Association between neutrophil-to-lymphocyte ratio and 30-day mortality in patients with pneumonia receiving glucocorticoid therapy

Association between neutrophil-to-lymphocyte ratio and 30-day mortality in patients with pneumonia receiving glucocorticoid therapy | 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 Association between neutrophil-to-lymphocyte ratio and 30-day mortality in patients with pneumonia receiving glucocorticoid therapy Cailian Wang¹, Fengwang Xue, Ruoqing Lu, Shengmin Guo, Shifang Mao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7965583/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 neutrophil-to-lymphocyte ratio (NLR) is a recognized prognostic marker in infectious and inflammatory diseases; however, its value for short-term mortality in pneumonia patients receiving glucocorticoid therapy remains unclear. This study evaluated the predictive role of admission NLR for 30-day all-cause mortality in this population. Methods: We conducted a retrospective cohort study using data from 696 hospitalized patients with pneumonia who received glucocorticoids, obtained from the Dryad database (Li et al.). Demographics, comorbidities, laboratory results, and corticosteroid use were collected. Prognostic effects of NLR were assessed with multivariate Cox regression, restricted cubic splines (RCS), Kaplan–Meier survival analysis, and subgroup and sensitivity analyses. Results: After multivariable adjustment, log₂ NLR was significantly associated with 30-day mortality (HR = 1.20, 95% CI: 1.06–1.35). Patients with NLR ≥ 10 had a 69% higher mortality risk compared with those with NLR < 10 (HR = 1.69, 95% CI: 1.19–2.40). RCS analysis demonstrated a linear association between NLR and mortality. Subgroup and sensitivity analyses confirmed the robustness of these findings. Conclusion: Admission NLR is an independent prognostic indicator for pneumonia patients receiving glucocorticoid therapy, particularly in those with NLR ≥ 10 who are at substantially higher risk. Neutrophil-to-Lymphocyte Ratio Pneumonia Glucocorticoids Mortality Risk Factors Figures Figure 1 Figure 2 Figure 3 1. Introduction Pneumonia is a significant public health issue that has led to a significant increase in morbidity and mortality worldwide. According to the latest global burden of disease estimates, there were approximately 218 million cases of non-COVID-19 pneumonia and approximately 2.18 million deaths worldwide in 2021 1 Despite ongoing updates to treatment protocols and advancements in supportive therapies in recent years, pneumonia continues to result in a substantial number of preventable deaths 2 . Particularly among hospitalized patients, those with severe pneumonia or complications such as respiratory failure face more critical conditions and higher mortality rates. In some regions, the mortality rate for severely ill pneumonia patients admitted to intensive care units (ICUs) can reach 30%–50% 3 . Glucocorticoids, due to their anti-inflammatory and immunomodulatory effects, have been widely used in recent years for severe pneumonia, refractory pneumonia, and patients with acute respiratory distress syndrome 4 . Studies have found 5 that in adult patients with severe community-acquired pneumonia, adjunctive glucocorticoid therapy can reduce the risk of death by 61%, decrease the need for mechanical ventilation and ICU admission, and shorten hospital stays. Another study indicated 6 that glucocorticoid therapy reduces the 30-day mortality risk (RR = 0.61, 95% CI 0.44–0.85), with patients requiring less mechanical ventilation support and shorter ICU and hospital stays compared to the control group. Subgroup analysis showed that treatment benefits were concentrated in severe subgroups with concomitant septic shock or requiring mechanical ventilation. However, the adverse effects of corticosteroids should not be overlooked, as patients experienced a significant increase in the risk of hyperglycemia, new-onset diabetes, and new-onset insulin dependence postoperatively, as well as a notable increase in the incidence of recurrent pneumonia and secondary infections 7 . Additionally, studies have found 7 , 8 that in a 180-day follow-up, patients with community-acquired pneumonia (CAP) treated with corticosteroids had higher readmission rates and CAP recurrence rates. Therefore, while corticosteroid therapy may provide short-term benefits, the long-term adverse effects may offset some of the benefits, and this remains uncertain 8 . Therefore, the challenge lies in balancing benefits and risks to maximize patient outcomes, avoid overtreatment of low-risk patients, and ensure timely benefits for high-risk patients. To achieve this goal, there is an urgent need for a simple, cost-effective indicator that reflects the balance between inflammatory response and immune status. The neutrophil-to-lymphocyte ratio (NLR) meets these criteria 9 . Previous studies have confirmed 10 , 11 that the NLR has good prognostic predictive value in various infectious diseases, including community-acquired pneumonia, sepsis, and acute respiratory failure. However, the independent predictive role of the NLR in pneumonia patients receiving glucocorticoid therapy remains unclear. Therefore, this study utilized multi-center retrospective cohort data to assess the independent predictive value of NLR at admission for 30-day all-cause mortality in pneumonia patients receiving glucocorticoid therapy. Subgroup analysis and sensitivity analysis were further conducted to explore its predictive efficacy and robustness under different clinical settings, aiming to provide evidence-based guidance for risk stratification and individualized management of pneumonia patients receiving glucocorticoid therapy. 2. Methods 2.1 Study Population The data for this study were obtained from the Dryad database and provided by Li et al 12 . The original authors consented to the use of the data for educational and clinical research purposes. The study was approved by the Ethics Committee of the China-Japan Friendship Hospital (approval number 2015-86). Patients who received glucocorticoid therapy for connective tissue diseases, nephrotic syndrome or chronic glomerulonephritis, idiopathic interstitial pneumonia, bronchial asthma, or chronic obstructive pulmonary disease developed pneumonia after a median treatment duration of 4 months (interquartile range [IQR], 2–18 months).. Inclusion criteria for this study were: (1) patients had already started oral or intravenous glucocorticoid therapy prior to hospitalization; (2) pneumonia was diagnosed at admission or during hospitalization; (3) age ≥ 16 years; (4) complete peripheral blood neutrophil and lymphocyte counts available for NLR calculation. Twenty cases with missing neutrophil or lymphocyte counts were excluded, resulting in a final inclusion of 696 cases (Figure 1). Figure 1 2.2 Data Sources Demographic information: age, gender, smoking, alcohol consumption; clinical indicators: body temperature, heart rate, respiratory rate, mean arterial pressure, oxygenation index, dyspnea, CURB-65 score; comorbidities: chronic obstructive pulmonary disease (COPD), coronary heart disease (CHD), Continuous Veno-Venous Hemofiltration (CVVH), respiratory failure, solid organ transplantation, Idiopathic Interstitial Pneumonia (IIP), septic shock, diabetes, etc.; Laboratory parameters: platelet count (PLT), albumin (ALB), blood urea nitrogen (BUN), procalcitonin (PCT), hemoglobin (HGB), lactate dehydrogenase (LDH), total white blood cell count and absolute neutrophil and lymphocyte counts; Hormone use: high-dose glucocorticoid use (defined as prednisolone ≥30 mg/day or equivalent medication within 30 days prior to admission), cumulative dose (total amount used from the start of hormone therapy to the diagnosis of pneumonia), etc. NLR (Neutrophil-to-Lymphocyte Ratio) = absolute neutrophil count (×10⁹/L) / absolute lymphocyte count (×10⁹/L)⁽⁴⁾. 2.3 Diagnostic criteria for pneumonia The diagnosis of pneumonia was based on the guidelines of the American Thoracic Society and the Infectious Diseases Society of America 13 , 14 . 2.4 Statistical analysis All data analysis in this study was performed using R software (Version 4.2.2) and the Free Statistics platform (Version 2.1.1). All tests were two-sided, and P < 0.05 was considered statistically significant. For descriptive statistics, continuous variables with a normal distribution were expressed as mean ± standard deviation (SD), non-normal continuous variables were expressed as median (interquartile range, IQR), and categorical variables were described as frequency and percentage (%). Intergroup comparisons were performed using appropriate statistical tests based on the distribution characteristics of the variables: independent samples t-tests for normally distributed continuous variables, Mann-whitney U tests for non-normally distributed continuous variables, and chi-square tests for categorical variables. Kaplan–Meier survival curves and log-rank tests were applied to compare survival differences between groups. Univariable and multivariable Cox regression models were used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) and, together with restricted cubic spline (RCS) analysis, to evaluate the association between NLR and 30-day mortality. First, following previous studies 15 , 16 , 17 , 18 , NLR was divided into two groups using 10 as the cutoff value. The Shapiro–Wilk test was used to assess the distribution characteristics of continuous variables. Since NLR showed a significantly right-skewed distribution, it was log-transformed using base 2 (log₂ NLR), and normality was re-tested to confirm whether it approximated a normal distribution. Subsequently, univariate and Cox regression analyses were conducted to identify candidate variables associated with 30-day mortality, and a multivariable Cox proportional hazards model was constructed to evaluate the independent predictive value of log₂NLR. Covariate selection was based on two criteria: (1) when inclusion or exclusion of a covariate resulted in a ≥10% change in the regression coefficient of log₂NLR, the variable was retained in the model; and (2) clinical relevance was also taken into consideration. The final covariates included age, sex, oxygenation index, CVVH, respiratory failure, organ transplantation, IIP, septic shock, mechanical ventilation, platelet count (PLT), albumin (ALB), blood urea nitrogen (BUN), CURB-65 score, high-dose glucocorticoid use, and history of coronary heart disease. For sensitivity analysis, four sequential models were constructed: Model 1 was unadjusted; Model 2 was adjusted for age, sex, and oxygenation index; Model 3 was further adjusted for coronary heart disease, CVVH, respiratory failure, solid organ transplantation, IIP, septic shock, mechanical ventilation, and CURB-65 score; and Model 4 was fully adjusted by additionally including procalcitonin, albumin, BUN, and high-dose glucocorticoid use. Additionally, to validate the robustness of the model and the predictive consistency of log₂NLR, subgroup analyses were further conducted. Subgrouping was performed based on gender, age (<60 years vs ≥60 years), presence of respiratory failure, use of high-dose corticosteroids, and presence of IIP, and multivariate Cox regression analysis was conducted within each subgroup. Finally, the likelihood ratio test was used to assess potential interactions between log₂NLR and subgroup variables. 2.5 Handling of missing values In this study, we excluded variables with more than 30% missing values and imputed the missing data using the Multivariate Imputation by Chained Equations (MICE) method, creating five datasets for analysis. 3. Results 3.1 Baseline Characteristics The final analysis included 696 patients with pneumonia who received glucocorticoid therapy. A total of 696 patients were included in this study, and they were divided into an NLR<10 group(n =538) and an NLR≥10group (n=158). There were no statistically significant differences between the two groups in terms of age and gender distribution. Regarding comorbidities, there were no statistically significant differences between the two groups in terms of smoking history, alcohol history, coronary heart disease (CHD), solid organ transplantation, idiopathic pulmonary fibrosis (IIP), and diabetes mellitus (DM) ( P>0.05 ). However, the NLR ≥ 10 group had significantly higher rates of dyspnea (81.0% vs. 45.9%, P < 0.001 ), COPD (91.1% vs. 85.3%, P = 0.022), respiratory failure (96.8% vs. 61.9%, P<0.001 ), CVVH (27.2% vs. 4.5%, P<0.001 ), and shock (10.1% vs. 5.5%, P<0.001 ) were significantly higher in the NLR≥10 group than in the NLR<10 group. In terms of physiological and laboratory indicators, patients in the NLR ≥ 10 group had significantly lower oxygenation indices ( P<0.001 ), significantly higher white blood cell counts ( P<0.001 ), neutrophil percentages ( P<0.001 ), lactate dehydrogenase levels ( P<0.001 ), and neutrophil-to-lymphocyte ratios ( P<0.001 ); while albumin levels were significantly lower ( P<0.001 ). Additionally, the NLR≥10 group had higher body temperature, heart rate, respiratory rate, platelet count, procalcitonin (PCT), hemoglobin (HGB), blood urea nitrogen (BUN), and proportion of patients receiving high-dose corticosteroids compared to the NLR<10 group ( P<0.05 ). In summary, patients in the NLR ≥ 10 group exhibited more severe respiratory dysfunction, inflammatory response, and organ dysfunction, indicating a more severe condition. ( Table 1) Table 1 Baseline Characteristics of the Study Population Variables Total (n = 696) NLR<10 (n = 538) NLR≥10 (n = 158) p Age（years）, n (%) 0.249 < 60 332 (47.7) 263 (48.9) 69 (43.7) ≥ 60 364 (52.3) 275 (51.1) 89 (56.3) Gender, n (%) 0.478 male 366 (52.6) 279 (51.9) 87 (55.1) female 330 (47.4) 259 (48.1) 71 (44.9) smoke, n (%) 0.257 No 509 (73.1) 399 (74.2) 110 (69.6) Yes 187 (26.9) 139 (25.8) 48 (30.4) Alcoholism, n (%) 0.313 No 639 (91.8) 497 (92.4) 142 (89.9) Yes 57 ( 8.2) 41 (7.6) 16 (10.1) Dyspnea, n (%) < 0.001 No 276 (39.7) 247 (45.9) 29 (18.4) Yes 420 (60.3) 291 (54.1) 129 (81.6) COPD, n (%) 0.022 No 595 (85.5) 451 (83.8) 144 (91.1) Yes 101 (14.5) 87 (16.2) 14 (8.9) Oxygenationindex, Mean ± SD 248.8 ± 138.8 284.1 ± 135.5 153.1 ± 96.4 < 0.001 CHD, n (%) 0.896 No 610 (87.6) 472 (87.7) 138 (87.3) Yes 86 (12.4) 66 (12.3) 20 (12.7) CVVH, n (%) < 0.001 No 630 (90.5) 516 (95.9) 114 (72.2) Yes 66 ( 9.5) 22 (4.1) 44 (27.8) Respiratory failure, n (%) < 0.001 No 338 (48.6) 333 (61.9) 5 (3.2) Yes 358 (51.4) 205 (38.1) 153 (96.8) Solid organ transplantation, n (%) 0.054 No 634 (91.1) 484 (90) 150 (94.9) Yes 62 ( 8.9) 54 (10) 8 (5.1) IIP, n (%) 0.972 No 625 (89.8) 483 (89.8) 142 (89.9) Yes 71 (10.2) 55 (10.2) 16 (10.1) Septic shock, n (%) 0.547 No 638 (94.5) 490 (94.2) 148 (95.5) Yes 37 ( 5.5) 30 (5.8) 7 (4.5) CURB-65 score >1, n (%) < 0.001 No 491 (70.5) 409 (76) 82 (51.9) Yes 205 (29.5) 129 (24) 76 (48.1) Platelets (×10 9 g/L), Mean ± SD 190.5 ± 90.9 199.1 ± 88.9 160.7 ± 91.6 < 0.001 Albumin (g/L), Mean ± SD 32.8 ± 6.4 33.6 ± 6.2 29.9 ± 5.9 < 0.001 BUN (mmol/L), Median (IQR) 6.3 (4.6, 9.8) 6.0 (4.4, 8.6) 8.2 (5.7, 13.6) < 0.001 Glucocorticoid accumulation (g), Median (IQR) 3.8 (2.0, 8.8) 4.3 (2.2, 9.6) 2.9 (1.5, 4.8) < 0.001 High dose glucocorticoid, n (%) < 0.001 No 450 (64.7) 371 (69) 79 (50) Yes 246 (35.3) 167 (31) 79 (50) Diabetes , n (%) 0.435 No 523 (75.1) 408 (75.8) 115 (72.8) Yes 173 (24.9) 130 (24.2) 43 (27.2) MAP, Mean ± SD 90.8 ± 13.5 90.8 ± 13.2 90.6 ± 14.2 0.894 Temperature, Mean ± SD 37.3 ± 1.0 37.2 ± 1.0 37.6 ± 1.1 < 0.001 Heartrate, Mean ± SD 89.3 ± 24.4 88.2 ± 23.8 92.9 ± 26.1 0.032 Respiratoryrate, Mean ± SD 23.0 ± 6.2 22.4 ± 6.0 25.1 ± 6.3 < 0.001 White cell(×10 9 / L), Mean ± SD 9.2 ± 5.4 8.7 ± 4.6 10.7 ± 7.4 < 0.001 Procalcitonin (ng/ml), Median (IQR) 0.3 (0.1, 0.8) 0.3 (0.1, 0.6) 0.4 (0.2, 1.8) 0.005 Hemoglobin(g/L), Mean ± SD 112.1 ± 23.5 113.0 ± 23.8 109.0 ± 22.2 0.059 NUET (×10 9 /L), Median (IQR) 6.5 (4.3, 10.1) 6.2 (4.0, 9.1) 8.3 (5.4, 12.0) < 0.001 LYM (×10 9 /L), Median (IQR) 0.9 (0.5, 1.4) 1.0 (0.6, 1.5) 0.6 (0.4, 0.9) < 0.001 LDH (U/L), Median (IQR) 329.0 (229.0, 502.0) 295.0 (213.0, 430.0) 483.5 (357.2, 645.2) < 0.001 NLR, Median (IQR) 7.7 (3.7, 15.5) 6.5 (3.3, 11.9) 15.6 (7.9, 24.0) < 0.001 COPD, chronic obstructive pulmonary disease; MBP, mean blood pressure; SPo2, blood oxygen saturation; ALB, BUN, Blood urea nitrogen; CHD, Coronary Heart Disease; CHF, congestive heart failure; CRF, Chronic renal failure; INR, international normalized ratio; PNI, Prognostic nutritional index; WBC, white blood cells. 3.2 30-day mortality rate in pneumonia patients receiving glucocorticoids After performing univariate analysis of each variable, it was found that log₂(NLR), dyspnea, CVVH use, respiratory failure, CURB-65 score>1, high-dose glucocorticoid use, fever, increased respiratory rate, increased heart rate, WBC, neutrophils, PCT, BUN, and elevated LDH were positively correlated with 30-day mortality rate; PLT, ALB, and lymphocytes were negatively correlated with 30-day mortality. However, age, gender, coronary heart disease, diabetes, septic shock, IIP, organ transplantation, mean arterial pressure (MAP), HGB, and smoking were not significantly correlated with 30-day mortality. Kaplan-Meier curves showed that as the NLR value increased, the 30-day mortality risk of pneumonia patients treated with glucocorticoids increased (Log-rank, P<0.0001 ). (Figure 2) Figure 2 3.3 Relationship between NLR ratio and 30-day mortality Multivariate Cox regression analysis demonstrated a significant association between NLR levels and 30-day mortality (Table 2). In the crude model, log-transformed NLR was positively associated with 30-day mortality (HR = 1.55, 95% CI: 1.40–1.73). This association remained significant in the fully adjusted model (Model 4; HR = 1.20, 95% CI: 1.06–1.35, P=0.004). Subgroup analysis further revealed that patients with NLR ≥ 10 had a markedly higher risk of 30-day mortality compared with the reference group (NLR<10) (HR=1.71, 95% CI: 1.21–2.42, P<0.05 ). Restricted cubic spline (RCS) analysis indicated a linear relationship between log-transformed NLR and 30-day mortality (P for non-linear=0.673; Supplementary Figure 1 ). Table 2 Multivariable Cox Regression of NLR and 30-Day Mortality. 30-day mortality Model 1 Model 2 Model 3 Model 4 Variable HR（95%CI） P-value HR（95%CI） P-value HR（95%CI） P-value HR（95%CI） P-value Log 2 NLR 1.55 (1.4~1.73) <0.001 1.3 (1.16~1.45) <0.001 1.21 (1.08~1.37) 0.002 1.20 (1.06~1.35) 0.004 NLR < 10 1(Ref) 1(Ref) 1(Ref) 1(Ref) ≥ 10 3.65 (2.62~5.07) <0.001 2.52 (1.67~3.81) <0.001 1.76 (1.25~2.47) 0.001 1.71 (1.21~2.42) 0.003 HR hazard ratio; CI confidence index; Ref: reference. Model 1: the unadjusted (crude) model. Model 2: adjusted for age, sex, oxygenation index. Model 3: adjusted as for Model 2, additionally adjusted for CHD, CVVH, respiratory failure, solid organ transplantation, IIP, septic shock, mechanical ventilation, CURB-65 score. Model 4: adjusted as for Model 3, additionally adjusted for procalcitonin, albumin, BUN, high dose glucocorticoid. 3.4 Subgroup Analysis This study further constructed a forest plot to visualize the results of the multivariable Cox regression analysis across different clinical subgroups, aiming to assess the robustness of the association between NLR and 30-day mortality risk. Subgroup analysis was stratified by age, gender, presence of respiratory failure, receipt of high-dose corticosteroid therapy, and presence of interstitial pneumonia. The overall analysis revealed that elevated NLR was significantly positively associated with 30-day mortality risk, and this association persisted even after adjusting for confounding factors such as age, gender, oxygenation index, comorbidities, treatment factors, and laboratory indicators. These results suggest that NLR as a prognostic indicator demonstrates good stability and applicability across different clinical populations, regardless of differences in patients' basic demographic characteristics, disease severity, or certain treatment factors. （ Figure 3 ） Figure 3 3.5 Sensitivity analysis After excluding individuals with missing data on all potential confounding factors, the remaining 484 pneumonia patients receiving glucocorticoid therapy showed a stable association between NLR and 90-day mortality risk. In the fully adjusted model, a 1-unit increase in log-transformed NLR was associated with a 20% increase in 30-day mortality risk (HR 1.20, 95% CI 1.05–1.38). When NLR was treated as a dichotomous variable, in the fully adjusted model, patients with NLR≥10 had a 67% higher 90-day mortality risk compared to those with NLR<10(HR=1.67, 95% CI: 1.13–2.48) (Supplementary Table 1). 4. Discussion This study is based on a large-scale retrospective cohort study that systematically assessed the NLR of patients at admission and explored the relationship between NLR and 30-day mortality in pneumonia patients receiving glucocorticoid therapy. The study found that an elevated NLR was not only closely associated with disease severity (reduced oxygenation index, increased CURB-65 score, respiratory failure, and increased use of continuous Veno venous hemofiltration) but also remained an independent predictor of short-term mortality after multivariable adjustment. This finding is consistent with the predictive value of NLR observed in previous studies 4 , 5 ,6 in community-acquired pneumonia, sepsis, and acute respiratory distress syndrome. Most existing studies have focused on CAP 19 , sepsis 20 , and ARDS 21 populations, while evidence regarding NLR in pneumonia patients receiving glucocorticoid therapy remains relatively limited. This study systematically assessed the prognostic value of NLR in this specific population, providing new evidence supporting the clinical application of NLR in pneumonia patients receiving glucocorticoid therapy. Elevated NLR reflects excessive activation of neutrophil-driven inflammatory responses and impaired lymphocyte-mediated immune regulatory functions 22 . In severe infection states, neutrophils release large amounts of reactive oxygen species, proteases, and inflammatory mediators, which can directly damage alveolar epithelium and capillary endothelium, leading to acute lung injury and impaired gas exchange 23 ; while lymphocyte depletion indicates impaired immune function and reduced anti-infective defense capacity, closely associated with secondary infections and multi-organ dysfunction 24 . Although glucocorticoids have anti-inflammatory effects, they can suppress lymphocyte proliferation and function 25 , potentially exacerbating the immune imbalance reflected by elevated NLR in some patients, thereby increasing the risk of adverse outcomes. Additionally, the NLR shows a linear increasing relationship with 30-day mortality, without a clear threshold effect, meaning that even mild to moderate increases in the NLR indicate an increased risk. This relationship has stronger predictive ability in patients without respiratory failure, suggesting that the NLR can identify high-risk populations before traditional clinical indicators become abnormal, providing an opportunity window for early intervention. Clinically, NLR testing is convenient, low-cost, and easily accessible, and has the potential to serve as an important component of risk stratification tools for pneumonia patients, especially those receiving glucocorticoid therapy 26 , 27 . Combining NLR with other inflammatory or nutritional markers can further enhance the accuracy of short-term mortality risk prediction 28 , 29 thereby guides individualized glucocorticoid usage strategies and optimize prognosis management. Future prospective studies can validate the predictive efficacy of NLR in such populations, explore its dynamic changes and their relationship with prognosis, and incorporate it into clinical scoring systems (e.g., CURB-65, PSI) or combine it with other biomarkers to construct precise predictive models, providing more robust evidence-based support for clinical risk-benefit balancing. In summary, this study demonstrates that NLR is an independent predictor of 30-day mortality in pneumonia patients receiving glucocorticoid therapy, exhibiting good stability and clinical applicability. Its implementation in clinical practice can provide a basis for the early identification and precise treatment of such patients. 5. Conclusion NLR is an independent predictor of 30-day mortality in pneumonia patients receiving glucocorticoid therapy, demonstrating good stability and clinical applicability. Its implementation in clinical practice is expected to provide scientific evidence for the early identification of high-risk populations, individualized treatment, and optimized resource allocation. Declarations Contributions CW: study conception and design, public database curation, drafting and revision. FX: data analysis, result interpretation, drafting and revision. RL data acquisition, data verification, critical revision of the manuscript. SG & SM: study conception and design, overall guidance, drafting and revision. Corresponding authors Correspondence to Shifang Mao or Shengmin Guo. Funding This work was supported by the Luzhou Municipal Key Research and Development Science and Technology Program (Grant No. 2022-NYF-26). Ethics declarations This study received ethics approval from the Ethics Committee of China-Japan Friendship Hospital (Approval No. 2015-86). As a retrospective investigation, it involved multi-institutional collaboration under the coordination of the committee, which also authorized the anonymized submission and collection of patient data. In accordance with Dryad’s policy, the reuse of anonymized data for secondary analysis does not require additional ethical approval, provided that the rights of the original authors are respected. Therefore, no further ethics approval was sought for the present secondary analysis. Both the original and the current studies adhered to the principles of the Declaration of Helsinki. All procedures conformed to established ethical standards and regulatory requirements. Competing interests The authors declare no competing interests. Data Availability The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: the datasets analyzed during the current study are available in the Dryad database repository, https://datadryad.org/dataset/doi: 10.5061/dryad.mkkwh70x2,Alternatively, the data are available from the corresponding author upon reasonable request. References GBD 2021 Lower Respiratory Infections and Antimicrobial Resistance Collaborators. Global, regional, and national incidence and mortality burden of non-COVID-19 lower respiratory infections and aetiologies, 1990-2021: a systematic analysis from the Global Burden of Disease Study 2021. Lancet Infect Dis. 24 (9), 974–1002(2024). Abdalla, J. S. et al. Narrative Review of the Epidemiology of Hospital-Acquired Pneumonia and Ventilator-Associated Pneumonia in Gulf Cooperation Council Countries. Infect Dis Ther . 12(7),1741-1773(2023). Fagerli, K. et al. 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Lee, H., Kim, I., Kang, B. H. & Um, S. J. Prognostic value of serial neutrophil-to-lymphocyte ratio measurements in hospitalized community-acquired pneumonia. PloS one . 16 (4), e0250067(2021). Tekin, A., Wireko, F. W., Gajic, O., & Odeyemi, Y. E. The Neutrophil/Lymphocyte Ratio and Outcomes in Hospitalized Patients with Community-Acquired Pneumonia: A Retrospective Cohort Study. Biomedicines . 12 (2), 260(2024). Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1.tif SupplementaryTable1.doc Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Introduction","content":"\u003cp\u003ePneumonia is a significant public health issue that has led to a significant increase in morbidity and mortality worldwide. According to the latest global burden of disease estimates, there were approximately 218 million cases of non-COVID-19 pneumonia and approximately 2.18 million deaths worldwide in 2021\u003csup\u003e1\u003c/sup\u003e Despite ongoing updates to treatment protocols and advancements in supportive therapies in recent years, pneumonia continues to result in a substantial number of preventable deaths\u003csup\u003e2\u003c/sup\u003e. Particularly among hospitalized patients, those with severe pneumonia or complications such as respiratory failure face more critical conditions and higher mortality rates. In some regions, the mortality rate for severely ill pneumonia patients admitted to intensive care units (ICUs) can reach 30%\u0026ndash;50%\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eGlucocorticoids, due to their anti-inflammatory and immunomodulatory effects, have been widely used in recent years for severe pneumonia, refractory pneumonia, and patients with acute respiratory distress syndrome\u003csup\u003e4\u003c/sup\u003e. Studies have found\u003csup\u003e5\u003c/sup\u003ethat in adult patients with severe community-acquired pneumonia, adjunctive glucocorticoid therapy can reduce the risk of death by 61%, decrease the need for mechanical ventilation and ICU admission, and shorten hospital stays. Another study indicated\u0026nbsp;\u003csup\u003e6\u003c/sup\u003ethat glucocorticoid therapy reduces the 30-day mortality risk (RR = 0.61, 95% CI 0.44\u0026ndash;0.85), with patients requiring less mechanical ventilation support and shorter ICU and hospital stays compared to the control group. Subgroup analysis showed that treatment benefits were concentrated in severe subgroups with concomitant septic shock or requiring mechanical ventilation.\u003c/p\u003e\n\u003cp\u003eHowever, the adverse effects of corticosteroids should not be overlooked, as patients experienced a significant increase in the risk of hyperglycemia, new-onset diabetes, and new-onset insulin dependence postoperatively, as well as a notable increase in the incidence of recurrent pneumonia and secondary infections\u003csup\u003e7\u003c/sup\u003e. Additionally, studies have found\u003csup\u003e7\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e8\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003ethat in a 180-day follow-up, patients with community-acquired pneumonia (CAP) treated with corticosteroids had higher readmission rates and CAP recurrence rates. Therefore, while corticosteroid therapy may provide short-term benefits, the long-term adverse effects may offset some of the benefits, and this remains uncertain\u003csup\u003e8\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTherefore, the challenge lies in balancing benefits and risks to maximize patient outcomes, avoid overtreatment of low-risk patients, and ensure timely benefits for high-risk patients. To achieve this goal, there is an urgent need for a simple, cost-effective indicator that reflects the balance between inflammatory response and immune status. The neutrophil-to-lymphocyte ratio (NLR) meets these criteria\u003csup\u003e9\u003c/sup\u003e. Previous studies have confirmed\u003csup\u003e10\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e11\u003c/sup\u003ethat the NLR has good prognostic predictive value in various infectious diseases, including community-acquired pneumonia, sepsis, and acute respiratory failure. However, the independent predictive role of the NLR in pneumonia patients receiving glucocorticoid therapy remains unclear.\u003c/p\u003e\n\u003cp\u003eTherefore, this study utilized multi-center retrospective cohort data to assess the independent predictive value of NLR at admission for 30-day all-cause mortality in pneumonia patients receiving glucocorticoid therapy. Subgroup analysis and sensitivity analysis were further conducted to explore its predictive efficacy and robustness under different clinical settings, aiming to provide evidence-based guidance for risk stratification and individualized management of pneumonia patients receiving glucocorticoid therapy.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for this study were obtained from the Dryad database and provided by Li et al\u003csup\u003e12\u003c/sup\u003e. The original authors consented to the use of the data for educational and clinical research purposes. The study was approved by the Ethics Committee of the China-Japan Friendship Hospital (approval number 2015-86). Patients who received glucocorticoid therapy for connective tissue diseases, nephrotic syndrome or chronic glomerulonephritis, idiopathic interstitial pneumonia, bronchial asthma, or chronic obstructive pulmonary disease developed pneumonia after a median treatment duration of 4 months (interquartile range [IQR], 2\u0026ndash;18 months).. Inclusion criteria for this study were: (1) patients had already started oral or intravenous glucocorticoid therapy prior to hospitalization; (2) pneumonia was diagnosed at admission or during hospitalization; (3) age \u0026ge; 16 years; (4) complete peripheral blood neutrophil and lymphocyte counts available for NLR calculation. Twenty cases with missing neutrophil or lymphocyte counts were excluded, resulting in a final inclusion of 696 cases \u003cstrong\u003e(Figure 1).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Data Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic information: age, gender, smoking, alcohol consumption; clinical indicators: body temperature, heart rate, respiratory rate, mean arterial pressure, oxygenation index, dyspnea, CURB-65 score; comorbidities: chronic obstructive pulmonary disease (COPD), coronary heart disease (CHD), Continuous Veno-Venous Hemofiltration (CVVH), respiratory failure, solid organ transplantation, Idiopathic Interstitial Pneumonia (IIP), septic shock, diabetes, etc.; Laboratory parameters: platelet count (PLT), albumin (ALB), blood urea nitrogen (BUN), procalcitonin (PCT), hemoglobin (HGB), lactate dehydrogenase (LDH), total white blood cell count and absolute neutrophil and lymphocyte counts; Hormone use: high-dose glucocorticoid use (defined as prednisolone \u0026ge;30 mg/day or equivalent medication within 30 days prior to admission), cumulative dose (total amount used from the start of hormone therapy to the diagnosis of pneumonia), etc. NLR (Neutrophil-to-Lymphocyte Ratio) = absolute neutrophil count (\u0026times;10⁹/L) / absolute lymphocyte count (\u0026times;10⁹/L)⁽⁴⁾.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Diagnostic criteria for pneumonia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe diagnosis of pneumonia was based on the guidelines of the American Thoracic Society and the Infectious Diseases Society of America\u003csup\u003e13\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data analysis in this study was performed using R software (Version 4.2.2) and the Free Statistics platform (Version 2.1.1). All tests were two-sided, and\u003cem\u003e\u0026nbsp;P \u0026lt; 0.05\u0026nbsp;\u003c/em\u003ewas considered statistically significant. For descriptive statistics, continuous variables with a normal distribution were expressed as mean \u0026plusmn; standard deviation (SD), non-normal continuous variables were expressed as median (interquartile range, IQR), and categorical variables were described as frequency and percentage (%). Intergroup comparisons were performed using appropriate statistical tests based on the distribution characteristics of the variables: independent samples t-tests for normally distributed continuous variables, \u003cstrong\u003eMann-whitney U\u0026nbsp;\u003c/strong\u003etests for non-normally distributed continuous variables, and chi-square tests for categorical variables.\u003c/p\u003e\n\u003cp\u003eKaplan\u0026ndash;Meier survival curves and log-rank tests were applied to compare survival differences between groups. Univariable and multivariable Cox regression models were used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) and, together with restricted cubic spline (RCS) analysis, to evaluate the association between NLR and 30-day mortality.\u003c/p\u003e\n\u003cp\u003eFirst, following previous studies\u003csup\u003e15\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e16\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e17\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e18\u003c/sup\u003e, NLR was divided into two groups using 10 as the cutoff value. The Shapiro\u0026ndash;Wilk test was used to assess the distribution characteristics of continuous variables. Since NLR showed a significantly right-skewed distribution, it was log-transformed using base 2 (log₂ NLR), and normality was re-tested to confirm whether it approximated a normal distribution.\u003c/p\u003e\n\u003cp\u003eSubsequently, univariate and Cox regression analyses were conducted to identify candidate variables associated with 30-day mortality, and a multivariable Cox proportional hazards model was constructed to evaluate the independent predictive value of log₂NLR. Covariate selection was based on two criteria: (1) when inclusion or exclusion of a covariate resulted in a \u0026ge;10% change in the regression coefficient of log₂NLR, the variable was retained in the model; and (2) clinical relevance was also taken into consideration. The final covariates included age, sex, oxygenation index, CVVH, respiratory failure, organ transplantation, IIP, septic shock, mechanical ventilation, platelet count (PLT), albumin (ALB), blood urea nitrogen (BUN), CURB-65 score, high-dose glucocorticoid use, and history of coronary heart disease.\u003c/p\u003e\n\u003cp\u003eFor sensitivity analysis, four sequential models were constructed: Model 1 was unadjusted; Model 2 was adjusted for age, sex, and oxygenation index; Model 3 was further adjusted for coronary heart disease, CVVH, respiratory failure, solid organ transplantation, IIP, septic shock, mechanical ventilation, and CURB-65 score; and Model 4 was fully adjusted by additionally including procalcitonin, albumin, BUN, and high-dose glucocorticoid use.\u003c/p\u003e\n\u003cp\u003eAdditionally, to validate the robustness of the model and the predictive consistency of log₂NLR, subgroup analyses were further conducted. Subgrouping was performed based on gender, age (\u0026lt;60 years vs \u0026ge;60 years), presence of respiratory failure, use of high-dose corticosteroids, and presence of IIP, and multivariate Cox regression analysis was conducted within each subgroup. Finally, the likelihood ratio test was used to assess potential interactions between log₂NLR and subgroup variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Handling of missing values\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we excluded variables with more than 30% missing values and imputed the missing data using the Multivariate Imputation by Chained Equations (MICE) method, creating five datasets for analysis.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Baseline Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final analysis included 696 patients with pneumonia who received glucocorticoid therapy. A total of 696 patients were included in this study, and they were divided into an NLR\u0026lt;10 group(n =538) and an NLR\u0026ge;10group (n=158). There were no statistically significant differences between the two groups in terms of age and gender distribution. Regarding comorbidities, there were no statistically significant differences between the two groups in terms of smoking history, alcohol history, coronary heart disease (CHD), solid organ transplantation, idiopathic pulmonary fibrosis (IIP), and diabetes mellitus (DM) (\u003cem\u003eP\u0026gt;0.05\u003c/em\u003e). However, the NLR \u0026ge; 10 group had significantly higher rates of dyspnea (81.0% vs. 45.9%, \u003cem\u003eP \u0026lt; 0.001\u003c/em\u003e), COPD (91.1% vs. 85.3%, P = 0.022), respiratory failure (96.8% vs. 61.9%, \u003cem\u003eP\u0026lt;0.001\u003c/em\u003e), CVVH (27.2% vs. 4.5%, \u003cem\u003eP\u0026lt;0.001\u003c/em\u003e), and shock (10.1% vs. 5.5%, \u003cem\u003eP\u0026lt;0.001\u003c/em\u003e) were significantly higher in the NLR\u0026ge;10 group than in the NLR\u0026lt;10 group.\u003c/p\u003e\n\u003cp\u003eIn terms of physiological and laboratory indicators, patients in the NLR \u0026ge; 10 group had significantly lower oxygenation indices (\u003cem\u003eP\u0026lt;0.001\u003c/em\u003e), significantly higher white blood cell counts (\u003cem\u003eP\u0026lt;0.001\u003c/em\u003e), neutrophil percentages (\u003cem\u003eP\u0026lt;0.001\u003c/em\u003e), lactate dehydrogenase levels (\u003cem\u003eP\u0026lt;0.001\u003c/em\u003e), and neutrophil-to-lymphocyte ratios (\u003cem\u003eP\u0026lt;0.001\u003c/em\u003e); while albumin levels were significantly lower (\u003cem\u003eP\u0026lt;0.001\u003c/em\u003e). Additionally, the NLR\u0026ge;10 group had higher body temperature, heart rate, respiratory rate, platelet count, procalcitonin (PCT), hemoglobin (HGB), blood urea nitrogen (BUN), and proportion of patients receiving high-dose corticosteroids compared to the NLR\u0026lt;10 group (\u003cem\u003eP\u0026lt;0.05\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eIn summary, patients in the NLR \u0026ge; 10 group exhibited more severe respiratory dysfunction, inflammatory response, and organ dysfunction, indicating a more severe condition. (\u003cstrong\u003eTable 1)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 Baseline Characteristics of the Study Population\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003eTotal (n = 696)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003eNLR\u0026lt;10\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n = 538)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003eNLR\u0026ge;10\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(n = 158)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eAge（years）, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003e\u0026lt; 60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e332 (47.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e263 (48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e69 (43.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003e\u0026ge; 60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e364 (52.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e275 (51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e89 (56.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e366 (52.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e279 (51.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e87 (55.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e330 (47.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e259 (48.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e71 (44.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003esmoke, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e509 (73.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e399 (74.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e110 (69.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e187 (26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e139 (25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e48 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eAlcoholism, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e639 (91.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e497 (92.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e142 (89.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e57 ( 8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e41 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e16 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eDyspnea, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e276 (39.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e247 (45.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e29 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e420 (60.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e291 (54.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e129 (81.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eCOPD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e595 (85.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e451 (83.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e144 (91.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e101 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e87 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e14 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eOxygenationindex, Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e248.8 \u0026plusmn; 138.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e284.1 \u0026plusmn; 135.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e153.1 \u0026plusmn; 96.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eCHD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e610 (87.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e472 (87.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e138 (87.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e86 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e66 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e20 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eCVVH, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e630 (90.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e516 (95.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e114 (72.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e66 ( 9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e22 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e44 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eRespiratory failure, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e338 (48.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e333 (61.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e5 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e358 (51.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e205 (38.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e153 (96.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eSolid organ transplantation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e634 (91.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e484 (90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e150 (94.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e62 ( 8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e54 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e8 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eIIP, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e625 (89.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e483 (89.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e142 (89.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e71 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e55 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e16 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5%;\"\u003e\n \u003cp\u003eSeptic shock, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e638 (94.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e490 (94.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e148 (95.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e37 ( 5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e30 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e7 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eCURB-65 score \u0026gt;1, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e491 (70.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e409 (76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e82 (51.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e205 (29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e129 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e76 (48.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5%;\"\u003e\n \u003cp\u003ePlatelets (\u0026times;10\u003csup\u003e9\u0026nbsp;\u003c/sup\u003eg/L), Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e190.5 \u0026plusmn; 90.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e199.1 \u0026plusmn; 88.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e160.7 \u0026plusmn; 91.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5%;\"\u003e\n \u003cp\u003eAlbumin (g/L), Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e32.8 \u0026plusmn; 6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e33.6 \u0026plusmn; 6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e29.9 \u0026plusmn; 5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5%;\"\u003e\n \u003cp\u003eBUN (mmol/L), Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e6.3 (4.6, 9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e6.0 (4.4, 8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e8.2 (5.7, 13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eGlucocorticoid accumulation (g), Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e3.8 (2.0, 8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e4.3 (2.2, 9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e2.9 (1.5, 4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eHigh dose glucocorticoid, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e450 (64.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e371 (69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e79 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e246 (35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e167 (31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e79 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5%;\"\u003e\n \u003cp\u003eDiabetes , n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e523 (75.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e408 (75.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e115 (72.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e173 (24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e130 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e43 (27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eMAP, Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e90.8 \u0026plusmn; 13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e90.8 \u0026plusmn; 13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e90.6 \u0026plusmn; 14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eTemperature, Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e37.3 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e37.2 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e37.6 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eHeartrate, Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e89.3 \u0026plusmn; 24.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e88.2 \u0026plusmn; 23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e92.9 \u0026plusmn; 26.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eRespiratoryrate, Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e23.0 \u0026plusmn; 6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e22.4 \u0026plusmn; 6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e25.1 \u0026plusmn; 6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5%;\"\u003e\n \u003cp\u003eWhite cell(\u0026times;10\u003csup\u003e9\u003c/sup\u003e\u003cem\u003e/\u003c/em\u003eL), Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e9.2 \u0026plusmn; 5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e8.7 \u0026plusmn; 4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e10.7 \u0026plusmn; 7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5%;\"\u003e\n \u003cp\u003eProcalcitonin (ng/ml), Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e0.3 (0.1, 0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e0.3 (0.1, 0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e0.4 (0.2, 1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5%;\"\u003e\n \u003cp\u003eHemoglobin(g/L), Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e112.1 \u0026plusmn; 23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e113.0 \u0026plusmn; 23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e109.0 \u0026plusmn; 22.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5%;\"\u003e\n \u003cp\u003eNUET (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L), Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e6.5 (4.3, 10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e6.2 (4.0, 9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e8.3 (5.4, 12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5%;\"\u003e\n \u003cp\u003eLYM (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L), Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e0.9 (0.5, 1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e1.0 (0.6, 1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e0.6 (0.4, 0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.5%;\"\u003e\n \u003cp\u003eLDH (U/L), Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e329.0 (229.0, 502.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e295.0 (213.0, 430.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e483.5 (357.2, 645.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 37.5%;\"\u003e\n \u003cp\u003eNLR, Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e7.7 (3.7, 15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e6.5 (3.3, 11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.7083%;\"\u003e\n \u003cp\u003e15.6 (7.9, 24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.375%;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCOPD, chronic obstructive pulmonary disease; MBP, mean blood pressure; SPo2, blood oxygen saturation; ALB, BUN, Blood urea nitrogen; CHD, Coronary Heart Disease; CHF, congestive heart failure; CRF, Chronic renal failure; INR, international normalized ratio; PNI, Prognostic nutritional index; WBC, white blood cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 30-day mortality rate in pneumonia patients receiving glucocorticoids\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter performing univariate analysis of each variable, it was found that log₂(NLR), dyspnea, CVVH use, respiratory failure, CURB-65 score\u0026gt;1, high-dose glucocorticoid use, fever, increased respiratory rate, increased heart rate, WBC, neutrophils, PCT, BUN, and elevated LDH were positively correlated with 30-day mortality rate; PLT, ALB, and lymphocytes were negatively correlated with 30-day mortality. However, age, gender, coronary heart disease, diabetes, septic shock, IIP, organ transplantation, mean arterial pressure (MAP), HGB, and smoking were not significantly correlated with 30-day mortality.\u003c/p\u003e\n\u003cp\u003eKaplan-Meier curves showed that as the NLR value increased, the 30-day mortality risk of pneumonia patients treated with glucocorticoids increased (Log-rank, \u003cem\u003eP\u0026lt;0.0001\u003c/em\u003e).\u003cstrong\u003e\u0026nbsp;(Figure 2)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Relationship between NLR ratio and 30-day mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariate Cox regression analysis demonstrated a significant association between NLR levels and 30-day mortality (Table 2). In the crude model, log-transformed NLR was positively associated with 30-day mortality (HR = 1.55, 95% CI: 1.40\u0026ndash;1.73). This association remained significant in the fully adjusted model (Model 4; HR = 1.20, 95% CI: 1.06\u0026ndash;1.35, P=0.004). Subgroup analysis further revealed that patients with NLR \u0026ge; 10 had a markedly higher risk of 30-day mortality compared with the reference group (NLR\u0026lt;10) (HR=1.71, 95% CI: 1.21\u0026ndash;2.42, \u003cem\u003eP\u0026lt;0.05\u003c/em\u003e). Restricted cubic spline (RCS) analysis indicated a linear relationship between log-transformed NLR and 30-day mortality (P for non-linear=0.673; \u003cstrong\u003eSupplementary Figure 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eTable 2 Multivariable Cox Regression of NLR and 30-Day Mortality.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e30-day mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 22px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 22px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 22px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 22px;\"\u003e\n \u003cp\u003eModel 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eHR（95%CI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eHR（95%CI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eHR（95%CI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eHR（95%CI）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eLog\u003csub\u003e2\u003c/sub\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.55 (1.4~1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.3 (1.16~1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.21 (1.08~1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.20 (1.06~1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026lt; 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1(Ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026ge; 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e3.65 (2.62~5.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e2.52 (1.67~3.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.76 (1.25~2.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e1.71 (1.21~2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHR hazard ratio; CI confidence index; Ref: reference.\u003c/p\u003e\n\u003cp\u003eModel 1: the unadjusted (crude) model.\u003c/p\u003e\n\u003cp\u003eModel 2: adjusted for age, sex, oxygenation index.\u003c/p\u003e\n\u003cp\u003eModel 3: adjusted as for Model 2, additionally adjusted for CHD, CVVH, respiratory failure, solid organ transplantation, IIP, septic shock, mechanical ventilation, CURB-65 score.\u003c/p\u003e\n\u003cp\u003eModel 4: adjusted as for Model 3, additionally adjusted for procalcitonin, albumin, BUN, high dose glucocorticoid.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Subgroup Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study further constructed a forest plot to visualize the results of the multivariable Cox regression analysis across different clinical subgroups, aiming to assess the robustness of the association between NLR and 30-day mortality risk. \u0026nbsp;Subgroup analysis was stratified by age, gender, presence of respiratory failure, receipt of high-dose corticosteroid therapy, and presence of interstitial pneumonia. The overall analysis revealed that elevated NLR was significantly positively associated with 30-day mortality risk, and this association persisted even after adjusting for confounding factors such as age, gender, oxygenation index, comorbidities, treatment factors, and laboratory indicators. These results suggest that NLR as a prognostic indicator demonstrates good stability and applicability across different clinical populations, regardless of differences in patients\u0026apos; basic demographic characteristics, disease severity, or certain treatment factors. \u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eFigure 3\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 3\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Sensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter excluding individuals with missing data on all potential confounding factors, the remaining 484 pneumonia patients receiving glucocorticoid therapy showed a stable association between NLR and 90-day mortality risk. In the fully adjusted model, a 1-unit increase in log-transformed NLR was associated with a 20% increase in 30-day mortality risk (HR 1.20, 95% CI 1.05\u0026ndash;1.38). When NLR was treated as a dichotomous variable, in the fully adjusted model, patients with NLR\u0026ge;10 had a 67% higher 90-day mortality risk compared to those with NLR\u0026lt;10(HR=1.67, 95% CI: 1.13\u0026ndash;2.48)\u003cstrong\u003e\u0026nbsp;(Supplementary Table 1).\u003c/strong\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study is based on a large-scale retrospective cohort study that systematically assessed the NLR of patients at admission and explored the relationship between NLR and 30-day mortality in pneumonia patients receiving glucocorticoid therapy. The study found that an elevated NLR was not only closely associated with disease severity (reduced oxygenation index, increased CURB-65 score, respiratory failure, and increased use of continuous Veno venous hemofiltration) but also remained an independent predictor of short-term mortality after multivariable adjustment. This finding is consistent with the predictive value of NLR observed in previous studies \u003csup\u003e4\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e5\u003c/sup\u003e\u003csup\u003e,6\u003c/sup\u003ein community-acquired pneumonia, sepsis, and acute respiratory distress syndrome. Most existing studies have focused on CAP\u003csup\u003e19\u003c/sup\u003e, sepsis \u003csup\u003e20\u003c/sup\u003e, and ARDS \u003csup\u003e21\u003c/sup\u003epopulations, while evidence regarding NLR in pneumonia patients receiving glucocorticoid therapy remains relatively limited.\u003c/p\u003e\n\u003cp\u003eThis study systematically assessed the prognostic value of NLR in this specific population, providing new evidence supporting the clinical application of NLR in pneumonia patients receiving glucocorticoid therapy. Elevated NLR reflects excessive activation of neutrophil-driven inflammatory responses and impaired lymphocyte-mediated immune regulatory functions\u003csup\u003e22\u003c/sup\u003e. In severe infection states, neutrophils release large amounts of reactive oxygen species, proteases, and inflammatory mediators, which can directly damage alveolar epithelium and capillary endothelium, leading to acute lung injury and impaired gas exchange\u003csup\u003e23\u003c/sup\u003e; while lymphocyte depletion indicates impaired immune function and reduced anti-infective defense capacity, closely associated with secondary infections and multi-organ dysfunction\u003csup\u003e24\u003c/sup\u003e. Although glucocorticoids have anti-inflammatory effects, they can suppress lymphocyte proliferation and function\u003csup\u003e25\u003c/sup\u003e, potentially exacerbating the immune imbalance reflected by elevated NLR in some patients, thereby increasing the risk of adverse outcomes.\u003c/p\u003e\n\u003cp\u003eAdditionally, the NLR shows a linear increasing relationship with 30-day mortality, without a clear threshold effect, meaning that even mild to moderate increases in the NLR indicate an increased risk. This relationship has stronger predictive ability in patients without respiratory failure, suggesting that the NLR can identify high-risk populations before traditional clinical indicators become abnormal, providing an opportunity window for early intervention. Clinically, NLR testing is convenient, low-cost, and easily accessible, and has the potential to serve as an important component of risk stratification tools for pneumonia patients, especially those receiving glucocorticoid therapy\u003csup\u003e26\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e27\u003c/sup\u003e. Combining NLR with other inflammatory or nutritional markers can further enhance the accuracy of short-term mortality risk prediction\u003csup\u003e28\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e29\u003c/sup\u003e thereby guides individualized glucocorticoid usage strategies and optimize prognosis management.\u003c/p\u003e\n\u003cp\u003eFuture prospective studies can validate the predictive efficacy of NLR in such populations, explore its dynamic changes and their relationship with prognosis, and incorporate it into clinical scoring systems (e.g., CURB-65, PSI) or combine it with other biomarkers to construct precise predictive models, providing more robust evidence-based support for clinical risk-benefit balancing.\u003c/p\u003e\n\u003cp\u003eIn summary, this study demonstrates that NLR is an independent predictor of 30-day mortality in pneumonia patients receiving glucocorticoid therapy, exhibiting good stability and clinical applicability. Its implementation in clinical practice can provide a basis for the early identification and precise treatment of such patients.\u0026nbsp;\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eNLR is an independent predictor of 30-day mortality in pneumonia patients receiving glucocorticoid therapy, demonstrating good stability and clinical applicability. Its implementation in clinical practice is expected to provide scientific evidence for the early identification of high-risk populations, individualized treatment, and optimized resource allocation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCW: study conception and design, public database curation, drafting and revision.\u003c/p\u003e\n\u003cp\u003eFX: data analysis, result interpretation, drafting and revision.\u003c/p\u003e\n\u003cp\u003eRL data acquisition, data verification, critical revision of the manuscript.\u003c/p\u003e\n\u003cp\u003eSG \u0026amp; SM: study conception and design, overall guidance, drafting and revision.\u003c/p\u003e\n\u003cp\u003eCorresponding authors Correspondence to Shifang Mao or Shengmin Guo.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Luzhou Municipal Key Research and Development Science and Technology Program (Grant No. 2022-NYF-26).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received ethics approval from the Ethics Committee of China-Japan Friendship Hospital (Approval No. 2015-86). As a retrospective investigation, it involved multi-institutional collaboration under the coordination of the committee, which also authorized the anonymized submission and collection of patient data.\u003c/p\u003e\n\u003cp\u003eIn accordance with Dryad\u0026rsquo;s policy, the reuse of anonymized data for secondary analysis does not require additional ethical approval, provided that the rights of the original authors are respected. Therefore, no further ethics approval was sought for the present secondary analysis. Both the original and the current studies adhered to the principles of the Declaration of Helsinki. All procedures conformed to established ethical standards and regulatory requirements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: the datasets analyzed during the current study are available in the Dryad database repository, https://datadryad.org/dataset/doi: 10.5061/dryad.mkkwh70x2,Alternatively, the data are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eGBD 2021 Lower Respiratory Infections and Antimicrobial Resistance Collaborators. Global, regional, and national incidence and mortality burden of non-COVID-19 lower respiratory infections and aetiologies, 1990-2021: a systematic analysis from the Global Burden of Disease Study 2021. \u003cem\u003eLancet Infect Dis. 24\u003c/em\u003e(9), 974\u0026ndash;1002(2024).\u003c/li\u003e\n \u003cli\u003eAbdalla, J. S. et al. Narrative Review of the Epidemiology of Hospital-Acquired Pneumonia and Ventilator-Associated Pneumonia in Gulf Cooperation Council Countries. \u003cem\u003eInfect Dis Ther\u003c/em\u003e. 12(7),1741-1773(2023).\u003c/li\u003e\n \u003cli\u003eFagerli, K. et al. Epidemiology of pneumonia in hospitalized adults \u0026ge;18 years old in four districts of Ulaanbaatar, Mongolia, 2015-2019. \u003cem\u003eLancet Reg Health West Pac\u003c/em\u003e. 30,100591(2022).\u003c/li\u003e\n \u003cli\u003eHorita, N. et al. Adjunctive Systemic Corticosteroids for Hospitalized Community-Acquired Pneumonia: Systematic Review and Meta-Analysis 2015 Update. \u003cem\u003eSci Rep\u003c/em\u003e. 5,14061(2015).\u003c/li\u003e\n \u003cli\u003eSiemieniuk, R. A . et al. Corticosteroid Therapy for Patients Hospitalized With Community-Acquired Pneumonia: A Systematic Review and Meta-analysis. \u003cem\u003eAnn Intern Med\u003c/em\u003e. 163(7),519-528(2015).\u003c/li\u003e\n \u003cli\u003eWu, J. Y. et al. Efficacy and safety of adjunctive corticosteroids in the treatment of severe community-acquired pneumonia: a systematic review and meta-analysis of randomized controlled trials. \u003cem\u003eCrit Care\u003c/em\u003e. 27(1),274(2023).\u003c/li\u003e\n \u003cli\u003eChaudhuri, D. et al. Adverse Effects Related to Corticosteroid Use in Sepsis, Acute Respiratory Distress Syndrome, and Community-Acquired Pneumonia: A Systematic Review and Meta-Analysis.\u003cem\u003e\u0026nbsp;Crit Care Explor\u003c/em\u003e. 6(4), e1071(2024).\u003c/li\u003e\n \u003cli\u003eBlum, C. A. et al. Adjunct prednisone in community-acquired pneumonia: 180-day outcome of a multicentre, double-blind, randomized, placebo-controlled trial. \u003cem\u003eBMC Pulm Med.\u003c/em\u003e 23(1),500(2023).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eForget, P., Khalifa, C., Defour, J. P., Latinne, D., Van Pel, M. C., \u0026amp; De Kock, M. What is the normal value of the neutrophil-to-lymphocyte ratio. \u003cem\u003eBMC Res Notes\u003c/em\u003e. 10(1),12(2017).\u003c/li\u003e\n \u003cli\u003eDe Jager, C.P. et al. The neutrophil-lymphocyte count ratio in patients with community-acquired pneumonia. \u003cem\u003ePLoS One\u003c/em\u003e. 7(10), e46561(2012).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCataudella, E. et al. Neutrophil-To-Lymphocyte Ratio: An Emerging Marker Predicting Prognosis in Elderly Adults with Community-Acquired Pneumonia. \u003cem\u003eJ Am Geriatr Soc\u003c/em\u003e. 65(8),1796-1801(2017).\u003c/li\u003e\n \u003cli\u003eLi, L. et al. Aetiology and prognostic risk factors of mortality in patients with pneumonia receiving glucocorticoids alone or glucocorticoids and other immunosuppressants: a retrospective cohort study.\u003cem\u003e\u0026nbsp;BMJ Open\u003c/em\u003e. 10(10), e037419(2020).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSousa, D. et al. Community-acquired pneumonia in immunocompromised older patients: incidence, causative organisms and outcome. \u003cem\u003eClin Microbiol Infect\u003c/em\u003e. 9(2),187-192(2013).\u003c/li\u003e\n \u003cli\u003eAmerican Thoracic Society, \u0026amp; Infectious Diseases Society of America. Guidelines for the management of adults with hospital-acquired, ventilator-associated, and healthcare-associated pneumonia. \u003cem\u003eAm J Respir Crit Care Med. 171\u003c/em\u003e(4), 388\u0026ndash;416(2005)\u003c/li\u003e\n \u003cli\u003eRussell, C. D. et al. The utility of peripheral blood leucocyte ratios as biomarkers in infectious diseases: A systematic review and meta-analysis. \u003cem\u003eJ Infect\u003c/em\u003e. 78(5),339-348(2019).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCurbelo, J. et al. Inflammation biomarkers in blood as mortality predictors in community-acquired pneumonia admitted patients: Importance of comparison with neutrophil count percentage or neutrophil-lymphocyte ratio. \u003cem\u003ePLoS One\u003c/em\u003e. 12(3), e0173947(2017).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eEnersen, C. C. et al. The ratio of neutrophil-to-lymphocyte and platelet-to-lymphocyte and association with mortality in community-acquired pneumonia: a derivation-validation cohort study. \u003cem\u003eInfection\u003c/em\u003e. 51(5),1339-1347(2023).\u003c/li\u003e\n \u003cli\u003eDrăgoescu, A. N. et al. Neutrophil to Lymphocyte Ratio (NLR)-A Useful Tool for the Prognosis of Sepsis in the ICU. \u003cem\u003eBiomedicines\u003c/em\u003e. 10(1),75(2021).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWang, Q. et al. 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The Neutrophil/Lymphocyte Ratio and Outcomes in Hospitalized Patients with Community-Acquired Pneumonia: A Retrospective Cohort Study. \u003cem\u003eBiomedicines\u003c/em\u003e. \u003cem\u003e12\u003c/em\u003e(2), 260(2024). \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neutrophil-to-Lymphocyte Ratio, Pneumonia, Glucocorticoids, Mortality, Risk Factors","lastPublishedDoi":"10.21203/rs.3.rs-7965583/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7965583/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The neutrophil-to-lymphocyte ratio (NLR) is a recognized prognostic marker in infectious and inflammatory diseases; however, its value for short-term mortality in pneumonia patients receiving glucocorticoid therapy remains unclear. This study evaluated the predictive role of admission NLR for 30-day all-cause mortality in this population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We conducted a retrospective cohort study using data from 696 hospitalized patients with pneumonia who received glucocorticoids, obtained from the Dryad database (Li et al.). Demographics, comorbidities, laboratory results, and corticosteroid use were collected. Prognostic effects of NLR were assessed with multivariate Cox regression, restricted cubic splines (RCS), Kaplan–Meier survival analysis, and subgroup and sensitivity analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e After multivariable adjustment, log₂ NLR was significantly associated with 30-day mortality (HR = 1.20, 95% CI: 1.06–1.35). Patients with NLR ≥ 10 had a 69% higher mortality risk compared with those with NLR \u0026lt; 10 (HR = 1.69, 95% CI: 1.19–2.40). RCS analysis demonstrated a linear association between NLR and mortality. Subgroup and sensitivity analyses confirmed the robustness of these findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Admission NLR is an independent prognostic indicator for pneumonia patients receiving glucocorticoid therapy, particularly in those with NLR ≥ 10 who are at substantially higher risk.\u003c/p\u003e","manuscriptTitle":"Association between neutrophil-to-lymphocyte ratio and 30-day mortality in patients with pneumonia receiving glucocorticoid therapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-09 04:47:41","doi":"10.21203/rs.3.rs-7965583/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":"aadaf799-3842-4ced-ad33-9104d33c4465","owner":[],"postedDate":"December 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-30T13:53:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-09 04:47:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7965583","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7965583","identity":"rs-7965583","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}