Eosinopenia Predicts Prognosis in Severe Community-Acquired Pneumonia

Eosinopenia Predicts Prognosis in Severe Community-Acquired Pneumonia | 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 Eosinopenia Predicts Prognosis in Severe Community-Acquired Pneumonia Catia Cillóniz, Tatiana Castañeda, Antonio Campanella, Riccardo Guglielmi, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7899511/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Mar, 2026 Read the published version in Pneumonia → Version 1 posted 9 You are reading this latest preprint version Abstract Background Eosinopenia has been reported as a marker of severity in infections, but its prognostic value in patients with severe community-acquired pneumonia (CAP) admitted to the intensive care unit (ICU) is unclear. Methods We retrospectively analyzed 496 patients with severe CAP admitted to the ICU, stratified by presence of eosinopenia at admission. Clinical characteristics, laboratory data, microbiological etiology, treatment, and outcomes were compared. Multivariable Cox regression identified independent predictors of 30-day mortality. Predictive performance of eosinophil counts, severity scores (CRB-65, CURB-65, PSI), and a modified CURB-65 incorporating eosinopenia (CURB-65Eos) were assessed. Results Eosinopenia was detected in 33% of patients. There were no differences in age and comorbidities between eosinopenic and non eosinopenic patients. Compared to non-eosinopenic patients, these patients had lower leukocyte, neutrophil, and lymphocyte counts and more frequent viral or polymicrobial infections. They more often required invasive mechanical ventilation (58% vs. 45%, p = 0.009) and developed pleural effusions (30% vs. 19%, p = 0.008). In-hospital and 30-day mortality were higher in the eosinopenia group (21% vs. 13%, p = 0.036; 20% vs. 12%, p = 0.022). Eosinopenia independently predicted 30-day mortality (HR 1.98; 95% CI 1.23–3.16; p = 0.005). Eosinophil counts alone had poor predictive accuracy (AUC 0.562), while established severity scores performed moderately. CURB-65Eos showed a numerical but nonsignificant improvement over CURB-65. Conclusions Eosinopenia is common in patients with severe CAP admitted to the ICU and is strongly associated with increased severity and mortality independent of age and comorbidities. It may serve as simple and inexpensive biomarker for the early identification of high-risk patients and could help guide more intensive therapeutic interventions. Pneumonia community-acquired pneumonia eosinopenia prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Identify patients with community-acquired pneumonia (CAP) at risk of severe disease and worse prognosis remain a main challenges for the management of pneumonia, especially in cases of severe pneumonia[ 1 , 2 ]. Several prognostic tools were develop over time including the pneumonia severity index (PSI), CURB65 score, and the major and minor criteria for severe CAP of the Infectious Disease Society of America (IDSA) and American Thoracic Society (ATS)[ 3 ]. However, their ability to detect high-risk patients who may suffer clinical deterioration in the short and long-term is limited. Other adjunctive tools such as biomarkers like C-reactive protein (CRP), procalcitonin (PCT), neutrophil-lymphocyte ratio, lymphocytes count are being investigated for prognostic value in CAP patients[ 4 , 5 ]. Recently, several studies focus on the prognostic value of eosinophil count as a predictor of clinical outcomes in CAP patients, mainly because previous scientific evidence demonstrate that eosinopenia is a good predictor of worse outcome in different clinical conditions such as sepsis[ 6 ], acute respiratory distress syndrome (ARDS)[ 7 ], an in specific population such as patients with chronic obstructive pulmonary disease (COPD) and CAP[ 8 ] or patients with COVID-19[ 9 ]. However, there are controversy results about the prognostic value of eosinopenia in patients with CAP, and limited data in patients with severe CAP. Since identifying high-risk severe CAP (SCAP) patients enables intensified interventions that prevent organ failure and clinical deterioration, our study aims to evaluate the association between eosinopenia and clinical outcomes in patients with SCAP. Methods Study design and patients This was a retrospective observational study of prospectively collected data from the Hospital Clinic of Barcelona, Spain. We enrolled all consecutive adult patients with a diagnosis of CAP admitted to hospital via the emergency department between January 2005 and February 2025. We included patients from nursing homes as we previously demonstrated that the microbial etiology in this population is similar to that of CAP arising in people living in their own homes [ 10 ]. Among all patients with CAP, we selected those patients with severe CAP that were admitted to the intensive care unit (ICU). We excluded patients with a confirmed alternative diagnosis. For publication purposes, the study was approved by the Ethics Committee of our institution ( Comité Ètic d’Investigació Clínica , register: 2009/5451). The need for written informed consent was waived because of the non-interventional study design. Definitions Pneumonia (CAP) was defined as a new pulmonary infiltrate on chest X-ray during hospital admission with symptoms and signs of a lower respiratory tract infection. Severe CAP was diagnosed by the presence of at least one major or three minor criteria of the Infectious Disease Society of America/American Thoracic Society (IDSA/ATS) guidelines[ 11 ]. Polymicrobial pneumonia was defined as pneumonia due to more than one pathogen. Prior antibiotic treatment was defined as antibiotic intake during the week before hospital admission. The appropriateness of empiric antibiotic treatment was determined according to multidisciplinary guidelines for the management of CAP [ 12 ]. Sepsis was defined according to the criteria of the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) as the presence of pneumonia and an increase ≥ 2 points in the Sequential Organ Failure Assessment (SOFA) score [ 13 ]. The presence of an acute respiratory distress syndrome (ARDS) was evaluated within the first 24 h of hospital admission based on the Berlin definition [ 14 ]. Eosinopenia was defined as eosinophil count ≤ 50/µL and non-eosinopenia as eosinophil count ≥ 50/µL[ 15 , 16 ]. Data collection, evaluation, and microbiological diagnosis Demographic variables, comorbidities, and physiologic parameters were collected in the Emergency Department within 24 hours of admission. The Pneumonia Severity Index (PSI), CURB-65 score (i.e., confusion, urea nitrogen, respiratory rate, blood pressure, and age ≥ 65 years,) and SOFA score at admission were calculated [ 17 – 19 ]. During hospitalization, we recorded whether the patients had specific complications, including multilobar infiltration, pleural effusions, ARDS [ 14 ], septic shock [ 20 ], and acute renal failure [ 21 ]. Additional details are provided elsewhere [ 22 ]. All surviving patients were visited or contacted by telephone 30 days after discharge, and the hospital records and the database of Catalunya Health Department reviewed at 1 year. The criteria for etiological diagnosis are described elsewhere [ 22 , 23 ] and in Supplementary data. Outcomes The primary outcome was 30-day mortality. Secondary outcomes were in-hospital mortality, 1-year mortality, and need for mechanical ventilation. Statistical analysis Categorical variables were described using frequencies and percentages, while continuous variables were reported as means and standard deviations (SD) or as medians with interquartile ranges (IQR) when not normally distributed, based on the Kolmogorov-Smirnov test. Comparisons between categorical variables were made using the chi-square test or Fisher’s exact test, as appropriate. For continuous variables, the Student’s t -test was used when assumption of normality was met; otherwise, the nonparametric Mann-Whitney U test was applied. Linear regression models were used to assess the association between log-transformed eosinophil counts and other hematological parameters (lymphocytes, leukocytes, neutrophils, and C-reactive protein). Interaction terms were included to evaluate whether systemic corticosteroid use modified these associations. Differences in 30-day and 1-year mortality between study groups were evaluated using the Kaplan-Meier method, with comparisons made via the Gehan-Breslow-Wilcoxon test. Univariable and Cox proportional hazards regression analyses[ 24 ] were conducted to identify predictors of 30-day mortality (dependent variable). Variables with a p-value < 0.25 in univariable analyses were included in the multivariable Cox regression model. Final variable selection was performed using a backward stepwise method based on the likelihood ratio test (entry criterion: p in 0.10). Patients lost to follow-up were censored in the survival analyses. Hazard ratios (HRs) with 95% confidence intervals (CIs) were reported. Model discrimination in the multivariable Cox regression was assessed using Harrell’s C-index and Somers’ D statistics. Collinearity was evaluated using Pearson’s correlation coefficient ( r ), and multicollinearity was assessed using the variance inflation factor (VIF). Internal validation of the prediction model was conducted using ordinary nonparametric bootstrapping with 1,000 bootstrap samples and bias-corrected, accelerated 95% CIs[ 25 ]. Missing data were handled using multiple imputation[ 26 , 27 ]. The ability of eosinophil counts to differentiate survivors from non-survivors was assessed using the area under the receiver operating characteristic (ROC) curve (AUC). Comparisons of the AUCs were performed according to the method described by DeLong et al.[ 28 ] All statistical tests were two-tailed, with a significance level set at p < 0.05. All analyses were performed using SPSS version 26.0 for Windows (IBM Corp., Armonk, NY, USA) and R version 4.4.2 (R Foundation for Statistical Computing, Vienna, Austria). Results Study population A total of 4,984 patients with CAP were admitted to our hospital during the study period. After excluding 4,488 patients who did not meet eligibility criteria, 496 patients with severe CAP admitted to the ICU were included in the final analysis (Fig. 1 ). Of these, 163 patients (33%) had eosinopenia, while 333 (67%) did not. Comparison of clinical characteristics, laboratory findings, treatment, and outcomes between patients with and without eosinopenia at admission are shown in Table 1 . Clinical characteristics Patients in the eosinopenia group had a median age of 69 years (IQR 57–79), which was not significantly different from the non-eosinopenia group (66 years, IQR 54–79; p = 0.136). The proportion of male patients was also similar (63% vs. 68%; p = 0.241). No significant differences were observed in smoking status, alcohol abuse, or the presence of chronic pulmonary, neurologic, liver, or renal disease. However, eosinopenic patients had a lower overall rate of comorbidities (63% vs. 75%; p = 0.005) but showed a higher prevalence of previous neoplasms (17% vs. 7%; p < 0.001). Chronic heart disease was less common in this group (9% vs. 16%; p = 0.029). They were also less likely to use inhaled corticosteroids prior to admission (9% vs. 20%; p = 0.005) and had received pneumococcal vaccination more frequently (27% vs. 15%; p = 0.004). A decreased level of consciousness at admission was significantly more common among non-eosinopenic patients (41% vs. 26%; p = 0.001). Laboratory and severity scores At admission, eosinopenic patients had significantly lower leukocyte (7.8 vs. 14.1 ×10⁹/L; p < 0.001), neutrophil (6.5 vs. 11.4 ×10⁹/L; p < 0.001), and lymphocyte counts (0.51 vs. 0.91 ×10⁹/L; p < 0.001), with eosinophil counts effectively absent (0 vs. 0.12 ×10⁹/L; p < 0.001). The PSI score was lower in the eosinopenia group (median 111.5 vs. 123; p = 0.049), while SOFA scores were similar between groups. No differences were observed in C-reactive protein, serum creatinine, glycemia, or PaO₂/FiO₂ ratios. In linear regression analysis (Supplementary Table 1), log-transformed eosinophil counts were positively associated with lymphocyte, leukocyte, and neutrophil counts (all p 0.1), indicating that these associations were independent of corticosteroid treatment. (Supplementary Figs. 1 to 4). Microbial etiology A defined microbiological etiology was more frequently established in eosinopenic patients (71% vs. 56%; p = 0.002). Respiratory viral infections were significantly more common in the eosinopenia group (12% vs. 5%; p = 0.003), as were polymicrobial infections (17% vs. 8%; p = 0.003). The frequency of Streptococcus pneumoniae and other specific pathogens did not differ between groups. Treatment and clinical course Empirical antibiotic therapy did not differ between groups in terms of monotherapy (10% in both; p = 0.984) or overall use of combination therapy (90% in both; p = 0.984). However, β-lactam plus macrolide regimens were more commonly used in eosinopenic patients (41% vs. 22%; p < 0.001), whereas β-lactam plus fluoroquinolone combinations were more frequent in non-eosinopenic patients (54% vs. 37%; p < 0.001). Invasive mechanical ventilation was significantly more frequent in the eosinopenia group (58% vs. 45%; p = 0.009), although the need for non-invasive ventilation was similar (p = 0.350). Complications such as pleural effusion (30% vs. 19%; p = 0.008) and septic shock (34% vs. 43%; p = 0.046) occurred more frequently in eosinopenic patients. There were no significant differences in rates of ARDS, bacteremia, sepsis, or acute renal failure. Outcomes Eosinopenia was associated with worse clinical outcomes, including higher in-hospital mortality (21% vs. 13%; p = 0.036) and 30-day mortality (20% vs. 12%; p = 0.022). One-year mortality was also higher in the eosinopenia group, although the difference did not reach statistical significance (25% vs. 18%; p = 0.056). Median length of hospital stay did not differ significantly between groups. However, among survivors, eosinopenic patients tended to have longer hospitalizations (median 17 vs. 14 days; p = 0.053). Kaplan–Meier survival curves showed significantly lower survival in patients with eosinopenia both at 30 days (p = 0.010; Fig. 2 ) and at 1 year (p = 0.030; Fig. 3 ), compared to non-eosinopenic patients. Predictors of 30-day mortality: multivariable analysis Multivariable Cox regression identified eosinopenia as an independent predictor of 30-day mortality (HR 1.98; 95% CI 1.23 to 3.16; p = 0.005), along with age ≥ 65 years (HR 2.92; 95% CI 1.67 to 5.11; p < 0.001), chronic liver disease (HR 3.20; 95% CI 1.69 to 6.05; p < 0.001), and absence of fever at admission (HR 0.61; 95% CI 0.39 to 0.98; p = 0.042). Internal validation of the Cox regression model using bootstrapping with 1,000 samples demonstrated robust results for all the variables included in the model, with small 95% CIs around the original coefficients (Supplementary Table 2). Comparison of eosinophil count and clinical severity scores in predictive accuracy Figure 4 presents ROC curve analyses comparing eosinophil count, CRB-65, CURB-65, and PSI as predictors. The AUC for eosinophil count was 0.562 (95% CI 0.487 to 0.637), which is close to random chance and indicates poor predictive ability. In contrast, CRB-65 (AUC 0.635, 95% CI 0.560 to 0.711), CURB-65 (AUC 0.655, 95% CI 0.581 to 0.728), and PSI (AUC 0.654, 95% CI 0.576 to 0.733) all show moderate predictive accuracy and outperformed eosinophil count. When comparing AUCs, eosinophil count was not significantly different from CRB-65 (p = 0.121), but it was significantly worse than both CURB-65 (p = 0.043) and PSI (p = 0.029). Overall, eosinophil count alone is a weak predictor of 30-day mortality, whereas established severity scores, particularly CURB-65 and PSI, provide substantially better prognostic discrimination. Improvement of the CURB-65 score’s ability to predict mortality by adding eosinophil count Patients with eosinopenia were assigned an additional extra point in the CURB-65 score to create the “CURB-65Eos” score (“Eos” indicating “eosinopenia”). AUROC analysis comparing CURB-65 with CURB-65Eos showed a numerically higher AUC for the new score; however, this difference did not reach statistical significance according to the DeLong test[ 28 ] (Fig. 5 ). Discussion Our findings showed that patients with eosinopenia and SCAP had a more severe clinical course, including higher rates of invasive mechanical ventilation, septic shock, pleural effusion, and in-hospital mortality, despite having lower rate of chronic comorbidities compared with non-eosinopenic patients. Additionally, microbial etiology was identified in a higher proportion of patients with eosinopenia, polymicrobial and viral infections being the most frequent etiologies identified, this may suggest a potential link between eosinopenia and increased pathogen burden. Age ≥ 65 years, chronic liver disease, fever and eosinopenia (< 0.05×10⁹/L) were independent predictors of 30-day mortality in patients with SCAP, eosinopenia was a factor nearly double the risk of mortality even after adjusting for age and comorbidities. Previous studies have reported eosinopenia as a good predictor of worse outcome in patients with sepsis[ 6 ], ARDS[ 7 ], COPD[ 8 ], COVID-19[ 9 ], and in patients with pneumonia[ 8 , 29 ]. However, there are controversial results about the prognostic value of eosinopenia in patients with CAP, and there are limited data in patients with severe CAP. A small retrospective study reported that eosinopenia was a strong predictor of 18-month mortality and was associated with severe infection in patients with acute exacerbation of COPD and CAP[ 8 ]. More recently, a large retrospective multicenter study, reported that eosinopenia was associated with an increase in in-hospital mortality, need for mechanical ventilation, risk of sepsis, as well as longer hospital stay in survivors and shorter time to in-hospital death[ 30 ]. However, in the study, the comorbidities, especially COPD and asthma may confound the association between eosinopenia and outcomes, particularly because there was not and adjusted for in multivariable analyses, and the use of corticosteroids in patients with chronic lung disease also could create bias, since decrease the eosinophil counts[ 31 ]. Our findings expand the results of this previous study, since we adjusted for age and comorbidities in multivariable Cox regression and confirmed eosinopenia as an independent predictor of 30-day mortality. While our cohort included patients with chronic pulmonary disease, the prevalence of corticosteroid use was relatively low, and eosinopenia remained predictive of poor outcomes even after accounting for prior inhaled corticosteroid treatment. Importantly, in our linear regression analyses, eosinophil counts were positively correlated with lymphocyte, leukocyte, and neutrophil counts, but not with C-RP levels, suggesting that eosinophil depletion occurs in parallel with other leukocyte populations rather than as a reflection of systemic inflammation. Moreover, the use of corticosteroid did not affect these associations, which suggest that the observed eosinophil–leukocyte relationships were independent of steroid use. This confirm that eosinopenia reflects an intrinsic dysregulation of the immune response in severe infection rather than a pharmacologic effect. Our results highlight the potential of eosinopenia as a reliable and inexpensive biomarker to identify patients with severe pneumonia who are at risk of severe disease and poor outcomes. Together with age ≥ 65 years and chronic liver disease, as previous reports suggest that severity of pneumonia increases with age and number of comorbidities[ 3 , 32 , 33 ]. Interestingly, the presence of fever was associated with lower risk of mortality, which may be explain with the results of a previous study of our group that reported fever associated with lower risk of drug resistant pathogens[ 34 ]. Our results show that eosinopenia is a factor associated with nearly double the risk of mortality, independent of age and comorbidities. This suggests the possibility to incorporate the eosinophil count as a prognostic biomarker to improve the early identification of high-risk patients and help in the intensification of therapeutic interventions. In our study, microbial etiology was identified in a higher proportion of SCAP patients with eosinopenia, with polymicrobial and viral infections being the most frequent identified. This suggests a possible potential link between eosinopenia and increased pathogen burden. A similar observation was reported in a study of patients with severe exacerbation of COPD, where the authors found a higher identification of bacteria in patients with eosinopenia[ 35 ]. During the COVID-19 pandemic, eosinopenia was reported as a reliable biomarker of SARS-CoV-2 infection[ 36 , 37 ]. This may be explained by the fact that viral infection can suppress eosinophils, potentially indicating increased viral load and severe disease[ 38 ]. In our study, the prognostic value of eosinopenia alone was lower than that of other severity scores, and even when added to CURB-65, its predictive power remained limited. This indicates that eosinopenia may serve as a complementary biomarker to pneumonia severity scores. The strengths of our study include the number of real-world patients with severe CAP. Our study has some limitations, though, beginning with its retrospective nature. However, the data of all patients included were collected consecutively and prospectively according to our study protocol for CAP which reduce potential bias. Second, the study was carried out in a single-center teaching hospital in Spain, consequently the findings of our study need to be confirmed in an external validation and in other populations. However, we performed an internal validation using a bootstrap resampling approach with 1,000 iterations, which provided a robust assessment of the model’s stability and potential bias despite the absence of external validation. In conclusion, eosinopenia is strongly associated with increased severity and mortality in patients with severe CAP, independent of age and comorbidities. It may serve as simple and inexpensive biomarker for the early identification of high-risk patients and could help guide more intensive therapeutic interventions. Abbreviations Community-acquired pneumonia (CAP) Intensive care unit (ICU) Pneumonia severity index (PSI) Confusion, Urea, Respiratory rate, Blood pressure, and age 65 years or older (CURB65) Infectious Disease Society of America (IDSA) American Thoracic Society (ATS) C-reactive protein (CRP) Procalcitonin (PCT) Acute respiratory distress syndrome (ARDS) Chronic obstructive pulmonary disease (COPD) Severe CAP (SCAP) Sequential Organ Failure Assessment (SOFA) Standard deviations (SD) Interquartile ranges (IQR) Hazard ratios (HRs) confidence intervals (CIs) Variance inflation factor (VIF) Receiver operating characteristic (ROC) Declarations Ethics approval and consent to participate For publication purposes, the study was approved by the Ethics Committee of our institution ( Comité Ètic d’Investigació Clínica , register: 2009/5451). The need for written informed consent was waived because of the non-interventional study design. Data Availability The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no conflicts of interest Funding This study was supported by Ciber de Enfermedades Respiratorias (CibeRes CB06/06/0028), and 2009 Support to Research Groups of Catalonia 911; IDIBAPS. Dr Cilloniz is the recipient of a SEPAR fellowship 2024. Acknowledgments We are indebted to all medical and nursing colleagues for their assistance and cooperation in this study. References Pletz MW, Jensen AV, Bahrs C, et al. Unmet needs in pneumonia research: a comprehensive approach by the CAPNETZ study group. Respir Res. 2022;23:239. Cillóniz C, Torres A, Niederman MS. Management of pneumonia in critically ill patients. BMJ. 2021;375:e065871. Torres A, Cilloniz C, Niederman MS, et al. Pneumonia. Nat Rev Dis Primers. 2021;7:25. Cilloniz C, Videla A, Pericàs JM. 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Chin Med J (Engl). 2021;135:462–4. Soni M. Evaluation of eosinopenia as a diagnostic and prognostic indicator in COVID-19 infection. Int J Lab Hematol. 2021;43(Suppl 1):137–41. Karakonstantis S, Gryllou N, Papazoglou G, et al. Eosinophil count (EC) as a diagnostic and prognostic marker for infection in the internal medicine department setting. Rom J Intern Med. 2019;57:166–74. Macchia I, La Sorsa V, Urbani F, et al. Eosinophils as potential biomarkers in respiratory viral infections. Front Immunol. 2023;14:1170035. Tables Table 1. Clinical characteristics, treatment, and outcomes according to the presence of eosinopenia at hospital admission Eosinopenia (<0.05 x10 9 /L) (N=163) Non-eosinopenia (≥0.05 x10 9 /L) (N=333) P-value Age, median (Q1; Q3), years 69 (57; 79) 66 (54; 79) 0.136 Male sex, n (%) 103 (63) 228 (68) 0.241 Smoking habit (current smoke), n (%) 48 (30) 81 (25) 0.250 Alcohol abuse (current alcohol), n (%) 29 (18) 59 (18) 0.982 Comorbidities, n (%) a 103 (63) 249 (75) 0.005 Diabetes mellitus 41 (26) 83 (25) 0.879 Chronic lung disease 56 (35) 132 (41) 0.232 Neurologic disease 24 (15) 54 (17) 0.580 Chronic liver disease 14 (9) 20 (6) 0.306 Chronic heart disease 14 (9) 52 (16) 0.029 Chronic renal disease 17 (10) 36 (11) 0.854 Previous neoplasm 26 (17) 21 (7) <0.001 Nursing-home, n (%) 6 (4) 25 (8) 0.089 Previous pneumonia, n (%) 13 (8) 34 (11) 0.361 Days since initial symptoms to admission, median (Q1; Q3) 3 (2; 5) 4 (2; 7) 0.372 Cough, n (%) 110 (69) 230 (73) 0.304 Dyspnoea, n (%) 122 (76) 240 (77) 0.827 Fever, n (%) 111 (69) 208 (65) 0.361 Decreased level of consciousness, n (%) 42 (26) 135 (41) 0.001 Treatment before admission, n (%) Systemic corticosteroids 11 (7) 32 (10) 0.283 Inhaled Corticosteroids 15 (9) 62 (20) 0.005 Previous Antibiotic 24 (16) 55 (18) 0.502 Influenza vaccine 46 (33) 108 (41) 0.123 Pneumococcal vaccine 37 (27) 40 (15) 0.004 Characteristics at admission Respiratory rate ≥30 rpm, n (%) 63 (46) 149 (50) 0.458 PSI score, median (Q1; Q3) 111.5 (86.5; 147.5) 123 (104; 146) 0.049 SOFA score, median (Q1; Q3) 4 (3; 6) 4 (3; 6) 0.355 Laboratory findings, median (Q1; Q3) Glycemia, g/dL 13.3 (10.8; 18.9) 14.2 (11; 20.4) 0.166 Leucocyte count, 10 9 /L 7.8 (3.6; 13) 14.1 (9.9; 20.2) <0.001 Neutrophil count, 10 9 /L 6.5 (2.7; 11.5) 11.4 (7.8; 16.5) <0.001 Lymphocyte count, 10 9 /L 0.51 (0.31; 0.76) 0.91 (0.5; 1.5) <0.001 Neutrophil/Lymphocyte ratio 12.6 (6.2; 21.9) 13.1 (7.3; 22.3) 0.261 Eosinophil count, 10 9 /L 0 (0; 0) 0.12 (0.1; 0.2) <0.001 C-reactive protein, mg/L 19.3 (12.8; 28.6) 19.9 (9.1; 28.9) 0.615 Serum creatinine, mg/dL 1.5 (0.9; 2.1) 1.5 (1.0;2.0) 0.717 PaO 2 /FiO 2 ratio 210 (161; 280) 233 (173; 281) 0.187 Empiric antibiotic therapy, n (%) Monotherapy 16 (10) 33 (10) 0.984 Fluoroquinolones 2 (1) 17 (5) 0.035 β-lactams 14 (9) 15 (5) 0.067 Other therapy 0 (0) 1 (0.3) >0.999 Combination therapies 142 (90) 291 (90) 0.984 β-lactams plus fluoroquinolones 58 (37) 175 (54) <0.001 β-lactams plus macrolides 65 (41) 70 (22) <0.001 Other combination therapies 19 (12) 46 (14) 0.512 Respiratory support, n (%) b 0.031 Non-invasive mechanical ventilation 19 (13) 53 (16) 0.350 Invasive mechanical ventilation 85 (58) 146 (45) 0.009 Complications during admission, n (%) Pleural effusion 47 (30) 62 (19) 0.008 Sepsis 89 (90) 285 (94) 0.123 Multilobar infiltration 99 (61) 186 (56) 0.302 ARDS 45 (30) 74 (23) 0.101 Septic shock 54 (34) 139 (43) 0.046 Bacteremia 38 (27) 57 (20) 0.142 Acute renal failure 83 (52) 168 (53) 0.951 Outcomes In-hospital mortality, n (%) 33 (21) 44 (13) 0.036 30-day mortality, n (%) c 32 (20) 40 (12) 0.022 1-year mortality, n (%) d 40 (25) 59 (18) 0.056 Length of hospital stay, median (Q1; Q3), days All patients 16 (10; 29) 14.5 (10; 27) 0.514 Surviving patients 17 (10; 35) 14 (10; 27) 0.053 Non-surviving patients 11 (4; 19) 16 (9.5; 24.5) 0.071 Length of ICU stay, median (Q1; Q3), days All patients 16 (10; 29) 14.5 (10; 27) 0.514 Surviving patients 17 (10; 35) 14 (10; 27) 0.053 Non-surviving patients 11 (4; 19) 16.5 (9.5; 24.5) 0.071 Abbreviations. Q1 indicates first quartile; Q3, third quartile; PSI, pneumonia severity index; SOFA, sequential organ failure assessment; PaO 2 , partial pressure of arterial oxygen; FiO 2 , fraction of inspired oxygen; ARDS, acute respiratory distress syndrome; ICU, intensive care unit. Note. Percentages calculated on non-missing data. P-values marked in bold indicate numbers that are statistically significant at the 95% confidence limit. a May have >1 comorbid condition. b Patients who initially received non-invasive ventilation but subsequently needed intubation were included in the invasive mechanical ventilation group. c Calculated only for patients with 30-day follow-up (162 in the eosipenia group and 333 in the non-eosipenia group). d Calculated only for patients with 1-year follow-up (158 in the eosipenia group and 330 in the non-eosipenia group). Table 2. Microbial etiology according to eosinopenia status Eosinopenia (<0.05 x10 9 /L) (N=163) Non-eosinopenia (≥0.05 x10 9 /L) (N=333) P-value Defined etiology, n (%) 115 (71) 187 (56) 0.002 Monomicrobial, n (%) 87 (53) 160 (48) 0.256 Streptococcus pneumoniae , n (%) 44 (27) 93 (28) 0.827 Respiratory virus, n (%) 19 (12) 15 (5) 0.003 Legionella pneumophila , n (%) 7 (4) 6 (2) 0.134 Haemophilus influenzae , n (%) 1 (1) 8 (2) 0.283 Pseudomonas aeruginosa , n (%) 3 (2) 6 (2) >0.999 Atypical bacteria, n (%) 2 (1) 5 (2) >0.999 Staphylococcus aureus , n (%) 3 (2) 12 (4) 0.405 GNEB, n (%) 4 (2) 5 (2) 0.485 Escherichia coli , n (%) 1 (1) 3 (1) >0.999 Klebsiella pneumoniae , n (%) 3 (2) 2 (1) 0.337 Other pathogens, n (%) 4 (2) 10 (3) >0.999 Polymicrobial, n (%) 28 (17) 27 (8) 0.003 Abbreviations. GNEB, Gram-negative enteric bacteria. Note. Percentages calculated on non-missing data. P-values marked in bold indicate numbers that are statistically significant at the 95% confidence limit. Atypical bacteria include Mycoplasma pneumoniae , Chlamydophila pneumoniae , and Coxiella burnetti . Table 3. Significant univariable Cox regression analyses for variables associated with 30-day mortality and independent predictors of 30-day mortality determined by multivariable Cox regression analysis Variable Univariable a Multivariable b HR 95% CI P-value HR 95% CI P-value Age ≥65 years 2.39 1.39 to 4.11 0.002 2.92 1.67 to 5.11 <0.001 Pneumococcal vaccine 1.59 0.95 to 2.66 0.080 - - - Chronic heart disease 1.43 0.78 to 2.60 0.247 - - - Chronic renal disease 1.70 0.91 to 3.15 0.095 - - - Chronic liver disease 2.71 1.46 to 5.03 0.002 3.20 1.69 to 6.05 <0.001 Diabetes mellitus 1.63 1.01 to 2.63 0.047 - - - Chronic neurological disease 1.58 0.91 to 2.75 0.108 - - - Fever 0.58 0.37 to 0.93 0.023 0.61 0.39 to 0.98 0.042 Reduced level of consciousness 1.71 1.07 to 2.71 0.024 - - - Creatinine ≥1.5 mg/dL 1.37 0.86 to 2.19 0.181 - - - Etiology 0.512 - Unknown / Non-infectious etiology 1.00 - - - - - Bacterial 1.56 0.90 to 2.70 0.109 - - - Respiratory virus 1.63 0.66 to 4.04 0.291 - - - Other pathogens 1.45 0.34 to 6.18 0.617 - - - Polymicrobial 1.69 0.80 to 3.60 0.170 - - - Adequate therapy 0.63 0.32 to 1.22 0.170 - - - Eosinopenia (<0.05 x10 9 /L) 1.75 1.10 to 2.78 0.019 1.98 1.23 to 3.16 0.005 Abbreviations. HR indicates hazard ratio. CI, confidence interval. Note. Data are presented as estimated HRs (95% CIs) of the explanatory variables in the 30-day mortality group. The HR is defined as the ratio of the hazard rates corresponding to the conditions described by two levels of an explanatory variable (the hazard rate is the risk of death [i.e., the probability of death], given that the patient has survived up to a specific time). The P-value is based on the null hypothesis that all HRs relating to an explanatory variable equal unity (no effect). a The variables analyzed in the univariable analyses were age, sex, smoking habit, alcohol habit, pneumococcal vaccine, influenza vaccine, systemic corticosteroid, inhaled corticosteroid, previous antibiotic, chronic pulmonary disease, chronic heart disease, chronic renal disease, chronic liver disease, diabetes mellitus, neurological disease, previous neoplasm, fever, confusion, PSI, creatinine, C-reactive protein, leucocytes count, lymphocytes count, multilobar infiltration, etiology, and appropriate therapy. b Harrell's C statistic is 0.68 and Somers' D statistic is 0.37. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":17940,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the study population\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations.\u003c/em\u003e CAP indicates community acquired pneumonia.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7899511/v1/28a35702f06ca6b6c62715bb.png"},{"id":95894673,"identity":"1ac337d8-1dd4-4dba-be2a-65ef8eb00fd4","added_by":"auto","created_at":"2025-11-14 07:09:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":16206,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curves for 30-day mortality by study group\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote. \u003c/em\u003eP-values are based on Breslow (Wilcoxon) test. \u003cem\u003eY-axis truncated at 80% to enhance visibility.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7899511/v1/00371c7ff32624ef28a0979f.png"},{"id":95894677,"identity":"19233236-f365-4936-ad54-7819777d6a19","added_by":"auto","created_at":"2025-11-14 07:09:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":18842,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curves for 1-year mortality by study group\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote. \u003c/em\u003eP-values are based on Breslow (Wilcoxon) test. \u003cem\u003eY-axis truncated at 50% to enhance visibility.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7899511/v1/f1c46b90a86e23af5363f355.png"},{"id":95894687,"identity":"8beefb3e-e236-46d7-b79a-46248785d877","added_by":"auto","created_at":"2025-11-14 07:09:27","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":549063,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of AUCs of Eosinophil count, CURB-65, CRB-65 and PSI to Predict 30-Day Mortality\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7899511/v1/d2f7ebdba95b7555156c2e7c.jpeg"},{"id":95894683,"identity":"f5e2a4a4-cf99-421f-bdb1-31700f0134b1","added_by":"auto","created_at":"2025-11-14 07:09:27","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":370453,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of AUCs of CURB-65 and CURB-65Eos to Predict 30-Day mortality: One extra point was added to the CURB-65 score of those patients with eosinopenia to build the CURB-65Eos\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7899511/v1/d3aa7f0f9ee5f59e567622f2.jpeg"},{"id":105755478,"identity":"52a41a6c-3f97-4a67-a774-93781a668048","added_by":"auto","created_at":"2026-03-30 16:27:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2356295,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7899511/v1/7784a33b-da1f-43c7-8fd1-a7712a29d0cf.pdf"},{"id":96241851,"identity":"6d376414-e403-4cd6-8f2e-eb855af110c9","added_by":"auto","created_at":"2025-11-19 07:11:31","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":179549,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary141025.docx","url":"https://assets-eu.researchsquare.com/files/rs-7899511/v1/3a306e56534b15a0e04b671a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Eosinopenia Predicts Prognosis in Severe Community-Acquired Pneumonia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIdentify patients with community-acquired pneumonia (CAP) at risk of severe disease and worse prognosis remain a main challenges for the management of pneumonia, especially in cases of severe pneumonia[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Several prognostic tools were develop over time including the pneumonia severity index (PSI), CURB65 score, and the major and minor criteria for severe CAP of the Infectious Disease Society of America (IDSA) and American Thoracic Society (ATS)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, their ability to detect high-risk patients who may suffer clinical deterioration in the short and long-term is limited.\u003c/p\u003e\u003cp\u003eOther adjunctive tools such as biomarkers like C-reactive protein (CRP), procalcitonin (PCT), neutrophil-lymphocyte ratio, lymphocytes count are being investigated for prognostic value in CAP patients[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Recently, several studies focus on the prognostic value of eosinophil count as a predictor of clinical outcomes in CAP patients, mainly because previous scientific evidence demonstrate that eosinopenia is a good predictor of worse outcome in different clinical conditions such as sepsis[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], acute respiratory distress syndrome (ARDS)[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], an in specific population such as patients with chronic obstructive pulmonary disease (COPD) and CAP[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] or patients with COVID-19[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, there are controversy results about the prognostic value of eosinopenia in patients with CAP, and limited data in patients with severe CAP.\u003c/p\u003e\u003cp\u003eSince identifying high-risk severe CAP (SCAP) patients enables intensified interventions that prevent organ failure and clinical deterioration, our study aims to evaluate the association between eosinopenia and clinical outcomes in patients with SCAP.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and patients\u003c/h2\u003e\u003cp\u003eThis was a retrospective observational study of prospectively collected data from the Hospital Clinic of Barcelona, Spain. We enrolled all consecutive adult patients with a diagnosis of CAP admitted to hospital via the emergency department between January 2005 and February 2025. We included patients from nursing homes as we previously demonstrated that the microbial etiology in this population is similar to that of CAP arising in people living in their own homes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Among all patients with CAP, we selected those patients with severe CAP that were admitted to the intensive care unit (ICU). We excluded patients with a confirmed alternative diagnosis.\u003c/p\u003e\u003cp\u003eFor publication purposes, the study was approved by the Ethics Committee of our institution (\u003cem\u003eComit\u0026eacute; \u0026Egrave;tic d\u0026rsquo;Investigaci\u0026oacute; Cl\u0026iacute;nica\u003c/em\u003e, register: 2009/5451). The need for written informed consent was waived because of the non-interventional study design.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDefinitions\u003c/h3\u003e\n\u003cp\u003ePneumonia (CAP) was defined as a new pulmonary infiltrate on chest X-ray during hospital admission with symptoms and signs of a lower respiratory tract infection. Severe CAP was diagnosed by the presence of at least one major or three minor criteria of the Infectious Disease Society of America/American Thoracic Society (IDSA/ATS) guidelines[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Polymicrobial pneumonia was defined as pneumonia due to more than one pathogen. Prior antibiotic treatment was defined as antibiotic intake during the week before hospital admission. The appropriateness of empiric antibiotic treatment was determined according to multidisciplinary guidelines for the management of CAP [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Sepsis was defined according to the criteria of the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) as the presence of pneumonia and an increase\u0026thinsp;\u0026ge;\u0026thinsp;2 points in the Sequential Organ Failure Assessment (SOFA) score [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The presence of an acute respiratory distress syndrome (ARDS) was evaluated within the first 24 h of hospital admission based on the Berlin definition [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Eosinopenia was defined as eosinophil count\u0026thinsp;\u0026le;\u0026thinsp;50/\u0026micro;L and non-eosinopenia as eosinophil count\u0026thinsp;\u0026ge;\u0026thinsp;50/\u0026micro;L[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eData collection, evaluation, and microbiological diagnosis\u003c/h3\u003e\n\u003cp\u003eDemographic variables, comorbidities, and physiologic parameters were collected in the Emergency Department within 24 hours of admission. The Pneumonia Severity Index (PSI), CURB-65 score (i.e., confusion, urea nitrogen, respiratory rate, blood pressure, and age\u0026thinsp;\u0026ge;\u0026thinsp;65 years,) and SOFA score at admission were calculated [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. During hospitalization, we recorded whether the patients had specific complications, including multilobar infiltration, pleural effusions, ARDS [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], septic shock [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and acute renal failure [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Additional details are provided elsewhere [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. All surviving patients were visited or contacted by telephone 30 days after discharge, and the hospital records and the database of Catalunya Health Department reviewed at 1 year. The criteria for etiological diagnosis are described elsewhere [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and in Supplementary data.\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was 30-day mortality. Secondary outcomes were in-hospital mortality, 1-year mortality, and need for mechanical ventilation.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eCategorical variables were described using frequencies and percentages, while continuous variables were reported as means and standard deviations (SD) or as medians with interquartile ranges (IQR) when not normally distributed, based on the Kolmogorov-Smirnov test. Comparisons between categorical variables were made using the chi-square test or Fisher\u0026rsquo;s exact test, as appropriate. For continuous variables, the Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test was used when assumption of normality was met; otherwise, the nonparametric Mann-Whitney \u003cem\u003eU\u003c/em\u003e test was applied.\u003c/p\u003e\u003cp\u003eLinear regression models were used to assess the association between log-transformed eosinophil counts and other hematological parameters (lymphocytes, leukocytes, neutrophils, and C-reactive protein). Interaction terms were included to evaluate whether systemic corticosteroid use modified these associations.\u003c/p\u003e\u003cp\u003eDifferences in 30-day and 1-year mortality between study groups were evaluated using the Kaplan-Meier method, with comparisons made via the Gehan-Breslow-Wilcoxon test. Univariable and Cox proportional hazards regression analyses[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] were conducted to identify predictors of 30-day mortality (dependent variable). Variables with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.25 in univariable analyses were included in the multivariable Cox regression model. Final variable selection was performed using a backward stepwise method based on the likelihood ratio test (entry criterion: p\u003csub\u003ein\u003c/sub\u003e\u0026lt;0.05; removal criterion: p\u003csub\u003eout\u003c/sub\u003e\u0026gt;0.10). Patients lost to follow-up were censored in the survival analyses. Hazard ratios (HRs) with 95% confidence intervals (CIs) were reported. Model discrimination in the multivariable Cox regression was assessed using Harrell\u0026rsquo;s C-index and Somers\u0026rsquo; D statistics. Collinearity was evaluated using Pearson\u0026rsquo;s correlation coefficient (\u003cem\u003er\u003c/em\u003e), and multicollinearity was assessed using the variance inflation factor (VIF). Internal validation of the prediction model was conducted using ordinary nonparametric bootstrapping with 1,000 bootstrap samples and bias-corrected, accelerated 95% CIs[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Missing data were handled using multiple imputation[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe ability of eosinophil counts to differentiate survivors from non-survivors was assessed using the area under the receiver operating characteristic (ROC) curve (AUC). Comparisons of the AUCs were performed according to the method described by DeLong et al.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eAll statistical tests were two-tailed, with a significance level set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All analyses were performed using SPSS version 26.0 for Windows (IBM Corp., Armonk, NY, USA) and R version 4.4.2 (R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy population\u003c/h2\u003e\n \u003cp\u003eA total of 4,984 patients with CAP were admitted to our hospital during the study period. After excluding 4,488 patients who did not meet eligibility criteria, 496 patients with severe CAP admitted to the ICU were included in the final analysis (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Of these, 163 patients (33%) had eosinopenia, while 333 (67%) did not.\u003c/p\u003e\n \u003cp\u003eComparison of clinical characteristics, laboratory findings, treatment, and outcomes between patients with and without eosinopenia at admission are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eClinical characteristics\u003c/h3\u003e\n\u003cp\u003ePatients in the eosinopenia group had a median age of 69 years (IQR 57\u0026ndash;79), which was not significantly different from the non-eosinopenia group (66 years, IQR 54\u0026ndash;79; p\u0026thinsp;=\u0026thinsp;0.136). The proportion of male patients was also similar (63% vs. 68%; p\u0026thinsp;=\u0026thinsp;0.241). No significant differences were observed in smoking status, alcohol abuse, or the presence of chronic pulmonary, neurologic, liver, or renal disease.\u003c/p\u003e\n\u003cp\u003eHowever, eosinopenic patients had a lower overall rate of comorbidities (63% vs. 75%; p\u0026thinsp;=\u0026thinsp;0.005) but showed a higher prevalence of previous neoplasms (17% vs. 7%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Chronic heart disease was less common in this group (9% vs. 16%; p\u0026thinsp;=\u0026thinsp;0.029). They were also less likely to use inhaled corticosteroids prior to admission (9% vs. 20%; p\u0026thinsp;=\u0026thinsp;0.005) and had received pneumococcal vaccination more frequently (27% vs. 15%; p\u0026thinsp;=\u0026thinsp;0.004). A decreased level of consciousness at admission was significantly more common among non-eosinopenic patients (41% vs. 26%; p\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eLaboratory and severity scores\u003c/h2\u003e\n \u003cp\u003eAt admission, eosinopenic patients had significantly lower leukocyte (7.8 vs. 14.1 \u0026times;10⁹/L; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), neutrophil (6.5 vs. 11.4 \u0026times;10⁹/L; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and lymphocyte counts (0.51 vs. 0.91 \u0026times;10⁹/L; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with eosinophil counts effectively absent (0 vs. 0.12 \u0026times;10⁹/L; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The PSI score was lower in the eosinopenia group (median 111.5 vs. 123; p\u0026thinsp;=\u0026thinsp;0.049), while SOFA scores were similar between groups. No differences were observed in C-reactive protein, serum creatinine, glycemia, or PaO₂/FiO₂ ratios.\u003c/p\u003e\n \u003cp\u003eIn linear regression analysis (Supplementary Table\u0026nbsp;1), log-transformed eosinophil counts were positively associated with lymphocyte, leukocyte, and neutrophil counts (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but not with C-reactive protein levels (p\u0026thinsp;=\u0026thinsp;0.055). Interaction terms with systemic corticosteroid use were not statistically significant (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.1), indicating that these associations were independent of corticosteroid treatment. (Supplementary Figs.\u0026nbsp;1 to 4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eMicrobial etiology\u003c/h2\u003e\n \u003cp\u003eA defined microbiological etiology was more frequently established in eosinopenic patients (71% vs. 56%; p\u0026thinsp;=\u0026thinsp;0.002). Respiratory viral infections were significantly more common in the eosinopenia group (12% vs. 5%; p\u0026thinsp;=\u0026thinsp;0.003), as were polymicrobial infections (17% vs. 8%; p\u0026thinsp;=\u0026thinsp;0.003). The frequency of \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e and other specific pathogens did not differ between groups.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eTreatment and clinical course\u003c/h2\u003e\n \u003cp\u003eEmpirical antibiotic therapy did not differ between groups in terms of monotherapy (10% in both; p\u0026thinsp;=\u0026thinsp;0.984) or overall use of combination therapy (90% in both; p\u0026thinsp;=\u0026thinsp;0.984). However, \u0026beta;-lactam plus macrolide regimens were more commonly used in eosinopenic patients (41% vs. 22%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas \u0026beta;-lactam plus fluoroquinolone combinations were more frequent in non-eosinopenic patients (54% vs. 37%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cp\u003eInvasive mechanical ventilation was significantly more frequent in the eosinopenia group (58% vs. 45%; p\u0026thinsp;=\u0026thinsp;0.009), although the need for non-invasive ventilation was similar (p\u0026thinsp;=\u0026thinsp;0.350). Complications such as pleural effusion (30% vs. 19%; p\u0026thinsp;=\u0026thinsp;0.008) and septic shock (34% vs. 43%; p\u0026thinsp;=\u0026thinsp;0.046) occurred more frequently in eosinopenic patients. There were no significant differences in rates of ARDS, bacteremia, sepsis, or acute renal failure.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eOutcomes\u003c/h2\u003e\n \u003cp\u003eEosinopenia was associated with worse clinical outcomes, including higher in-hospital mortality (21% vs. 13%; p\u0026thinsp;=\u0026thinsp;0.036) and 30-day mortality (20% vs. 12%; p\u0026thinsp;=\u0026thinsp;0.022). One-year mortality was also higher in the eosinopenia group, although the difference did not reach statistical significance (25% vs. 18%; p\u0026thinsp;=\u0026thinsp;0.056). Median length of hospital stay did not differ significantly between groups. However, among survivors, eosinopenic patients tended to have longer hospitalizations (median 17 vs. 14 days; p\u0026thinsp;=\u0026thinsp;0.053).\u003c/p\u003e\n \u003cp\u003eKaplan\u0026ndash;Meier survival curves showed significantly lower survival in patients with eosinopenia both at 30 days (p\u0026thinsp;=\u0026thinsp;0.010; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) and at 1 year (p\u0026thinsp;=\u0026thinsp;0.030; Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), compared to non-eosinopenic patients.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003ePredictors of 30-day mortality: multivariable analysis\u003c/h2\u003e\n \u003cp\u003eMultivariable Cox regression identified eosinopenia as an independent predictor of 30-day mortality (HR 1.98; 95% CI 1.23 to 3.16; p\u0026thinsp;=\u0026thinsp;0.005), along with age\u0026thinsp;\u0026ge;\u0026thinsp;65 years (HR 2.92; 95% CI 1.67 to 5.11; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), chronic liver disease (HR 3.20; 95% CI 1.69 to 6.05; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and absence of fever at admission (HR 0.61; 95% CI 0.39 to 0.98; p\u0026thinsp;=\u0026thinsp;0.042).\u003c/p\u003e\n \u003cp\u003eInternal validation of the Cox regression model using bootstrapping with 1,000 samples demonstrated robust results for all the variables included in the model, with small 95% CIs around the original coefficients (Supplementary Table\u0026nbsp;2).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eComparison of eosinophil count and clinical severity scores in predictive accuracy\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presents ROC curve analyses comparing eosinophil count, CRB-65, CURB-65, and PSI as predictors. The AUC for eosinophil count was 0.562 (95% CI 0.487 to 0.637), which is close to random chance and indicates poor predictive ability. In contrast, CRB-65 (AUC 0.635, 95% CI 0.560 to 0.711), CURB-65 (AUC 0.655, 95% CI 0.581 to 0.728), and PSI (AUC 0.654, 95% CI 0.576 to 0.733) all show moderate predictive accuracy and outperformed eosinophil count.\u003c/p\u003e\n \u003cp\u003eWhen comparing AUCs, eosinophil count was not significantly different from CRB-65 (p\u0026thinsp;=\u0026thinsp;0.121), but it was significantly worse than both CURB-65 (p\u0026thinsp;=\u0026thinsp;0.043) and PSI (p\u0026thinsp;=\u0026thinsp;0.029). Overall, eosinophil count alone is a weak predictor of 30-day mortality, whereas established severity scores, particularly CURB-65 and PSI, provide substantially better prognostic discrimination.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eImprovement of the CURB-65 score\u0026rsquo;s ability to predict mortality by adding eosinophil count\u003c/h2\u003e\n \u003cp\u003ePatients with eosinopenia were assigned an additional extra point in the CURB-65 score to create the \u0026ldquo;CURB-65Eos\u0026rdquo; score (\u0026ldquo;Eos\u0026rdquo; indicating \u0026ldquo;eosinopenia\u0026rdquo;). AUROC analysis comparing CURB-65 with CURB-65Eos showed a numerically higher AUC for the new score; however, this difference did not reach statistical significance according to the DeLong test[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e] (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings showed that patients with eosinopenia and SCAP had a more severe clinical course, including higher rates of invasive mechanical ventilation, septic shock, pleural effusion, and in-hospital mortality, despite having lower rate of chronic comorbidities compared with non-eosinopenic patients. Additionally, microbial etiology was identified in a higher proportion of patients with eosinopenia, polymicrobial and viral infections being the most frequent etiologies identified, this may suggest a potential link between eosinopenia and increased pathogen burden. Age\u0026thinsp;\u0026ge;\u0026thinsp;65 years, chronic liver disease, fever and eosinopenia (\u0026lt;\u0026thinsp;0.05\u0026times;10⁹/L) were independent predictors of 30-day mortality in patients with SCAP, eosinopenia was a factor nearly double the risk of mortality even after adjusting for age and comorbidities.\u003c/p\u003e\u003cp\u003ePrevious studies have reported eosinopenia as a good predictor of worse outcome in patients with sepsis[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], ARDS[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], COPD[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], COVID-19[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and in patients with pneumonia[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, there are controversial results about the prognostic value of eosinopenia in patients with CAP, and there are limited data in patients with severe CAP. A small retrospective study reported that eosinopenia was a strong predictor of 18-month mortality and was associated with severe infection in patients with acute exacerbation of COPD and CAP[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. More recently, a large retrospective multicenter study, reported that eosinopenia was associated with an increase in in-hospital mortality, need for mechanical ventilation, risk of sepsis, as well as longer hospital stay in survivors and shorter time to in-hospital death[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, in the study, the comorbidities, especially COPD and asthma may confound the association between eosinopenia and outcomes, particularly because there was not and adjusted for in multivariable analyses, and the use of corticosteroids in patients with chronic lung disease also could create bias, since decrease the eosinophil counts[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Our findings expand the results of this previous study, since we adjusted for age and comorbidities in multivariable Cox regression and confirmed eosinopenia as an independent predictor of 30-day mortality. While our cohort included patients with chronic pulmonary disease, the prevalence of corticosteroid use was relatively low, and eosinopenia remained predictive of poor outcomes even after accounting for prior inhaled corticosteroid treatment. Importantly, in our linear regression analyses, eosinophil counts were positively correlated with lymphocyte, leukocyte, and neutrophil counts, but not with C-RP levels, suggesting that eosinophil depletion occurs in parallel with other leukocyte populations rather than as a reflection of systemic inflammation. Moreover, the use of corticosteroid did not affect these associations, which suggest that the observed eosinophil\u0026ndash;leukocyte relationships were independent of steroid use. This confirm that eosinopenia reflects an intrinsic dysregulation of the immune response in severe infection rather than a pharmacologic effect.\u003c/p\u003e\u003cp\u003eOur results highlight the potential of eosinopenia as a reliable and inexpensive biomarker to identify patients with severe pneumonia who are at risk of severe disease and poor outcomes. Together with age\u0026thinsp;\u0026ge;\u0026thinsp;65 years and chronic liver disease, as previous reports suggest that severity of pneumonia increases with age and number of comorbidities[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Interestingly, the presence of fever was associated with lower risk of mortality, which may be explain with the results of a previous study of our group that reported fever associated with lower risk of drug resistant pathogens[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur results show that eosinopenia is a factor associated with nearly double the risk of mortality, independent of age and comorbidities. This suggests the possibility to incorporate the eosinophil count as a prognostic biomarker to improve the early identification of high-risk patients and help in the intensification of therapeutic interventions.\u003c/p\u003e\u003cp\u003eIn our study, microbial etiology was identified in a higher proportion of SCAP patients with eosinopenia, with polymicrobial and viral infections being the most frequent identified. This suggests a possible potential link between eosinopenia and increased pathogen burden. A similar observation was reported in a study of patients with severe exacerbation of COPD, where the authors found a higher identification of bacteria in patients with eosinopenia[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. During the COVID-19 pandemic, eosinopenia was reported as a reliable biomarker of SARS-CoV-2 infection[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This may be explained by the fact that viral infection can suppress eosinophils, potentially indicating increased viral load and severe disease[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn our study, the prognostic value of eosinopenia alone was lower than that of other severity scores, and even when added to CURB-65, its predictive power remained limited. This indicates that eosinopenia may serve as a complementary biomarker to pneumonia severity scores.\u003c/p\u003e\u003cp\u003eThe strengths of our study include the number of real-world patients with severe CAP. Our study has some limitations, though, beginning with its retrospective nature. However, the data of all patients included were collected consecutively and prospectively according to our study protocol for CAP which reduce potential bias. Second, the study was carried out in a single-center teaching hospital in Spain, consequently the findings of our study need to be confirmed in an external validation and in other populations. However, we performed an internal validation using a bootstrap resampling approach with 1,000 iterations, which provided a robust assessment of the model\u0026rsquo;s stability and potential bias despite the absence of external validation.\u003c/p\u003e\u003cp\u003eIn conclusion, eosinopenia is strongly associated with increased severity and mortality in patients with severe CAP, independent of age and comorbidities. It may serve as simple and inexpensive biomarker for the early identification of high-risk patients and could help guide more intensive therapeutic interventions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCommunity-acquired pneumonia (CAP)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIntensive care unit (ICU)\u003c/p\u003e\n\u003cp\u003ePneumonia severity index (PSI)\u003c/p\u003e\n\u003cp\u003eConfusion, Urea, Respiratory rate, Blood pressure, and age 65 years or older (CURB65)\u003c/p\u003e\n\u003cp\u003eInfectious Disease Society of America (IDSA)\u003c/p\u003e\n\u003cp\u003eAmerican Thoracic Society (ATS)\u003c/p\u003e\n\u003cp\u003eC-reactive protein (CRP)\u003c/p\u003e\n\u003cp\u003eProcalcitonin (PCT)\u003c/p\u003e\n\u003cp\u003eAcute respiratory distress syndrome (ARDS)\u003c/p\u003e\n\u003cp\u003eChronic obstructive pulmonary disease (COPD)\u003c/p\u003e\n\u003cp\u003eSevere CAP (SCAP)\u003c/p\u003e\n\u003cp\u003eSequential Organ Failure Assessment (SOFA)\u003c/p\u003e\n\u003cp\u003eStandard deviations (SD)\u003c/p\u003e\n\u003cp\u003eInterquartile ranges (IQR)\u003c/p\u003e\n\u003cp\u003eHazard ratios (HRs)\u003c/p\u003e\n\u003cp\u003econfidence intervals (CIs)\u003c/p\u003e\n\u003cp\u003eVariance inflation factor (VIF)\u003c/p\u003e\n\u003cp\u003eReceiver operating characteristic (ROC)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor publication purposes, the study was approved by the Ethics Committee of our institution (\u003cem\u003eComit\u0026eacute; \u0026Egrave;tic d\u0026rsquo;Investigaci\u0026oacute; Cl\u0026iacute;nica\u003c/em\u003e, register: 2009/5451). The need for written informed consent was waived because of the non-interventional study design.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Ciber de Enfermedades Respiratorias (CibeRes CB06/06/0028), and 2009 Support to Research Groups of Catalonia 911; IDIBAPS. Dr Cilloniz is the recipient of a SEPAR fellowship 2024.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are indebted to all medical and nursing colleagues for their assistance and cooperation in this study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePletz MW, Jensen AV, Bahrs C, et al. Unmet needs in pneumonia research: a comprehensive approach by the CAPNETZ study group. Respir Res. 2022;23:239.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCill\u0026oacute;niz C, Torres A, Niederman MS. Management of pneumonia in critically ill patients. BMJ. 2021;375:e065871.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTorres A, Cilloniz C, Niederman MS, et al. Pneumonia. Nat Rev Dis Primers. 2021;7:25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCilloniz C, Videla A, Peric\u0026agrave;s JM. 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JAMA. 2016;315:801\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eARDS Definition Task Force, Ranieri VM, Rubenfeld GD, et al. Acute respiratory distress syndrome: the Berlin Definition. JAMA. 2012;307:2526\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeckler BC, Pott H, Race A, et al. Eosinopenia as Predictor of Disease Severity in Patients With Community-Acquired Pneumonia: An Observational Study. CHEST Elsevier. 2024;166:1329\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMao Y, Qian Y, Sun X, et al. Eosinopenia Predicting Long-term Mortality in Hospitalized Acute Exacerbation of COPD Patients with Community-acquired Pneumonia-A Retrospective Analysis. Int J Chron Obstruct Pulmon Dis. 2021;16:3551\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVincent JL, Moreno R, Takala J, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med. 1996;22:707\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFine MJ, Auble TE, Yealy DM, et al. A prediction rule to identify low-risk patients with community-acquired pneumonia. N Engl J Med. 1997;336:243\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLim WS, van der Eerden MM, Laing R, et al. Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax. 2003;58:377\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRhodes A, Evans LE, Alhazzani W, et al. Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016. Intensive Care Med. 2017;43:304\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBellomo R, Ronco C, Kellum JA et al. Acute renal failure - definition, outcome measures, animal models, fluid therapy and information technology needs: the Second International Consensus Conference of the Acute Dialysis Quality Initiative (ADQI) Group. \u003cem\u003eCrit Care\u003c/em\u003e 2004; 8: R204-212.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCill\u0026oacute;niz C, Ewig S, Polverino E, et al. Microbial aetiology of community-acquired pneumonia and its relation to severity. Thorax. 2011;66:340\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCill\u0026oacute;niz C, Polverino E, Ewig S, et al. Impact of age and comorbidity on cause and outcome in community-acquired pneumonia. Chest. 2013;144:999\u0026ndash;1007.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDavid Collett H. Modelling Survival Data in Medical Research. Stat Med. 1995;14:1147\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEfron B, Tibshirani R. An introduction to the bootstrap. Monographs on statistics and applied probability. New York: Chapman and Hall.; 1993.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSterne JAC, White IR, Carlin JB, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWebb BJ, Sorensen J, Jephson A, et al. Broad-spectrum antibiotic use and poor outcomes in community-onset pneumonia: a cohort study. Eur Respir J. 2019;54:1900057.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach. \u003cem\u003eBiometrics\u003c/em\u003e [Wiley, International Biometric Society]; 1988; 44: 837\u0026ndash;845.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePinte L, Dumitru A-C, Usurelu A-C, et al. Low eosinophils and their dynamic as a predictor of death in patients with infections: a systematic review and meta-analysis of cohort studies. Ann Med. 2025;57:2541084.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeckler BC, Pott H, Race A, et al. Eosinopenia as Predictor of Disease Severity in Patients With Community-Acquired Pneumonia: An Observational Study. Chest. 2024;166:1329\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKirac A, Satici C. Do Eosinopenic Patients With Community-Acquired Pneumonia Really Have a Worse Outcome? \u003cem\u003eCHEST\u003c/em\u003e 2025; 168: e15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCill\u0026oacute;niz C, Domined\u0026ograve; C, Ielpo A, et al. Risk and Prognostic Factors in Very Old Patients with Sepsis Secondary to Community-Acquired Pneumonia. J Clin Med. 2019;8:961.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCill\u0026oacute;niz C, Calabretta D, Palomeque A, et al. Risk Factors and Outcomes Associated With Polymicrobial Infection in Community-Acquired Pneumonia. Arch Bronconeumol. 2025;61:408\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePrina E, Ranzani OT, Polverino E, et al. Risk factors associated with potentially antibiotic-resistant pathogens in community-acquired pneumonia. Ann Am Thorac Soc. 2015;12:153\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang F, Yang L, Wang M, et al. Association between blood eosinophil count and bacterial infection and clinical outcomes in patients with severe exacerbations of chronic obstructive pulmonary disease. Chin Med J (Engl). 2021;135:462\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSoni M. Evaluation of eosinopenia as a diagnostic and prognostic indicator in COVID-19 infection. Int J Lab Hematol. 2021;43(Suppl 1):137\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarakonstantis S, Gryllou N, Papazoglou G, et al. Eosinophil count (EC) as a diagnostic and prognostic marker for infection in the internal medicine department setting. Rom J Intern Med. 2019;57:166\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMacchia I, La Sorsa V, Urbani F, et al. Eosinophils as potential biomarkers in respiratory viral infections. Front Immunol. 2023;14:1170035.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Clinical characteristics, treatment, and outcomes according to the presence of eosinopenia at hospital admission\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"640\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEosinopenia\u003cbr\u003e (\u0026lt;0.05 x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=163)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-eosinopenia\u003cbr\u003e (\u0026ge;0.05 x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(N=333)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eAge, median (Q1; Q3), years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e69 (57; 79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e66 (54; 79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eMale sex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e103 (63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e228 (68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eSmoking habit (current smoke), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e48 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e81 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eAlcohol abuse (current alcohol), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e29 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e59 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eComorbidities, n (%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e103 (63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e249 (75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e41 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e83 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eChronic lung disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e56 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e132 (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eNeurologic disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e24 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e54 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eChronic liver disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e14 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e20 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eChronic heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e14 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e52 (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eChronic renal disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e17 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e36 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003ePrevious neoplasm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e26 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e21 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eNursing-home, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e6 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e25 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003ePrevious pneumonia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e13 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e34 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eDays since initial symptoms to admission, median (Q1; Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e3 (2; 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e4 (2; 7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eCough, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e110 (69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e230 (73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eDyspnoea, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e122 (76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e240 (77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eFever, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e111 (69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e208 (65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eDecreased level of consciousness, n (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e42 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e135 (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eTreatment before admission, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 283px;\"\u003e\n \u003cp\u003eSystemic corticosteroids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e11 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e32 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eInhaled Corticosteroids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e15 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e62 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003ePrevious Antibiotic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e24 (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e55 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eInfluenza vaccine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e46 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e108 (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003ePneumococcal vaccine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e37 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e40 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eCharacteristics at admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eRespiratory rate \u0026ge;30 rpm, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e63 (46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e149 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003ePSI score, median (Q1; Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e111.5 (86.5; 147.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e123 (104; 146)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eSOFA score, median (Q1; Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e4 (3; 6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e4 (3; 6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eLaboratory findings, median (Q1; Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 283px;\"\u003e\n \u003cp\u003eGlycemia, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e13.3 (10.8; 18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e14.2 (11; 20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eLeucocyte count, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e7.8 (3.6; 13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e14.1 (9.9; 20.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eNeutrophil count, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e6.5 (2.7; 11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e11.4 (7.8; 16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eLymphocyte count, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0.51 (0.31; 0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e0.91 (0.5; 1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eNeutrophil/Lymphocyte ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e12.6 (6.2; 21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e13.1 (7.3; 22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eEosinophil count, 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0 (0; 0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e0.12 (0.1; 0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eC-reactive protein, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e19.3 (12.8; 28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e19.9 (9.1; 28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eSerum creatinine, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e1.5 (0.9; 2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e1.5 (1.0;2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003ePaO\u003csub\u003e2\u003c/sub\u003e/FiO\u003csub\u003e2\u003c/sub\u003e ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e210 (161; 280)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e233 (173; 281)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eEmpiric antibiotic therapy, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 283px;\"\u003e\n \u003cp\u003eMonotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e16 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e33 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eFluoroquinolones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e2 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e17 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.035\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u0026beta;-lactams\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e14 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e15 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eOther therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e1 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026gt;0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eCombination therapies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e142 (90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e291 (90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u0026beta;-lactams plus fluoroquinolones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e58 (37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e175 (54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e\u0026beta;-lactams plus macrolides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e65 (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e70 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eOther combination therapies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e19 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e46 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eRespiratory support, n (%)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eNon-invasive mechanical ventilation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e19 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e53 (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eInvasive mechanical ventilation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e85 (58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e146 (45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eComplications during admission, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 283px;\"\u003e\n \u003cp\u003ePleural effusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e47 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e62 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eSepsis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e89 (90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e285 (94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eMultilobar infiltration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e99 (61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e186 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eARDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e45 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e74 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eSeptic shock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e54 (34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e139 (43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.046\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eBacteremia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e38 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e57 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eAcute renal failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e83 (52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e168 (53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eOutcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 283px;\"\u003e\n \u003cp\u003eIn-hospital mortality, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e33 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e44 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.036\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e30-day mortality, n (%)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e32 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e40 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003e1-year mortality, n (%)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e40 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e59 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eLength of hospital stay, median (Q1; Q3), days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 283px;\"\u003e\n \u003cp\u003eAll patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e16 (10; 29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e14.5 (10; 27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eSurviving patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e17 (10; 35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e14 (10; 27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eNon-surviving patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e11 (4; 19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e16 (9.5; 24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eLength of ICU stay, median (Q1; Q3), days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\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: 283px;\"\u003e\n \u003cp\u003eAll patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e16 (10; 29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e14.5 (10; 27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eSurviving patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e17 (10; 35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e14 (10; 27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 283px;\"\u003e\n \u003cp\u003eNon-surviving patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e11 (4; 19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e16.5 (9.5; 24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations.\u003c/em\u003e Q1 indicates first quartile; Q3, third quartile; PSI, pneumonia severity index; SOFA, sequential organ failure assessment; PaO\u003csub\u003e2\u003c/sub\u003e, partial pressure of arterial oxygen; FiO\u003csub\u003e2\u003c/sub\u003e, fraction of inspired oxygen; ARDS, acute respiratory distress syndrome; ICU, intensive care unit.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Percentages calculated on non-missing data. P-values marked in bold indicate numbers that are statistically significant at the 95% confidence limit. \u003csup\u003ea\u003c/sup\u003e May have \u0026gt;1 comorbid condition. \u003csup\u003eb\u003c/sup\u003e Patients who initially received non-invasive ventilation but subsequently needed intubation were included in the invasive mechanical ventilation group. \u003csup\u003ec\u003c/sup\u003e Calculated only for patients with 30-day follow-up (162 in the eosipenia group and 333 in the non-eosipenia group).\u0026nbsp;\u003csup\u003ed\u003c/sup\u003e Calculated only for patients with 1-year follow-up (158 in the eosipenia group and 330 in the non-eosipenia group).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Microbial etiology\u003c/strong\u003e \u003cstrong\u003eaccording to eosinopenia status\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"569\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEosinopenia\u003cbr\u003e (\u0026lt;0.05 x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N=163)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-eosinopenia\u003cbr\u003e (\u0026ge;0.05 x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(N=333)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003eDefined etiology, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e115 (71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e187 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003eMonomicrobial, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e87 (53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e160 (48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e44 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e93 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003eRespiratory virus, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e19 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e15 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u003cem\u003eLegionella pneumophila\u003c/em\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e7 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e6 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u003cem\u003eHaemophilus influenzae\u003c/em\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e8 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e3 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e6 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026gt;0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003eAtypical bacteria, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e2 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e5 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026gt;0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e3 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e12 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u003cem\u003eGNEB, n (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e4 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e5 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e3 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026gt;0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e3 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e2 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003eOther pathogens, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e4 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e10 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026gt;0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 249px;\"\u003e\n \u003cp\u003ePolymicrobial, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e28 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e27 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations.\u003c/em\u003e GNEB, Gram-negative enteric bacteria.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Percentages calculated on non-missing data. P-values marked in bold indicate numbers that are statistically significant at the 95% confidence limit. Atypical bacteria include \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e, \u003cem\u003eChlamydophila pneumoniae\u003c/em\u003e, and \u003cem\u003eCoxiella burnetti\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Significant univariable Cox regression analyses for variables associated with 30-day mortality and independent predictors of 30-day mortality determined by multivariable Cox regression analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"655\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariable \u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariable \u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eAge \u0026ge;65 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.39 to 4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e2.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.67 to 5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003ePneumococcal vaccine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.95 to 2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eChronic heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.78 to 2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eChronic renal disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.91 to 3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eChronic liver disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e2.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.46 to 5.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.69 to 6.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.01 to 2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eChronic neurological disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.91 to 2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eFever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.37 to 0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.39 to 0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eReduced level of consciousness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.07 to 2.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eCreatinine \u0026ge;1.5 mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.86 to 2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eEtiology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eUnknown / Non-infectious etiology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eBacterial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.90 to 2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eRespiratory virus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.66 to 4.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eOther pathogens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.34 to 6.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003ePolymicrobial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.80 to 3.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eAdequate therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.32 to 1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eEosinopenia (\u0026lt;0.05 x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.10 to 2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e1.23 to 3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations.\u003c/em\u003e HR indicates hazard ratio. CI, confidence interval.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Data are presented as estimated HRs (95% CIs) of the explanatory variables in the 30-day mortality group. The HR is defined as the ratio of the hazard rates corresponding to the conditions described by two levels of an explanatory variable (the hazard rate is the risk of death [i.e., the probability of death], given that the patient has survived up to a specific time). The P-value is based on the null hypothesis that all HRs relating to an explanatory variable equal unity (no effect). \u003csup\u003ea\u003c/sup\u003e The variables analyzed in the univariable analyses were age, sex, smoking habit, alcohol habit, pneumococcal vaccine, influenza vaccine, systemic corticosteroid, inhaled corticosteroid, previous antibiotic, chronic pulmonary disease, chronic heart disease, chronic renal disease, chronic liver disease, diabetes mellitus, neurological disease, previous neoplasm, fever, confusion, PSI, creatinine, C-reactive protein, leucocytes count, lymphocytes count, multilobar infiltration, etiology, and appropriate therapy. \u003csup\u003eb\u003c/sup\u003e Harrell\u0026apos;s C statistic is 0.68 and Somers\u0026apos; D statistic is 0.37.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"pneumonia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pneu","sideBox":"Learn more about [Pneumonia](http://pneumonia.biomedcentral.com)","snPcode":"41479","submissionUrl":"https://submission.nature.com/new-submission/41479/3","title":"Pneumonia","twitterHandle":"@pneumoniajourn","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pneumonia, community-acquired pneumonia, eosinopenia, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-7899511/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7899511/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eEosinopenia has been reported as a marker of severity in infections, but its prognostic value in patients with severe community-acquired pneumonia (CAP) admitted to the intensive care unit (ICU) is unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe retrospectively analyzed 496 patients with severe CAP admitted to the ICU, stratified by presence of eosinopenia at admission. Clinical characteristics, laboratory data, microbiological etiology, treatment, and outcomes were compared. Multivariable Cox regression identified independent predictors of 30-day mortality. Predictive performance of eosinophil counts, severity scores (CRB-65, CURB-65, PSI), and a modified CURB-65 incorporating eosinopenia (CURB-65Eos) were assessed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eEosinopenia was detected in 33% of patients. There were no differences in age and comorbidities between eosinopenic and non eosinopenic patients. Compared to non-eosinopenic patients, these patients had lower leukocyte, neutrophil, and lymphocyte counts and more frequent viral or polymicrobial infections. They more often required invasive mechanical ventilation (58% vs. 45%, p\u0026thinsp;=\u0026thinsp;0.009) and developed pleural effusions (30% vs. 19%, p\u0026thinsp;=\u0026thinsp;0.008). In-hospital and 30-day mortality were higher in the eosinopenia group (21% vs. 13%, p\u0026thinsp;=\u0026thinsp;0.036; 20% vs. 12%, p\u0026thinsp;=\u0026thinsp;0.022). Eosinopenia independently predicted 30-day mortality (HR 1.98; 95% CI 1.23\u0026ndash;3.16; p\u0026thinsp;=\u0026thinsp;0.005). Eosinophil counts alone had poor predictive accuracy (AUC 0.562), while established severity scores performed moderately. CURB-65Eos showed a numerical but nonsignificant improvement over CURB-65.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eEosinopenia is common in patients with severe CAP admitted to the ICU and is strongly associated with increased severity and mortality independent of age and comorbidities. It may serve as simple and inexpensive biomarker for the early identification of high-risk patients and could help guide more intensive therapeutic interventions.\u003c/p\u003e","manuscriptTitle":"Eosinopenia Predicts Prognosis in Severe Community-Acquired Pneumonia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 07:09:22","doi":"10.21203/rs.3.rs-7899511/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-30T11:19:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-14T12:54:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-03T08:03:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95886542202186677080331309207644384238","date":"2025-12-02T00:05:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"290730332487780272931356825299088052677","date":"2025-11-22T07:44:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-04T00:56:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-20T11:53:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-20T11:51:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Pneumonia","date":"2025-10-19T15:14:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"pneumonia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pneu","sideBox":"Learn more about [Pneumonia](http://pneumonia.biomedcentral.com)","snPcode":"41479","submissionUrl":"https://submission.nature.com/new-submission/41479/3","title":"Pneumonia","twitterHandle":"@pneumoniajourn","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"99ba7703-6fb8-46e0-8232-79d9fa697dcd","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:24:16+00:00","versionOfRecord":{"articleIdentity":"rs-7899511","link":"https://doi.org/10.1186/s41479-026-00196-0","journal":{"identity":"pneumonia","isVorOnly":false,"title":"Pneumonia"},"publishedOn":"2026-03-25 16:12:26","publishedOnDateReadable":"March 25th, 2026"},"versionCreatedAt":"2025-11-14 07:09:22","video":"","vorDoi":"10.1186/s41479-026-00196-0","vorDoiUrl":"https://doi.org/10.1186/s41479-026-00196-0","workflowStages":[]},"version":"v1","identity":"rs-7899511","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7899511","identity":"rs-7899511","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}