The protective role of basophil against sepsis mortality

The protective role of basophil against sepsis mortality | 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 Article The protective role of basophil against sepsis mortality Mingmin Pang, Shaohua Fan, Shihan Zhang, Yanan Li, Hao Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4647257/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: The role of basophils on sepsis prognosis remains understudied and we aimed to investigate the effects of basophil on sepsis mortality. Methodology : Initially, a prospective local cohort was conducted to establish primary connection between basophil count and 28-day mortality. In addition, sepsis patients from Medical Information Mart for Intensive Care (MIMIC) database were extracted for validation purposes. Thirdly, 2-sample Mendelian randomization (MR) was applied from UK Biobank cohort to confirm the causative link between basophil and sepsis death. Lastly, prognostic effect of human granulocyte colony-stimulating factor (G-CSF) by ameliorating basopenia was assessed utilizing MIMIC data. Findings: Independent verification from both MIMIC and local cohort revealed basophil count as a protective factor against mortality (HR = 0.35, 95% CI = 0.28 - 0.42; HR = 0.40, 95% CI = 0.30–0.52). The MR analysis substantiated the causal relationship between basophil and death (OR=0.776, 95% CI [0.637,0.946], P=0.002). Administration of human G-CSF led to an increase in basophil counts and resulted in a notable decrease in mortality among patients with basopenia (HR = 0.74, 95% CI = 0.58 - 0.93). Conclusion: Basophils significantly contribute to protecting against sepsis mortality, and bolstering basophil numbers may be a feasible strategy in reducing mortality. Health sciences/Anatomy/Cells Health sciences/Diseases Health sciences/Medical research Sepsis Septic shock Severe infection Basophils Blood cell counts Mortality Figures Figure 1 Figure 2 Figure 3 Introduction Sepsis, a life-threatening condition that may ensue following various infections, represents a significant portion of the critically ill population. It is a principal cause of health loss globally, characterized by high morbidity and mortality rates[ 1 , 2 ]. The defective host response to infectious agents greatly contributes to the incidence and unfavorable outcomes associated with sepsis. Despite recent advancements in understanding the pathophysiology of sepsis, there is a scarcity of validated treatments targeting the restoration of immune dysregulation. Basophils are immune cells capable of releasing an array of effector molecules, including prostaglandins, proteolytic enzymes, histamine, leukotrienes, tumor necrosis factor-α (TNF-α), and several interleukins (IL-4, IL-6, and IL-13)[ 3 , 4 ]. Known previously for their role in allergic reactions and parasitic immunity 9,10 , more recent studies highlight their significant involvement in a variety of disorders11, most notably autoimmune diseases, allograft fibrosis, and diverse host responses to pathogens[ 5 , 6 ]. Basophils also aid in type 2 (T2) immune responses that serve numerous host-protective functions such as maintenance of metabolic homeostasis, suppression of excessive type 1 inflammation, barrier defense upkeep, and regulation of tissue regeneration[ 4 , 7 ]. Prior studies report an increase in basophil count being associated with worsened prognosis for conditions like coronary artery disease[ 8 ] and prostate cancer [ 9 ]. Yet, the role of basophils in sepsis prognosis remains largely unexplored. Recent literature suggests that patients with diseases exhibiting a T2 immune response have reduced mortality risk from infections [ 10 ]. For instance, in a mouse model of cecal ligation and puncture, basophils were found to boost innate immune responses to bacterial infection, thereby preventing sepsis [ 11 ]. Moreover, in cases of human immunodeficiency virus and Epstein-Barr virus infections, elevated basophil levels often indicate heightened immunoglobulin E levels and increased immunological responses, which are linked to disease progression [ 12 , 13 ]. Given this information, in light of the limited clinical evidence connecting basophils and sepsis outcomes, we hypothesize that basophils might perform a protective role against sepsis mortality. This study aims to substantiate this relationship across different observational cohorts. Material and methods Study population and data source We embarked upon a single-center prospective observational cohort study, hosted in a 20-bed mixed intensive care unit at Qilu Hospital of Shandong University (Qilu Cohort, January 2021 to December 2021). This was undertaken for our primary analysis. The findings were cross-verified with the Medical Information Mart for Intensive Care (MIMIC cohort, 2001–2019) database for further confirmation. Inclusion criteria entailed sepsis patients admitted to the ICU who were aged 16 years or older. Exclusion criteria included: survival time of less than 48 hours, existing conditions that may influence basophil count such as acquired immunodeficiency syndrome and hematologic malignancy, and instances where more than 20% of data was missing. For patients with recurring ICU admissions, only data from the initial ICU admission was inspected. The MIMIC database, comprising MIMIC-III 1.4 and MIMIC IV 1.0 versions, is an open, single-center repository of data on intensive care patients admitted to Beth Israel Deaconess Medical Center (BIDMC). The required authorizations for the utilization of the MIMIC database for research have been granted by the Institutional Review Boards at BIDMC and MIT. A Collaborative Institution Training Initiative Program course must be completed to gain access to this data (Certificate Number 11057215 belongs to one of the authors of this paper, Pang). This project received an informed consent waiver. For our local prospective observational cohort, ethical approval was obtained from the Ethics Committee of Qilu Hospital, Shandong University (approval number KYLL-2018153).Informed Consent was secured from all participants or their legal guardians ahead of their inclusion in the study. The Institutional Review Boards at BIDMC and MIT have approved the use of the MIMIC database for research purposes, and accessing the data requires completion of the collaborative institution training initiative program course (certification number 11057215 for Pang, one of the authors of this paper). For our local prospective observational cohort, the protocol was approved by the Ethics Committee of (approval number KYLL-2018153). The UK Biobank has full ethical approval from the NHS National Research Ethics Service (16/NW/0274). All methods were performed in accordance with the relevant guidelines and regulations. We leveraged summary genetic data to perform Mendelian randomization (MR), affirming the causal link between basophil count and sepsis. Genetic data of basophil were identified from the genome-wide association study (GWAS) on Blood-cell genetics ( http://www.mhi-humangenetics.org/en/resources/ ) [ 14 ] and sepsis 28d-mortality ( https://gwas.mrcieu.ac.uk/datasets/ieu-b-4981/ ). The detailed data sources are shown in Supplemental Table S1 . Variables and outcome Covariates were chosen through consensus, factoring in biological plausibility and known associations, incorporating demographic characteristics (age, gender, ethnicity, insurance status, marital status), underlying medical conditions (Charlson comorbidities), clinical risk factors, laboratory findings, acute severity indicators, and treatment details. The comorbidities, comprising congestive heart failure, chronic obstructive pulmonary disease, diabetes, renal failure, liver disease, solid tumors, and immunosuppression, were pinpointed and extracted in line with the ICD-9 and ICD-10 coding system. Acute severity was gauged using a range of scores: Sequential Organ Failure Assessment (SOFA) score, Simplified Acute Physiology Score II (SAPS II) score, Oxford Acute Severity of Illness Score (OASIS), and Acute Physiology and Chronic Health Evaluation II (APACHE II) score. This study employed extreme laboratory values of certain blood parameters—white blood cell count, basophil count, platelet count, and lactate level—registered within the initial two days following sepsis onset. Clinical treatments encompassed the use of vasopressors, continuous renal replacement therapy (CRRT), and mechanical ventilation during the ICU stay. Given the augmenting impact of granulocyte colony-stimulating factor (G-CSF) on basophil numbers, we recorded human G-CSF usage data from the MIMIC database. Sepsis patients with low basophil counts (counts < 0.33 ×10 9 /L) were stratified into two groups based on G-CSF exposure: one involving patients who received Human G-CSF administration post-ICU admission (labelled as the Human G-CSF group) and the other consisting of patients who never underwent Human G-CSF administration throughout their ICU stay (labelled as the no-Human G-CSF group). The primary outcome evaluated was the 28-day all-cause mortality rate, defined as the occurrence of any cause of death within 28 days since sepsis onset. Statistical analysis The data acquired was meticulously examined using R 4.3.1 software. Patients were categorized into quartiles in accordance with their basophil count. For continuous and categorical variables, the median (Q1–Q3), mean (SD), and number (percentage) were respectively represented. To evaluate continuous parametric data, one-way ANOVA or Student's t-test was utilized. The Wilcoxon rank-sum test was employed to scrutinize continuous nonparametric data while categorical data was appraised using the chi-square (χ2) test. A p-value less than 0.05 (two-tailed) was deemed statistically significant. The determination of survival was facilitated through the application of the Kaplan-Meier method (log-rank test) along with Cox regression analysis. By stratifying patients based on their basophil counts distributed among quartiles, Kaplan-Meier curves were generated to illustrate survival plots. The relationship between basophil count and 28-day mortality was analyzed by employing univariate and multivariate Cox proportional hazards models. To accommodate potential confounding variables, these were added to the models through a stepwise backward selection technique. Hazards ratio (HR) coupled with 95% confidence intervals (CIs) was reported. Multivariable Cox proportional hazard models were used in conjunction with restricted cubic spline (RCS) to investigate the correlation between basophil count and 28-day all-cause mortality. A Propensity Score Matching (PSM) analysis was performed to reduce the disparity between groups with elevated and normal basophil levels. For this purpose, we produced a propensity score with the help of a logistic regression model that integrated all potential covariables and baseline variables linked with disease severity, including demographic parameters, background history, and underlying conditions. This was done to match subjects in both groups utilizing the optimal match method at a 1:1 match rate, through R package MatchIt and optmatch. The standard mean differences were calculated for the evaluation of balance between groups. An additional subgroup analysis was conducted considering patients with baseline basophil levels less than 0.3 * 10 9 /L, to gain greater insights into the role of basophils in sepsis. A 2-sample MR was also performed to authenticate the causal associations between basophils and sepsis. The introduction of all Genome-wide association studies (GWAS) used for MR is detailed out in Supplemental Table S1 . A comprehensive explanation of MR analyses is presented in Supplemental methods. Results Patient characteristics Initially, 361 patients were enrolled in the local cohort. However, we carried out our final analysis using data from only 202 patients (Fig. 1 a). The attributes of the patients, distributed according to their basophil count, are enumerated in Table 1 . A total of 92 patients (45.5%) had a basophil count within 0–0.2 × 10 9 /L, 46 patients (22.8%) within 0.2–0.4 × 10 9 /L, and 67 patients (31.7%) exhibited a basophil count > 0.4 × 10 9 /L. Interestingly, patients with lower basophil counts manifested more severe illness and registered a higher mortality rate ( P 0.4 P N = 92 N = 46 N = 67 Baseline variables Age, years, mean (SD) 58.7 (18.3) 61.0 (17.2) 60.7 (17.8) 0.7 Gender, n (%) 0.141 Female 53 (58.2%) 28 (60.9%) 49 (73.1%) Male 38 (41.8%) 18 (39.1%) 18 (26.9%) Height, median (IQR) 167 (7.68) 169 (7.86) 169 (5.53) 0.277 Weight, median (IQR) 66.8 (15.2) 69.5 (17.1) 71.7 (15.7) 0.131 CCI, median (IQR) 6.00 [4.00–8.00] 6.00 [5.00–7.00] 6.00 [5.00–8.00] 0.883 Underlying diseases, n (%) Congestive heart failure 18 (19.8%) 7 (15.2%) 25 (37.3%) 0.016 Chronic pulmonary disease 17 (18.7%) 10 (21.7%) 12 (17.9%) 0.87 Rheumatic disease 1 (1.09%) 4 (6.06%) 3 (6.52%) 0.225 Liver disease 3 (3.30%) 3 (6.52%) 7 (10.4%) 0.162 Diabetes 25 (27.5%) 9 (19.6%) 15 (22.4%) 0.557 Renal diseases 3 (3.30%) 1 (2.17%) 3 (4.48%) 0.791 Malignant cancer 18 (19.8%) 10 (21.7%) 16 (23.9%) 0.536 Primary sources of infection, n (%) Lower respiratory tract 40 (44.0%) 24 (52.2%) 30 (44.8%) 0.865 Intra-abdominal 18 (19.8%) 4 (8.70%) 7 (10.4%) 0.134 Urinary tract 1 (1.10%) 1 (2.17%) 2 (2.99%) 0.396 Skin and soft tissue 6 (6.59%) 3 (6.52%) 1 (1.49%) 0.315 Central nervous system 7 (7.69%) 5 (10.9%) 5 (7.46%) 0.804 Others 20(21.7%) 9(19.5%) 22(32.8%) 0.368 Severity evaluation and intensity of care SOFA, median [IQR] 7 [5;10] 7 [5;10] 8 [5;13] 0.777 APACHE II, mean (SD) 22.1 (8.66) 21.1 (7.54) 23.1 (8.37) 0.453 Mechanical ventilation, n (%) 67 (73.6%) 34 (73.9%) 51 (77.3%) 0.614 Renal replacement therapy, n (%) 31 (34.1%) 14 (30.4%) 22 (33.8%) 0.904 Laboratory value , median [IQR] WBC, *10 9 /L 15.0 [9.80–21.0] 11.2 [7.30–15.9] 11.2 [6.64–16.1] 0.01 Basophils, *10 9 /L 0.10 [0.00-0.20] 0.20 [0.20–0.40] 0.63 [0.53–0.79] < 0.001 Platelets, *10 9 /L 94.0 [43.0-145] 145 [61.5–212] 188 [120–344] 0.013 Lactate, mmol/L 2.00 [1.30–5.12] 1.40 [1.10–2.35] 1.25 [1.00-1.88] 0.012 Outcome 28d mortality 35 (39.3%) 18 (29.0%) 10 (22.7%) 0.049 Data expressed as median (IQR), mean (sd) or number (percentage). Definition of abbreviations: IQR: interquartile range, CCI: Charlson comorbidity index, SOFA: Sequential Organ Failure Assessment Score, APACHE II: acute physiology and chronic health evaluation II, WBC: White blood cells. Using the MIMIC database, we collected data for 42,393 patients diagnosed with sepsis as per the Sepsis-3 criteria. The final analyzed sample consisted of 20,239 patients (Fig. 1 b). Basic characteristics, clinical trajectory, and disease severity based on basophil levels are represented in Table 2 . Of these, 7,439 (36.7%) had a basophil count between 0–0.1 × 10 9 /L, 4,784 (23.6%) between 0.1–0.2 × 10 9 /L, 5,051(24.9%) within 0.2–0.4 × 10 9 /L and 15,755 (14.8%) > 0.4 x 10 9 /L. As in the Qilu cohort, patients within lower basophil counts displayed more severe illness, indicated by elevated SAPS II, SOFA scores, higher prevalence of mechanical ventilation use, septic shock, and notably increased mortality rates ( P 0.4 P N = 7439 N = 4784 N = 5051 N = 5755 Baseline variables Age, years, median (IQR) 69.6 [57.5–80.0] 68.7 [56.4–79.7] 66.9 [54.6–78.6] 65.8 [54.0-77.4] < 0.001 Gender, n (%) < 0.001 Female 3421 (46.0%) 2100 (43.9%) 2203 (43.6%) 2452 (42.6%) Male 4018 (54.0%) 2684 (56.1%) 2848 (56.4%) 3303 (57.4%) Insurance, n (%): 0.002 Private 2662 (35.8%) 1577 (33.0%) 1753 (34.7%) 1901 (33.0%) Medicaid 328 (4.41%) 212 (4.43%) 240 (4.75%) 314 (5.46%) Medicare 2313 (31.1%) 1516 (31.7%) 1487 (29.4%) 1702 (29.6%) Others 2136 (28.7%) 1479 (30.9%) 1571 (31.1%) 1838 (31.9%) Marital status, n (%) 0.531 Married 3295 (44.3%) 2117 (44.3%) 2222 (44.0%) 2475 (43.0%) Divorced 522 (7.02%) 333 (6.96%) 368 (7.29%) 437 (7.59%) Single 1765 (23.7%) 1188 (24.8%) 1385 (27.4%) 1573 (27.3%) Widowed 1110 (14.9%) 699 (14.6%) 613 (12.1%) 677 (11.8%) Others 747 (10.0%) 447 (9.34%) 463 (9.17%) 593 (10.3%) Race, n (%) 0.503 White 5035 (67.7%) 3193 (66.7%) 3375 (66.8%) 3910 (67.9%) Black 597 (8.03%) 411 (8.59%) 391 (7.74%) 432 (7.51%) Asian 212 (2.85%) 138 (2.88%) 124 (2.45%) 135 (2.35%) Others 1595 (21.4%) 1042 (21.8%) 1161 (23.0%) 1278 (22.2%) CCI, median (IQR) 4.00 [0.00–7.00] 4.00 [0.00–7.00] 4.00 [0.00–7.00] 4.00 [0.00–7.00] 0.719 Underlying diseases, n (%) Myocardial infarct 953 (17.2%) 618 (17.4%) 640 (17.7%) 735 (17.7%) 0.442 Congestive heart failure 1473 (19.8%) 1008 (21.1%) 1056 (20.9%) 1211 (21.0%) 0.085 Chronic pulmonary disease 1507 (27.2%) 973 (27.4%) 966 (26.8%) 1055 (25.5%) 0.053 Rheumatic disease 193 (2.59%) 106 (2.22%) 107 (2.12%) 139 (2.42%) 0.379 Liver disease 954 (17.2%) 554 (15.6%) 597 (16.6%) 776 (18.7%) 0.062 Diabetes 1518(20.38%) 1098(22.95%) 1081(21.36%) 1317(22.89%) 0.09 Renal diseases 1052 (14.1%) 693 (14.5%) 714 (14.1%) 822 (14.3%) 0.919 Malignant cancer 864 (11.6%) 447 (9.34%) 446 (8.83%) 452 (7.85%) < 0.001 Primary sources of infection, n (%) Lower respiratory tract 2909 (39.1%) 1887 (39.4%) 2006 (39.7%) 2223 (38.6%) 0.682 Intra-abdominal 1382 (18.6%) 938 (19.6%) 943 (18.7%) 1140 (19.8%) 0.206 Urinary tract 1586 (21.3%) 1007 (21.0%) 1023 (20.3%) 1150 (20.0%) 0.211 Skin and soft tissue 462 (6.21%) 314 (6.56%) 340 (6.73%) 384 (6.67%) 0.621 Central nervous system 86 (1.16%) 74 (1.55%) 45 (0.89%) 72 (1.25%) 0.027 Bloodstream 365 (4.91%) 215 (4.49%) 241 (4.77%) 299 (5.20%) 0.407 Others 649 (8.72%) 349 (7.29%) 453(8.96%) 487 (8.46%) 0.338 Severity evaluation and intensity of care SOFA, median (IQR) 4.00 [2.00–6.00] 4.00 [2.00–6.00] 4.00 [2.00–6.00] 3.00 [2.00–5.00] < 0.001 SAPS II, median (IQR) 55.0 [40.0–73.0] 54.0 [40.0-72.2] 52.0 [39.0–71.0] 52.0 [37.0–70.0] < 0.001 OASIS, median (IQR) 37.0 [31.0–44.0] 37.0 [31.0–43.0] 36.0 [30.0–42.0] 35.0 [30.0–41.0] < 0.001 Mechanical ventilation, n (%) 4349 (58.5%) 2931 (61.3%) 3139 (62.1%) 3369 (58.5%) 0.004 Renal replacement therapy, n (%) 804 (10.8%) 515 (10.8%) 542 (10.7%) 541 (9.40%) 0.006 Septic shock, n (%) 1680 (23.0%) 795 (17.0%) 812 (16.5%) 743 (13.2%) < 0.001 Laboratory value, median [IQR] WBC, *10 9 /L 14.7 [10.3–21.0] 14.5 [10.4–19.5] 13.7 [10.1–18.5] 12.8 [9.20–17.2] < 0.001 Basophils, *10 9 /L 0.03 [0.00-0.10] 0.15 [0.13–0.20] 0.30 [0.25–0.32] 0.50 [0.40–0.67] < 0.001 Lactate, mmol/L 2.00 [1.40–3.01] 1.80 [1.30–2.68] 1.80 [1.29–2.60] 1.72 [1.25–2.50] < 0.001 Platelets, *10 9 /L 212 [148–294] 216 [156–301] 216 [155–294] 214 [151–288] 0.309 Outcome 28d mortality, n (%) 3227 (43.4%) 1755 (36.7%) 1600 (31.7%) 1804 (31.3%) < 0.001 Data expressed as median (IQR) or number (percentage). Definition of abbreviations: IQR : interquartile range, CCI : Charlson comorbidity index, SOFA : Sequential Organ Failure Assessment Score, SAPS : Simplified Physiology Score, OASIS : oxford acute severity of illness score, WBC : White blood cells. Association between basophil count and 28-day all-cause mortality Supplemental Table S2 lists the risk factors identified for 28-day mortality in our local cohort, as revealed by univariate analysis. Multivariate analysis demonstrated that basophil count independently predicted 28-day mortality in patients with sepsis (HR = 0.40, 95% CI = 0.30–0.52; Fig. 2 a), after adjusting for age, CRRT usage, mechanical ventilation usage, APACHE II scores, and CCI score. Kaplan-Meier analysis delivered results, shown in Fig. S1 , illustrating the 28-day survival curve of patients as per basophil count ( P < 0.001, as per the log-rank test). An L-shaped correlation surfaced when assessing basophil count against 28-day mortality, through the use of RCS based on Cox proportional hazards models (Fig. 3 a). Both Kaplan-Meier analysis and RCS findings affirm that patients with heightened basophil counts incur a diminished risk of mortality. Supplemental Table S3 enlists the 28-day mortality risk factors detected through univariate analysis within the MIMIC cohort. It was found that patients who survived had significantly high basophil counts ( P < 0.001). Furthermore, the multivariate Cox regression model reveals that basophil count is an independent predictor for 28-day mortality (HR = 0.35, 95% CI = 0.3–0.42), even after adjustments for age, Charlson comorbidity index (CCI), liver disease, diabetes, SOFA lactate levels, mechanical ventilation usage, and presence of septic shock (Fig. 2 b). Subsequent to the initial analysis, we estimated the risk of 28-day mortality concerning basophil count utilizing Propensity Score Matching (PSM) cohorts. This was done to harmonize baseline differences between the groups based on their respective basophil counts. Patient characteristics in the PSM MIMIC cohort can be found in Supplemental Table S4 . Univariate analysis was shown in Supplemental Table S5. Multivariate Cox regression analysis executed on the matched cohort reaffirmed that high basophil count offered protection, even after taking into account potential confounders (HR = 0.35, 95% CI = 0.28–0.42; Fig. S2 ). An L-shaped relationship between basophil count and 28-day mortality emerged at a cutoff value of 0.33 × 10 9 /L when we made adjustments for confounding variables (Fig. 3 b). The 28-day survival curves of patients per basophil count are depicted in Fig S3 and S4 ( P < 0.001, according to the log-rank test). Findings from both Kaplan-Meier analysis and RCS indicated a decreased mortality risk in patients with elevated basophil counts. Causal association between basophil and sepsis outcomes The primary MR results can be found in Figure S5 . In the 2-sample MR, the inverse-variance weighted (IVW) estimates were aligned with a causally protective effect of basophils on sepsis patient prognosis (OR = 0.776, 95% CI [0.637,0.946], P = 0.002, Table S6 ). The sensitivity analysis, employing MR-Egger regression, indicated no horizontal pleiotropism (intercept P = 0.115, Table S7 ). The leave-one-out method suggests that no single SNP has a dominant role in the overall assessment ( Fig S6 ). Human granulocyte colony stimulating factor (Human G-CSF) may decrease mortality in patient with basopenia by increasing basophils count To further substantiate the influence of interventions on basophils in sepsis, we conducted an additional analysis in patients with basopenia (basophil counts < 0.33 × 10 9 /L), dividing them into Human G-CSF group and non-Human G-CSF groups. In the Human G-CSF group, there was a significant increase in basophil counts compared with the non-Human G-CSF group ( Fig. S7 ). Basic characteristics are provided in Table S8 . Univariable and multivariable Cox regression analyses of 28-day mortality in basopenia patients revealed that Human G-CSF was an independent protective factor (HR = 0.74, 95% CI = 0.58–0.93; Tables S9 and S10 ). The protective effect of Human G-CSF was also assessed using PSM cohorts. Patient characteristics in these cohorts are presented in Table S11 . Univariable and multivariable Cox regression analysis of 28-day mortality confirmed that Human G-CSF remained an independent protective factor in the PSM cohort (HR = 0.74, 95% CI = 0.58–0.94; Tables S12 and S13 ). Discussion In utilizing a local cohort, supplemented by two expansive cohorts of participants (n > 40,000 and n > 420,000, respectively), we discovered a robust observational correlation between basophils and a heightened threat of sepsis and associated fatalities. MR data further endorses the possibility of a causal influence of basophils per se on sepsis. Importantly, our observations also indicate that administering Human G-CSF caused a rise in basophil counts, which could potentially result in reduced mortality rates among patients suffering from basopenia. Our study contributes the most significant evidence to date supporting the protective capabilities of basophils against sepsis and its mortality. The insights derived from our data suggest that enhancing basophil counts could be a viable strategy to decrease mortality rates. Previous studies have demonstrated basophils playing a role in the prognosis and progression of several disorders. For instance, numerous research findings suggest an association between basophil count and the extent of tumor invasion and progression [ 9 , 15 ]. The effects of basophils are likely mediated through various factors secreted by these cells, including cytokines, lipid inflammatory mediators, and histamines, which regulate inflammatory responses, adaptive immunity, and cell apoptosis. Yet, the underlying mechanisms that enable basophils to exert these effects remain hazy. In a mouse model for myocardial infarction (MI) [ 16 ], lesser basophil levels corresponded to poorer cardiac prognosis. There are propositions suggesting that basophils might enhance tissue repair post-MI by elevating the levels of cardiac IL-4 and IL-13, thereby promoting a shift from inflammatory Ly6Chi monocytes to reparative Ly6Clo macrophages in the cell population. Basophils have also exhibited beneficial function in liver tissue regeneration post Listeria monocytogenes infection [ 17 ]. However, the role of basophils in sepsis remains ambiguous. Acting as initial effector cells in T2 immunity, basophils have been reported in a number of in vitro and in vivo studies to potentially contribute to host responses, such as anti-bacterial mechanisms. For instance, an in vitro study incorporating co-culturing human basophils with Escherichia coli or Staphylococcus aureus demonstrated basophil activation and anti-bacterial activity via the formation of functional extracellular traps[ 18 ]. Moreover, in an invasive pneumococcal disease (IPD) mouse model, basophil transfusion was found to provide protection against IPD by amplifying protein-based memory response against pneumococcal protein antigens[ 19 ]. In a separate sepsis mouse model, basophil transfusion appeared to enhance the innate immune response to bacterial infection by producing tumor necrosis factor and bolstering the anti-bacterial effects of other myeloid cells [ 11 ]. These investigational outcomes reveal the crucial role basophils hold for bacteria eradication. However, the role of basophils in bacterial clearance in human sepsis remains indeterminate. Basophils are known to produce effector molecules, such as IL-4 and IL-13, which are generally attributed to having anti-inflammatory characteristics [ 20 , 21 ]. These insights suggest that basophils may play a protective role in sepsis [ 22 , 23 ]. Clinical studies have shown that patients with severe infections who exhibit a potent T2 immune response tend to have lower mortality rates. Moreover, basophils have been identified as significant players in numerous T2 immunological conditions [ 24 , 25 ]. These findings align seamlessly with our current observations, indicating that basophils may reduce mortality in sepsis. In our analysis utilizing both MIMIC and local cohorts, septic patients with high basophil counts exhibited reduced 28-day mortality, and an L-shaped association was observed between basophil count and 28-day mortality. Additionally, both Kaplan-Meier analysis and RCS indicated a decreased risk of mortality in patients with elevated basophil counts. Therefore, our data illuminates basophils as an independent protective factor against mortality in septic patients. To our knowledge, this constitutes the most comprehensive study to date elucidating the protective effect of basophils in patients with sepsis. Basophils can be triggered through a variety of molecular pathways. Numerous in vitro studies have demonstrated that IL-3 [ 26 , 27 ] or granulocyte/macrophage colony-stimulating factor [ 18 ] is capable of inducing a proliferation in the number of basophils originated from bone marrow forerunners. An in vivo experiment involving mice revealed that thymic stromal lymphopoietin could stimulate an increase in the quantity of peripheral basophils, thereby initiating and rejuvenating T2 immune functionality [ 28 , 29 ]. Looking forward, enhanced in vivo and in vitro studies are indispensable for gaining profound insights into the biological traits of basophils. This knowledge might assist in implementing early interventions, such as basophil transfusion, for sepsis patients to mitigate mortality. Limitations This research harbors several constraints. Initially, it was an observational investigation. Therefore, confounding factors potentially influencing the effect of basophil count on mortality still persist even after accounting for possible confounders through PSM and multivariable Cox regression analysis. To diminish the impact of a likely confounding bias, we have excluded patients known to be exposed to several prominent risk factors. Secondly, our dependency on a single basophil count measurement might overlook the influence of prolonged periods of low basophil levels on mortality. Thirdly, given the retrospective nature of the MIMIC cohort, we couldn't scrutinize the effect of treatment on mortality. Fourthly, our local prospective cohort encompassed a small sample size, mandating further prospective inquiries on a larger scale including diverse ethnic groups longer follow-up durations. Conclusion In conclusion, we've elucidated that basophils are linked to an improved prognosis in sepsis patients. Our findings indicate significant clinical implications, suggesting that basophils exert a protective effect against sepsis and sepsis-induced mortality. Moreover, dynamic monitoring of basophil count could aid clinicians in pinpointing patients with a heightened risk of unfavorable sepsis prognosis. The administration of Human G-CSFs in patients suffering from basopenia may emerge as a revolutionary therapeutic strategy to lessen sepsis mortality, however, future randomized controlled intervention studies are warranted. Abbreviations T2 immune response Type 2 immune response MIMIC Medical Information Mart for Intensive Care PSM Propensity Score Matching TNF-α Tumor Necrosis Factor-α IL Interleukin ICU Intensive Care Unit BIDMC Beth Israel Deaconess Medical Center SOFA Sequential Organ Failure Assessment SAPS II Simplified Acute Physiology Score II OASIS Oxford Acute Severity of Illness Score APACHE II Acute Physiology and Chronic Health Evaluation II HR Hazard Ratio CI Confidence Interval RCS Restricted Cubic Spline MI Myocardial Infarction IPD Invasive Pneumococcal Disease Declarations Disclosure The authors declare that they have no competing interests. Funding This work was supported by the National Natural Science Foundation of China (grant nos. 82072231), Natural Science Foundation of Shandong Province (ZR2023YQ068), and Taishan Scholars Program of Shandong Province (award no. tsqn202103165). Author Contribution Mm P wrote the original draft, collected data and analyzed the data. Sh F wrote the original draft, collected data, and analyzed the data. Sh Z collected Data. Yn L collected Data. HW was responsible for writing the original draft, writing review, project administration, and supervision. All authors read and approved the final manuscript. Acknowledgement We would like to thank the Beth Israel Deaconess Medical Center staff and its MIMIC collaborators who prepared these publicly available data. We also thank UK Biobank participants. This research has been conducted using the UK Biobank Resource (application No 7155). Data Availability The MIMIC datasets used and/or analyzed during the current study are available in the MIMIC database, https://github.com/MIT-LCP/mimic-code/. Researchers are required to complete a collaborative institution training initiative program course in order to access the database. The data of Qilu cohort used and analyzed during our study are available from the corresponding author up-on reasonable request. Genetic data of basophil were identified from the genome-wide association study (GWAS) on (http://www.mhi-humangenetics.org/en/resources/) and sepsis 28day mortality (https://gwas.mrcieu.ac.uk/datasets/ieu-b-4981/). References Rudd KE, Johnson SC, Agesa KM, et al (2020) Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study. Lancet 395:200–211. https://doi.org/10.1016/S0140-6736(19)32989-7 Evans L, Rhodes A, Alhazzani W, et al (2021) Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Med 47:1181–1247. https://doi.org/10.1007/s00134-021-06506-y Shibata S, Miyake K, Tateishi T, et al (2018) Basophils trigger emphysema development in a murine model of COPD through IL-4-mediated generation of MMP-12-producing macrophages. Proc Natl Acad Sci U S A 115:13057–13062. https://doi.org/10.1073/pnas.1813927115 Cianferoni A (2021) Non-IgE-mediated anaphylaxis. J Allergy Clin Immunol 147:1123–1131. https://doi.org/10.1016/j.jaci.2021.02.012 Schiechl G, Hermann FJ, Rodriguez Gomez M, et al (2016) Basophils Trigger Fibroblast Activation in Cardiac Allograft Fibrosis Development. Am J Transplant 16:2574–2588. https://doi.org/10.1111/ajt.13764 Atta AM, Santiago MB, Guerra FG, et al (2010) Autoimmune Response of IgE Antibodies to Cellular Self-Antigens in Systemic Lupus Erythematosus. Int Arch Allergy Immunol 152:401–406. https://doi.org/10.1159/000288293 Sokol CL, Medzhitov R (2010) Role of basophils in the initiation of Th2 responses. Curr Opin Immunol 22:73–77. https://doi.org/10.1016/j.coi.2010.01.012 F P, A C, O O, et al (2021) Basophil Blood Cell Count Is Associated With Enhanced Factor II Plasma Coagulant Activity and Increased Risk of Mortality in Patients With Stable Coronary Artery Disease: Not Only Neutrophils as Prognostic Marker in Ischemic Heart Disease. Journal of the American Heart Association 10:. https://doi.org/10.1161/JAHA.120.018243 Hadadi A, Smith KE, Wan L, et al (2022) Baseline basophil and basophil-to-lymphocyte status is associated with clinical outcomes in metastatic hormone sensitive prostate cancer. Urologic Oncology: Seminars and Original Investigations 40:271.e9-271.e18. https://doi.org/10.1016/j.urolonc.2022.03.016 Verhoef PA, Bhavani SV, Carey KA, Churpek MM (2019) Allergic Immune Diseases and the Risk of Mortality Among Patients Hospitalized for Acute Infection. Crit Care Med 47:1735–1742. https://doi.org/10.1097/CCM.0000000000004020 Piliponsky AM, Shubin NJ, Lahiri AK, et al (2019) Basophil-derived tumor necrosis factor can enhance survival in a sepsis model in mice. Nat Immunol 20:129–140. https://doi.org/10.1038/s41590-018-0288-7 Sakakibara Y, Wada T, Muraoka M, et al (2015) Basophil activation by mosquito extracts in patients with hypersensitivity to mosquito bites. Cancer Sci 106:965–971. https://doi.org/10.1111/cas.12696 Lucey DR, Zajac RA, Melcher GP, et al (1990) Serum IgE levels in 622 persons with human immunodeficiency virus infection: IgE elevation with marked depletion of CD4 + T-cells. AIDS Res Hum Retroviruses 6:427–429. https://doi.org/10.1089/aid.1990.6.427 Constantinescu A-E, Bull CJ, Jones N, et al (2023) Circulating white blood cell traits and colorectal cancer risk: A Mendelian randomisation study. Int J Cancer. https://doi.org/10.1002/ijc.34691 Liu Q, Luo D, Cai S, et al (2020) Circulating basophil count as a prognostic marker of tumor aggressiveness and survival outcomes in colorectal cancer. Clin Transl Med 9:6. https://doi.org/10.1186/s40169-019-0255-4 Sicklinger F, Meyer IS, Li X, et al (2021) Basophils balance healing after myocardial infarction via IL-4/IL-13. J Clin Invest 131:e136778. https://doi.org/10.1172/JCI136778 Blériot C, Dupuis T, Jouvion G, et al (2015) Liver-resident macrophage necroptosis orchestrates type 1 microbicidal inflammation and type-2-mediated tissue repair during bacterial infection. Immunity 42:145–158. https://doi.org/10.1016/j.immuni.2014.12.020 Yousefi S, Morshed M, Amini P, et al (2015) Basophils exhibit antibacterial activity through extracellular trap formation. Allergy 70:1184–1188. https://doi.org/10.1111/all.12662 Bischof A, Brumshagen C, Ding N, et al (2014) Basophil expansion protects against invasive pneumococcal disease in mice. J Infect Dis 210:14–24. https://doi.org/10.1093/infdis/jiu056 Phillips C, Coward WR, Pritchard DI, Hewitt CRA (2003) Basophils express a type 2 cytokine profile on exposure to proteases from helminths and house dust mites. J Leukoc Biol 73:165–171. https://doi.org/10.1189/jlb.0702356 Voehringer D, Reese TA, Huang X, et al (2006) Type 2 immunity is controlled by IL-4/IL-13 expression in hematopoietic non-eosinophil cells of the innate immune system. J Exp Med 203:1435–1446. https://doi.org/10.1084/jem.20052448 Gause WC, Wynn TA, Allen JE (2013) Type 2 immunity and wound healing: evolutionary refinement of adaptive immunity by helminths. Nat Rev Immunol 13:607–614. https://doi.org/10.1038/nri3476 Jogdand P, Siddhuraj P, Mori M, et al (2020) Eosinophils, basophils and type 2 immune microenvironments in COPD-affected lung tissue. Eur Respir J 55:1900110. https://doi.org/10.1183/13993003.00110-2019 Denzel A, Maus UA, Rodriguez Gomez M, et al (2008) Basophils enhance immunological memory responses. Nat Immunol 9:733–742. https://doi.org/10.1038/ni.1621 Siracusa MC, Kim BS, Spergel JM, Artis D (2013) Basophils and allergic inflammation. J Allergy Clin Immunol 132:789–801; quiz 788. https://doi.org/10.1016/j.jaci.2013.07.046 Lantz CS, Boesiger J, Song CH, et al (1998) Role for interleukin-3 in mast-cell and basophil development and in immunity to parasites. Nature 392:90–93. https://doi.org/10.1038/32190 Voehringer D (2012) Basophil modulation by cytokine instruction. Eur J Immunol 42:2544–2550. https://doi.org/10.1002/eji.201142318 Varricchi G, Pecoraro A, Marone G, et al (2018) Thymic Stromal Lymphopoietin Isoforms, Inflammatory Disorders, and Cancer. Front Immunol 9:1595. https://doi.org/10.3389/fimmu.2018.01595 Siracusa MC, Saenz SA, Hill DA, et al (2011) TSLP promotes interleukin-3-independent basophil haematopoiesis and type 2 inflammation. Nature 477:229–233. https://doi.org/10.1038/nature10329 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4647257","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":331707203,"identity":"b733278e-2f4d-454a-92ba-9fc1110ddd07","order_by":0,"name":"Mingmin Pang","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mingmin","middleName":"","lastName":"Pang","suffix":""},{"id":331707204,"identity":"d7a69443-f7a9-4278-9191-298327f588b2","order_by":1,"name":"Shaohua Fan","email":"","orcid":"","institution":"Central Hospital affiliated to Shandong First Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shaohua","middleName":"","lastName":"Fan","suffix":""},{"id":331707205,"identity":"45db97ac-a090-4907-850a-c9f2cfc7b229","order_by":2,"name":"Shihan Zhang","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shihan","middleName":"","lastName":"Zhang","suffix":""},{"id":331707206,"identity":"cee11e61-fbf0-4fab-a728-50b8eb550243","order_by":3,"name":"Yanan Li","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yanan","middleName":"","lastName":"Li","suffix":""},{"id":331707207,"identity":"81916029-dbdc-4b74-899f-333cb6ba749f","order_by":4,"name":"Hao Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYJACZiC2Y2xvbHzwgRQtycw9h5sNZ5CihbF9RnqbNAcxyg2Onz38uqDGhpl35sMGaaAD5XQbCGk5k5dmPeNYGp/k7MQG4wKGZGOzAwS0mB3IMTPmYTvMbAjUkjyD4UDiNoJazr8Bavl3mHH/zYMNh3mI0nIjx/gxb9thxsYZjI3NRGmxv/HGjJm3Ly2ZsSexmXGGARF+kezPMf7M880GGJXHn//4UGEnR1ALELBJINgGhJWDADPRyWQUjIJRMApGKAAAj0dFcmsXjVoAAAAASUVORK5CYII=","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-06-27 09:11:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4647257/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4647257/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62123222,"identity":"b19b4dd3-450a-4f22-bbdc-37e2a117535f","added_by":"auto","created_at":"2024-08-09 14:20:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":581472,"visible":true,"origin":"","legend":"\u003cp\u003ea. Flow chart of Qilu Cohort. \u003cstrong\u003eb.\u003c/strong\u003e Flow chart of MIMIC Cohort.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4647257/v1/b52d37ff0dafc71c90c5b7f6.png"},{"id":62123225,"identity":"e2c18075-5282-474e-9eba-36d3b68cd9ea","added_by":"auto","created_at":"2024-08-09 14:20:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3289991,"visible":true,"origin":"","legend":"\u003cp\u003ea. Multivariate analysis for Risk factors of 28d mortality in Qilu Cohort. \u003cstrong\u003eb. \u003c/strong\u003eMultivariate analysis for Risk factors of 28d mortality in MIMIC Cohort.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4647257/v1/20b3f2c8ee7505697227d39a.png"},{"id":62123224,"identity":"3f5f6465-d05b-49bb-9d08-f850f41c77c7","added_by":"auto","created_at":"2024-08-09 14:20:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1579952,"visible":true,"origin":"","legend":"\u003cp\u003ea. RCS curve of Qilu cohort of the relationship between basophil count and 28d mortality.\u003cstrong\u003e b. \u003c/strong\u003eRCS curve of MIMIC cohort of the relationship between basophil count and 28d mortality.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCCI\u003c/em\u003e: Charlson comorbidity index, \u003cem\u003eSOFA\u003c/em\u003e: Sequential Organ Failure Assessment Score, \u003cem\u003eAPACHE II\u003c/em\u003e: Acute Physiology and Chronic Health Evaluation II, \u003cem\u003eCRRT\u003c/em\u003e: Continuous Renal Replacement Therapy.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4647257/v1/d5ef60f4b90fac91d36c598c.png"},{"id":66524586,"identity":"d079fc71-e1f7-48e1-a66a-043d434d8647","added_by":"auto","created_at":"2024-10-14 04:39:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1745255,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4647257/v1/0dec3bf9-cb9a-4ca9-b0fb-6be6fa7a911f.pdf"},{"id":62123223,"identity":"3fbfbd48-fb04-4687-934d-4fc1648635b9","added_by":"auto","created_at":"2024-08-09 14:20:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":754775,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4647257/v1/ea20ddf4bfd4e853a57c30a6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The protective role of basophil against sepsis mortality","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSepsis, a life-threatening condition that may ensue following various infections, represents a significant portion of the critically ill population. It is a principal cause of health loss globally, characterized by high morbidity and mortality rates[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The defective host response to infectious agents greatly contributes to the incidence and unfavorable outcomes associated with sepsis. Despite recent advancements in understanding the pathophysiology of sepsis, there is a scarcity of validated treatments targeting the restoration of immune dysregulation.\u003c/p\u003e \u003cp\u003eBasophils are immune cells capable of releasing an array of effector molecules, including prostaglandins, proteolytic enzymes, histamine, leukotrienes, tumor necrosis factor-α (TNF-α), and several interleukins (IL-4, IL-6, and IL-13)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Known previously for their role in allergic reactions and parasitic immunity\u003csup\u003e9,10\u003c/sup\u003e, more recent studies highlight their significant involvement in a variety of disorders11, most notably autoimmune diseases, allograft fibrosis, and diverse host responses to pathogens[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Basophils also aid in type 2 (T2) immune responses that serve numerous host-protective functions such as maintenance of metabolic homeostasis, suppression of excessive type 1 inflammation, barrier defense upkeep, and regulation of tissue regeneration[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Prior studies report an increase in basophil count being associated with worsened prognosis for conditions like coronary artery disease[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and prostate cancer [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Yet, the role of basophils in sepsis prognosis remains largely unexplored.\u003c/p\u003e \u003cp\u003eRecent literature suggests that patients with diseases exhibiting a T2 immune response have reduced mortality risk from infections [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For instance, in a mouse model of cecal ligation and puncture, basophils were found to boost innate immune responses to bacterial infection, thereby preventing sepsis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Moreover, in cases of human immunodeficiency virus and Epstein-Barr virus infections, elevated basophil levels often indicate heightened immunoglobulin E levels and increased immunological responses, which are linked to disease progression [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Given this information, in light of the limited clinical evidence connecting basophils and sepsis outcomes, we hypothesize that basophils might perform a protective role against sepsis mortality. This study aims to substantiate this relationship across different observational cohorts.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population and data source\u003c/h2\u003e \u003cp\u003eWe embarked upon a single-center prospective observational cohort study, hosted in a 20-bed mixed intensive care unit at Qilu Hospital of Shandong University (Qilu Cohort, January 2021 to December 2021). This was undertaken for our primary analysis. The findings were cross-verified with the Medical Information Mart for Intensive Care (MIMIC cohort, 2001\u0026ndash;2019) database for further confirmation. Inclusion criteria entailed sepsis patients admitted to the ICU who were aged 16 years or older. Exclusion criteria included: survival time of less than 48 hours, existing conditions that may influence basophil count such as acquired immunodeficiency syndrome and hematologic malignancy, and instances where more than 20% of data was missing. For patients with recurring ICU admissions, only data from the initial ICU admission was inspected.\u003c/p\u003e \u003cp\u003eThe MIMIC database, comprising MIMIC-III 1.4 and MIMIC IV 1.0 versions, is an open, single-center repository of data on intensive care patients admitted to Beth Israel Deaconess Medical Center (BIDMC). The required authorizations for the utilization of the MIMIC database for research have been granted by the Institutional Review Boards at BIDMC and MIT. A Collaborative Institution Training Initiative Program course must be completed to gain access to this data (Certificate Number 11057215 belongs to one of the authors of this paper, Pang). This project received an informed consent waiver. For our local prospective observational cohort, ethical approval was obtained from the Ethics Committee of Qilu Hospital, Shandong University (approval number KYLL-2018153).Informed Consent was secured from all participants or their legal guardians ahead of their inclusion in the study.\u003c/p\u003e \u003cp\u003eThe Institutional Review Boards at BIDMC and MIT have approved the use of the MIMIC database for research purposes, and accessing the data requires completion of the collaborative institution training initiative program course (certification number 11057215 for Pang, one of the authors of this paper). For our local prospective observational cohort, the protocol was approved by the Ethics Committee of (approval number KYLL-2018153). The UK Biobank has full ethical approval from the NHS National Research Ethics Service (16/NW/0274). All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e \u003cp\u003eWe leveraged summary genetic data to perform Mendelian randomization (MR), affirming the causal link between basophil count and sepsis. Genetic data of basophil were identified from the genome-wide association study (GWAS) on Blood-cell genetics (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.mhi-humangenetics.org/en/resources/\u003c/span\u003e\u003cspan address=\"http://www.mhi-humangenetics.org/en/resources/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and sepsis 28d-mortality (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/datasets/ieu-b-4981/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/datasets/ieu-b-4981/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The detailed data sources are shown in \u003cb\u003eSupplemental Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eVariables and outcome\u003c/h2\u003e \u003cp\u003eCovariates were chosen through consensus, factoring in biological plausibility and known associations, incorporating demographic characteristics (age, gender, ethnicity, insurance status, marital status), underlying medical conditions (Charlson comorbidities), clinical risk factors, laboratory findings, acute severity indicators, and treatment details. The comorbidities, comprising congestive heart failure, chronic obstructive pulmonary disease, diabetes, renal failure, liver disease, solid tumors, and immunosuppression, were pinpointed and extracted in line with the ICD-9 and ICD-10 coding system.\u003c/p\u003e \u003cp\u003eAcute severity was gauged using a range of scores: Sequential Organ Failure Assessment (SOFA) score, Simplified Acute Physiology Score II (SAPS II) score, Oxford Acute Severity of Illness Score (OASIS), and Acute Physiology and Chronic Health Evaluation II (APACHE II) score. This study employed extreme laboratory values of certain blood parameters\u0026mdash;white blood cell count, basophil count, platelet count, and lactate level\u0026mdash;registered within the initial two days following sepsis onset. Clinical treatments encompassed the use of vasopressors, continuous renal replacement therapy (CRRT), and mechanical ventilation during the ICU stay.\u003c/p\u003e \u003cp\u003eGiven the augmenting impact of granulocyte colony-stimulating factor (G-CSF) on basophil numbers, we recorded human G-CSF usage data from the MIMIC database. Sepsis patients with low basophil counts (counts\u0026thinsp;\u0026lt;\u0026thinsp;0.33 \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L) were stratified into two groups based on G-CSF exposure: one involving patients who received Human G-CSF administration post-ICU admission (labelled as the Human G-CSF group) and the other consisting of patients who never underwent Human G-CSF administration throughout their ICU stay (labelled as the no-Human G-CSF group).\u003c/p\u003e \u003cp\u003eThe primary outcome evaluated was the 28-day all-cause mortality rate, defined as the occurrence of any cause of death within 28 days since sepsis onset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe data acquired was meticulously examined using R 4.3.1 software. Patients were categorized into quartiles in accordance with their basophil count. For continuous and categorical variables, the median (Q1\u0026ndash;Q3), mean (SD), and number (percentage) were respectively represented. To evaluate continuous parametric data, one-way ANOVA or Student's t-test was utilized. The Wilcoxon rank-sum test was employed to scrutinize continuous nonparametric data while categorical data was appraised using the chi-square (χ2) test. A p-value less than 0.05 (two-tailed) was deemed statistically significant. The determination of survival was facilitated through the application of the Kaplan-Meier method (log-rank test) along with Cox regression analysis. By stratifying patients based on their basophil counts distributed among quartiles, Kaplan-Meier curves were generated to illustrate survival plots. The relationship between basophil count and 28-day mortality was analyzed by employing univariate and multivariate Cox proportional hazards models. To accommodate potential confounding variables, these were added to the models through a stepwise backward selection technique. Hazards ratio (HR) coupled with 95% confidence intervals (CIs) was reported. Multivariable Cox proportional hazard models were used in conjunction with restricted cubic spline (RCS) to investigate the correlation between basophil count and 28-day all-cause mortality. A Propensity Score Matching (PSM) analysis was performed to reduce the disparity between groups with elevated and normal basophil levels. For this purpose, we produced a propensity score with the help of a logistic regression model that integrated all potential covariables and baseline variables linked with disease severity, including demographic parameters, background history, and underlying conditions. This was done to match subjects in both groups utilizing the optimal match method at a 1:1 match rate, through R package MatchIt and optmatch. The standard mean differences were calculated for the evaluation of balance between groups. An additional subgroup analysis was conducted considering patients with baseline basophil levels less than 0.3 * 10\u003csup\u003e9\u003c/sup\u003e/L, to gain greater insights into the role of basophils in sepsis. A 2-sample MR was also performed to authenticate the causal associations between basophils and sepsis. The introduction of all Genome-wide association studies (GWAS) used for MR is detailed out in \u003cb\u003eSupplemental Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e. A comprehensive explanation of MR analyses is presented in Supplemental methods.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eInitially, 361 patients were enrolled in the local cohort. However, we carried out our final analysis using data from only 202 patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The attributes of the patients, distributed according to their basophil count, are enumerated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 92 patients (45.5%) had a basophil count within 0\u0026ndash;0.2 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L, 46 patients (22.8%) within 0.2\u0026ndash;0.4 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L, and 67 patients (31.7%) exhibited a basophil count\u0026thinsp;\u0026gt;\u0026thinsp;0.4 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L. Interestingly, patients with lower basophil counts manifested more severe illness and registered a higher mortality rate (\u003cb\u003eP\u003c/b\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient characteristics stratified by basophil count in Qilu cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eBasophil count, *10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0-0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u0026ndash;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge, years, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.7 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.0 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.7 (17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (58.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (60.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49 (73.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (41.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (39.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (26.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHeight, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167 (7.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e169 (7.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e169 (5.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWeight, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.8 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.5 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.7 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCCI, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.00 [4.00\u0026ndash;8.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.00 [5.00\u0026ndash;7.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.00 [5.00\u0026ndash;8.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnderlying diseases, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCongestive heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (37.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eChronic pulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (18.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (17.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRheumatic disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (6.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (6.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLiver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (6.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (10.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (27.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (22.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRenal diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (4.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMalignant cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (23.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary sources of infection, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLower respiratory tract\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (44.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (52.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (44.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eIntra-abdominal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (8.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (10.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUrinary tract\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (2.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSkin and soft tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (6.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (6.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCentral nervous system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (7.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (10.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (7.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(19.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22(32.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeverity evaluation and intensity of care\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSOFA, median [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 [5;10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 [5;10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 [5;13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAPACHE II, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.1 (8.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.1 (7.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.1 (8.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMechanical ventilation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (73.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (73.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51 (77.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRenal replacement therapy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (34.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (30.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (33.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory value\u003c/b\u003e, median [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWBC, *10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0 [9.80\u0026ndash;21.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.2 [7.30\u0026ndash;15.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.2 [6.64\u0026ndash;16.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBasophils, *10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10 [0.00-0.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20 [0.20\u0026ndash;0.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63 [0.53\u0026ndash;0.79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePlatelets, *10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.0 [43.0-145]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e145 [61.5\u0026ndash;212]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e188 [120\u0026ndash;344]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLactate, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00 [1.30\u0026ndash;5.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.40 [1.10\u0026ndash;2.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.25 [1.00-1.88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e28d mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (39.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (29.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eData expressed as median (IQR), mean (sd) or number (percentage). Definition of abbreviations: IQR: interquartile range, CCI: Charlson comorbidity index, SOFA: Sequential Organ Failure Assessment Score, APACHE II: acute physiology and chronic health evaluation II, WBC: White blood cells.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUsing the MIMIC database, we collected data for 42,393 patients diagnosed with sepsis as per the Sepsis-3 criteria. The final analyzed sample consisted of 20,239 patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Basic characteristics, clinical trajectory, and disease severity based on basophil levels are represented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Of these, 7,439 (36.7%) had a basophil count between 0\u0026ndash;0.1 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L, 4,784 (23.6%) between 0.1\u0026ndash;0.2 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L, 5,051(24.9%) within 0.2\u0026ndash;0.4 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L and 15,755 (14.8%)\u0026thinsp;\u0026gt;\u0026thinsp;0.4 x 10\u003csup\u003e9\u003c/sup\u003e/L. As in the Qilu cohort, patients within lower basophil counts displayed more severe illness, indicated by elevated SAPS II, SOFA scores, higher prevalence of mechanical ventilation use, septic shock, and notably increased mortality rates (\u003cb\u003eP\u003c/b\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient characteristics stratified by basophil count in MIMIC cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eBasophil count, *10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u0026ndash;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u0026ndash;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;7439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;4784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;5051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;5755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.6 [57.5\u0026ndash;80.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.7 [56.4\u0026ndash;79.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.9 [54.6\u0026ndash;78.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.8 [54.0-77.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3421 (46.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2100 (43.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2203 (43.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2452 (42.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4018 (54.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2684 (56.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2848 (56.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3303 (57.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsurance, n (%):\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2662 (35.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1577 (33.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1753 (34.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1901 (33.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e328 (4.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e212 (4.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e240 (4.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e314 (5.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2313 (31.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1516 (31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1487 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1702 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2136 (28.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1479 (30.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1571 (31.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1838 (31.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3295 (44.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2117 (44.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2222 (44.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2475 (43.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e522 (7.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e333 (6.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e368 (7.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e437 (7.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1765 (23.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1188 (24.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1385 (27.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1573 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1110 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e699 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e613 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e677 (11.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e747 (10.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e447 (9.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e463 (9.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e593 (10.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5035 (67.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3193 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3375 (66.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3910 (67.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e597 (8.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e411 (8.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e391 (7.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e432 (7.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212 (2.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 (2.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124 (2.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e135 (2.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1595 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1042 (21.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1161 (23.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1278 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.00 [0.00\u0026ndash;7.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.00 [0.00\u0026ndash;7.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00 [0.00\u0026ndash;7.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.00 [0.00\u0026ndash;7.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnderlying diseases, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial infarct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e953 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e618 (17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e640 (17.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e735 (17.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1473 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1008 (21.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1056 (20.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1211 (21.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic pulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1507 (27.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e973 (27.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e966 (26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1055 (25.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRheumatic disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e193 (2.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106 (2.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107 (2.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e139 (2.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e954 (17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e554 (15.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e597 (16.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e776 (18.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1518(20.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1098(22.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1081(21.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1317(22.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1052 (14.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e693 (14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e714 (14.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e822 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignant cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e864 (11.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e447 (9.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e446 (8.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e452 (7.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary sources of infection, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower respiratory tract\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2909 (39.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1887 (39.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2006 (39.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2223 (38.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntra-abdominal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1382 (18.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e938 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e943 (18.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1140 (19.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary tract\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1586 (21.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1007 (21.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1023 (20.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1150 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin and soft tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e462 (6.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e314 (6.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e340 (6.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e384 (6.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral nervous system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86 (1.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (1.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (0.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72 (1.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBloodstream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e365 (4.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e215 (4.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e241 (4.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e299 (5.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e649 (8.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e349 (7.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e453(8.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e487 (8.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeverity evaluation and intensity of care\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.00 [2.00\u0026ndash;6.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.00 [2.00\u0026ndash;6.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00 [2.00\u0026ndash;6.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.00 [2.00\u0026ndash;5.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAPS II, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.0 [40.0\u0026ndash;73.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.0 [40.0-72.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.0 [39.0\u0026ndash;71.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.0 [37.0\u0026ndash;70.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOASIS, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.0 [31.0\u0026ndash;44.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.0 [31.0\u0026ndash;43.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.0 [30.0\u0026ndash;42.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.0 [30.0\u0026ndash;41.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4349 (58.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2931 (61.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3139 (62.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3369 (58.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal replacement therapy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e804 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e515 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e542 (10.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e541 (9.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptic shock, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1680 (23.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e795 (17.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e812 (16.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e743 (13.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory value, median [IQR]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, *10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.7 [10.3\u0026ndash;21.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.5 [10.4\u0026ndash;19.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.7 [10.1\u0026ndash;18.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.8 [9.20\u0026ndash;17.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasophils, *10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03 [0.00-0.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15 [0.13\u0026ndash;0.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30 [0.25\u0026ndash;0.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50 [0.40\u0026ndash;0.67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.00 [1.40\u0026ndash;3.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.80 [1.30\u0026ndash;2.68]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.80 [1.29\u0026ndash;2.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.72 [1.25\u0026ndash;2.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets, *10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212 [148\u0026ndash;294]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e216 [156\u0026ndash;301]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e216 [155\u0026ndash;294]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e214 [151\u0026ndash;288]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28d mortality, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3227 (43.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1755 (36.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1600 (31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1804 (31.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eData expressed as median (IQR) or number (percentage). Definition of abbreviations: \u003cem\u003eIQR\u003c/em\u003e: interquartile range, \u003cem\u003eCCI\u003c/em\u003e: Charlson comorbidity index, \u003cem\u003eSOFA\u003c/em\u003e: Sequential Organ Failure Assessment Score, \u003cem\u003eSAPS\u003c/em\u003e: Simplified Physiology Score, \u003cem\u003eOASIS\u003c/em\u003e: oxford acute severity of illness score, \u003cem\u003eWBC\u003c/em\u003e: White blood cells.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between basophil count and 28-day all-cause mortality\u003c/h2\u003e \u003cp\u003e \u003cb\u003eSupplemental Table S2\u003c/b\u003e lists the risk factors identified for 28-day mortality in our local cohort, as revealed by univariate analysis. Multivariate analysis demonstrated that basophil count independently predicted 28-day mortality in patients with sepsis (HR\u0026thinsp;=\u0026thinsp;0.40, 95% CI\u0026thinsp;=\u0026thinsp;0.30\u0026ndash;0.52; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), after adjusting for age, CRRT usage, mechanical ventilation usage, APACHE II scores, and CCI score. Kaplan-Meier analysis delivered results, shown in \u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e, illustrating the 28-day survival curve of patients as per basophil count (\u003cb\u003eP\u003c/b\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, as per the log-rank test). An L-shaped correlation surfaced when assessing basophil count against 28-day mortality, through the use of RCS based on Cox proportional hazards models (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Both Kaplan-Meier analysis and RCS findings affirm that patients with heightened basophil counts incur a diminished risk of mortality.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSupplemental Table S3\u003c/b\u003e enlists the 28-day mortality risk factors detected through univariate analysis within the MIMIC cohort. It was found that patients who survived had significantly high basophil counts (\u003cb\u003eP\u003c/b\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, the multivariate Cox regression model reveals that basophil count is an independent predictor for 28-day mortality (HR\u0026thinsp;=\u0026thinsp;0.35, 95% CI\u0026thinsp;=\u0026thinsp;0.3\u0026ndash;0.42), even after adjustments for age, Charlson comorbidity index (CCI), liver disease, diabetes, SOFA lactate levels, mechanical ventilation usage, and presence of septic shock (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eSubsequent to the initial analysis, we estimated the risk of 28-day mortality concerning basophil count utilizing Propensity Score Matching (PSM) cohorts. This was done to harmonize baseline differences between the groups based on their respective basophil counts. Patient characteristics in the PSM MIMIC cohort can be found in \u003cb\u003eSupplemental Table S4\u003c/b\u003e. Univariate analysis was shown in \u003cb\u003eSupplemental Table S5.\u003c/b\u003e Multivariate Cox regression analysis executed on the matched cohort reaffirmed that high basophil count offered protection, even after taking into account potential confounders (HR\u0026thinsp;=\u0026thinsp;0.35, 95% CI\u0026thinsp;=\u0026thinsp;0.28\u0026ndash;0.42; \u003cb\u003eFig. S2\u003c/b\u003e). An L-shaped relationship between basophil count and 28-day mortality emerged at a cutoff value of 0.33 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L when we made adjustments for confounding variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The 28-day survival curves of patients per basophil count are depicted in \u003cb\u003eFig S3\u003c/b\u003e and \u003cb\u003eS4\u003c/b\u003e (\u003cb\u003eP\u003c/b\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, according to the log-rank test). Findings from both Kaplan-Meier analysis and RCS indicated a decreased mortality risk in patients with elevated basophil counts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCausal association between basophil and sepsis outcomes\u003c/h2\u003e \u003cp\u003eThe primary MR results can be found in \u003cb\u003eFigure S5\u003c/b\u003e. In the 2-sample MR, the inverse-variance weighted (IVW) estimates were aligned with a causally protective effect of basophils on sepsis patient prognosis (OR\u0026thinsp;=\u0026thinsp;0.776, 95% CI [0.637,0.946], \u003cb\u003eP\u003c/b\u003e\u0026thinsp;=\u0026thinsp;0.002, \u003cb\u003eTable S6\u003c/b\u003e). The sensitivity analysis, employing MR-Egger regression, indicated no horizontal pleiotropism (intercept \u003cb\u003eP\u003c/b\u003e\u0026thinsp;=\u0026thinsp;0.115, \u003cb\u003eTable S7\u003c/b\u003e). The leave-one-out method suggests that no single SNP has a dominant role in the overall assessment (\u003cb\u003eFig S6\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eHuman granulocyte colony stimulating factor (Human G-CSF) may decrease mortality in patient with basopenia by increasing basophils count\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo further substantiate the influence of interventions on basophils in sepsis, we conducted an additional analysis in patients with basopenia (basophil counts\u0026thinsp;\u0026lt;\u0026thinsp;0.33 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L), dividing them into Human G-CSF group and non-Human G-CSF groups. In the Human G-CSF group, there was a significant increase in basophil counts compared with the non-Human G-CSF group (\u003cb\u003eFig. S7\u003c/b\u003e). Basic characteristics are provided in \u003cb\u003eTable S8\u003c/b\u003e. Univariable and multivariable Cox regression analyses of 28-day mortality in basopenia patients revealed that Human G-CSF was an independent protective factor (HR\u0026thinsp;=\u0026thinsp;0.74, 95% CI\u0026thinsp;=\u0026thinsp;0.58\u0026ndash;0.93; \u003cb\u003eTables S9\u003c/b\u003e and \u003cb\u003eS10\u003c/b\u003e). The protective effect of Human G-CSF was also assessed using PSM cohorts. Patient characteristics in these cohorts are presented in \u003cb\u003eTable S11\u003c/b\u003e. Univariable and multivariable Cox regression analysis of 28-day mortality confirmed that Human G-CSF remained an independent protective factor in the PSM cohort (HR\u0026thinsp;=\u0026thinsp;0.74, 95% CI\u0026thinsp;=\u0026thinsp;0.58\u0026ndash;0.94; \u003cb\u003eTables S12\u003c/b\u003e and \u003cb\u003eS13\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn utilizing a local cohort, supplemented by two expansive cohorts of participants (n\u0026thinsp;\u0026gt;\u0026thinsp;40,000 and n\u0026thinsp;\u0026gt;\u0026thinsp;420,000, respectively), we discovered a robust observational correlation between basophils and a heightened threat of sepsis and associated fatalities. MR data further endorses the possibility of a causal influence of basophils per se on sepsis. Importantly, our observations also indicate that administering Human G-CSF caused a rise in basophil counts, which could potentially result in reduced mortality rates among patients suffering from basopenia. Our study contributes the most significant evidence to date supporting the protective capabilities of basophils against sepsis and its mortality. The insights derived from our data suggest that enhancing basophil counts could be a viable strategy to decrease mortality rates.\u003c/p\u003e \u003cp\u003ePrevious studies have demonstrated basophils playing a role in the prognosis and progression of several disorders. For instance, numerous research findings suggest an association between basophil count and the extent of tumor invasion and progression [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The effects of basophils are likely mediated through various factors secreted by these cells, including cytokines, lipid inflammatory mediators, and histamines, which regulate inflammatory responses, adaptive immunity, and cell apoptosis. Yet, the underlying mechanisms that enable basophils to exert these effects remain hazy. In a mouse model for myocardial infarction (MI) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], lesser basophil levels corresponded to poorer cardiac prognosis. There are propositions suggesting that basophils might enhance tissue repair post-MI by elevating the levels of cardiac IL-4 and IL-13, thereby promoting a shift from inflammatory Ly6Chi monocytes to reparative Ly6Clo macrophages in the cell population. Basophils have also exhibited beneficial function in liver tissue regeneration post Listeria monocytogenes infection [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, the role of basophils in sepsis remains ambiguous.\u003c/p\u003e \u003cp\u003eActing as initial effector cells in T2 immunity, basophils have been reported in a number of in vitro and in vivo studies to potentially contribute to host responses, such as anti-bacterial mechanisms. For instance, an in vitro study incorporating co-culturing human basophils with Escherichia coli or Staphylococcus aureus demonstrated basophil activation and anti-bacterial activity via the formation of functional extracellular traps[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Moreover, in an invasive pneumococcal disease (IPD) mouse model, basophil transfusion was found to provide protection against IPD by amplifying protein-based memory response against pneumococcal protein antigens[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In a separate sepsis mouse model, basophil transfusion appeared to enhance the innate immune response to bacterial infection by producing tumor necrosis factor and bolstering the anti-bacterial effects of other myeloid cells [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These investigational outcomes reveal the crucial role basophils hold for bacteria eradication. However, the role of basophils in bacterial clearance in human sepsis remains indeterminate.\u003c/p\u003e \u003cp\u003eBasophils are known to produce effector molecules, such as IL-4 and IL-13, which are generally attributed to having anti-inflammatory characteristics [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These insights suggest that basophils may play a protective role in sepsis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Clinical studies have shown that patients with severe infections who exhibit a potent T2 immune response tend to have lower mortality rates. Moreover, basophils have been identified as significant players in numerous T2 immunological conditions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These findings align seamlessly with our current observations, indicating that basophils may reduce mortality in sepsis.\u003c/p\u003e \u003cp\u003eIn our analysis utilizing both MIMIC and local cohorts, septic patients with high basophil counts exhibited reduced 28-day mortality, and an L-shaped association was observed between basophil count and 28-day mortality. Additionally, both Kaplan-Meier analysis and RCS indicated a decreased risk of mortality in patients with elevated basophil counts. Therefore, our data illuminates basophils as an independent protective factor against mortality in septic patients. To our knowledge, this constitutes the most comprehensive study to date elucidating the protective effect of basophils in patients with sepsis.\u003c/p\u003e \u003cp\u003eBasophils can be triggered through a variety of molecular pathways. Numerous \u003cem\u003ein vitro\u003c/em\u003e studies have demonstrated that IL-3 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] or granulocyte/macrophage colony-stimulating factor [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] is capable of inducing a proliferation in the number of basophils originated from bone marrow forerunners. An in vivo experiment involving mice revealed that thymic stromal lymphopoietin could stimulate an increase in the quantity of peripheral basophils, thereby initiating and rejuvenating T2 immune functionality [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Looking forward, enhanced in vivo and in vitro studies are indispensable for gaining profound insights into the biological traits of basophils. This knowledge might assist in implementing early interventions, such as basophil transfusion, for sepsis patients to mitigate mortality.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis research harbors several constraints. Initially, it was an observational investigation. Therefore, confounding factors potentially influencing the effect of basophil count on mortality still persist even after accounting for possible confounders through PSM and multivariable Cox regression analysis. To diminish the impact of a likely confounding bias, we have excluded patients known to be exposed to several prominent risk factors. Secondly, our dependency on a single basophil count measurement might overlook the influence of prolonged periods of low basophil levels on mortality. Thirdly, given the retrospective nature of the MIMIC cohort, we couldn't scrutinize the effect of treatment on mortality. Fourthly, our local prospective cohort encompassed a small sample size, mandating further prospective inquiries on a larger scale including diverse ethnic groups longer follow-up durations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, we've elucidated that basophils are linked to an improved prognosis in sepsis patients. Our findings indicate significant clinical implications, suggesting that basophils exert a protective effect against sepsis and sepsis-induced mortality. Moreover, dynamic monitoring of basophil count could aid clinicians in pinpointing patients with a heightened risk of unfavorable sepsis prognosis. The administration of Human G-CSFs in patients suffering from basopenia may emerge as a revolutionary therapeutic strategy to lessen sepsis mortality, however, future randomized controlled intervention studies are warranted.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT2 immune response\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eType 2 immune response\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMIMIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMedical Information Mart for Intensive Care\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePropensity Score Matching\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTNF-α\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor Necrosis Factor-α\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterleukin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntensive Care Unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBIDMC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBeth Israel Deaconess Medical Center\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSOFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSequential Organ Failure Assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSAPS II\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSimplified Acute Physiology Score II\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOASIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOxford Acute Severity of Illness Score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPACHE II\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute Physiology and Chronic Health Evaluation II\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHazard Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRestricted Cubic Spline\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMyocardial Infarction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInvasive Pneumococcal Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDisclosure\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China (grant nos. 82072231), Natural Science Foundation of Shandong Province (ZR2023YQ068), and Taishan Scholars Program of Shandong Province (award no. tsqn202103165).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMm P wrote the original draft, collected data and analyzed the data. Sh F wrote the original draft, collected data, and analyzed the data. Sh Z collected Data. Yn L collected Data. HW was responsible for writing the original draft, writing review, project administration, and supervision. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank the Beth Israel Deaconess Medical Center staff and its MIMIC collaborators who prepared these publicly available data. We also thank UK Biobank participants. This research has been conducted using the UK Biobank Resource (application No 7155).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe MIMIC datasets used and/or analyzed during the current study are available in the MIMIC database, https://github.com/MIT-LCP/mimic-code/. Researchers are required to complete a collaborative institution training initiative program course in order to access the database. The data of Qilu cohort used and analyzed during our study are available from the corresponding author up-on reasonable request. Genetic data of basophil were identified from the genome-wide association study (GWAS) on (http://www.mhi-humangenetics.org/en/resources/) and sepsis 28day mortality (https://gwas.mrcieu.ac.uk/datasets/ieu-b-4981/).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRudd KE, Johnson SC, Agesa KM, et al (2020) Global, regional, and national sepsis incidence and mortality, 1990\u0026ndash;2017: analysis for the Global Burden of Disease Study. 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Nature 477:229\u0026ndash;233. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature10329\u003c/span\u003e\u003cspan address=\"10.1038/nature10329\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sepsis, Septic shock, Severe infection, Basophils, Blood cell counts, Mortality","lastPublishedDoi":"10.21203/rs.3.rs-4647257/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4647257/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThe role of basophils on sepsis prognosis remains understudied and we aimed to investigate the effects of basophil on sepsis mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology\u003c/strong\u003e: Initially, a prospective local cohort was conducted to establish primary connection between basophil count and 28-day mortality. In addition, sepsis patients from Medical Information Mart for Intensive Care (MIMIC) database were extracted for validation purposes. Thirdly, 2-sample Mendelian randomization (MR) was applied from UK Biobank cohort to confirm the causative link between basophil and sepsis death. Lastly, prognostic effect of human granulocyte colony-stimulating factor (G-CSF) by ameliorating basopenia was assessed utilizing MIMIC data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings: \u003c/strong\u003eIndependent verification from both MIMIC and local cohort revealed basophil count as a protective factor against mortality (HR = 0.35, 95% CI = 0.28 - 0.42; HR = 0.40, 95% CI = 0.30–0.52). The MR analysis substantiated the causal relationship between basophil and death (OR=0.776, 95% CI [0.637,0.946], P=0.002). Administration of human G-CSF led to an increase in basophil counts and resulted in a notable decrease in mortality among patients with basopenia (HR = 0.74, 95% CI = 0.58 - 0.93).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eBasophils significantly contribute to protecting against sepsis mortality, and bolstering basophil numbers may be a feasible strategy in reducing mortality.\u003c/p\u003e","manuscriptTitle":"The protective role of basophil against sepsis mortality","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 14:20:33","doi":"10.21203/rs.3.rs-4647257/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"92b94c51-213c-4cd1-9d6a-9ef80028ed1e","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":35120545,"name":"Health sciences/Anatomy/Cells"},{"id":35120546,"name":"Health sciences/Diseases"},{"id":35120547,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2024-10-14T04:39:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-09 14:20:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4647257","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4647257","identity":"rs-4647257","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}