis. COPD was confirmed using clinical history, imaging, and spirometry where available. Disease severity was classified by post-bronchodilator FEV₁ % predicted according to GOLD grades 1–4.(Figure 1) Inclusion Criteria In this study, the sample was included according to the following considerations: 1. Hospitalized between December 2022 and February 2025. 2. Confirmed COPD diagnosis according to GOLD criteria, supported by spirometry. 3. Confirmed COVID-19 infection, based on PCR and/or bronchoalveolar lavage (BAL) results. 4. Required hospitalization due to severe COVID-19, including oxygen therapy. 5. Availability of complete medical records, including corticosteroid usage history. Exclusion Criteria: 1. Missing or incomplete medical records (n = 35). 2. Asthma diagnosis. 3. Mild or non-severe COVID-19 cases not requiring hospitalization or oxygen support. Patient Grouping Based on Corticosteroid Usage Patients were classified by corticosteroid exposure during the 12 months before hospitalization. Of 166 patients, 82 had prior corticosteroid use and 84 had none (control group).(Figure 1) Corticosteroid users were defined as having ≥ 3 prescriptions in the previous year, consistent with established COPD–COVID-19 criteria. Budesonide was the predominant inhaled corticosteroid (1 mg, 120 µg, or 160 µg BID). Other corticosteroids (e.g., methylprednisolone, dexamethasone, fluticasone, prednisone) were used less often for COPD complications or comorbidities. A subgroup of 26 patients who received both budesonide and other corticosteroids—including either systemic agents (e.g., methylprednisolone, dexamethasone, prednisone) or other inhaled corticosteroids (e.g., fluticasone)—was analyzed separately to evaluate the potential effects of combined corticosteroid therapy. Non-users were defined as those with ≤ 2 corticosteroid prescriptions during the year prior to admission, indicating minimal or no prior exposure. Data Collection Data were retrospectively obtained from the electronic medical records of the First Affiliated Hospital of Wenzhou Medical University, using the hospital information system and institutional laboratory databases. Demographic and Clinical Variables Collected variables included age, sex, body mass index, smoking history (never, less than ten years, ten to twenty years, or more than twenty years), and duration of chronic obstructive pulmonary disease (10 years). Documented comorbidities comprised hypertension, diabetes mellitus, cardiovascular disease, stroke, chronic respiratory disease (such as silicosis), malignancy, chronic kidney disease, rheumatologic or immunological disorders, and gastrointestinal diseases (including liver cirrhosis or gastritis). Additional parameters included the frequency of COVID-19 polymerase chain reaction testing during hospitalization, site of admission (ward or intensive care unit), length of hospital stay, and type of oxygen therapy administered (low flow, high flow, non-invasive, or mechanical ventilation). One-year survival status was categorized as survived, deceased, or lost to follow-up. Laboratory Parameters Laboratory data obtained within the first twenty-four hours of admission were grouped into four categories. Inflammatory markers included C-reactive protein, procalcitonin, D-dimer, and interleukin-6. Immune status was assessed through total T lymphocyte, CD4, and CD8 cell counts. Hematological parameters comprised white blood cell, neutrophil, and lymphocyte counts. Cardiac function was evaluated using troponin T, troponin I, and N-terminal pro–B-type natriuretic peptide. Renal function was assessed by serum urea and creatinine, and hepatic function by alanine aminotransferase and aspartate aminotransferase levels. Outcome measures This study aims to: 1. Assess the need for oxygen therapy and the duration of hospitalization. 2. Monitor changes in immune, infection, cardiac, renal, hepatic, and coagulation markers. 3. Evaluate 1-year survival following COVID-19 infection in COPD patients. Interpretation Context Given that long-term corticosteroid use is known to suppress immune function, the study also seeks to explore whether prior corticosteroid exposure is associated with a more aggressive course of COVID-19 in COPD patients. Statistical Analysis Continuous variables were tested for normality using the Shapiro–Wilk method. Normally distributed data were compared using independent t-tests and presented as mean with standard deviation, whereas non-normally distributed data, including inflammatory, immune, and biochemical parameters, were analyzed using the Mann–Whitney U test and expressed as median with interquartile range. Categorical variables such as sex, comorbidities, spirometry severity, symptoms, and outcomes were compared using the Chi-square or Fisher’s exact test as appropriate. Associations between corticosteroid use and outcomes were examined through binary logistic regression for dichotomous variables (intensive care admission and survival) and ordinal logistic regression for ordered outcomes (oxygen therapy level). Both unadjusted and multivariable models adjusted for age, sex, and clinically relevant comorbidities were applied, and results were expressed as odds ratios with ninety-five percent confidence intervals. To ensure model stability, multicollinearity was assessed using tolerance and variance inflation factor statistics; all predictors showed tolerance above 0.7 and variance inflation factor below 2, confirming the reliability of the regression estimates. A subgroup analysis compared patients receiving both inhaled and systemic corticosteroids with those receiving inhaled therapy alone or none. As this was a retrospective study, all eligible cases during the study period were included without prior sample-size estimation. Statistical significance was defined as a p-value below 0.05. Analyses were conducted using IBM SPSS Statistics (Version 26) and R software (Version 4.5.1). Results A total of 166 patients met the inclusion criteria and were analyzed, comprising 84 patients in the non-corticosteroid group and 82 patients in the corticosteroid group. The mean age of the study population was 76.8 years, with no significant difference observed between the two groups (77.1 ± 10.0 vs. 76.4 ± 7.2 years, p = 0.551). Gender distribution, however, differed significantly, with the corticosteroid group consisting of a higher proportion of males compared with the non-corticosteroid group (93.9% vs. 79.8%, p = 0.007). Body mass index (BMI) and smoking history did not significantly differ between groups (p = 0.649 and p = 0.334, respectively). Among comorbidities, chronic kidney disease (CKD) was more prevalent in the corticosteroid group (17.1% vs. 7.1%, p = 0.049). Other comorbidities showed no statistically significant differences between groups. Spirometry-based classification indicated a higher prevalence of severe and very severe airflow obstruction in the corticosteroid group compared to the non-corticosteroid group (p = 0.001). (Table 1 ) Clinical Variables The most common presenting symptoms in both groups were cough and sputum production, reported in 94.0% of patients in each group (p = 0.969). Fever was slightly more common in the non-corticosteroid group (66.7% vs. 64.8%, p = 0.783). Dyspnea and chest tightness were more frequent in the corticosteroid group (97.6% vs. 90.5%), with borderline statistical significance (p = 0.055). Other presenting symptoms were reported in 14.3% of non-corticosteroid patients and 12.2% of corticosteroid patients (p = 0.891). The duration of symptoms prior to admission was similar between groups (p = 0.280). Regarding hospitalization, 71.4% of patients in the non-corticosteroid group and 80.5% in the corticosteroid group were admitted to general wards, while ICU admission was higher in the non-corticosteroid group (28.6% vs. 19.5%), although this difference was not statistically significant (p = 0.172). The median hospital stay was slightly longer in the corticosteroid group (11 [IQR: 6–18] days) compared to the non-corticosteroid group (10 [IQR: 6–14] days), but this difference was also not significant (p = 0.278). One-year survival was comparable between groups (61.0% alive in the corticosteroid group vs. 52.4% in the non-corticosteroid group, p = 0.535). A significant difference was observed in the type of oxygen therapy administered upon admission (p = 0.047). Low-flow oxygen was more frequently used in the corticosteroid group (63.4% vs. 45.2%), whereas non-invasive ventilation (BiPAP/CPAP) was more common in the non-corticosteroid group (23.8% vs. 14.6%). Mechanical ventilation usage was similar between groups (20.7% vs. 23.8%), and high-flow oxygen was rarely used. Detailed comparisons of clinical presentation, hospitalization, and oxygen therapy are summarized in `1(Table 1 ) Laboratory variables Comparison of laboratory parameters between the corticosteroid and non-corticosteroid groups revealed no statistically significant differences across inflammatory markers, immune markers, complete blood counts, cardiac biomarkers, or liver and kidney function tests. Inflammatory markers such as C-reactive protein (CRP), procalcitonin, D-dimer, and IL-6 showed slightly higher median values in the corticosteroid group, but none reached statistical significance (all p > 0.05). Similarly, immune markers including total T-lymphocyte counts, CD4, and CD8 were lower in the corticosteroid group, suggesting a possible trend toward immune suppression, though not statistically significant (p = 0.379, 0.302, and 0.251, respectively). Complete blood counts, including white blood cells, neutrophils, and lymphocytes, were comparable between groups (p > 0.05). Cardiac biomarkers such as Troponin T, Troponin I, and NT-proBNP were slightly lower in the corticosteroid group but did not show significant differences (p = 0.176, 0.092, and 0.123, respectively). Renal markers (urea and creatinine) and liver enzymes (ALT and AST) were also similar between groups. Overall, corticosteroid use was not associated with marked biochemical disturbances in this sample. Detailed laboratory results are presented in (Table 1 ). Multivariable logistic regression for ICU admission Crude analysis indicated a lower proportion of intensive care unit admissions among patients with prior corticosteroid use compared with non-users (19.5 percent versus 28.6 percent, p = 0.172) (Table 1 ). In unadjusted logistic regression, corticosteroid use was associated with reduced odds of intensive care admission (odds ratio = 0.61, 95 percent confidence interval 0.29–1.24, p = 0.174). After adjustment for age, sex, and comorbidities, this association persisted but did not reach statistical significance (adjusted odds ratio = 0.56, 95 percent confidence interval 0.25–1.23, p = 0.155). None of the other covariates were statistically significant in the adjusted model (Table 2 ). Multivariable ordinal logistic regression for oxygen therapy Crude analysis suggested that patients who received corticosteroids were less likely to require higher levels of oxygen therapy than those without prior exposure (p = 0.047) (Table 1 ). In unadjusted ordinal logistic regression, corticosteroid use was linked to lower odds of requiring advanced oxygen support (odds ratio = 0.54, 95 percent confidence interval 0.30–0.98, p = 0.044). After adjustment for age, sex, and comorbidities, the association remained in the same direction but lost statistical significance (adjusted odds ratio = 0.65, 95 percent confidence interval 0.34–1.25, p = 0.197). None of the additional covariates were significantly associated with oxygen therapy requirements in the adjusted model (Table 3 ). Multivariable logistic regression for survival status Crude analysis showed a slightly higher survival rate among corticosteroid users compared with non-users (61.0 percent versus 52.4 percent, p = 0.5351) (Table 1 ). After excluding patients lost to follow-up, unadjusted logistic regression revealed that corticosteroid use was associated with higher odds of survival (odds ratio = 1.45, 95 percent confidence interval 0.60–3.58, p = 0.415), though the result was not statistically significant. Following adjustment for age, sex, and comorbidities, the association remained positive but non-significant (adjusted odds ratio = 1.45, 95 percent confidence interval 0.55–3.91, p = 0.453). No other covariates reached statistical significance; however, pre-existing respiratory disease showed a trend toward lower survival (odds ratio = 0.36, 95 percent confidence interval 0.12–1.14, p = 0.076) (Table 4 ). Subgroup Analysis of Combination Corticosteroid Users To further examine whether the combined use of systemic and inhaled corticosteroids influenced outcomes, a subgroup analysis was performed among 26 patients who had received both budesonide and at least one additional corticosteroid (prednisone, methylprednisolone, dexamethasone, beclomethasone, or fluticasone) during the 12 months prior to admission. Compared with the remaining 140 patients, this subgroup exhibited a higher prevalence of malignancy (50.0% vs 26.8%, p < 0.001) and gastrointestinal comorbidities (42.3% vs 30.5%, p = 0.023), suggesting a greater baseline disease burden. Inflammatory markers showed a pattern toward higher procalcitonin values (median 0.27 ng/mL vs 0.14 ng/mL, p = 0.011) and slightly lower lymphocyte counts (p = 0.051).(Table 1 ) Multivariable ordinal logistic regression demonstrated that prior use of other corticosteroids was not significantly associated with the severity of oxygen therapy requirements (adjusted OR 0.97, 95% CI 0.38–2.50, p = 0.950) or with ICU admission (adjusted OR 1.13, 95% CI 0.35–3.28, p = 0.846). Likewise, multivariable logistic regression for one-year survival showed no significant association between combination corticosteroid use and mortality (adjusted OR 2.30, 95% CI 0.49–16.99, p = 0.339). None of the covariates reached statistical significance.(Table 2 )(Table 3 )(Table 4 ) Overall, these findings indicate that dual or mixed corticosteroid exposure did not alter oxygen demand, ICU admission risk, or one-year survival in this cohort, though the wide confidence intervals reflect limited subgroup size and reduced statistical power. Table 1 Baseline Characteristics and Clinical Parameters of Patients Stratified by Corticosteroid Exposure and Subgroup Use of Other Corticosteroids Characteristics Without corticosteroids group (N = 84) With corticosteroids group (N = 82) P-Value Other Corticosteroids group(N = 26) P-Value Age(Mean) 77.13 ± 10.03(49–94) 76.39 ± 7.24(61–93) 0.551 76.12 ± 6.34 (66–92) 0.661 Gender 0.007* 0.363 Male(%) 67(79.8%) 77(93.9%) 0.015* 24 (92.3%) Female(%) 17(20.2%) 5(6.1%) 0.018* 2 (7.7%) BMI 0.649 0.861 < 18.5 5(6.0%) 8(9.8%) 2 (7.7%) 18.5–24.9 66(78.6%) 61(74.4%) 19 (73.1%) 25.0-29.9 13(15.7%) 13(15.9%) 5 (19.2%) Smoking history 0.334 0.329 No smoking(%) 25(29.8%) 22(26.8%) 6 (23.1%) 20 years(%) 39(46.4%) 47(57.3%) 12 (46.2%) COPD duration 0.195 0.075 10 years 13(15.5%) 22(26.8%) 7 (26.9%) COVID-19 PCR(Frequency) 0.524 0.815 Once 50(60.2%) 47(57.3%) 14 (53.8%) Twice 18(21.7%) 23(28.0%) 7 (26.9%) Thrice 15(18.1%) 11(13.4%) 7 (26.9%) Comorbidities Hypertension 47(56.0%) 44(53.7%) 0.767 14 (53.8%) 0.914 Diabetes Mellitus 25(29.8%) 15(18.3%) 0.084 5 (19.2%) 0.528 Cardiovascular diseases 25(29.8%) 25(30.5%) 0.919 3 (11.5%) 0.025* Stroke(cerebral infarction) 12(14.3%) 11(13.4%) 0.871 2 (7.7%) 0.322 Other Respiratory disease 11(13.1%) 19(23.2%) 0.092 7 (26.9%) 0.202 Malignancy 13 (15.5%) 22 (26.8%) 0.073 13 (50.0%) < 0.001* Chronic Kidney disease 6(7.1%) 14(17.1%) 0.049* 4 (15.4%) 0.569 Rheumatologic or Immunological disorders 5(6.0%) 8(9.8%) 0.362 3 (11.5%) 0.444 Gastrointestinal diseases(liver chirosis or gastritis) 16(19.0%) 25(30.5%) 0.087 11 (42.3%) 0.023* Spirometry iresults in the past 0.001* 0.787 Not found 50(59.5%) 27(32.9%) 13 (50.0%) Mild 6(7.1%) 3(3.7%) 2 (7.7%) Moderate 14(16.7%) 14(17.1%) 4 (15.4%) Severe 13(15.5%) 32(39.0%) 7 (26.9%) Very severe 1(1.2%) 6(7.3%) 0 (0.0%) Symptoms Cough and excessive sputum 79(94.0%) 77(93.9%) 0.969 24 (92.3%) 0.657 Fever 56(66.7%) 53(64.8%) 0.783 18 (69.2%) 0.677 Dyspnea and chest tightness 76(90.5%) 80(97.6%) 0.055 25 (96.2%) 1.000 Other presenting symptoms 12(14.3%) 10(12.2%) 0.891 3 (11.5%) 1.000 Duration of symptoms before admission 0.28 0.081 Less than 5 days 55(65.5%) 47(57.3%) 12 (46.2%) More than 5 days 29(34.5%) 35(42.7%) 14 (53.8%) Hospitalization 0.172 0.895 Ward 60(71.4%) 66(80.5%) 20 (76.9%) ICU 24(28.6%) 16(19.5%) 6 (23.1%) Hospitalization duration 10 [6–14] 11 [6–18] 0.278 8 [5–19] 0.384 Oxygen Therapy on admission 0.047* 0.603 Low flow 38(45.2%) 52(53.4%) 16 (61.5%) High flow 6(7.1%) 1(1.2%) 0 (0.0%) Non invasive ventilation (BiPAP, CPAP) 20(23.8%) 12(14.6%) 4 (15.4%) Mechanical ventilation 20(23.8%) 17(20.7%) 6 (23.1%) Survival status within 1 year 0.5351 0.321 Dead 14(16.7%) 11(13.4%) 2 (7.7%) Alive 44(52.4%) 50(61.0%) 14 (53.8%) Lost follow up 26(31.0%) 21(25.6%) 10 (38.5%) Inflammatory markers CRP (mg/L) 44.4 (14.18–100.78) 47.5 (11.53–119.0) 0.804 86.6 (16.3–123.3) 0.080 Procalcitonin (ng/mL) 0.155 (0.020–0.450) 0.140 (0.020–0.255) 0.807 0.270 (0.127–1.11) 0.011* D-dimer (mg/L) 0.945 (0.11–1.88) 1.20 (0.14–1.75) 0.613 1.54 (0.78–2.91) 0.099 IL-6 (pg/mL) 12.14 (0.10–34.38) 16.17 (0.10–41.83) 0.313 14.3 (5.8–47.2) 0.544 Immune markers T lymphocytes (cells/µL) 346.0 (72.0–400.5) 259.0 (36.0–464.8) 0.379 219.0 (103.0–514.5) 0.276 CD4 (cells/µL) 165.0 (29.0–220.5) 149.5 (16.0–301.5) 0.302 131.0 (51.2–347.0) 0.521 CD8 (cells/µL) 111.0 (28.0–175.5) 98.0 (11.8–220.6) 0.251 96.0 (31.0–200.0) 0.225 Complete blood counts WBC (×10⁹/L) 9.20 (3.22–10.20) 9.52 (2.25–9.72) 0.495 8.75 (6.47–12.82) 0.812 Neutrophils (×10⁹/L) 8.07 (2.03–9.21) 8.16 (0.29–7.38) 0.669 7.34 (5.97–11.87) 0.885 Lymphocytes (×10⁹/L) 1.12 (0.68–1.59) 1.00 (0.58–1.44) 0.295 0.52 (0.36–0.99) 0.051 Cardiac markers Troponin T (ng/L) 29.35 (5.90–44.80) 22.00 (7.00–27.8) 0.176 23.5 (15.2–36.4) 0.982 Troponin I (ng/L) 59.0 (21.0–181.0) 55.0 (21.0–78.5) 0.092 58.0 (34.0–112.0) 0.749 NT-proBNP (pg/mL) 1144.5 (571.7–3415.5) 637.0 (350.0–1869.0) 0.123 727.0 (189.0–3443.0) 0.817 Renal & liver function Urea (mmol/L) 8.3 (4.9–10.9) 7.6 (5.3–10.0) 0.105 7.6 (5.0–9.8) 0.292 Creatinine (µmol/L) 81.2 (63.7–101.0) 76.9 (62.0–94.2) 0.065 78.0 (54.0–137.0) 0.591 ALT (U/L) 31.0 (20.5–48.0) 30.0 (21.0–46.0) 0.774 20.5 (14.0–27.0) 0.906 AST (U/L) 28.5 (21.0–35.0) 26.0 (16.0–37.0) 0.573 34.5 (22.0–50.0) 0.056 * indicates statistical significance (P < 0.05) BMI: body mass index; ICU: intensive care unit; CRP: C-reactive protein; IL-6: interleukin-6; WBC: white blood cells; ALT: alanine aminotransferase; AST: aspartate aminotransferase; NT-proBNP: N-terminal pro-B-type natriuretic peptide. Table 2 Multivariable logistic regression for ICU admission Predictor Corticosteroids Group Sub Group analysis Adjusted OR 95% CI p-value Adjusted OR 95% CI p-value Corticosteroid use (Yes vs No) 0.56 0.25–1.23 0.155 1.13 0.35–3.28 0.846 Age (per year) 1.01 0.96–1.05 0.768 1.01 0.96–1.06 0.711 Male sex (vs Female) 1.34 0.43–4.84 0.629 1.12 0.37–3.93 0.844 Hypertension (Yes vs No) 0.98 0.43–2.20 0.957 0.95 0.43–2.12 0.901 Diabetes mellitus (Yes vs No) 0.64 0.23–1.64 0.371 0.69 0.25–1.74 0.433 Cardiovascular disease (Yes vs No) 1.07 0.45–2.48 0.869 1.08 0.45–2.51 0.856 Stroke (Yes vs No) 2.11 0.74–5.75 0.148 2.05 0.73–5.53 0.169 Respiratory disease (Yes vs No) 0.71 0.23–1.90 0.513 0.64 0.21–1.70 0.391 Malignancy (Yes vs No) 1.10 0.41–2.77 0.844 0.99 0.36–2.56 0.985 Chronic kidney disease (Yes vs No) 1.15 0.31–3.78 0.824 1.02 0.28–3.24 0.975 Rheumatologic/immune disorder (Yes vs No) 2.80 0.75–9.83 0.111 2.65 0.72–9.10 0.137 Gastrointestinal disease (Yes vs No) 0.58 0.20–1.47 0.269 0.52 0.18–1.33 0.186 No predictor reached statistical significance (P < 0.05). Table 3 Multivariable ordinal logistic regression for oxygen therapy Predictor Corticosteroids Group Sub Group analysis Adjusted OR 95% CI p-value Adjusted OR 95% CI p-value Corticosteroid use (Yes vs No) 0.65 0.34–1.25 0.197 0.97 0.37–2.28 0.950 Age (per year) 1.00 0.96–1.04 0.953 1.00 0.96–1.04 0.960 Male sex (vs Female) 0.49 0.20–1.20 0.118 0.45 0.19–1.03 0.068 Hypertension (Yes vs No) 1.38 0.71–2.71 0.347 1.33 0.69–2.63 0.395 Diabetes mellitus (Yes vs No) 0.58 0.26–1.23 0.165 0.60 0.28–1.21 0.165 Cardiovascular disease (Yes vs No) 0.79 0.38–1.61 0.528 0.79 0.38–1.61 0.509 Stroke (Yes vs No) 0.66 0.25–1.66 0.388 0.81 0.37–1.80 0.388 Respiratory disease (Yes vs No) 0.96 0.42–2.10 0.912 0.87 0.42–2.10 0.913 Malignancy (Yes vs No) 0.93 0.41–2.05 0.859 0.90 0.41–1.99 0.860 Chronic kidney disease (Yes vs No) 0.50 0.16–1.47 0.227 0.46 0.17–1.49 0.227 Rheumatologic/immune disorder (Yes vs No) 0.77 0.22–2.40 0.656 0.74 0.23–2.35 0.656 Gastrointestinal disease (Yes vs No) 0.52 0.23–1.12 0.104 0.57 0.26–1.12 0.105 No predictor reached statistical significance (P < 0.05). Table 4 Multivariable logistic regression for survival status Predictor Corticosteroids Group Sub Group analysis Adjusted OR 95% CI p-value Adjusted OR 95% CI p-value Corticosteroid treatment (Yes vs No) 1.45 0.55–3.91 0.453 2.30 0.49–16.99 0.339 Age (per year increase) 0.96 0.91–1.02 0.215 0.99 0.94–1.04 0.770 Male sex (vs Female) 1.17 0.23–4.72 0.830 0.78 0.27–2.10 0.641 Hypertension (Yes vs No) 1.67 0.60–4.82 0.329 0.89 0.33–2.25 0.801 Diabetes mellitus (Yes vs No) 1.00 0.33–3.35 0.999 1.10 0.38–3.06 0.854 Cardiovascular disease (Yes vs No) 1.85 0.63–6.12 0.285 0.77 0.27–2.12 0.608 Stroke (Yes vs No) 1.37 0.34–7.32 0.681 0.85 0.23–2.81 0.792 Respiratory disease (Yes vs No) 0.36 0.12–1.14 0.076 0.36 0.12–1.15 0.079 Malignancy (Yes vs No) 1.03 0.30–4.16 0.964 0.91 0.28–2.85 0.870 Chronic kidney disease (Yes vs No) 0.81 0.18–4.51 0.794 1.21 0.32–5.21 0.782 Rheumatologic/immune disorder (Yes vs No) 2.41 0.35–49.90 0.449 0.87 0.19–3.83 0.854 Gastrointestinal disease (Yes vs No) 1.59 0.48–6.32 0.473 0.66 0.19–2.04 0.447 No predictor reached statistical significance (P < 0.05). Discussion This study evaluated the impact of prior corticosteroid use on clinical outcomes—including intensive care admission, oxygen therapy requirements, and one-year survival among 166 hospitalized patients with chronic obstructive pulmonary disease and COVID-19. Corticosteroid users included a greater proportion of males (93.9% versus 79.8%; p = 0.007) and more cases of severe or very severe airflow obstruction (severe: 39.0% versus 15.5%; very severe: 7.3% versus 1.2%; p = 0.001), consistent with the clinical rationale for corticosteroid therapy in advanced disease. Regression analyses suggested a possible favorable association between prior corticosteroid use and several outcomes: lower odds of requiring advanced oxygen therapy (odds ratio = 0.54, 95% confidence interval 0.30–0.98; p = 0.044), reduced likelihood of intensive care admission (odds ratio = 0.61, 95% confidence interval 0.29–1.24; p = 0.174), and higher one-year survival (odds ratio = 1.45, 95% confidence interval 0.60–3.58; p = 0.415). After adjusting for age, sex, and comorbidities, the direction of these associations persisted but lost statistical significance (oxygen therapy: adjusted odds ratio = 0.65, 95% confidence interval 0.34–1.25; p = 0.197; intensive care admission: adjusted odds ratio = 0.56, 95% confidence interval 0.25–1.23; p = 0.155; survival: adjusted odds ratio = 1.45, 95% confidence interval 0.55–3.91; p = 0.453). Pre-existing respiratory disease showed a trend toward lower survival (odds ratio = 0.36, 95% confidence interval 0.12–1.14; p = 0.076), highlighting the influence of underlying pulmonary comorbidity on prognosis. The attenuation of significance in the multivariable model likely reflects the small sample size and the higher comorbidity burden among corticosteroid users. These patients tended to have more severe airflow limitation and systemic disease both strong predictors of higher oxygen demand so adjustment for these confounders may have diluted the apparent protective association. Similar attenuation has been observed in previous studies, where controlling for disease severity and comorbidities reduced the observed benefit of corticosteroid therapy ( 8 , 9 ). Despite the lack of statistical significance, the consistent direction of effect across all primary clinical outcomes (reduced oxygen therapy, lower ICU admission, and improved survival) strengthens the plausibility of a true protective association, which was likely undetected due to limited statistical power. In the subgroup of 26 patients who received both inhaled budesonide and additional systemic corticosteroids (prednisone, methylprednisolone, dexamethasone, beclomethasone, or fluticasone), no significant differences were found in oxygen therapy level, intensive care admission, or one-year survival compared with the remaining cohort. Although this subgroup showed higher rates of malignancy and gastrointestinal disease, adjusted analyses did not demonstrate any independent effect of combined corticosteroid use. These findings indicate that dual exposure did not substantially alter clinical outcomes. From an immunological perspective, corticosteroid users displayed numerically lower T-lymphocyte, CD4-positive, and CD8-positive cell counts, suggesting a mild immunosuppressive trend consistent with the pharmacological actions of corticosteroids ( 24 , 25 ). Mechanistically, corticosteroids may exert a dual effect: they can attenuate excessive inflammation by suppressing pro-inflammatory cytokines and downregulating angiotensin-converting enzyme 2 and TMPRSS2 expression ( 26 ), thereby reducing viral entry and cytokine-mediated tissue injury, but they may also dampen antiviral immune responses. In this cohort, these immunological trends did not translate into worse clinical outcomes, suggesting that the anti-inflammatory benefits may outweigh potential immune suppression or that the extent of suppression remained clinically mild. Routine laboratory indicators including complete blood counts, cardiac, renal, and hepatic biomarkers did not differ significantly between groups, supporting the view that prior corticosteroid therapy did not worsen early clinical presentation. Although laboratory parameters did not reach statistical significance, inflammatory markers were modestly higher and immune cell counts lower among corticosteroid users. These trends may reflect both the limited sample size and inherent differences in disease stage. Long-term corticosteroid users typically represent patients with more advanced chronic obstructive pulmonary disease and greater baseline systemic inflammation, which could contribute to these findings independent of acute infection. The modest sample size and variability in timing of laboratory sampling relative to corticosteroid exposure further limited statistical power to detect subtle yet biologically meaningful effects. Nevertheless, these patterns are physiologically plausible and merit further investigation in larger, prospective cohorts. Our findings are consistent with prior reports showing no clear harm from corticosteroid therapy in chronic obstructive pulmonary disease during COVID-19. Sen et al. ( 27 ) found no increase in healthcare utilization or mortality, and Bloom et al. ( 28 ) similarly reported no significant effect on outcomes among patients hospitalized with COVID-19. Husby et al. (2021) likewise found no association between inhaled corticosteroid therapy and either benefit or harm with respect to intensive care admission or mortality ( 29 ). Comparable results were observed by Choi et al. (2020) ( 15 ). Collectively, these data support the interpretation that prior corticosteroid exposure is not detrimental in this population. In contrast, some population-based studies have reported increased mortality among corticosteroid users. Schultze et al. ( 9 ) and Aveyard et al. ( 8 ) observed higher risks of death or severe COVID-19 in users of inhaled corticosteroids, although these associations were largely attributed to residual confounding related to worse baseline health. The present study differs from those analyses in several ways. Community-based studies typically defined exposure by recent prescriptions, potentially including frailer patients with poorer prognosis. By contrast, this study focused exclusively on hospitalized patients and employed a stricter exposure threshold (three or more prescriptions per year) to identify consistent long-term users. In addition, our investigation was conducted during a later phase of the pandemic, when inpatient management of COVID-19—including standardized corticosteroid protocols—was better established. Furthermore, the distinct pandemic phases and evolving standards of care, including the widespread in-hospital use of systemic corticosteroids for severe COVID-19 later in the pandemic, represent another critical contextual difference that may account for the discrepant findings between early community-based studies and our later hospital-based cohort. These methodological and temporal differences may explain why our findings align more closely with later hospital-based cohorts such as Labor et al. ( 14 ), which reported a reduction in intensive care admissions and mortality among corticosteroid-treated patients (hazard ratio = 0.82, 95% confidence interval 0.67–0.99). Mechanistic studies by Rogliani et al. (2021) further support potential protective pathways, demonstrating that corticosteroids can downregulate viral receptor expression and inhibit pro-inflammatory cytokine release ( 26 ). Importantly, the timing and chronicity of corticosteroid use appear critical; long-term therapy for disease control may have different consequences than short-term initiation at the onset of infection ( 30 ). Overall, these findings suggest that, conditional on infection, prior corticosteroid therapy may confer some protection in patients with chronic obstructive pulmonary disease, particularly when continued during hospitalization. Heterogeneity across studies likely reflects differences in patient selection, disease severity, corticosteroid formulation, and adjustment for confounders. Larger, well-powered investigations are required to confirm whether the observed associations represent a true protective effect or residual confounding. This study has several strengths, including a clearly defined patient cohort, comprehensive clinical and laboratory characterization, and multivariable analyses accounting for major comorbidities. The integration of both laboratory and clinical endpoints provides a multidimensional understanding of corticosteroid effects. Nonetheless, several limitations must be acknowledged. The retrospective design entails potential residual confounding, especially indication bias, as more severe cases were more likely to receive corticosteroids. The modest sample size limited statistical power to detect small or moderate effects. In addition, lack of detailed data on corticosteroid type, dosage, and duration prevented differentiation between inhaled and systemic therapy or evaluation of dose–response relationships. Consequently, the findings apply broadly to corticosteroid exposure rather than specific regimens. As this was a single-center study, generalizability to other populations and treatment settings may be limited. Therefore, our findings should be interpreted as generating the hypothesis that corticosteroid exposure in general is not harmful and potentially protective, rather than providing evidence for the efficacy of any specific regimen. This hypothesis, alongside the unresolved question of differential effects by corticosteroid type, must be tested in future, adequately powered studies designed specifically for that purpose. Future research should extend beyond increasing sample size. Prospective, multicenter cohort studies or pragmatic clinical trials stratified by corticosteroid formulation (inhaled versus systemic), dosage, and treatment duration are needed to validate our findings. Incorporating biomarker-defined subgroups such as baseline eosinophil counts or T-cell profiles would enhance mechanistic insight and external validity. Adaptive or pragmatic trial designs similar to RECOVERY, focused on patients with chronic obstructive pulmonary disease, could help determine whether corticosteroids yield a net clinical benefit. Long-term outcomes, including pulmonary function, diffusion capacity, and quality of life, also warrant further evaluation, given corticosteroids’ dual potential to reduce inflammation while predisposing to infection or metabolic complications. Conclusion In summary, among hospitalized COPD patients with COVID-19, prior corticosteroid use was not associated with worse outcomes and consistently showed a potential protective trend across oxygen therapy requirements, ICU admission, and survival. These data provide preliminary support for the hypothesis that prior corticosteroid exposure may confer protection in severe COVID-19, but this needs to be confirmed in prospective studies or larger multicenter cohorts. At the current stage, our findings strongly support that COPD patients should not discontinue necessary corticosteroid therapy out of concern for COVID-19 prognosis, with careful consideration of individual patient characteristics. Future well-powered, multicenter studies are warranted to confirm these observations and to optimize corticosteroid strategies in this high-risk population. Abbreviations ACE2 Angiotensin-Converting Enzyme 2 ALT Alanine Aminotransferase ARDS Acute Respiratory Distress Syndrome AST Aspartate Aminotransferase aOR Adjusted Odds Ratio BAL Bronchoalveolar Lavage BiPAP Bilevel Positive Airway Pressure BMI Body Mass Index CD4 Cluster of Differentiation 4 (Helper T Cell) CD8 Cluster of Differentiation 8 (Cytotoxic T Cell) CI Confidence Interval CKD Chronic Kidney Disease COPD Chronic Obstructive Pulmonary Disease COVID-19 Coronavirus Disease 2019 CRP C-Reactive Protein DLCO Diffusing Capacity of the Lung for Carbon Monoxide FEV₁ Forced Expiratory Volume in One Second GOLD Global Initiative for Chronic Obstructive Lung Disease ICS Inhaled Corticosteroids ICU Intensive Care Unit IL-6 Interleukin-6 IQR Interquartile Range NT-proBNP N-terminal pro–B-type Natriuretic Peptide OR Odds Ratio PCR Polymerase Chain Reaction RECOVERY Randomised Evaluation of COVID-19 Therapy Trial SARS-CoV-2 Severe Acute Respiratory Syndrome Coronavirus 2 SD Standard Deviation TMPRSS2 Transmembrane Serine Protease 2 WBC White Blood Cells WHO World Health Organization Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and relevant national regulations. Ethical approval was obtained from the Ethics Committee of the First Affiliated Hospital of Wenzhou Medical University (Approval No. KY2025-R088). Given the retrospective nature of the study and the use of anonymized clinical data, the requirement for written informed consent was waived by the Ethics Committee. 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. Funding This study was supported by the National Key Research and Development Program of China grants2016YFC1304000 (C Chen); The National Natural Scientific Foundation of China 82170017,82370085 (CChen); Zhejiang Provincial Key Research and Development Program 2020C03067 (C Chen); Wenzhou Science and Technology Bureau program Y2023113(CL Chen) Declarations of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author contributions ZT and CLC conceived and designed the study. ZT and MAG performed data acquisition and research execution. ZT and CLC contributed to the formal analysis and manuscript drafting. CLC and CSC are the guarantors of this work and assume responsibility for the integrity of the data and accuracy of the data analysis. All authors contributed to data interpretation, critically reviewed the manuscript, and approved the final version. Acknowledgments The authors would like to thank Dr. Hongjun Zhao for his valuable guidance and constructive suggestions during the data arrangement and interpretation process. We also express our gratitude to Tianye Li for assistance with data collection and organization. Their support contributed significantly to the completion of this study. References Alturki SO, Alturki SO, Connors J, Cusimano G, Kutzler MA, Izmirly AM, et al. The 2020 pandemic: current SARS-CoV-2 vaccine development. Frontiers in immunology. 2020;11:1880. O'Horo JC. Coronavirus: novel coronavirus (COVID-19) infection. Elsevier. 2020. Lai C-C, Liu YH, Wang C-Y, Wang Y-H, Hsueh S-C, Yen M-Y, et al. Asymptomatic carrier state, acute respiratory disease, and pneumonia due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2): Facts and myths. 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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-9661542","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":637281017,"identity":"2d1bf827-72fa-427c-9c24-7ef3244584ee","order_by":0,"name":"Talha Zaman","email":"","orcid":"","institution":"Zhejiang Provincial Key Laboratory of Interventional Pulmonology, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China","correspondingAuthor":false,"prefix":"","firstName":"Talha","middleName":"","lastName":"Zaman","suffix":""},{"id":637283676,"identity":"11d8dada-b0db-4d2a-8347-7a04e1018492","order_by":1,"name":"Muhammad Arslan Ghaffar","email":"","orcid":"","institution":"Department of Urology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Arslan","lastName":"Ghaffar","suffix":""},{"id":637285002,"identity":"527cf225-61d3-45f0-b7a8-c9844b55f528","order_by":2,"name":"Chengshui Chen","email":"","orcid":"","institution":"Zhejiang Provincial Key Laboratory of Interventional Pulmonology, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China","correspondingAuthor":false,"prefix":"","firstName":"Chengshui","middleName":"","lastName":"Chen","suffix":""},{"id":637290142,"identity":"a6842bdf-cbf1-4f7e-8b21-a249e69d29d8","order_by":3,"name":"ChaoLei Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYBACAwglIcfP3nyAJC0WxpI9xxJI0lKRuOFGjgFxWswlco895mGQYGw4c+bjjTcMdnK6DQS0WM7ISzcGamFmbO/dbDmHIdnY7AAhh93IMZMGamFj5jm7Dcg4kLiNWC08bBI5z0jTIsEjkcNGnBbLnjdmknMYJAwkeI4ZW84xIMIv5uw5ZhJvGOrq9x9vfnjjTYWdHEEtIMDE+w/CkOAhMmoYGH8wwLQQqWMUjIJRMApGFgAArjI5/AtbvFcAAAAASUVORK5CYII=","orcid":"","institution":"Zhejiang Provincial Key Laboratory of Interventional Pulmonology, Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China","correspondingAuthor":true,"prefix":"","firstName":"ChaoLei","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-05-09 08:34:14","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9661542/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9661542/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109102228,"identity":"3162c393-e74c-404f-92a5-d580549a6785","added_by":"auto","created_at":"2026-05-12 14:31:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92392,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Design Diagram\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9661542/v1/718e37163f5cc5021730d970.png"},{"id":109102345,"identity":"f4af80fe-a54b-4869-b369-675556042b02","added_by":"auto","created_at":"2026-05-12 14:31:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":810779,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9661542/v1/2fe9d068-4f33-4658-b9b1-fdfaba4b66c2.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eA Retrospective Cohort Study on the Effect of Prior Corticosteroid Use on COVID-19 Severity and Long-Term Outcomes in COPD Patients\u003c/p\u003e","fulltext":[{"header":"Plain Language Summary","content":"\u003cp\u003ePeople living with chronic obstructive pulmonary disease (COPD) are at higher risk of severe illness when they develop COVID-19. Many people with COPD regularly use corticosteroids to control breathing symptoms and reduce flare-ups. However, there has been concern that using these medicines before getting COVID-19 might worsen infection outcomes by weakening the immune system. This uncertainty led us to carry out this study.\u003c/p\u003e\n\u003cp\u003eWe reviewed the medical records of 166 people with COPD who were hospitalized with COVID-19. About half of these patients had used corticosteroids regularly in the year before they were admitted to hospital, while the others had not. We compared the two groups to see whether previous corticosteroid use was linked to the need for higher levels of oxygen, admission to intensive care, or survival one year after infection. We also took into account other health conditions that could affect outcomes.\u003c/p\u003e\n\u003cp\u003eWe found that people who had used corticosteroids before hospitalization did not have worse outcomes. In fact, they showed a tendency toward needing less intensive oxygen support and had similar or slightly better survival over one year, although these differences were not statistically certain.\u003c/p\u003e\n\u003cp\u003eThese findings suggest that continuing necessary corticosteroid treatment for COPD does not appear to increase the risk of poor outcomes from COVID-19. Larger studies are needed to confirm these results and to better understand which patients may benefit the most.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eThe emergence of SARS-CoV-2 in late 2019 rapidly evolved into a global pandemic, posing a major public health threat worldwide.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) COVID-19 presents with a wide clinical spectrum, ranging from asymptomatic infection to severe pneumonia and acute respiratory distress syndrome (ARDS), which can lead to death.(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Viral entry is mediated by the angiotensin-converting enzyme 2 (ACE2) receptor, which is highly expressed in type II alveolar, bronchial, and tracheal epithelial cells, rendering the respiratory tract a principal target for infection.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) The expression level of ACE2 influences susceptibility and disease severity and can be modified by environmental and clinical factors such as smoking.(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) Chronic obstructive pulmonary disease (COPD), commonly associated with smoking, has been linked to upregulated ACE2 expression and heightened COVID-19 vulnerability.(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eInhaled corticosteroids (ICS) are a cornerstone in COPD management and have been shown experimentally to downregulate the expression of viral entry\u0026ndash;related genes, potentially influencing SARS-CoV-2 infection dynamics.(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) However, clinical evidence remains inconsistent. Early reports suggested that high-dose ICS use might increase COVID-19\u0026ndash;related mortality in COPD,(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) while large-scale studies indicated that systemic corticosteroids such as dexamethasone reduced mortality among hospitalized and critically ill COVID-19 patients.(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) These findings prompted global guidelines, including from the WHO and NIH, to recommend corticosteroids for severe or critical COVID-19 cases but to avoid routine use in mild infections.(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eDespite the well-documented role of systemic corticosteroids in acute COVID-19, the effects of pre-existing ICS therapy in COPD patients remain uncertain. Some studies have suggested a neutral or even protective association between regular ICS use and COVID-19 outcomes.(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) A Swedish population study and a Korean national cohort both found that ongoing ICS exposure did not increase COVID-19 susceptibility or worsen clinical outcomes in COPD or asthma, though results varied depending on disease severity.(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eCorticosteroids exert potent anti-inflammatory and immunosuppressive effects.(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) While these actions alleviate airway inflammation, they may also dampen immune defenses against viral pathogens. Severe COVID-19 is characterized by excessive cytokine release, particularly interleukin-6 (IL-6), which correlates strongly with disease severity and mortality.(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) ICS may theoretically modulate this cytokine surge but could also alter baseline immune function in COPD patients. Corticosteroid-induced T-cell apoptosis and suppression of CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;activity can impair viral clearance and prolong infection.(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) Moreover, systemic inflammation in severe COVID-19 can cause multi-organ injury, affecting the liver, kidneys, and cardiovascular system.(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) The intersection of COPD-related inflammation, ICS-induced immunomodulation, and COVID-19 cytokine dysregulation therefore remains clinically relevant but poorly understood.\u003c/p\u003e \u003cp\u003eGiven these uncertainties, it is essential to clarify whether prior ICS exposure influences COVID-19 severity and recovery in COPD patients. This study aimed to evaluate the association between pre-admission corticosteroid use including inhaled and systemic forms and both in-hospital outcomes and long-term survival among COPD patients hospitalized with COVID-19.\u003c/p\u003e"},{"header":"Methodology","content":"\u003ch3\u003eStudy Design and Setting\u003c/h3\u003e\n\u003cp\u003eThis retrospective cohort study was conducted at the First Affiliated Hospital of Wenzhou Medical University between December 2022 and February 2025. The objective was to evaluate the association between prior corticosteroid use and clinical, laboratory, and survival outcomes among hospitalized patients with chronic obstructive pulmonary disease (COPD) and confirmed COVID-19.\u003c/p\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eA total of 201 patients were screened, and 166 with complete records were included in the final analysis. COPD was confirmed using clinical history, imaging, and spirometry where available. Disease severity was classified by post-bronchodilator FEV₁ % predicted according to GOLD grades 1–4.(Figure 1)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, the sample was included according to the following considerations:\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;\u0026nbsp;Hospitalized between December 2022 and February 2025.\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;\u0026nbsp;Confirmed COPD diagnosis according to GOLD criteria, supported by spirometry.\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp;\u0026nbsp;Confirmed COVID-19 infection, based on PCR and/or bronchoalveolar lavage (BAL) results.\u003c/p\u003e\n\u003cp\u003e4.\u0026nbsp; \u0026nbsp;\u0026nbsp;Required hospitalization due to severe COVID-19, including oxygen therapy.\u003c/p\u003e\n\u003cp\u003e5.\u0026nbsp; \u0026nbsp;\u0026nbsp;Availability of complete medical records, including corticosteroid usage history.\u003c/p\u003e\n\u003ch3 id=\"_Toc2753\"\u003eExclusion Criteria:\u003c/h3\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;\u0026nbsp;Missing or incomplete medical records (n = 35).\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;\u0026nbsp;Asthma diagnosis.\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp;\u0026nbsp;Mild or non-severe COVID-19 cases not requiring hospitalization or oxygen support.\u003c/p\u003e\n\u003ch3 id=\"_Toc21011\"\u003ePatient Grouping Based on Corticosteroid Usage\u003c/h3\u003e\n\u003cp\u003ePatients were classified by corticosteroid exposure during the 12 months before hospitalization. Of 166 patients, 82 had prior corticosteroid use and 84 had none (control group).(Figure 1)\u003cbr\u003e\u0026nbsp;Corticosteroid users were defined as having ≥ 3 prescriptions in the previous year, consistent with established COPD–COVID-19 criteria. Budesonide was the predominant inhaled corticosteroid (1 mg, 120 µg, or 160 µg BID). Other corticosteroids (e.g., methylprednisolone, dexamethasone, fluticasone, prednisone) were used less often for COPD complications or comorbidities.\u003cbr\u003e\u0026nbsp;A subgroup of 26 patients who received both budesonide and other corticosteroids—including either systemic agents (e.g., methylprednisolone, dexamethasone, prednisone) or other inhaled corticosteroids (e.g., fluticasone)—was analyzed separately to evaluate the potential effects of combined corticosteroid therapy. Non-users were defined as those with ≤ 2 corticosteroid prescriptions during the year prior to admission, indicating minimal or no prior exposure.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eData were retrospectively obtained from the electronic medical records of the First Affiliated Hospital of Wenzhou Medical University, using the hospital information system and institutional laboratory databases.\u003c/p\u003e\n\u003ch3\u003eDemographic and Clinical Variables\u003c/h3\u003e\n\u003cp\u003eCollected variables included age, sex, body mass index, smoking history (never, less than ten years, ten to twenty years, or more than twenty years), and duration of chronic obstructive pulmonary disease (\u0026lt;5 years, 5–10 years, \u0026gt;10 years). Documented comorbidities comprised hypertension, diabetes mellitus, cardiovascular disease, stroke, chronic respiratory disease (such as silicosis), malignancy, chronic kidney disease, rheumatologic or immunological disorders, and gastrointestinal diseases (including liver cirrhosis or gastritis). Additional parameters included the frequency of COVID-19 polymerase chain reaction testing during hospitalization, site of admission (ward or intensive care unit), length of hospital stay, and type of oxygen therapy administered (low flow, high flow, non-invasive, or mechanical ventilation). One-year survival status was categorized as survived, deceased, or lost to follow-up.\u003c/p\u003e\n\u003ch3\u003eLaboratory Parameters\u003c/h3\u003e\n\u003cp\u003eLaboratory data obtained within the first twenty-four hours of admission were grouped into four categories. Inflammatory markers included C-reactive protein, procalcitonin, D-dimer, and interleukin-6. Immune status was assessed through total T lymphocyte, CD4, and CD8 cell counts. Hematological parameters comprised white blood cell, neutrophil, and lymphocyte counts. Cardiac function was evaluated using troponin T, troponin I, and N-terminal pro–B-type natriuretic peptide. Renal function was assessed by serum urea and creatinine, and hepatic function by alanine aminotransferase and aspartate aminotransferase levels.\u003c/p\u003e\n\u003ch3\u003eOutcome measures\u003c/h3\u003e\n\u003cp\u003eThis study aims to:\u003cbr\u003e\u0026nbsp;1. \u0026nbsp;Assess the need for oxygen therapy and the duration of hospitalization.\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp;Monitor changes in immune, infection, cardiac, renal, hepatic, and coagulation markers.\u003c/p\u003e\n\u003cp\u003e3. \u0026nbsp; Evaluate 1-year survival following COVID-19 infection in COPD patients.\u003c/p\u003e\n\u003ch3 id=\"_Toc3708\"\u003eInterpretation Context\u003c/h3\u003e\n\u003cp\u003eGiven that long-term corticosteroid use is known to suppress immune function, the study also seeks to explore whether prior corticosteroid exposure is associated with a more aggressive course of COVID-19 in COPD patients.\u003c/p\u003e\n\u003ch3\u003eStatistical Analysis\u003c/h3\u003e\n\u003cp\u003eContinuous variables were tested for normality using the Shapiro–Wilk method. Normally distributed data were compared using independent t-tests and presented as mean with standard deviation, whereas non-normally distributed data, including inflammatory, immune, and biochemical parameters, were analyzed using the Mann–Whitney U test and expressed as median with interquartile range. Categorical variables such as sex, comorbidities, spirometry severity, symptoms, and outcomes were compared using the Chi-square or Fisher’s exact test as appropriate.\u003c/p\u003e\n\u003cp\u003eAssociations between corticosteroid use and outcomes were examined through binary logistic regression for dichotomous variables (intensive care admission and survival) and ordinal logistic regression for ordered outcomes (oxygen therapy level). Both unadjusted and multivariable models adjusted for age, sex, and clinically relevant comorbidities were applied, and results were expressed as odds ratios with ninety-five percent confidence intervals. To ensure model stability, multicollinearity was assessed using tolerance and variance inflation factor statistics; all predictors showed tolerance above 0.7 and variance inflation factor below 2, confirming the reliability of the regression estimates.\u003c/p\u003e\n\u003cp\u003eA subgroup analysis compared patients receiving both inhaled and systemic corticosteroids with those receiving inhaled therapy alone or none. As this was a retrospective study, all eligible cases during the study period were included without prior sample-size estimation. Statistical significance was defined as a p-value below 0.05. Analyses were conducted using IBM SPSS Statistics (Version 26) and R software (Version 4.5.1).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 166 patients met the inclusion criteria and were analyzed, comprising 84 patients in the non-corticosteroid group and 82 patients in the corticosteroid group. The mean age of the study population was 76.8 years, with no significant difference observed between the two groups (77.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0 vs. 76.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2 years, p\u0026thinsp;=\u0026thinsp;0.551). Gender distribution, however, differed significantly, with the corticosteroid group consisting of a higher proportion of males compared with the non-corticosteroid group (93.9% vs. 79.8%, p\u0026thinsp;=\u0026thinsp;0.007). Body mass index (BMI) and smoking history did not significantly differ between groups (p\u0026thinsp;=\u0026thinsp;0.649 and p\u0026thinsp;=\u0026thinsp;0.334, respectively). Among comorbidities, chronic kidney disease (CKD) was more prevalent in the corticosteroid group (17.1% vs. 7.1%, p\u0026thinsp;=\u0026thinsp;0.049). Other comorbidities showed no statistically significant differences between groups. Spirometry-based classification indicated a higher prevalence of severe and very severe airflow obstruction in the corticosteroid group compared to the non-corticosteroid group (p\u0026thinsp;=\u0026thinsp;0.001). (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eClinical Variables\u003c/h2\u003e \u003cp\u003eThe most common presenting symptoms in both groups were cough and sputum production, reported in 94.0% of patients in each group (p\u0026thinsp;=\u0026thinsp;0.969). Fever was slightly more common in the non-corticosteroid group (66.7% vs. 64.8%, p\u0026thinsp;=\u0026thinsp;0.783). Dyspnea and chest tightness were more frequent in the corticosteroid group (97.6% vs. 90.5%), with borderline statistical significance (p\u0026thinsp;=\u0026thinsp;0.055). Other presenting symptoms were reported in 14.3% of non-corticosteroid patients and 12.2% of corticosteroid patients (p\u0026thinsp;=\u0026thinsp;0.891). The duration of symptoms prior to admission was similar between groups (p\u0026thinsp;=\u0026thinsp;0.280).\u003c/p\u003e \u003cp\u003eRegarding hospitalization, 71.4% of patients in the non-corticosteroid group and 80.5% in the corticosteroid group were admitted to general wards, while ICU admission was higher in the non-corticosteroid group (28.6% vs. 19.5%), although this difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.172). The median hospital stay was slightly longer in the corticosteroid group (11 [IQR: 6\u0026ndash;18] days) compared to the non-corticosteroid group (10 [IQR: 6\u0026ndash;14] days), but this difference was also not significant (p\u0026thinsp;=\u0026thinsp;0.278). One-year survival was comparable between groups (61.0% alive in the corticosteroid group vs. 52.4% in the non-corticosteroid group, p\u0026thinsp;=\u0026thinsp;0.535).\u003c/p\u003e \u003cp\u003eA significant difference was observed in the type of oxygen therapy administered upon admission (p\u0026thinsp;=\u0026thinsp;0.047). Low-flow oxygen was more frequently used in the corticosteroid group (63.4% vs. 45.2%), whereas non-invasive ventilation (BiPAP/CPAP) was more common in the non-corticosteroid group (23.8% vs. 14.6%). Mechanical ventilation usage was similar between groups (20.7% vs. 23.8%), and high-flow oxygen was rarely used. Detailed comparisons of clinical presentation, hospitalization, and oxygen therapy are summarized in `1(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLaboratory variables\u003c/h2\u003e \u003cp\u003eComparison of laboratory parameters between the corticosteroid and non-corticosteroid groups revealed no statistically significant differences across inflammatory markers, immune markers, complete blood counts, cardiac biomarkers, or liver and kidney function tests.\u003c/p\u003e \u003cp\u003eInflammatory markers such as C-reactive protein (CRP), procalcitonin, D-dimer, and IL-6 showed slightly higher median values in the corticosteroid group, but none reached statistical significance (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Similarly, immune markers including total T-lymphocyte counts, CD4, and CD8 were lower in the corticosteroid group, suggesting a possible trend toward immune suppression, though not statistically significant (p\u0026thinsp;=\u0026thinsp;0.379, 0.302, and 0.251, respectively).\u003c/p\u003e \u003cp\u003eComplete blood counts, including white blood cells, neutrophils, and lymphocytes, were comparable between groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Cardiac biomarkers such as Troponin T, Troponin I, and NT-proBNP were slightly lower in the corticosteroid group but did not show significant differences (p\u0026thinsp;=\u0026thinsp;0.176, 0.092, and 0.123, respectively). Renal markers (urea and creatinine) and liver enzymes (ALT and AST) were also similar between groups. Overall, corticosteroid use was not associated with marked biochemical disturbances in this sample. Detailed laboratory results are presented in (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable logistic regression for ICU admission\u003c/h2\u003e \u003cp\u003eCrude analysis indicated a lower proportion of intensive care unit admissions among patients with prior corticosteroid use compared with non-users (19.5 percent versus 28.6 percent, p\u0026thinsp;=\u0026thinsp;0.172) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In unadjusted logistic regression, corticosteroid use was associated with reduced odds of intensive care admission (odds ratio\u0026thinsp;=\u0026thinsp;0.61, 95 percent confidence interval 0.29\u0026ndash;1.24, p\u0026thinsp;=\u0026thinsp;0.174). After adjustment for age, sex, and comorbidities, this association persisted but did not reach statistical significance (adjusted odds ratio\u0026thinsp;=\u0026thinsp;0.56, 95 percent confidence interval 0.25\u0026ndash;1.23, p\u0026thinsp;=\u0026thinsp;0.155). None of the other covariates were statistically significant in the adjusted model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable ordinal logistic regression for oxygen therapy\u003c/h2\u003e \u003cp\u003eCrude analysis suggested that patients who received corticosteroids were less likely to require higher levels of oxygen therapy than those without prior exposure (p\u0026thinsp;=\u0026thinsp;0.047) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In unadjusted ordinal logistic regression, corticosteroid use was linked to lower odds of requiring advanced oxygen support (odds ratio\u0026thinsp;=\u0026thinsp;0.54, 95 percent confidence interval 0.30\u0026ndash;0.98, p\u0026thinsp;=\u0026thinsp;0.044). After adjustment for age, sex, and comorbidities, the association remained in the same direction but lost statistical significance (adjusted odds ratio\u0026thinsp;=\u0026thinsp;0.65, 95 percent confidence interval 0.34\u0026ndash;1.25, p\u0026thinsp;=\u0026thinsp;0.197). None of the additional covariates were significantly associated with oxygen therapy requirements in the adjusted model (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable logistic regression for survival status\u003c/h2\u003e \u003cp\u003eCrude analysis showed a slightly higher survival rate among corticosteroid users compared with non-users (61.0 percent versus 52.4 percent, p\u0026thinsp;=\u0026thinsp;0.5351) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After excluding patients lost to follow-up, unadjusted logistic regression revealed that corticosteroid use was associated with higher odds of survival (odds ratio\u0026thinsp;=\u0026thinsp;1.45, 95 percent confidence interval 0.60\u0026ndash;3.58, p\u0026thinsp;=\u0026thinsp;0.415), though the result was not statistically significant. Following adjustment for age, sex, and comorbidities, the association remained positive but non-significant (adjusted odds ratio\u0026thinsp;=\u0026thinsp;1.45, 95 percent confidence interval 0.55\u0026ndash;3.91, p\u0026thinsp;=\u0026thinsp;0.453). No other covariates reached statistical significance; however, pre-existing respiratory disease showed a trend toward lower survival (odds ratio\u0026thinsp;=\u0026thinsp;0.36, 95 percent confidence interval 0.12\u0026ndash;1.14, p\u0026thinsp;=\u0026thinsp;0.076) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup Analysis of Combination Corticosteroid Users\u003c/h2\u003e \u003cp\u003eTo further examine whether the combined use of systemic and inhaled corticosteroids influenced outcomes, a subgroup analysis was performed among 26 patients who had received both budesonide and at least one additional corticosteroid (prednisone, methylprednisolone, dexamethasone, beclomethasone, or fluticasone) during the 12 months prior to admission.\u003c/p\u003e \u003cp\u003eCompared with the remaining 140 patients, this subgroup exhibited a higher prevalence of malignancy (50.0% vs 26.8%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and gastrointestinal comorbidities (42.3% vs 30.5%, p\u0026thinsp;=\u0026thinsp;0.023), suggesting a greater baseline disease burden. Inflammatory markers showed a pattern toward higher procalcitonin values (median 0.27 ng/mL vs 0.14 ng/mL, p\u0026thinsp;=\u0026thinsp;0.011) and slightly lower lymphocyte counts (p\u0026thinsp;=\u0026thinsp;0.051).(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eMultivariable ordinal logistic regression demonstrated that prior use of other corticosteroids was not significantly associated with the severity of oxygen therapy requirements (adjusted OR 0.97, 95% CI 0.38\u0026ndash;2.50, p\u0026thinsp;=\u0026thinsp;0.950) or with ICU admission (adjusted OR 1.13, 95% CI 0.35\u0026ndash;3.28, p\u0026thinsp;=\u0026thinsp;0.846). Likewise, multivariable logistic regression for one-year survival showed no significant association between combination corticosteroid use and mortality (adjusted OR 2.30, 95% CI 0.49\u0026ndash;16.99, p\u0026thinsp;=\u0026thinsp;0.339). None of the covariates reached statistical significance.(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)(Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eOverall, these findings indicate that dual or mixed corticosteroid exposure did not alter oxygen demand, ICU admission risk, or one-year survival in this cohort, though the wide confidence intervals reflect limited subgroup size and reduced statistical power.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics and Clinical Parameters of Patients Stratified by Corticosteroid Exposure and Subgroup Use of Other Corticosteroids\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithout corticosteroids group (N\u0026thinsp;=\u0026thinsp;84)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWith corticosteroids group\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;82)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOther Corticosteroids group(N\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(Mean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.13\u0026thinsp;\u0026plusmn;\u0026thinsp;10.03(49\u0026ndash;94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.39\u0026thinsp;\u0026plusmn;\u0026thinsp;7.24(61\u0026ndash;93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.12\u0026thinsp;\u0026plusmn;\u0026thinsp;6.34 (66\u0026ndash;92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67(79.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77(93.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (92.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(20.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5\u0026ndash;24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66(78.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61(74.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19 (73.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25.0-29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(15.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(15.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (19.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking history\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo smoking(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(29.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (23.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10 years(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026ndash;20 years(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(12.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (23.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;20 years(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39(46.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(57.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (46.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCOPD duration\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(22.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (34.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52(61.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(54.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (38.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(15.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (26.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCOVID-19 PCR(Frequency)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50(60.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(57.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (53.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTwice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(28.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (26.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThrice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(18.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(13.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (26.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47(56.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44(53.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (53.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes Mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(29.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (19.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(29.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(30.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.025*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke(cerebral infarction)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(13.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Respiratory disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(13.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(23.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (26.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (15.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic Kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(17.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.049*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRheumatologic or Immunological disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal diseases(liver chirosis or gastritis)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(30.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (42.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.023*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSpirometry iresults\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ein the past\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot found\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50(59.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(32.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(17.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(15.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(39.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (26.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery severe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSymptoms\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCough and excessive sputum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79(94.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77(93.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (92.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56(66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53(64.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (69.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyspnea and chest tightness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76(90.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80(97.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (96.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther presenting symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(12.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDuration of symptoms before admission\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 5 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55(65.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(57.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (46.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 5 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(42.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (53.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospitalization\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60(71.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66(80.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20 (76.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(19.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (23.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospitalization duration\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 [6\u0026ndash;14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 [6\u0026ndash;18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 [5\u0026ndash;19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxygen Therapy on 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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.047*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow flow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38(45.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52(53.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (61.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh flow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon invasive ventilation (BiPAP, CPAP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(23.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(23.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(20.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (23.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurvival status within 1 year\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(13.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(52.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50(61.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (53.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLost follow up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(31.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(25.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (38.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInflammatory markers\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.4 (14.18\u0026ndash;100.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.5 (11.53\u0026ndash;119.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.6 (16.3\u0026ndash;123.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcalcitonin (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.155 (0.020\u0026ndash;0.450)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.140 (0.020\u0026ndash;0.255)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.270 (0.127\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-dimer (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.945 (0.11\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20 (0.14\u0026ndash;1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.54 (0.78\u0026ndash;2.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6 (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.14 (0.10\u0026ndash;34.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.17 (0.10\u0026ndash;41.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.3 (5.8\u0026ndash;47.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eImmune markers\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT lymphocytes (cells/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e346.0 (72.0\u0026ndash;400.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e259.0 (36.0\u0026ndash;464.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e219.0 (103.0\u0026ndash;514.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4 (cells/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165.0 (29.0\u0026ndash;220.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149.5 (16.0\u0026ndash;301.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e131.0 (51.2\u0026ndash;347.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD8 (cells/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111.0 (28.0\u0026ndash;175.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.0 (11.8\u0026ndash;220.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.0 (31.0\u0026ndash;200.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComplete blood counts\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.20 (3.22\u0026ndash;10.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.52 (2.25\u0026ndash;9.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.75 (6.47\u0026ndash;12.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.07 (2.03\u0026ndash;9.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.16 (0.29\u0026ndash;7.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.34 (5.97\u0026ndash;11.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.12 (0.68\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.58\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.52 (0.36\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiac markers\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTroponin T (ng/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.35 (5.90\u0026ndash;44.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.00 (7.00\u0026ndash;27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.5 (15.2\u0026ndash;36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTroponin I (ng/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.0 (21.0\u0026ndash;181.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.0 (21.0\u0026ndash;78.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.0 (34.0\u0026ndash;112.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNT-proBNP (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1144.5 (571.7\u0026ndash;3415.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e637.0 (350.0\u0026ndash;1869.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e727.0 (189.0\u0026ndash;3443.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRenal \u0026amp; liver function\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.3 (4.9\u0026ndash;10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.6 (5.3\u0026ndash;10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.6 (5.0\u0026ndash;9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.2 (63.7\u0026ndash;101.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.9 (62.0\u0026ndash;94.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.0 (54.0\u0026ndash;137.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.0 (20.5\u0026ndash;48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.0 (21.0\u0026ndash;46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.5 (14.0\u0026ndash;27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.5 (21.0\u0026ndash;35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.0 (16.0\u0026ndash;37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.5 (22.0\u0026ndash;50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e* indicates statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBMI: body mass index; ICU: intensive care unit; CRP: C-reactive protein; IL-6: interleukin-6; WBC: white blood cells; ALT: alanine aminotransferase; AST: aspartate aminotransferase; NT-proBNP: N-terminal pro-B-type natriuretic peptide.\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\u003eMultivariable logistic regression for ICU admission\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eCorticosteroids Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eSub Group analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorticosteroid use (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25\u0026ndash;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.35\u0026ndash;3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u0026ndash;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.96\u0026ndash;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex (vs Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u0026ndash;4.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.37\u0026ndash;3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u0026ndash;2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.43\u0026ndash;2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u0026ndash;1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.25\u0026ndash;1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular disease (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.45\u0026ndash;2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.45\u0026ndash;2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u0026ndash;5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.73\u0026ndash;5.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory disease (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u0026ndash;1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.21\u0026ndash;1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u0026ndash;2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.36\u0026ndash;2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31\u0026ndash;3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.28\u0026ndash;3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRheumatologic/immune disorder (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75\u0026ndash;9.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.72\u0026ndash;9.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal disease (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u0026ndash;1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.18\u0026ndash;1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNo predictor reached statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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\u003eMultivariable ordinal logistic regression for oxygen therapy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eCorticosteroids Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eSub Group analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorticosteroid use (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.34\u0026ndash;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.37\u0026ndash;2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u0026ndash;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.96\u0026ndash;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex (vs Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u0026ndash;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.19\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.71\u0026ndash;2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.69\u0026ndash;2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.26\u0026ndash;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.28\u0026ndash;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular disease (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38\u0026ndash;1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.38\u0026ndash;1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25\u0026ndash;1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.37\u0026ndash;1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory disease (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u0026ndash;2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.42\u0026ndash;2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u0026ndash;2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.41\u0026ndash;1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16\u0026ndash;1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17\u0026ndash;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRheumatologic/immune disorder (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22\u0026ndash;2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u0026ndash;2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal disease (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u0026ndash;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.26\u0026ndash;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNo predictor reached statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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\u003eMultivariable logistic regression for survival status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eCorticosteroids Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eSub Group analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorticosteroid treatment (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u0026ndash;3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.49\u0026ndash;16.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (per year increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.94\u0026ndash;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale sex (vs Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u0026ndash;4.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u0026ndash;2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60\u0026ndash;4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.33\u0026ndash;2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u0026ndash;3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.38\u0026ndash;3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular disease (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.63\u0026ndash;6.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u0026ndash;2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.34\u0026ndash;7.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u0026ndash;2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory disease (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12\u0026ndash;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.12\u0026ndash;1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.30\u0026ndash;4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.28\u0026ndash;2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18\u0026ndash;4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.32\u0026ndash;5.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRheumatologic/immune disorder (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u0026ndash;49.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.19\u0026ndash;3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal disease (Yes vs No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48\u0026ndash;6.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.19\u0026ndash;2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNo predictor reached statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated the impact of prior corticosteroid use on clinical outcomes\u0026mdash;including intensive care admission, oxygen therapy requirements, and one-year survival among 166 hospitalized patients with chronic obstructive pulmonary disease and COVID-19. Corticosteroid users included a greater proportion of males (93.9% versus 79.8%; p\u0026thinsp;=\u0026thinsp;0.007) and more cases of severe or very severe airflow obstruction (severe: 39.0% versus 15.5%; very severe: 7.3% versus 1.2%; p\u0026thinsp;=\u0026thinsp;0.001), consistent with the clinical rationale for corticosteroid therapy in advanced disease.\u003c/p\u003e \u003cp\u003eRegression analyses suggested a possible favorable association between prior corticosteroid use and several outcomes: lower odds of requiring advanced oxygen therapy (odds ratio\u0026thinsp;=\u0026thinsp;0.54, 95% confidence interval 0.30\u0026ndash;0.98; p\u0026thinsp;=\u0026thinsp;0.044), reduced likelihood of intensive care admission (odds ratio\u0026thinsp;=\u0026thinsp;0.61, 95% confidence interval 0.29\u0026ndash;1.24; p\u0026thinsp;=\u0026thinsp;0.174), and higher one-year survival (odds ratio\u0026thinsp;=\u0026thinsp;1.45, 95% confidence interval 0.60\u0026ndash;3.58; p\u0026thinsp;=\u0026thinsp;0.415). After adjusting for age, sex, and comorbidities, the direction of these associations persisted but lost statistical significance (oxygen therapy: adjusted odds ratio\u0026thinsp;=\u0026thinsp;0.65, 95% confidence interval 0.34\u0026ndash;1.25; p\u0026thinsp;=\u0026thinsp;0.197; intensive care admission: adjusted odds ratio\u0026thinsp;=\u0026thinsp;0.56, 95% confidence interval 0.25\u0026ndash;1.23; p\u0026thinsp;=\u0026thinsp;0.155; survival: adjusted odds ratio\u0026thinsp;=\u0026thinsp;1.45, 95% confidence interval 0.55\u0026ndash;3.91; p\u0026thinsp;=\u0026thinsp;0.453). Pre-existing respiratory disease showed a trend toward lower survival (odds ratio\u0026thinsp;=\u0026thinsp;0.36, 95% confidence interval 0.12\u0026ndash;1.14; p\u0026thinsp;=\u0026thinsp;0.076), highlighting the influence of underlying pulmonary comorbidity on prognosis.\u003c/p\u003e \u003cp\u003eThe attenuation of significance in the multivariable model likely reflects the small sample size and the higher comorbidity burden among corticosteroid users. These patients tended to have more severe airflow limitation and systemic disease both strong predictors of higher oxygen demand so adjustment for these confounders may have diluted the apparent protective association. Similar attenuation has been observed in previous studies, where controlling for disease severity and comorbidities reduced the observed benefit of corticosteroid therapy (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Despite the lack of statistical significance, the consistent direction of effect across all primary clinical outcomes (reduced oxygen therapy, lower ICU admission, and improved survival) strengthens the plausibility of a true protective association, which was likely undetected due to limited statistical power.\u003c/p\u003e \u003cp\u003eIn the subgroup of 26 patients who received both inhaled budesonide and additional systemic corticosteroids (prednisone, methylprednisolone, dexamethasone, beclomethasone, or fluticasone), no significant differences were found in oxygen therapy level, intensive care admission, or one-year survival compared with the remaining cohort. Although this subgroup showed higher rates of malignancy and gastrointestinal disease, adjusted analyses did not demonstrate any independent effect of combined corticosteroid use. These findings indicate that dual exposure did not substantially alter clinical outcomes.\u003c/p\u003e \u003cp\u003eFrom an immunological perspective, corticosteroid users displayed numerically lower T-lymphocyte, CD4-positive, and CD8-positive cell counts, suggesting a mild immunosuppressive trend consistent with the pharmacological actions of corticosteroids (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Mechanistically, corticosteroids may exert a dual effect: they can attenuate excessive inflammation by suppressing pro-inflammatory cytokines and downregulating angiotensin-converting enzyme 2 and TMPRSS2 expression (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), thereby reducing viral entry and cytokine-mediated tissue injury, but they may also dampen antiviral immune responses. In this cohort, these immunological trends did not translate into worse clinical outcomes, suggesting that the anti-inflammatory benefits may outweigh potential immune suppression or that the extent of suppression remained clinically mild. Routine laboratory indicators including complete blood counts, cardiac, renal, and hepatic biomarkers did not differ significantly between groups, supporting the view that prior corticosteroid therapy did not worsen early clinical presentation.\u003c/p\u003e \u003cp\u003eAlthough laboratory parameters did not reach statistical significance, inflammatory markers were modestly higher and immune cell counts lower among corticosteroid users. These trends may reflect both the limited sample size and inherent differences in disease stage. Long-term corticosteroid users typically represent patients with more advanced chronic obstructive pulmonary disease and greater baseline systemic inflammation, which could contribute to these findings independent of acute infection. The modest sample size and variability in timing of laboratory sampling relative to corticosteroid exposure further limited statistical power to detect subtle yet biologically meaningful effects. Nevertheless, these patterns are physiologically plausible and merit further investigation in larger, prospective cohorts.\u003c/p\u003e \u003cp\u003eOur findings are consistent with prior reports showing no clear harm from corticosteroid therapy in chronic obstructive pulmonary disease during COVID-19. Sen et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) found no increase in healthcare utilization or mortality, and Bloom et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) similarly reported no significant effect on outcomes among patients hospitalized with COVID-19. Husby et al. (2021) likewise found no association between inhaled corticosteroid therapy and either benefit or harm with respect to intensive care admission or mortality (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Comparable results were observed by Choi et al. (2020) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Collectively, these data support the interpretation that prior corticosteroid exposure is not detrimental in this population.\u003c/p\u003e \u003cp\u003eIn contrast, some population-based studies have reported increased mortality among corticosteroid users. Schultze et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) and Aveyard et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) observed higher risks of death or severe COVID-19 in users of inhaled corticosteroids, although these associations were largely attributed to residual confounding related to worse baseline health. The present study differs from those analyses in several ways. Community-based studies typically defined exposure by recent prescriptions, potentially including frailer patients with poorer prognosis. By contrast, this study focused exclusively on hospitalized patients and employed a stricter exposure threshold (three or more prescriptions per year) to identify consistent long-term users. In addition, our investigation was conducted during a later phase of the pandemic, when inpatient management of COVID-19\u0026mdash;including standardized corticosteroid protocols\u0026mdash;was better established. Furthermore, the distinct pandemic phases and evolving standards of care, including the widespread in-hospital use of systemic corticosteroids for severe COVID-19 later in the pandemic, represent another critical contextual difference that may account for the discrepant findings between early community-based studies and our later hospital-based cohort. These methodological and temporal differences may explain why our findings align more closely with later hospital-based cohorts such as Labor et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), which reported a reduction in intensive care admissions and mortality among corticosteroid-treated patients (hazard ratio\u0026thinsp;=\u0026thinsp;0.82, 95% confidence interval 0.67\u0026ndash;0.99). Mechanistic studies by Rogliani et al. (2021) further support potential protective pathways, demonstrating that corticosteroids can downregulate viral receptor expression and inhibit pro-inflammatory cytokine release (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Importantly, the timing and chronicity of corticosteroid use appear critical; long-term therapy for disease control may have different consequences than short-term initiation at the onset of infection (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, these findings suggest that, conditional on infection, prior corticosteroid therapy may confer some protection in patients with chronic obstructive pulmonary disease, particularly when continued during hospitalization. Heterogeneity across studies likely reflects differences in patient selection, disease severity, corticosteroid formulation, and adjustment for confounders. Larger, well-powered investigations are required to confirm whether the observed associations represent a true protective effect or residual confounding.\u003c/p\u003e \u003cp\u003eThis study has several strengths, including a clearly defined patient cohort, comprehensive clinical and laboratory characterization, and multivariable analyses accounting for major comorbidities. The integration of both laboratory and clinical endpoints provides a multidimensional understanding of corticosteroid effects. Nonetheless, several limitations must be acknowledged. The retrospective design entails potential residual confounding, especially indication bias, as more severe cases were more likely to receive corticosteroids. The modest sample size limited statistical power to detect small or moderate effects. In addition, lack of detailed data on corticosteroid type, dosage, and duration prevented differentiation between inhaled and systemic therapy or evaluation of dose\u0026ndash;response relationships. Consequently, the findings apply broadly to corticosteroid exposure rather than specific regimens. As this was a single-center study, generalizability to other populations and treatment settings may be limited.\u003c/p\u003e \u003cp\u003eTherefore, our findings should be interpreted as generating the hypothesis that corticosteroid exposure in general is not harmful and potentially protective, rather than providing evidence for the efficacy of any specific regimen. This hypothesis, alongside the unresolved question of differential effects by corticosteroid type, must be tested in future, adequately powered studies designed specifically for that purpose.\u003c/p\u003e \u003cp\u003eFuture research should extend beyond increasing sample size. Prospective, multicenter cohort studies or pragmatic clinical trials stratified by corticosteroid formulation (inhaled versus systemic), dosage, and treatment duration are needed to validate our findings. Incorporating biomarker-defined subgroups such as baseline eosinophil counts or T-cell profiles would enhance mechanistic insight and external validity. Adaptive or pragmatic trial designs similar to RECOVERY, focused on patients with chronic obstructive pulmonary disease, could help determine whether corticosteroids yield a net clinical benefit. Long-term outcomes, including pulmonary function, diffusion capacity, and quality of life, also warrant further evaluation, given corticosteroids\u0026rsquo; dual potential to reduce inflammation while predisposing to infection or metabolic complications.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, among hospitalized COPD patients with COVID-19, prior corticosteroid use was not associated with worse outcomes and consistently showed a potential protective trend across oxygen therapy requirements, ICU admission, and survival. These data provide preliminary support for the hypothesis that prior corticosteroid exposure may confer protection in severe COVID-19, but this needs to be confirmed in prospective studies or larger multicenter cohorts. At the current stage, our findings strongly support that COPD patients should not discontinue necessary corticosteroid therapy out of concern for COVID-19 prognosis, with careful consideration of individual patient characteristics. Future well-powered, multicenter studies are warranted to confirm these observations and to optimize corticosteroid strategies in this high-risk population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eACE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAngiotensin-Converting Enzyme 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAlanine Aminotransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eARDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAcute Respiratory Distress Syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAspartate Aminotransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAdjusted Odds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBronchoalveolar Lavage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBiPAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBilevel Positive Airway Pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCluster of Differentiation 4 (Helper T Cell)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCD8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCluster of Differentiation 8 (Cytotoxic T Cell)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChronic Kidney Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChronic Obstructive Pulmonary Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCOVID-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCoronavirus Disease 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC-Reactive Protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDLCO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDiffusing Capacity of the Lung for Carbon Monoxide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFEV₁\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eForced Expiratory Volume in One Second\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGOLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGlobal Initiative for Chronic Obstructive Lung Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eICS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInhaled Corticosteroids\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIntensive Care Unit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIL-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInterleukin-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInterquartile Range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNT-proBNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN-terminal pro–B-type Natriuretic Peptide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePolymerase Chain Reaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRECOVERY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandomised Evaluation of COVID-19 Therapy Trial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSARS-CoV-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSevere Acute Respiratory Syndrome Coronavirus 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTMPRSS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTransmembrane Serine Protease 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWhite Blood Cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWorld Health Organization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003cbr\u003eThis study was conducted in accordance with the Declaration of Helsinki and relevant national regulations. Ethical approval was obtained from the Ethics Committee of the First Affiliated Hospital of Wenzhou Medical University (Approval No. KY2025-R088). Given the retrospective nature of the study and the use of anonymized clinical data, the requirement for written informed consent was waived by the Ethics Committee.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003cbr\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Key Research and Development Program of China grants2016YFC1304000 (C Chen); The National Natural Scientific Foundation of China 82170017,82370085 (CChen); Zhejiang Provincial Key Research and Development Program 2020C03067 (C Chen); Wenzhou Science and Technology Bureau program Y2023113(CL Chen)\u003c/p\u003e\n\u003cp\u003eDeclarations of competing interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eZT and CLC conceived and designed the study. ZT and MAG performed data acquisition and research execution. ZT and CLC contributed to the formal analysis and manuscript drafting. CLC and CSC are the guarantors of this work and assume responsibility for the integrity of the data and accuracy of the data analysis. All authors contributed to data interpretation, critically reviewed the manuscript, and approved the final version.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Dr. Hongjun Zhao for his valuable guidance and constructive suggestions during the data arrangement and interpretation process. We also express our gratitude to Tianye Li for assistance with data collection and organization. Their support contributed significantly to the completion of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlturki SO, Alturki SO, Connors J, Cusimano G, Kutzler MA, Izmirly AM, et al. The 2020 pandemic: current SARS-CoV-2 vaccine development. Frontiers in immunology. 2020;11:1880.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Horo JC. Coronavirus: novel coronavirus (COVID-19) infection. Elsevier. 2020.\u003c/li\u003e\n\u003cli\u003eLai C-C, Liu YH, Wang C-Y, Wang Y-H, Hsueh S-C, Yen M-Y, et al. Asymptomatic carrier state, acute respiratory disease, and pneumonia due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2): Facts and myths. Journal of Microbiology, Immunology and Infection. 2020;53(3):404-12.\u003c/li\u003e\n\u003cli\u003eMarson FAL. COVID-19 - 6 million cases worldwide and an overview of the diagnosis in Brazil: a tragedy to be announced. Diagn Microbiol Infect Dis. 2020;98(2):115113.\u003c/li\u003e\n\u003cli\u003eDavidson AM, Wysocki J, Batlle D. Interaction of SARS-CoV-2 and other coronavirus with ACE (angiotensin-converting enzyme)-2 as their main receptor: therapeutic implications. Hypertension. 2020;76(5):1339-49.\u003c/li\u003e\n\u003cli\u003eCai G, Boss\u0026eacute; Y, Xiao F, Kheradmand F, Amos CI. Tobacco smoking increases the lung gene expression of ACE2, the receptor of SARS-CoV-2. American journal of respiratory and critical care medicine. 2020;201(12):1557-9.\u003c/li\u003e\n\u003cli\u003eZheng M. ACE2 and COVID-19 susceptibility and severity. Aging and disease. 2022;13(2):360.\u003c/li\u003e\n\u003cli\u003eAveyard P, Gao M, Lindson N, Hartmann-Boyce J, Watkinson P, Young D, et al. Association between pre-existing respiratory disease and its treatment, and severe COVID-19: a population cohort study. The lancet Respiratory medicine. 2021;9(8):909-23.\u003c/li\u003e\n\u003cli\u003eSchultze A, Walker AJ, MacKenna B, Morton CE, Bhaskaran K, Brown JP, et al. Risk of COVID-19-related death among patients with chronic obstructive pulmonary disease or asthma prescribed inhaled corticosteroids: an observational cohort study using the OpenSAFELY platform. The Lancet Respiratory Medicine. 2020;8(11):1106-20.\u003c/li\u003e\n\u003cli\u003eAwatade NT, Wark PA, Chan AS, Mamun SAA, Mohd Esa NY, Matsunaga K, et al. The complex association between COPD and COVID-19. Journal of Clinical Medicine. 2023;12(11):3791.\u003c/li\u003e\n\u003cli\u003eSterne JA, Murthy S, Diaz JV, Slutsky AS, Villar J, Angus DC, et al. Association between administration of systemic corticosteroids and mortality among critically ill patients with COVID-19: a meta-analysis. Jama. 2020;324(13):1330-41.\u003c/li\u003e\n\u003cli\u003eGroup RC. Dexamethasone in hospitalized patients with Covid-19. New England journal of medicine. 2021;384(8):693-704.\u003c/li\u003e\n\u003cli\u003evan Paassen J, Vos JS, Hoekstra EM, Neumann KM, Boot PC, Arbous SM. Corticosteroid use in COVID-19 patients: a systematic review and meta-analysis on clinical outcomes. Critical Care. 2020;24(1):696.\u003c/li\u003e\n\u003cli\u003eLabor M, Kirui BK, Nyberg F, Vanfleteren LE. Regular inhaled corticosteroids use may protect against severe COVID-19 outcome in COPD. International Journal of Chronic Obstructive Pulmonary Disease. 2023:1701-12.\u003c/li\u003e\n\u003cli\u003eChoi JC, Jung S-Y, Yoon UA, You S-H, Kim M-S, Baek MS, et al. Inhaled corticosteroids and COVID-19 risk and mortality: a nationwide cohort study. Journal of Clinical Medicine. 2020;9(11):3406.\u003c/li\u003e\n\u003cli\u003eKunz LI, Ten Hacken NH, Lapperre TS, Timens W, Kerstjens HA, van Schadewijk A, et al. Airway inflammation in COPD after long-term withdrawal of inhaled corticosteroids. European Respiratory Journal. 2017;49(1).\u003c/li\u003e\n\u003cli\u003eSaito Z, Yoshida M, Kojima A, Tamura K, Hasegawa T, Kuwano K. Benefits and risks of inhaled corticosteroid treatment in patients with chronic obstructive pulmonary disease classified by blood eosinophil counts. Lung. 2020;198(6):925-31.\u003c/li\u003e\n\u003cli\u003eBroman N, Rantas\u0026auml;rkk\u0026auml; K, Feuth T, Valtonen M, Waris M, Hohenthal U, et al. IL-6 and other biomarkers as predictors of severity in COVID-19. Annals of medicine. 2021;53(1):410-2.\u003c/li\u003e\n\u003cli\u003eQin R, He L, Yang Z, Jia N, Chen R, Xie J, et al. Identification of parameters representative of immune dysfunction in patients with severe and fatal COVID-19 infection: a systematic review and meta-analysis. Clinical Reviews in Allergy \u0026amp; Immunology. 2023;64(1):33-65.\u003c/li\u003e\n\u003cli\u003eChen Z, Li J, Zheng J, Xiang F, Li X, Zhang M, et al. Characteristics of lymphocyte subsets and inflammatory factors in patients with COVID-19. Heliyon. 2024;10(6).\u003c/li\u003e\n\u003cli\u003eBhargava D, Ahirwal R, Dubey S, Gurjar P, Pandey A, Beena S, et al. Covid induced functional exhaustion and persistently reduced lymphocytes as vital contributing factors for post-covid rhino-orbital and cerebral mucormycosis in patients with diabetes: Report from the Indian Sub-continent. Head and Neck Pathology. 2022;16(3):645-50.\u003c/li\u003e\n\u003cli\u003eWang Y, Shen M, Li Y, Shao J, Zhang F, Guo M, et al. COVID‐19‐associated liver injury: Adding fuel to the flame. Cell Biochemistry and Function. 2023;41(8):1076-92.\u003c/li\u003e\n\u003cli\u003eMuscogiuri G, Bettini S, Boschetti M, Barrea L, Savastano S, Colao A, et al. Low-grade inflammation, CoVID-19, and obesity: clinical aspect and molecular insights in childhood and adulthood. International Journal of Obesity. 2022;46(7):1254-61.\u003c/li\u003e\n\u003cli\u003eSimpson JL, Carroll M, Yang IA, Reynolds PN, Hodge S, James AL, et al. Reduced antiviral interferon production in poorly controlled asthma is associated with neutrophilic inflammation and high-dose inhaled corticosteroids. Chest. 2016;149(3):704-13.\u003c/li\u003e\n\u003cli\u003eDavies JM, Carroll ML, Li H, Poh AM, Kirkegard D, Towers M, et al. Budesonide and formoterol reduce early innate anti-viral immune responses in vitro. PLoS One. 2011;6(11):e27898.\u003c/li\u003e\n\u003cli\u003eRogliani P, Lauro D, Di Daniele N, Chetta A, Calzetta L. Reduced risk of COVID-19 hospitalization in asthmatic and COPD patients: a benefit of inhaled corticosteroids? Expert Review of Respiratory Medicine. 2021;15(4):561-8.\u003c/li\u003e\n\u003cli\u003eSen P, Majumdar U, Zein J, Hatipoğlu U, Attaway AH. Inhaled corticosteroids do not adversely impact outcomes in COVID-19 positive patients with COPD: An analysis of Cleveland Clinic\u0026rsquo;s COVID-19 registry. PLoS One. 2021;16(6):e0252576.\u003c/li\u003e\n\u003cli\u003eBloom CI, Drake TM, Docherty AB, Lipworth BJ, Johnston SL, Nguyen-Van-Tam JS, et al. Risk of adverse outcomes in patients with underlying respiratory conditions admitted to hospital with COVID-19: a national, multicentre prospective cohort study using the ISARIC WHO Clinical Characterisation Protocol UK. The Lancet Respiratory Medicine. 2021;9(7):699-711.\u003c/li\u003e\n\u003cli\u003eHusby A, Potteg\u0026aring;rd A, Hviid A. Association between inhaled corticosteroid use and COVID‐19 outcomes. Pharmacoepidemiology and drug safety. 2021;30(11):1486-92.\u003c/li\u003e\n\u003cli\u003eSingh D, Halpin DM. Inhaled corticosteroids and COVID-19-related mortality: confounding or clarifying? The lancet Respiratory medicine. 2020;8(11):1065-6.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Corticosteroids, Chronic Obstructive Pulmonary Disease, COVID-19, Clinical Outcomes, Retrospective Cohort Study","lastPublishedDoi":"10.21203/rs.3.rs-9661542/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9661542/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u003c/p\u003e\n\u003cp\u003eThe impact of prior corticosteroid use on coronavirus disease 2019 (COVID-19) outcomes in patients with chronic obstructive pulmonary disease (COPD) remains uncertain.\u003c/p\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003cp\u003eWe conducted a retrospective cohort study of 166 hospitalized COPD patients with confirmed COVID-19, comparing those with prior corticosteroid use (n = 82) and without (n = 84). Clinical outcomes included severity of airflow limitation, oxygen therapy requirements, intensive care unit (ICU) admission, and one-year survival. Uni- and multivariable regression analyses were performed to adjust for potential confounders.\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eCorticosteroid users were more likely to have severe or very severe obstructive dysfunction (46.3% vs. 16.7%; p = 0.001). Prior corticosteroid therapy was associated with reduced odds of requiring advanced oxygen therapy (OR = 0.54, 95% CI: 0.30–0.98; p = 0.044; aOR = 0.65, 95% CI: 0.34–1.25; p = 0.197), lower risk of ICU admission (OR = 0.61, 95% CI: 0.29–1.24; p = 0.174; aOR = 0.56, 95% CI: 0.25–1.23; p = 0.155), and a trend toward improved one-year survival (OR = 1.45, 95% CI: 0.60–3.58; p = 0.415; aOR = 1.45, 95% CI: 0.55–3.91; p = 0.453). Patients with pre-existing respiratory disease showed a tendency toward lower survival (OR = 0.36, 95% CI: 0.12–1.14; p = 0.076). In a subgroup of 26 patients who received other corticosteroids, no statistically significant associations were observed for any of the studied outcomes is likely due to the very limited sample size of this subgroup and the consequently wide confidence intervals.. The adjusted odds ratios were 1.13 (95% CI: 0.35–3.28; p = 0.846) for ICU admission, 0.97 (95% CI: 0.38–2.50; p = 0.950) for oxygen-therapy severity, and 2.30 (95% CI: 0.49–16.99; p = 0.339) for one-year survival. The wide confidence intervals reflect the limited sample size, and results should therefore be interpreted with caution.\u003c/p\u003e\n\u003cp\u003eConclusion\u003c/p\u003e\n\u003cp\u003ePrior corticosteroid use in COPD patients hospitalized with COVID-19 was not associated with worse outcomes and showed non-significant trends toward potential protective benefits, including reduced oxygen therapy requirements and a trend toward improved survival. Observed heterogeneity across studies likely reflects differences in design, patient populations, and underlying disease severity. Larger prospective, multicenter studies are needed to clarify corticosteroids’ role in COVID-19 outcomes among COPD patients.\u003c/p\u003e","manuscriptTitle":"A Retrospective Cohort Study on the Effect of Prior Corticosteroid Use on COVID-19 Severity and Long-Term Outcomes in COPD Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-12 14:31:21","doi":"10.21203/rs.3.rs-9661542/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"09ec449f-d3c0-4e27-8d0c-e8243ce0cc7d","owner":[],"postedDate":"May 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":67832018,"name":"Pulmonology"},{"id":67832019,"name":"Infectious Diseases"},{"id":67832020,"name":"Clinical Pharmacology"},{"id":67832021,"name":"Immunology"},{"id":67832022,"name":"Internal Medicine"}],"tags":[],"updatedAt":"2026-05-12T14:31:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-12 14:31:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9661542","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9661542","identity":"rs-9661542","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}