Clinical value of galectin-9, soluble TREM-1, and soluble CD25 among critically ill patients with organ failure in the emergency department: a prospective observational study 

Clinical value of galectin-9, soluble TREM-1, and soluble CD25 among critically ill patients with organ failure in the emergency department: a prospective observational study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Clinical value of galectin-9, soluble TREM-1, and soluble CD25 among critically ill patients with organ failure in the emergency department: a prospective observational study Uihwan Kim, Sijin Lee, Kap Su Han, Su Jin Kim, Sungwoo Lee, Dae Won Park, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6534330/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 This study investigated clinical value of galectin-9 (Gal-9), a soluble triggering receptor expressed on myeloid cells-1 (sTREM-1), and soluble CD25 (sCD25) among critically ill patients with organ failure in the emergency department. Overall, 786 patients were enrolled and classified into: non-infectious organ failure (NIOF, n = 331), sepsis (n = 266), and septic shock (n = 189). The diagnostic value of Gal-9, sTREM-1, and sCD25 were evaluated by receiver operating characteristic curve analysis. The prognostic value of the biomarkers was evaluated using Kaplan–Meier survival curve and Cox proportional hazard model analyses. Gal-9, sTREM-1, and sCD25 could discriminate sepsis from NIOF (Gal-9, area under the curve [AUC], 0.599–0.678; sTREM-1, AUC, 0.616–0.695; sCD25, AUC, 0.710–0.781) and septic shock from sepsis (Gal-9, AUC, 0.562–0.667; sTREM-1, AUC, 0.572–0.676; sCD25, AUC, 0.555–0.660), respectively. Sepsis patients with higher levels of biomarkers over their cut-off value showed higher 30-day mortality compared to those with lower levels below the cut-off value (Gal-9 ≥ 14391.80 ng/L, p < 0.001; sTREM-1 ≥ 580.62 ng/L, p < 0.001; sCD25 ≥ 1639.29 ng/L, p < 0.001; respectively) (log-rank test). sCD25 is an independent risk factor for 30-day mortality in patients with sepsis or septic shock. Gal-9, sTREM-1, and sCD25 showed diagnostic and prognostic value in critically ill patients with organ failure. sCD25 can predict the 30-day mortality in patients with sepsis. Gal-9, sTREM-1, and sCD25 could serve as auxiliary biomarkers to support clinicians in effective sepsis management. Galectin-9 Mortality Organ failure Sepsis Soluble CD25 Soluble TREM-1 Figures Figure 1 Figure 2 Figure 3 Introduction Sepsis is a life-threatening disease characterized by organ dysfunction, induced by a dysregulated host response to infection [1]. Although the surviving sepsis campaign (SSC) guidelines emphasize the importance of the early diagnosis of sepsis [2], there is no gold standard for the diagnosis of sepsis and no reliable tools to predict clinical outcomes. Although blood cultures are useful to detect the presence of bacteremia, it requires a few days to obtain microbiological results, which might lead to false-negative outcomes. In clinical settings, biomarkers, such as C-reactive protein (CRP) and procalcitonin (PCT), have been widely used to identify and prognosticate sepsis patients [3]. Various other biomarkers, including CD64, presepsin, interleukin-6, and adrenomedullin have been evaluated and reported to provide valuable information for identifying sepsis and predicting clinical outcomes [4–7]. However, no single biomarker can perfectly reflect the pathological state of patients with sepsis, and they have several limitations in both diagnostic and prognostic value. Therefore, an investigation into the clinical value of novel biomarkers is required to establish a comprehensive care strategy for patients with sepsis. Galectin-9 (Gal-9) is a potential organ dysfunction biomarker known for its immunomodulatory role in various microbial infections and is expressed in whole organ systems by mediating host-pathogen interactions [8]. During acute infection, it is assumed that Gal-9 is rapidly released as a danger signal to initiate innate immune cell activity [8]. Gal-9 reflects the severity of infectious diseases, such as malaria, dengue, tuberculosis, and leptospirosis [9–12]. However, to our knowledge, the clinical value of Gal-9 has not been assessed in patients diagnosed with sepsis in the emergency department (ED). Triggering receptor expressed on myeloid cells-1 (TREM-1) is a member of the immunoglobulin superfamily that responds to the presence of bacterial components [13, 14]. Amplification of the inflammatory response by TREM-1 is considered a critical contributor to the dysregulated immune response in sepsis. Pediatric patients admitted to intensive care units (ICUs) with septic shock have higher TREM-1 levels than those with severe sepsis [15]. Elevated levels of soluble TREM-1 (sTREM-1) are associated with increased mortality in patients with septic shock [16]. sTREM-1 levels are also increased in other body fluids such as urine and cerebrospinal fluid. While some studies reported good diagnostic and prognostic values of sTREM-1, other meta-analyses, including nine studies, reported only moderate sensitivity and specificity in predicting mortality [17]. CD25 is an interleukin (IL)-2 receptor that is expressed in correlation with regulatory FOXP3 + T cells, and soluble CD25 (sCD25), which is emitted into the blood and is measurable, reflects compensatory regulatory responses [18]. Immunosuppression in sepsis may be closely linked to the development of acute kidney injury (AKI) and sCD25 or IL-10 may be useful as novel biomarkers for the development of septic AKI [19]. Combined CD25, CD64, and CD69 biomarker panels can be used to effectively diagnose sepsis [20]. Huang et al. suggested that increased sCD25 levels correlated with poor clinical outcomes in patients [18]. Although there have been several studies on the clinical value of sTREM-1, sCD24, and sCD25, they have some limitations, such as a relatively small sample size, application of previous sepsis definitions, and ICUs-based study. To the best of our knowledge, the clinical value of Gal-9 has not been assessed in critically ill patients with organ failure in the ED. Thus, we aimed to investigate the diagnostic and prognostic values of Gal-9, sTREM-1, and sCD25 in critically ill ED patients with non-infectious organ failure (NIOF), sepsis, and septic shock. Methods Study design and setting This prospective observational study was conducted in the ED of a tertiary care teaching hospital. Our study adhered to the Declaration of Helsinki (2013; Seventh revision, 64th Meeting, Fortaleza) and was approved by the Institutional Review Board (IRB) of Korea University Ansan Hospital (IRB no. 2022AS0313). Verbal information about the study was provided to all study participants or their legal representatives in advance and written informed consent was obtained. Adult patients over 18 years of age with a positive quick sepsis-related organ failure assessment (qSOFA) score (qSOFA score of ≥ 2 points) who visited the ED from July 2019 to December 2021 were initially screened by qSOFA alert system called Intelligent Sepsis Management System (i-SMS). The i-SMS system was designed as follows. Upon ED arrival, triage nurses check patient vital signs and mental status. For patients with an initial qSOFA score ≥ 2, the digital Order Communication System automatically highlights their name in violet to increase visibility to the ED physicians. Such patients are also automatically recorded into a qSOFA-positive registry group. The qSOFA alert system at our institution assists ED clinicians in identifying sepsis early and automatically enrolls patients with a positive qSOFA score [4]. A qSOFA scoring system evaluates the following three criteria and assigns one point for each criterion: altered mental status (Glasgow coma score under 15 points), high respiratory rate (≥ 22 breaths/min), and low blood pressure (systolic blood pressure ≤ 100 mmHg). Next, we set another inclusion criterion as an increase in the SOFA score by ≥ 2 points in the ED, regardless of the presence of current infection. Patients who refused to provide consent, had an increase in SOFA scores < 2, visited the hospital for trauma care, or had unknown 30-day mortality were excluded from this study. Consequently, all study participants had a qSOFA score of ≥ 2 points and an increase in the SOFA score by ≥ 2 points. Eligible participants were categorized into three groups according to the presence of infection and sepsis severity: noninfectious organ failure, sepsis, and septic shock. During the categorization process, all researchers were blinded to the levels of Gal-9, sTREM-1, and sCD25. The NIOF group was considered the control group, while the sepsis and septic shock groups were considered the experimental groups. Data collection We collected data on demographics (sex, age, and medical history), vital signs (blood pressure, heart rate, respiratory rate, body temperature, and saturation of percutaneous oxygen [SpO 2 ]), arterial blood gas analysis, laboratory results for several sepsis-related biomarkers (Gal-9, sTREM-1, sCD25, and CRP), and blood culture. Serum lactate levels were measured in all patients with sepsis according to the SSC guidelines [2]. By following up on the participants’ medical records after their ED presentation, their 30-day mortality rates were evaluated. If demographic or medical records were unavailable, clinical data were collected through telephone counseling with the patients or their legal representatives. Definitions Sepsis is now defined as a life-threatening condition marked by organ dysfunction due to a dysregulated immune response to infection, and septic shock is a more severe condition of sepsis characterized by profound cellular and metabolic abnormalities that greatly increase the risk of death [1]. Similar to our previous study [4], the criteria for NIOF included a positive qSOFA score and an increase in the SOFA score of ≥ 2 without the presence of current infection. The criteria for sepsis include a positive qSOFA score and an increase in SOFA score of ≥ 2 caused by the presence of current infection. The diagnostic criteria for septic shock include the use of vasopressors to maintain a mean arterial pressure of 65 mmHg and a serum lactate level above 2 mmol/L despite adequate fluid resuscitation. The presence of current infection was evaluated by reviewing medical records and laboratory and radiological results. The clinical severity of sepsis and septic shock was evaluated using the Acute Physiology and Chronic Health Evaluation II (APACHE II) and SOFA scores. Multiplex immunoassay We obtained peripheral venous blood for measuring Gal-9, sTREM-1, and sCD25 levels within 6 h of ED presentation. Serum was collected in the SST-Ⅱ vacutainer and stored in the Biobank of our institution at − 80 ℃ till performing analysis. Gal-9, sTREM-1, and sCD25 levels were measured using a multiplex immunoassay based on Luminex technology (xMAP, Luminex Austin TX, USA). All procedures strictly followed the Luminex Assay Human Premixed Multi-Analyte Kit protocol. Heterophilic immunoglobulins were preferentially absorbed from all samples using a heteroblock (Omega Biologicals, Bozeman, MT, USA). The samples were incubated with antibody-conjugated MagPlex microspheres for 1 h with continuous shaking at room temperature, biotinylated antibodies for 1 h, and phycoerythrin-conjugated streptavidin diluted in high-quality ELISA buffer (HPE, Sanquin, Netherlands) for 10 min. Statistical analysis Statistical analyses were performed using the SPSS (version 26.0; IBM, Armonk, NY, USA) and MedCalc (version 19.1.6; MedCalc Software, Mariakerke, Belgium) for Windows. Comparisons of continuous variables between the two groups were performed using the t-test or Mann–Whitney U test, according to the data distribution. Kolmogorov–Smirnov and Shapiro–Wilk tests were used to test for data normality. Comparisons of continuous variables among the three groups were performed using the Kruskal–Wallis test, which are represented as the median with an interquartile range (IQR). Statistical significance was accepted for p -value < 0.05, and the Bonferroni method was used to adjust p -value for post hoc analysis. Categorical variables were analyzed using the chi-squared test or Fisher’s exact test. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic and prognostic values of the biomarkers for sepsis and septic shock. Youden’s index was used to calculate the optimal cut-off value by balancing sensitivity and specificity in ROC curve analysis. Correlations between Gal-9, sTREM-1, sCD25, and SOFA scores were analyzed using Spearman’s rank test. The prognostic value of biomarkers was evaluated using the Kaplan-Meier survival curve and Cox proportional hazard model analyses. Survival curves for 30 days stratified by the cutoff values of biomarkers were evaluated using Kaplan-Meier curve analysis and the log-rank test. A multivariable Cox proportional hazards model analysis was conducted to identify the risk factors for 30-day mortality in patients with sepsis and septic shock. A logistic regression equation was constructed to predict the 30-day mortality probability. For this, we included biomarkers with a univariate significance of p < 0.1 as covariates, and 30-day mortality as the dependent variable using the backward elimination method. A logistic regression equation for predicting a logit transformation (logit ( p )) of the probability of 30-day mortality was created using the coefficients generated for each biomarker in the final step of the regression model. Finally, the Logit( p ) value was converted to 30-day mortality probability. The Hosmer–Lemeshow goodness-of-fit test was used to evaluate the fidelity of the regression model. Results Baseline characteristics of the study population A flowchart of the study population is shown in Fig. 1 . Initially, we screened 962 patients who met the positive qSOFA criteria for ED presentation using the i-SMS. Among them, 176 patients were excluded for the following reasons: refusal to provide consent (n = 96), increase in SOFA score < 2 (n = 58), ED visit for trauma care (n = 15), or unknown outcomes (n = 7). Finally, 786 patients were enrolled and classified into three groups: 1) NIOF, 2) sepsis, and 3) septic shock. Table 1 summarizes the baseline characteristics of the study population. Patients with sepsis or septic shock were older than those with NIOF. The Charlson Comorbidity Index was higher for sepsis and septic shock than for NIOF. Vasopressors were administered more frequently to patients with septic shock than to those with NIOF or sepsis. SOFA and APACHE Ⅱ scores and lactate levels were higher in septic shock than in NIOF or sepsis. SOFA score was higher in sepsis than in NIOF, while there was no significant difference in APACHE Ⅱ score between NIOF and sepsis. The lactate levels were higher in the NIOF group than in the sepsis group. Serum levels of Gal-9, sTREM-1, sCD25, and CRP ( p < 0.001) were higher in sepsis than in those with NIOF. Serum levels of Gal-9, sTREM-1, and sCD25 were higher in patients with septic shock than in those with sepsis. However, there was no significant difference in CRP levels between patients with sepsis and those with septic shock. Table 1 Baseline characteristics of the study population Variable NIOF (n = 331) Sepsis (n = 266) Septic shock (n = 189) p -value Age, median (IQR) 67 A,B (51–82) 77 A (69–84) 79 B (68–84) < 0.001 Male, n (%) 181 (54.7) 154 (57.9) 109 (57.7) 0.684 Charlson’s morbidity index, median (IQR) 5 A (3–7) 6 A (5–8) 6 (5–8) < 0.001 Past medical history, n (%) Myocardial infarction 11 (3.3) 7 (2.6) 1 (0.5) 0.131 Chronic heart disease 28 (8.5) 25 (9.4) 17 (9.0) 0.922 Peripheral vascular disease 161 (48.6) 138 (51.9) 107 (56.6) 0.215 Cerebrovascular disease 74 (22.4 A,B ) 101 (38.0 A ) 78 (41.3 B ) < 0.001 Dementia 42 (12.7 A,B ) 57 (21.4 A ) 51 (27.0 B ) < 0.001 COPD 27 (8.2) 34 (12.8) 17 (9.0) 0.152 Diabetes 90 (27.2 A ) 106 (39.8 A ) 66 (34.9) 0.004 Connective tissue disease 3 (0.9) 7 (2.6) 6 (3.2) 0.148 Peptic ulcer disease 18 (5.4) 13 (4.9) 6 (3.2) 0.496 Liver disease 38 (11.5 A ) 9 (3.4 A ) 13 (6.9) 0.001 Hemiplegia 27 (8.2 A,B ) 68 (25.6 A ) 55 (29.1 B ) < 0.001 Chronic kidney disease (stage ≥ 3) 31 (9.4) 20 (7.5) 12 (6.3) 0.445 Malignancy 73 (22.1) 57 (21.4) 38 (20.1) 0.873 Vital signs, median (IQR) Systolic blood pressure (mmHg) 100 B (91–145) 104 C (91–139) 91 B,C (80–115) < 0.001 Diastolic blood pressure (mmHg) 64 B (53–85) 63 C (54–79) 55 B,C (48–69) < 0.001 Heart rate (rate/min) 98 A,B (80–119) 108 A (89–124) 110 B (88–128) < 0.001 Respiratory rate (breath/min) 24 (22–26) 24 (20–28) 24 (20–30) 0.265 Body temperature (℃) 36.5 A,B (36.0–37.0) 37.2 A (36.4–38.2) 36.9 B (36.1–38.0) < 0.001 SpO 2 (%) 97 B (93–99) 96 C (93–99) 93 B,C (86–97) < 0.001 Vasopressor administration, n (%) 53 (16.0 B ) 32 (12.0 C ) 121 (64.0 B,C ) < 0.001 SOFA score, median (IQR) 6 A,B (3–8) 7 A,C (5–9) 10 B,C (7–12) < 0.001 APACHE Ⅱ score, median (IQR) 17 B (12–21) 17 C (14–22) 19 B,C (15–24) < 0.001 Platelet (×10 9 /L), median (IQR) 209 B (157–289) 206 C (138–287) 186 B,C (117–245) 0.001 Bilirubin (mg/L), median (IQR) 5.60 B (3.10–10.80) 6.50 (4.00–10.80) 6.90 B (4.90–12.90) 0.002 Creatinine (mg/L), median (IQR) 11.20 B (8.00–19.30) 12.30 C (8.00–20.40) 15.70 B,C (10.40–24.00) < 0.001 WBC (×10 9 /L), median (IQR) 10.9 A (8.0–15.5) 12.2 A (8.2–18.7) 11.0 (6.6–16.9) 0.016 CRP (mg/L), median (IQR) 6.70 A,B (1.50–30.10) 90.90 A (45.80–166.50) 111.90 B (55.40–207.00) < 0.001 Lactate (mmol/L), median (IQR) 2.7 A,B (1.6–5.4) 2.1 A,C (1.4–3.8) 4.7 B,C (2.6–8.3) < 0.001 Gal-9 (ng/L), median (IQR) 10433.76 A,B (6604.19–17164.89) 13904.70 A,C (9219.05–19641.90) 16642.54 B,C (11214.58–24755.72) < 0.001 sTREM-1 (ng/L), median (IQR) 373.25 A,B (219.91–641.78) 509.04 A,C (334.63–819.00) 673.30 B,C (431.82–1091.67) < 0.001 sCD25 (ng/L), median (IQR) 631.05 A,B (410.50–1113.20) 1313.19 A,C (747.33–2391.39) 1757.40 B,C (1013.64–3213.92) < 0.001 Length of hospital stay (days), median (IQR) 12 A,B (6–20) 13 A (8–23) 16 B (9–31) 0.002 A,B,C : The same letter indicates a significant difference between two groups. NIOF, noninfectious organ failure; IQR, interquartile range; COPD, chronic obstructive pulmonary disease; SpO 2 , saturation of percutaneous oxygen; SOFA, sepsis-related organ failure assessment; APACHE II, Acute Physiology and Chronic Health Evaluation II; WBC, white blood cell; CRP, C-reactive protein; Gal-9, galectin-9; sTREM-1, soluble triggering receptor expressed on myeloid cells-1; sCD25, soluble CD25. Correlation with biomarkers and severity scores Gal-9, sTREM-1, and sCD25 levels positively correlated with CRP (Gal-9, rho = 0.319, p < 0.001; sTREM-1, rho = 0.415, p < 0.001; sCD25, rho = 0.608, p < 0.001; respectively), SOFA score (Gal-9, rho = 0.351, p < 0.001; sTREM-1, rho = 0.454, p < 0.001; sCD25, rho = 0.346, p < 0.001; respectively), and APACEH Ⅱ score (Gal-9, rho = 0.274, p < 0.001; sTREM-1, rho = 0.331, p < 0.001; sCD25, rho = 0.222, p < 0.001; respectively). Gal-9 and sTREM-1 correlated with lactate levels (Gal-9, rho = 0.120, p = 0.001; sTREM-1, rho = 0.146, p < 0.001, respectively), but sCD25 did not. Diagnostic value of Gal-9, sTREM-1, and sCD25 The ROC curve analyses for discriminating sepsis from NIOF and septic shock from sepsis are shown in Fig. 2 a and Fig. 2 b, and Table 2 present the detailed optimal cutoff value, sensitivity, and specificity for discriminating sepsis from NIOF and septic shock from sepsis. The optimal cut-off value of Gal-9 for discriminating sepsis from NIOF was 9719.78 ng/L (AUC, 0.638; 95% confidence interval [CI], 0.599–0.678; sensitivity, 76.3%; specificity, 47.4%; p < 0.001) and that for discriminating septic shock from sepsis 15735.11 ng/L (AUC, 0.614; 95% CI, 0.562–0.667; sensitivity, 57.1%; specificity, 62.0%; p < 0.001). The optimal cut-off value of the sTREM-1 for discriminating sepsis from NIOF was 327.19 ng/L (AUC, 0.655; 95% CI, 0.616–0.695; sensitivity, 82.0%; specificity, 46.2%; p < 0.001), and that for discriminating septic shock from sepsis was 555.93 ng/L (AUC, 0.624; 95% CI, 0.572–0.676; sensitivity, 64.0; specificity, 56.0; p < 0.001), respectively. The optimal cut-off value of the sCD25 for discriminating sepsis from NIOF was 910.11 ng/L (AUC, 0.746; 95% CI, 0.710–0.781; sensitivity, 74.3%; specificity, 66.5%; p < 0.001), and that for discriminating septic shock from sepsis was 1560.53 ng/L (AUC, 0.607; 95% CI, 0.555–0.660; sensitivity, 56.6%; specificity, 63.5%; p < 0.001), respectively. The optimal cut-off value of the CRP for discriminating sepsis from NIOF was 36.95 mg/L (AUC, 0.843; 95% CI, 0.814–0.873; sensitivity, 81.1%; specificity, 78.9%; p < 0.001), and that for discriminating septic shock from sepsis was 227.35 mg/L (AUC, 0.559; 95% CI, 0.505–0.613; sensitivity, 23.3%; specificity, 89.1%; p = 0.032), respectively. The optimal cut-off value of the lactate for discriminating septic shock from sepsis was 2.10 mmol/L (AUC, 0.751; 95% CI, 0.707–0.795; sensitivity, 91.5%; specificity, 50.4%; p < 0.001). Table 2 Discriminating powers of the biomarkers presented as areas under the curve (95% CI). Biomarker AUC (95% CI) p- value Cut-off value Sensitivity Specificity Gal-9 NIOF vs * Sepsis 0.638 (0.599–0.678) < 0.001 9719.78 (ng/L) 76.3% 47.4% Sepsis vs Septic shock 0.614 (0.562–0.667) < 0.001 15735.11 (ng/L) 57.1% 62.0% sTREM-1 NIOF vs * Sepsis 0.655 (0.616–0.695) < 0.001 327.19 (ng/L) 82.0% 46.2% Sepsis vs Septic shock 0.624 (0.572–0.676) < 0.001 555.93 (ng/L) 64.0% 56.0% sCD25 NIOF vs * Sepsis 0.746 (0.710–0.781) < 0.001 910.11 (ng/L) 74.3% 66.5% Sepsis vs Septic shock 0.607 (0.555–0.660) < 0.001 1560.53 (ng/L) 56.6% 63.5% CRP NIOF vs * Sepsis 0.843 (0.814–0.873) < 0.001 36.95 (mg/L) 81.1% 78.9% Sepsis vs Septic shock 0.559 (0.505–0.613) 0.032 227.35 (mg/L) 23.3% 89.1% Lactate NIOF vs * Sepsis 0.519 (0.478–0.560) 0.361 2.12 (mmol/L) 65.9% 40.8% Sepsis vs Septic shock 0.751 (0.707–0.795) < 0.001 2.10 (mmol/L) 91.5% 50.4% AUC, area under the curve; Gal-9, galectin-9; sTREM-1, soluble triggering receptor expressed on myeloid cells-1; sCD25, soluble CD25; CRP, C-reactive protein; NIOF, noninfectious organ failure. * Sepsis, including septic shock. Prognostic value of Gal-9, sTREM-1, and sCD25 ROC curve analyses using Gal-9, sTREM-1, and sCD25 levels to predict 30-day mortality are presented for the sepsis and septic shock groups (Figure. 2c). The optimal cut-off values to predict 30-day mortality were 14391.80 ng/L for Gal-9 (AUC, 0.642; 95% CI, 0.585–0.698; sensitivity, 70%; specificity, 52.9%; p < 0.001), 580.62 ng/L for sTREM-1 (AUC, 0.656; 95% CI, 0.589–0.701; sensitivity, 69.8%; specificity, 58.4%; p < 0.001), 1639.29 ng/L for sCD25 (AUC, 0.626; 95% CI, 0.567–0.685; sensitivity, 60.3%; specificity, 63.5%; p < 0.001), 49.5 mg/L for CRP (AUC, 0.545; 95% CI, 0.487–0.602; sensitivity, 84.9%; specificity, 28.6%; p = 0.141), and 4.05 mmol/L for lactate (AUC, 0.710; 95% CI, 0.657–0.764; sensitivity, 58.7%; specificity, 72.6%; p < 0.001), respectively. A multivariable logistic regression model was constructed to predict the 30-day mortality rate using the SOFA scores and biomarkers (Gal-9, sTREM-1, sCD25, CRP, and lactate) (Figure. 2d). Using the regression equation, the log of the probability was converted to the probability of 30-day mortality. In the ROC curve analysis, the AUC of the SOFA score was 0.639 (95% confidence interval [CI], 0.582–0.696; p < 0.001), and the model was well calibrated (Hosmer-Lemeshow test; Χ 2 = 5.629; d f = 8; p = 0.689) for predicting 30-day mortality in patients with sepsis and septic shock. The AUC of the combination of the SOFA score and the two biomarkers (CRP and lactate) was 0.719 (95% CI, 0.666–0.773, p < 0.001), and the model was well calibrated (Hosmer-Lemeshow test, Χ 2 = 7.328; d f = 8; p = 0.502). The AUC of the combination of the SOFA score and the five biomarkers (Gal-9, sTREM-1, sCD25, CRP, and lactate) was 0.740 (95% CI, 0.689–0.792, p < 0.001), and model was well calibrated (Hosmer-Lemeshow test, Χ 2 = 10.977; d f = 8; p = 0.203). Figure 3 shows the Kaplan–Meier survival curves stratified by the cutoff values for the probability of 30-day mortality. In all of the biomarkers tested, patients with biomarker levels over the cut-off value showed higher mortality than those with biomarker levels below the cut-off value (Gal-9 ≥ 14391.80 ng/L, 35.9% vs 18.0%, p < 0.001; sTREM-1 ≥ 580.62 ng/L, 38.5% vs 17.0%, p < 0.001; sCD25 ≥ 1639.29 ng/L, 38.1% vs 19.7%, p < 0.001; CRP ≥ 49.5 mg/L, 31.1% vs 16.8%, p = 0.004; lactate ≥ 4.05 mmol/L, 44.3% vs 18.2%, p < 0.001; respectively) (log-rank test). Using Gal-9, sTREM-1, sCD25, CRP, and lactate, multivariable Cox proportional hazards model analysis was performed to identify the risk factors for 30-day mortality among the overall study population, including NIOF, sepsis, and septic shock (Table 3 ). In the overall patients, sCD25 (hazard ratio [HR], 1.000; 95% confidence interval [CI], 1.000–1.000; p < 0.001), CRP (HR, 1.020; 95% CI, 1.008–1.033; p = 0.001), and lactate levels (HR, 1.126; 95% CI, 1.100–1.154; p < 0.001) were identified as significant risk factors for 30-day mortality. In patients with NIOF, sTREM-1 (HR, 1.001; 95% CI, 1.000–1.001; p = 0.013), sCD25 (HR, 1.000; 95% CI, 1.000–1.000; p = 0.046), and lactate (HR, 1.107; 95% CI, 1.069–1.146; p < 0.001) were determined as significant risk factors for 30-day mortality. In patients with sepsis and septic shock, sCD25 (HR, 1.000; 95% CI, 1.000–1.000; p = 0.040) and lactate levels (HR, 1.178; 95% CI, 1.131–1.227; p < 0.001) were significant risk factors for 30-day mortality. Table 3 Multivariable Cox proportional hazards models of risk factors for 30-day mortality. Biomarker All patients (n = 786) NIOF (n = 331) * Sepsis (n = 455) Multivariable HR (95% CI) p -value Multivariable HR (95% CI) p -value Multivariable HR (95% CI) p -value Gal-9 - ** ns - ns - ns sTREM-1 - ns 1.001 (1.000–1.001) 0.013 - ns sCD25 1.000 (1.000–1.000) < 0.001 1.000 (1.000–1.000) 0.046 1.000 (1.000–1.000) 0.040 CRP 1.020 (1.008–1.033) 0.001 - ns - ns Lactate 1.126 (1.100–1.154) < 0.001 1.107 (1.069–1.146) < 0.001 1.178 (1.131–1.227) < 0.001 HR, hazard ratio; CI, confidence interval; Gal-9, galectin-9; sTREM-1, soluble triggering receptor expressed on myeloid cells-1; sCD25, soluble CD25; CRP, C-reactive protein; NIOF, noninfectious organ failure. * Sepsis, including septic shock; ** ns, not significant. Discussion To our knowledge, this is the largest prospective observational study on the diagnostic and prognostic value of Gal-9, sTREM-1, and sCD25 in critically ill patients with organ failure. Furthermore, this is the first study on the clinical value of Gal-9 in patients diagnosed with sepsis in the ED. Our study showed that Gal-9, sTREM-1, and sCD25 can help discriminate sepsis from NIOF, and septic shock from sepsis. These three biomarkers also have a prognostic value in patients with sepsis. The combination of biomarkers and SOFA scores showed improved performance in predicting the 30-day mortality. sCD25 is an independent risk factor for 30-day mortality in patients with sepsis. CRP and PCT levels are widely measured in various clinical settings. CRP provides useful information on a wide range of cardiovascular events and inflammatory conditions, including sepsis, and has analytical advantages as a “robust biomarker” that is minimally affected by sample or environmental conditions [21]. However, CRP has limited value in discriminating bacterial infections and predicting severity of sepsis [22–24]. Although PCT is relatively specific for bacterial infections, it has a limited prognostic value in patients with sepsis. Serum lactate levels are used to detect septic shock [1]. Furthermore, it has a better prognostic value than qSOFA in patients with sepsis [25]. Several studies have demonstrated the prognostic value of lactate levels in patients with sepsis. However, lactate levels have limited value in discriminating sepsis from noninfectious diseases. Owing to the limitations of the established biomarkers, novel biomarkers with better performance are required. This study investigated Gal-9, sTREM-1, and sCD25 as potential auxiliary biomarkers of sepsis and septic shock. Gal-9 is released from various organs during an immunologic crisis [8]. Gal-9 reflects the status of organ dysfunction; however, its clinical value has never been assessed in patients with sepsis or septic shock. In the present study, Gal-9 discriminated between sepsis and NIOF. It can also distinguish between septic shock and sepsis. Our study showed that Gal-9 could discriminate between sepsis severities. Furthermore, the 30-day mortality differed between the two sepsis groups stratified by the cut-off value. Although Gal-9 was not superior to CRP in discriminating sepsis from NIOF, it performed better than CPR in discriminating septic shock from sepsis. Previous experimental animal studies have shown that Gal-9 has therapeutic effects in sepsis and sepsis-like models [26, 27]. Moreover, T cell immunoglobulin and mucin domain 3 (Tim-3) on the T helper 1 cell surface are closely correlated with Gal-9 and form the Tim-3/Gal-9 signaling cascade [28, 29]. Similarly, the binding of Gal-9 to Tim-3 showed protective effects in CD4 T cells against HIV infection [30]. Thus, Gal-9 may be a suitable auxiliary biomarker for identifying septic shock. As Gal-9 works as a powerful therapeutic mediator in the immune cascade to alleviate disease severity at the same time, elevated levels of Gal-9 alone in septic conditions should not be simply interpreted as the pathological severity of sepsis. sTREM-1 could discriminate sepsis severity, and the 30-day survival curves differed between the two sepsis groups stratified by their cutoff values. sTREM-1 levels increase in the early phase of sepsis and decrease after adequate treatment [31]. Another study showed that sTREM-1 could effectively predict 28-day mortality in patients with sepsis, severe sepsis, and septic shock [16]. However, in that study, the prognostic value of sTREM-1 was not superior to that of the SOFA and APACHE II scores. sTREM-1 did not show better performance as a diagnostic biomarker for severe sepsis and septic shock compared to CRP and IL-6 [32]. Another prospective observational study suggested that sTREM-1 is a better predictor of 90-day mortality than CRP and PCT in septic shock [33]. These discrepancies might be partly caused by the different disease severities of the study populations or the different control group settings. A prospective cohort study suggested that sTREM-1 levels in patients with sepsis admitted to the ICUs could reflect infectious conditions more accurately than CRP and PCT levels [34]. Despite these controversial results, sTREM-1 appears to have significant diagnostic and prognostic value in sepsis. Several studies have assessed the clinical value of sCD25 as a sepsis-related biomarker. A previous study on serum protein markers suggested sCD25 as a complementary tool for diagnosing sepsis [35]. Increased plasma sCD25 levels and Treg percentages are associated with sepsis [36]. Similar to these studies, our study showed that sCD25 could discriminate sepsis from NIOF, and septic shock from sepsis. Although sCD25 was superior to sTREM-1 and sCD25 in discriminating sepsis from NIOF, it was not superior to either sTREM-1 or sCD25 in discriminating septic shock from sepsis. This may be explained by decreased sCD25 levels before death caused by sepsis, which reflects immune suppression and exhaustion of activated T-lymphocytes [18]. Among Gal-9, sTREM-1, and sCD25, only sCD25 was found to be an independent risk factor for 30-day mortality in patients with sepsis in the Cox proportional hazards model. Although sCD25 levels did not correlate with disease severity, sCD25 could effectively predict mortality in patients with sepsis in ICUs [37]. Another study suggested sIL-2Rα (i.e. sCD25) for the prediction of sepsis occurrence in multiple trauma patients [38]. Overall, sCD25 appears to have both diagnostic and prognostic value in patients with sepsis and septic shock. Furthermore, our study provides supportive evidence for the robust prognostic value of sCD25 levels. There are several limitations in the current study. First, this was a single-center, ED-based study, which has limited external validity. Therefore, further multi-center, ICUs- or other EDs-based studies are recommended to support our results. Second, only the initial levels of individual biomarkers were measured in the ED, and subsequent changes were not determined. Dynamic monitoring of biomarkers can help diagnose and prognosticate patients with sepsis; thus, further studies, including follow-up changes in these markers, are needed. Third, because our study included only patients with organ failure screened in the ED, this might have resulted in a selection bias. Although NIOF was used as a control group, there may have been patients who had an infection but no organ failure (infection without organ failure) during the study period. There may also have been patients without infection or organ failure. Because we did not enroll these patients, our results may have underestimated the discriminating power of the biomarkers. Lastly, as PCT levels were not measured in approximately 15% of our study population, we did not compare the clinical value of Gal-9, sTREM-1, and sCD25 with those of PCT. Conclusion Gal-9, sTREM-1, and sCD25 have diagnostic and prognostic value in critically ill patients with organ failure. Among these, sCD25 showed the best performance in distinguishing sepsis from NIOF. Furthermore, sCD25 level was an independent risk factor for 30-day mortality among patients with sepsis. Overall, Gal-9, sTREM-1, and sCD25 could be used as potential auxiliary biomarkers for supporting clinical decision in critically ill patients, including those with sepsis and septic shock. Abbreviations AKI Acute kidney injury APACHE Ⅱ Acute Physiology and Chronic Health Evaluation Ⅱ AUC Area under the curve CI Confidence interval CRP C-reactive protein ED Emergency department Gal-9 Galectin-9 HR Hazard ratio ICUs Intensive care units IQR Interquartile range i-SMS Intelligent Sepsis Management System NIOF Non-infectious organ failure ns Not significant PCT Procalcitonin qSOFA Quick sepsis-related organ failure assessment ROC Receiver operating characteristic sCD25 soluble CD25 SOFA Sepsis-related organ failure assessment SpO 2 Saturation of percutaneous oxygen SSC Surviving sepsis campaign sTREM-1 Soluble triggering receptor expressed on myeloid cells-1 Tim-3 T cell immunoglobulin and mucin domain 3 TREM-1 Triggering receptor expressed on myeloid cells-1 WBC White blood cell Declarations Acknowledgements: None. Author contributions: JS and UK were major contributors in writing the manuscript. SL (Sijin Lee), KSH, SJK and SL (Sungwoo Lee) helped with data analysis and curation. JS, DWP and UK provided the clinical data and collected samples. JS and DWP provided study design, technical support, and consultation. All authors read and approved the final manuscript. Research funding: This work was funded in part by a National Research Foundation (NRF) grant from the Korean government (MSIT) (No. RS-2023-00208807), National Research Foundation (NRF) grant funded by the Korean government (MSIT) (No. 2020R1C1C1010362), and Korea University Ansan Hospital Grant (No. K2211961) (all were received from JS). This work was also funded in part by a National Research Foundation (NRF) grant from the Korean government (MSIT) (No. 2020R1F1A1071620) (received from DWP). However, the funding organization did not play any role in the collection, management, analysis, or interpretation of the data; the preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication. Data availability: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethical Statement and Informed Consent: This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (IRB) of Korea University Medical Center (IRB no. 2022AS0313). Verbal information about the study was provided to all study participants or their legal representatives in advance and written informed consent was obtained. Conflicts of interest: The authors state no conflict of interest. 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Jedynak M, Siemiatkowski A, Milewski R, Mroczko B, Szmitkowski M: Diagnostic effectiveness of soluble triggering receptor expressed on myeloid cells-1 in sepsis, severe sepsis and septic shock . Arch Med Sci 2019, 15 (3):713–721. Brenner T, Uhle F, Fleming T, Wieland M, Schmoch T, Schmitt F, Schmidt K, Zivkovic AR, Bruckner T, Weigand MA et al : Soluble TREM-1 as a diagnostic and prognostic biomarker in patients with septic shock: an observational clinical study . Biomarkers 2017, 22 (1):63–69. Su L, Han B, Liu C, Liang L, Jiang Z, Deng J, Yan P, Jia Y, Feng D, Xie L: Value of soluble TREM-1, procalcitonin, and C-reactive protein serum levels as biomarkers for detecting bacteremia among sepsis patients with new fever in intensive care units: a prospective cohort study . BMC Infect Dis 2012, 12 :157. Garcia-Obregon S, Azkargorta M, Seijas I, Pilar-Orive J, Borrego F, Elortza F, Boyano MD, Astigarraga I: Identification of a panel of serum protein markers in early stage of sepsis and its validation in a cohort of patients . J Microbiol Immunol Infect 2018, 51 (4):465–472. Saito K, Wagatsuma T, Toyama H, Ejima Y, Hoshi K, Shibusawa M, Kato M, Kurosawa S: Sepsis is characterized by the increases in percentages of circulating CD4 + CD25 + regulatory T cells and plasma levels of soluble CD25 . Tohoku J Exp Med 2008, 216 (1):61–68. Llewelyn MJ, Berger M, Gregory M, Ramaiah R, Taylor AL, Curdt I, Lajaunias F, Graf R, Blincko SJ, Drage S et al : Sepsis biomarkers in unselected patients on admission to intensive or high-dependency care . Crit Care 2013, 17 (2):R60. Trancă S, Oever JT, Ciuce C, Netea M, Slavcovici A, Petrișor C, Hagău N: sTREM-1, sIL-2Rα, and IL-6, but not sCD163, might predict sepsis in polytrauma patients: a prospective cohort study . Eur J Trauma Emerg Surg 2017, 43 (3):363–370. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6534330","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451881006,"identity":"575b6f14-8e1b-4947-977d-e285f35c8467","order_by":0,"name":"Uihwan Kim","email":"","orcid":"","institution":"Korea University Anam Hospital","correspondingAuthor":false,"prefix":"","firstName":"Uihwan","middleName":"","lastName":"Kim","suffix":""},{"id":451881007,"identity":"a1a7fafc-6dd0-46f7-97c1-24166ef5a4a2","order_by":1,"name":"Sijin Lee","email":"","orcid":"","institution":"Korea University Anam Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sijin","middleName":"","lastName":"Lee","suffix":""},{"id":451881008,"identity":"652ea34b-b136-4ac0-94fb-217e08ecec4d","order_by":2,"name":"Kap Su Han","email":"","orcid":"","institution":"Korea University Anam Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kap","middleName":"Su","lastName":"Han","suffix":""},{"id":451881010,"identity":"3d780e70-721c-4c6c-93ec-9e079b97d04e","order_by":3,"name":"Su Jin Kim","email":"","orcid":"","institution":"Korea University Anam Hospital","correspondingAuthor":false,"prefix":"","firstName":"Su","middleName":"Jin","lastName":"Kim","suffix":""},{"id":451881013,"identity":"6a953a93-baf0-4359-a22f-b4251c350773","order_by":4,"name":"Sungwoo Lee","email":"","orcid":"","institution":"Korea University Anam Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sungwoo","middleName":"","lastName":"Lee","suffix":""},{"id":451881014,"identity":"75d0f16c-eb2a-4a7a-a864-a10762521343","order_by":5,"name":"Dae Won Park","email":"","orcid":"","institution":"Korea University Ansan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dae","middleName":"Won","lastName":"Park","suffix":""},{"id":451881015,"identity":"81a162fd-eb0d-4da7-a56d-42c66c3a8fab","order_by":6,"name":"Juhyun Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBADOfvjPWAGDx+xWowZzpxhYDgA1MJGrJbEhhs5YC0MBLXIt/cefvGj5k5i48y3Bx9/zLGTYWNgfvjoBh4tBmfOpVn2HHtm3Cydl2xwcFsy0GFsxsY5+LRI5JgZM7Adlm2TzjGTOLiNGaiFh00anxb5GSAt/w4z9kieAWmpJ6yF4UaO8WPGtsOKMyR4QFoOE9ZicOaMGWNv3zNjA54cY4Oz247zsDET8It8e4/xhx/f7sgZsJ8xfFC5rdqen7354WO8DgNGhAQkQmCAGb9ysJIPqFpGwSgYBaNgFKABAHP9ShB7YbZRAAAAAElFTkSuQmCC","orcid":"","institution":"Korea University Anam Hospital","correspondingAuthor":true,"prefix":"","firstName":"Juhyun","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2025-04-26 10:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6534330/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6534330/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82350229,"identity":"fde077f4-df3c-43ec-a5c2-56ec1149e9a7","added_by":"auto","created_at":"2025-05-09 10:51:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17667,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study population. qSOFA, quick sepsis-related organ failure assessment; SOFA, sepsis-related organ failure assessment; ED, emergency department.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6534330/v1/401c649c6fad81631f8a3db9.png"},{"id":82351693,"identity":"420360ef-f814-49c0-aae9-764fe63e74ee","added_by":"auto","created_at":"2025-05-09 10:59:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":78105,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analyses. \u003cstrong\u003e(a)\u003c/strong\u003e Discriminating sepsis from NIOF. \u003cstrong\u003e(b)\u003c/strong\u003e Discriminating septic shock from sepsis. \u003cstrong\u003e(c)\u003c/strong\u003e Predicting 30-day mortality in sepsis including septic shock. \u003cstrong\u003e(d)\u003c/strong\u003e combination of SOFA score and biomarkers for predicting 30-day mortality in sepsis including septic shock. Two biomarkers include CRP and lactate, and five biomarkers include Gal-9, sTREM-1, sCD25, CRP, and lactate. ROC, Receiver operating characteristic; Gal-9, galectin-9; sTREM-1, soluble triggering receptor expressed on myeloid cells-1; sCD25, soluble CD25; CRP, C-reactive protein; NIOF, non-infectious organ failure.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6534330/v1/9a3d4104ed26b64b5faf977a.png"},{"id":82350216,"identity":"fd831c36-4a64-40bc-96ae-0274be0421b9","added_by":"auto","created_at":"2025-05-09 10:51:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33295,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curve according to the optimal cut-off values of biomarkers to predict 30-day mortality. \u003cstrong\u003e(a)\u003c/strong\u003eGal-9. \u003cstrong\u003e(b)\u003c/strong\u003e sTREM-1. \u003cstrong\u003e(c)\u003c/strong\u003e sCD25. \u003cstrong\u003e(d)\u003c/strong\u003e CRP. \u003cstrong\u003e(e)\u003c/strong\u003eLactate. Gal-9, galectin-9; sTREM-1, soluble triggering receptor expressed on myeloid cells-1; sCD25, soluble CD25; CRP, C-reactive protein; NIOF, non-infectious organ failure.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6534330/v1/7d58d9f20c8588f52ba75634.png"},{"id":89351196,"identity":"4342efd7-de95-49b6-aa19-a6050d6ae9c4","added_by":"auto","created_at":"2025-08-19 06:17:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2892519,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6534330/v1/765cc8f0-f49d-4baf-ac3c-21ca107f917e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical value of galectin-9, soluble TREM-1, and soluble CD25 among critically ill patients with organ failure in the emergency department: a prospective observational study ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSepsis is a life-threatening disease characterized by organ dysfunction, induced by a dysregulated host response to infection [1]. Although the surviving sepsis campaign (SSC) guidelines emphasize the importance of the early diagnosis of sepsis [2], there is no gold standard for the diagnosis of sepsis and no reliable tools to predict clinical outcomes.\u003c/p\u003e \u003cp\u003eAlthough blood cultures are useful to detect the presence of bacteremia, it requires a few days to obtain microbiological results, which might lead to false-negative outcomes. In clinical settings, biomarkers, such as C-reactive protein (CRP) and procalcitonin (PCT), have been widely used to identify and prognosticate sepsis patients [3]. Various other biomarkers, including CD64, presepsin, interleukin-6, and adrenomedullin have been evaluated and reported to provide valuable information for identifying sepsis and predicting clinical outcomes [4\u0026ndash;7]. However, no single biomarker can perfectly reflect the pathological state of patients with sepsis, and they have several limitations in both diagnostic and prognostic value. Therefore, an investigation into the clinical value of novel biomarkers is required to establish a comprehensive care strategy for patients with sepsis.\u003c/p\u003e \u003cp\u003eGalectin-9 (Gal-9) is a potential organ dysfunction biomarker known for its immunomodulatory role in various microbial infections and is expressed in whole organ systems by mediating host-pathogen interactions [8]. During acute infection, it is assumed that Gal-9 is rapidly released as a danger signal to initiate innate immune cell activity [8]. Gal-9 reflects the severity of infectious diseases, such as malaria, dengue, tuberculosis, and leptospirosis [9\u0026ndash;12]. However, to our knowledge, the clinical value of Gal-9 has not been assessed in patients diagnosed with sepsis in the emergency department (ED).\u003c/p\u003e \u003cp\u003eTriggering receptor expressed on myeloid cells-1 (TREM-1) is a member of the immunoglobulin superfamily that responds to the presence of bacterial components [13, 14]. Amplification of the inflammatory response by TREM-1 is considered a critical contributor to the dysregulated immune response in sepsis. Pediatric patients admitted to intensive care units (ICUs) with septic shock have higher TREM-1 levels than those with severe sepsis [15]. Elevated levels of soluble TREM-1 (sTREM-1) are associated with increased mortality in patients with septic shock [16]. sTREM-1 levels are also increased in other body fluids such as urine and cerebrospinal fluid. While some studies reported good diagnostic and prognostic values of sTREM-1, other meta-analyses, including nine studies, reported only moderate sensitivity and specificity in predicting mortality [17].\u003c/p\u003e \u003cp\u003eCD25 is an interleukin (IL)-2 receptor that is expressed in correlation with regulatory FOXP3\u0026thinsp;+\u0026thinsp;T cells, and soluble CD25 (sCD25), which is emitted into the blood and is measurable, reflects compensatory regulatory responses [18]. Immunosuppression in sepsis may be closely linked to the development of acute kidney injury (AKI) and sCD25 or IL-10 may be useful as novel biomarkers for the development of septic AKI [19]. Combined CD25, CD64, and CD69 biomarker panels can be used to effectively diagnose sepsis [20]. Huang et al. suggested that increased sCD25 levels correlated with poor clinical outcomes in patients [18].\u003c/p\u003e \u003cp\u003eAlthough there have been several studies on the clinical value of sTREM-1, sCD24, and sCD25, they have some limitations, such as a relatively small sample size, application of previous sepsis definitions, and ICUs-based study. To the best of our knowledge, the clinical value of Gal-9 has not been assessed in critically ill patients with organ failure in the ED. Thus, we aimed to investigate the diagnostic and prognostic values of Gal-9, sTREM-1, and sCD25 in critically ill ED patients with non-infectious organ failure (NIOF), sepsis, and septic shock.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and setting\u003c/h2\u003e \u003cp\u003eThis prospective observational study was conducted in the ED of a tertiary care teaching hospital. Our study adhered to the Declaration of Helsinki (2013; Seventh revision, 64th Meeting, Fortaleza) and was approved by the Institutional Review Board (IRB) of Korea University Ansan Hospital (IRB no. 2022AS0313). Verbal information about the study was provided to all study participants or their legal representatives in advance and written informed consent was obtained.\u003c/p\u003e \u003cp\u003eAdult patients over 18 years of age with a positive quick sepsis-related organ failure assessment (qSOFA) score (qSOFA score of \u0026ge;\u0026thinsp;2 points) who visited the ED from July 2019 to December 2021 were initially screened by qSOFA alert system called Intelligent Sepsis Management System (i-SMS). The i-SMS system was designed as follows. Upon ED arrival, triage nurses check patient vital signs and mental status. For patients with an initial qSOFA score\u0026thinsp;\u0026ge;\u0026thinsp;2, the digital Order Communication System automatically highlights their name in violet to increase visibility to the ED physicians. Such patients are also automatically recorded into a qSOFA-positive registry group. The qSOFA alert system at our institution assists ED clinicians in identifying sepsis early and automatically enrolls patients with a positive qSOFA score [4]. A qSOFA scoring system evaluates the following three criteria and assigns one point for each criterion: altered mental status (Glasgow coma score under 15 points), high respiratory rate (\u0026ge;\u0026thinsp;22 breaths/min), and low blood pressure (systolic blood pressure\u0026thinsp;\u0026le;\u0026thinsp;100 mmHg). Next, we set another inclusion criterion as an increase in the SOFA score by \u0026ge;\u0026thinsp;2 points in the ED, regardless of the presence of current infection. Patients who refused to provide consent, had an increase in SOFA scores\u0026thinsp;\u0026lt;\u0026thinsp;2, visited the hospital for trauma care, or had unknown 30-day mortality were excluded from this study. Consequently, all study participants had a qSOFA score of \u0026ge;\u0026thinsp;2 points and an increase in the SOFA score by \u0026ge;\u0026thinsp;2 points. Eligible participants were categorized into three groups according to the presence of infection and sepsis severity: noninfectious organ failure, sepsis, and septic shock. During the categorization process, all researchers were blinded to the levels of Gal-9, sTREM-1, and sCD25. The NIOF group was considered the control group, while the sepsis and septic shock groups were considered the experimental groups.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eWe collected data on demographics (sex, age, and medical history), vital signs (blood pressure, heart rate, respiratory rate, body temperature, and saturation of percutaneous oxygen [SpO\u003csub\u003e2\u003c/sub\u003e]), arterial blood gas analysis, laboratory results for several sepsis-related biomarkers (Gal-9, sTREM-1, sCD25, and CRP), and blood culture. Serum lactate levels were measured in all patients with sepsis according to the SSC guidelines [2]. By following up on the participants\u0026rsquo; medical records after their ED presentation, their 30-day mortality rates were evaluated. If demographic or medical records were unavailable, clinical data were collected through telephone counseling with the patients or their legal representatives.\u003c/p\u003e\n\u003ch3\u003eDefinitions\u003c/h3\u003e\n\u003cp\u003eSepsis is now defined as a life-threatening condition marked by organ dysfunction due to a dysregulated immune response to infection, and septic shock is a more severe condition of sepsis characterized by profound cellular and metabolic abnormalities that greatly increase the risk of death [1]. Similar to our previous study [4], the criteria for NIOF included a positive qSOFA score and an increase in the SOFA score of \u0026ge;\u0026thinsp;2 without the presence of current infection. The criteria for sepsis include a positive qSOFA score and an increase in SOFA score of \u0026ge;\u0026thinsp;2 caused by the presence of current infection. The diagnostic criteria for septic shock include the use of vasopressors to maintain a mean arterial pressure of 65 mmHg and a serum lactate level above 2 mmol/L despite adequate fluid resuscitation. The presence of current infection was evaluated by reviewing medical records and laboratory and radiological results. The clinical severity of sepsis and septic shock was evaluated using the Acute Physiology and Chronic Health Evaluation II (APACHE II) and SOFA scores.\u003c/p\u003e\n\u003ch3\u003eMultiplex immunoassay\u003c/h3\u003e\n\u003cp\u003eWe obtained peripheral venous blood for measuring Gal-9, sTREM-1, and sCD25 levels within 6 h of ED presentation. Serum was collected in the SST-Ⅱ vacutainer and stored in the Biobank of our institution at \u0026minus;\u0026thinsp;80 ℃ till performing analysis. Gal-9, sTREM-1, and sCD25 levels were measured using a multiplex immunoassay based on Luminex technology (xMAP, Luminex Austin TX, USA). All procedures strictly followed the Luminex Assay Human Premixed Multi-Analyte Kit protocol. Heterophilic immunoglobulins were preferentially absorbed from all samples using a heteroblock (Omega Biologicals, Bozeman, MT, USA). The samples were incubated with antibody-conjugated MagPlex microspheres for 1 h with continuous shaking at room temperature, biotinylated antibodies for 1 h, and phycoerythrin-conjugated streptavidin diluted in high-quality ELISA buffer (HPE, Sanquin, Netherlands) for 10 min.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using the SPSS (version 26.0; IBM, Armonk, NY, USA) and MedCalc (version 19.1.6; MedCalc Software, Mariakerke, Belgium) for Windows. Comparisons of continuous variables between the two groups were performed using the t-test or Mann\u0026ndash;Whitney U test, according to the data distribution. Kolmogorov\u0026ndash;Smirnov and Shapiro\u0026ndash;Wilk tests were used to test for data normality. Comparisons of continuous variables among the three groups were performed using the Kruskal\u0026ndash;Wallis test, which are represented as the median with an interquartile range (IQR). Statistical significance was accepted for \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and the Bonferroni method was used to adjust \u003cem\u003ep\u003c/em\u003e-value for post hoc analysis. Categorical variables were analyzed using the chi-squared test or Fisher\u0026rsquo;s exact test. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic and prognostic values of the biomarkers for sepsis and septic shock. Youden\u0026rsquo;s index was used to calculate the optimal cut-off value by balancing sensitivity and specificity in ROC curve analysis. Correlations between Gal-9, sTREM-1, sCD25, and SOFA scores were analyzed using Spearman\u0026rsquo;s rank test. The prognostic value of biomarkers was evaluated using the Kaplan-Meier survival curve and Cox proportional hazard model analyses. Survival curves for 30 days stratified by the cutoff values of biomarkers were evaluated using Kaplan-Meier curve analysis and the log-rank test. A multivariable Cox proportional hazards model analysis was conducted to identify the risk factors for 30-day mortality in patients with sepsis and septic shock.\u003c/p\u003e \u003cp\u003eA logistic regression equation was constructed to predict the 30-day mortality probability. For this, we included biomarkers with a univariate significance of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1 as covariates, and 30-day mortality as the dependent variable using the backward elimination method. A logistic regression equation for predicting a logit transformation (logit (\u003cem\u003ep\u003c/em\u003e)) of the probability of 30-day mortality was created using the coefficients generated for each biomarker in the final step of the regression model. Finally, the Logit(\u003cem\u003ep\u003c/em\u003e) value was converted to 30-day mortality probability. The Hosmer\u0026ndash;Lemeshow goodness-of-fit test was used to evaluate the fidelity of the regression model.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of the study population\u003c/h2\u003e \u003cp\u003eA flowchart of the study population is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Initially, we screened 962 patients who met the positive qSOFA criteria for ED presentation using the i-SMS. Among them, 176 patients were excluded for the following reasons: refusal to provide consent (n\u0026thinsp;=\u0026thinsp;96), increase in SOFA score\u0026thinsp;\u0026lt;\u0026thinsp;2 (n\u0026thinsp;=\u0026thinsp;58), ED visit for trauma care (n\u0026thinsp;=\u0026thinsp;15), or unknown outcomes (n\u0026thinsp;=\u0026thinsp;7). Finally, 786 patients were enrolled and classified into three groups: 1) NIOF, 2) sepsis, and 3) septic shock.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the baseline characteristics of the study population. Patients with sepsis or septic shock were older than those with NIOF. The Charlson Comorbidity Index was higher for sepsis and septic shock than for NIOF. Vasopressors were administered more frequently to patients with septic shock than to those with NIOF or sepsis. SOFA and APACHE Ⅱ scores and lactate levels were higher in septic shock than in NIOF or sepsis. SOFA score was higher in sepsis than in NIOF, while there was no significant difference in APACHE Ⅱ score between NIOF and sepsis. The lactate levels were higher in the NIOF group than in the sepsis group. Serum levels of Gal-9, sTREM-1, sCD25, and CRP (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were higher in sepsis than in those with NIOF. Serum levels of Gal-9, sTREM-1, and sCD25 were higher in patients with septic shock than in those with sepsis. However, there was no significant difference in CRP levels between patients with sepsis and those with septic shock.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the study population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNIOF\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;331)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSepsis\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;266)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSeptic shock\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;189)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67\u003csup\u003eA,B\u003c/sup\u003e (51\u0026ndash;82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77\u003csup\u003eA\u003c/sup\u003e (69\u0026ndash;84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79\u003csup\u003eB\u003c/sup\u003e (68\u0026ndash;84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e181 (54.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154 (57.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (57.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharlson\u0026rsquo;s morbidity\u003c/p\u003e \u003cp\u003eindex, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003csup\u003eA\u003c/sup\u003e (3\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003csup\u003eA\u003c/sup\u003e (5\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (5\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003ePast medical history, 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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral vascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161 (48.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 (51.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107 (56.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (22.4\u003csup\u003eA,B\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (38.0\u003csup\u003eA\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78 (41.3\u003csup\u003eB\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eDementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (12.7\u003csup\u003eA,B\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (21.4\u003csup\u003eA\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (27.0\u003csup\u003eB\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.152\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\u003e90 (27.2\u003csup\u003eA\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106 (39.8\u003csup\u003eA\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66 (34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConnective tissue disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeptic ulcer disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.496\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\u003e38 (11.5\u003csup\u003eA\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (3.4\u003csup\u003eA\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemiplegia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (8.2\u003csup\u003eA,B\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (25.6\u003csup\u003eA\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (29.1\u003csup\u003eB\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003cp\u003e(stage\u0026thinsp;\u0026ge;\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVital signs, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure\u003c/p\u003e \u003cp\u003e(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u003csup\u003eB\u003c/sup\u003e (91\u0026ndash;145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104\u003csup\u003eC\u003c/sup\u003e (91\u0026ndash;139)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91\u003csup\u003eB,C\u003c/sup\u003e (80\u0026ndash;115)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eDiastolic blood pressure\u003c/p\u003e \u003cp\u003e(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64\u003csup\u003eB\u003c/sup\u003e (53\u0026ndash;85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63\u003csup\u003eC\u003c/sup\u003e (54\u0026ndash;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55\u003csup\u003eB,C\u003c/sup\u003e (48\u0026ndash;69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eHeart rate (rate/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98\u003csup\u003eA,B\u003c/sup\u003e (80\u0026ndash;119)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108\u003csup\u003eA\u003c/sup\u003e (89\u0026ndash;124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110\u003csup\u003eB\u003c/sup\u003e (88\u0026ndash;128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eRespiratory rate (breath/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (22\u0026ndash;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (20\u0026ndash;28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (20\u0026ndash;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody temperature (℃)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.5\u003csup\u003eA,B\u003c/sup\u003e (36.0\u0026ndash;37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.2\u003csup\u003eA\u003c/sup\u003e (36.4\u0026ndash;38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.9\u003csup\u003eB\u003c/sup\u003e (36.1\u0026ndash;38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eSpO\u003csub\u003e2\u003c/sub\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97\u003csup\u003eB\u003c/sup\u003e (93\u0026ndash;99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96\u003csup\u003eC\u003c/sup\u003e (93\u0026ndash;99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93\u003csup\u003eB,C\u003c/sup\u003e (86\u0026ndash;97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eVasopressor administration,\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (16.0\u003csup\u003eB\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (12.0\u003csup\u003eC\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121 (64.0\u003csup\u003eB,C\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eSOFA score, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003csup\u003eA,B\u003c/sup\u003e (3\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003csup\u003eA,C\u003c/sup\u003e (5\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003csup\u003eB,C\u003c/sup\u003e (7\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eAPACHE Ⅱ score,\u003c/p\u003e \u003cp\u003emedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003csup\u003eB\u003c/sup\u003e (12\u0026ndash;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003csup\u003eC\u003c/sup\u003e (14\u0026ndash;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003csup\u003eB,C\u003c/sup\u003e (15\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003ePlatelet (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L),\u003c/p\u003e \u003cp\u003emedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e209\u003csup\u003eB\u003c/sup\u003e (157\u0026ndash;289)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e206\u003csup\u003eC\u003c/sup\u003e (138\u0026ndash;287)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e186\u003csup\u003eB,C\u003c/sup\u003e (117\u0026ndash;245)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilirubin (mg/L),\u003c/p\u003e \u003cp\u003emedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.60\u003csup\u003eB\u003c/sup\u003e (3.10\u0026ndash;10.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.50 (4.00\u0026ndash;10.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.90\u003csup\u003eB\u003c/sup\u003e (4.90\u0026ndash;12.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/L),\u003c/p\u003e \u003cp\u003emedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.20\u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(8.00\u0026ndash;19.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.30\u003csup\u003eC\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(8.00\u0026ndash;20.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.70\u003csup\u003eB,C\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(10.40\u0026ndash;24.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eWBC (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.9\u003csup\u003eA\u003c/sup\u003e (8.0\u0026ndash;15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.2\u003csup\u003eA\u003c/sup\u003e (8.2\u0026ndash;18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.0 (6.6\u0026ndash;16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.70\u003csup\u003eA,B\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1.50\u0026ndash;30.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.90\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(45.80\u0026ndash;166.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111.90\u003csup\u003eB\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(55.40\u0026ndash;207.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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 \u003cp\u003emedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.7\u003csup\u003eA,B\u003c/sup\u003e (1.6\u0026ndash;5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003csup\u003eA,C\u003c/sup\u003e (1.4\u0026ndash;3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7\u003csup\u003eB,C\u003c/sup\u003e (2.6\u0026ndash;8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eGal-9 (ng/L), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10433.76\u003csup\u003eA,B\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(6604.19\u0026ndash;17164.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13904.70\u003csup\u003eA,C\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(9219.05\u0026ndash;19641.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16642.54\u003csup\u003eB,C\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(11214.58\u0026ndash;24755.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003esTREM-1 (ng/L),\u003c/p\u003e \u003cp\u003emedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e373.25\u003csup\u003eA,B\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(219.91\u0026ndash;641.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e509.04\u003csup\u003eA,C\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(334.63\u0026ndash;819.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e673.30\u003csup\u003eB,C\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(431.82\u0026ndash;1091.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003esCD25 (ng/L), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e631.05\u003csup\u003eA,B\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(410.50\u0026ndash;1113.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1313.19\u003csup\u003eA,C\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(747.33\u0026ndash;2391.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1757.40\u003csup\u003eB,C\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(1013.64\u0026ndash;3213.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eLength of hospital stay (days),\u003c/p\u003e \u003cp\u003emedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003csup\u003eA,B\u003c/sup\u003e (6\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003csup\u003eA\u003c/sup\u003e (8\u0026ndash;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003csup\u003eB\u003c/sup\u003e (9\u0026ndash;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003eA,B,C\u003c/sup\u003e: The same letter indicates a significant difference between two groups. NIOF, noninfectious organ failure; IQR, interquartile range; COPD, chronic obstructive pulmonary disease; SpO\u003csub\u003e2\u003c/sub\u003e, saturation of percutaneous oxygen; SOFA, sepsis-related organ failure assessment; APACHE II, Acute Physiology and Chronic Health Evaluation II; WBC, white blood cell; CRP, C-reactive protein; Gal-9, galectin-9; sTREM-1, soluble triggering receptor expressed on myeloid cells-1; sCD25, soluble CD25.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCorrelation with biomarkers and severity scores\u003c/h3\u003e\n\u003cp\u003eGal-9, sTREM-1, and sCD25 levels positively correlated with CRP (Gal-9, rho\u0026thinsp;=\u0026thinsp;0.319, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; sTREM-1, rho\u0026thinsp;=\u0026thinsp;0.415, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; sCD25, rho\u0026thinsp;=\u0026thinsp;0.608, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; respectively), SOFA score (Gal-9, rho\u0026thinsp;=\u0026thinsp;0.351, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; sTREM-1, rho\u0026thinsp;=\u0026thinsp;0.454, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; sCD25, rho\u0026thinsp;=\u0026thinsp;0.346, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; respectively), and APACEH Ⅱ score (Gal-9, rho\u0026thinsp;=\u0026thinsp;0.274, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; sTREM-1, rho\u0026thinsp;=\u0026thinsp;0.331, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; sCD25, rho\u0026thinsp;=\u0026thinsp;0.222, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; respectively). Gal-9 and sTREM-1 correlated with lactate levels (Gal-9, rho\u0026thinsp;=\u0026thinsp;0.120, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; sTREM-1, rho\u0026thinsp;=\u0026thinsp;0.146, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively), but sCD25 did not.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic value of Gal-9, sTREM-1, and sCD25\u003c/h2\u003e \u003cp\u003eThe ROC curve analyses for discriminating sepsis from NIOF and septic shock from sepsis are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e present the detailed optimal cutoff value, sensitivity, and specificity for discriminating sepsis from NIOF and septic shock from sepsis.\u003c/p\u003e \u003cp\u003eThe optimal cut-off value of Gal-9 for discriminating sepsis from NIOF was 9719.78 ng/L (AUC, 0.638; 95% confidence interval [CI], 0.599\u0026ndash;0.678; sensitivity, 76.3%; specificity, 47.4%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and that for discriminating septic shock from sepsis 15735.11 ng/L (AUC, 0.614; 95% CI, 0.562\u0026ndash;0.667; sensitivity, 57.1%; specificity, 62.0%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The optimal cut-off value of the sTREM-1 for discriminating sepsis from NIOF was 327.19 ng/L (AUC, 0.655; 95% CI, 0.616\u0026ndash;0.695; sensitivity, 82.0%; specificity, 46.2%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and that for discriminating septic shock from sepsis was 555.93 ng/L (AUC, 0.624; 95% CI, 0.572\u0026ndash;0.676; sensitivity, 64.0; specificity, 56.0; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. The optimal cut-off value of the sCD25 for discriminating sepsis from NIOF was 910.11 ng/L (AUC, 0.746; 95% CI, 0.710\u0026ndash;0.781; sensitivity, 74.3%; specificity, 66.5%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and that for discriminating septic shock from sepsis was 1560.53 ng/L (AUC, 0.607; 95% CI, 0.555\u0026ndash;0.660; sensitivity, 56.6%; specificity, 63.5%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. The optimal cut-off value of the CRP for discriminating sepsis from NIOF was 36.95 mg/L (AUC, 0.843; 95% CI, 0.814\u0026ndash;0.873; sensitivity, 81.1%; specificity, 78.9%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and that for discriminating septic shock from sepsis was 227.35 mg/L (AUC, 0.559; 95% CI, 0.505\u0026ndash;0.613; sensitivity, 23.3%; specificity, 89.1%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032), respectively. The optimal cut-off value of the lactate for discriminating septic shock from sepsis was 2.10 mmol/L (AUC, 0.751; 95% CI, 0.707\u0026ndash;0.795; sensitivity, 91.5%; specificity, 50.4%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminating powers of the biomarkers presented as areas under the curve (95% CI).\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiomarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCut-off value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGal-9\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\u003eNIOF vs \u003csup\u003e*\u003c/sup\u003eSepsis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.638 (0.599\u0026ndash;0.678)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9719.78 (ng/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSepsis vs Septic shock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.614 (0.562\u0026ndash;0.667)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15735.11 (ng/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e62.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esTREM-1\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\u003eNIOF vs \u003csup\u003e*\u003c/sup\u003eSepsis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.655 (0.616\u0026ndash;0.695)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e327.19 (ng/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSepsis vs Septic shock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.624 (0.572\u0026ndash;0.676)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e555.93 (ng/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e64.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esCD25\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\u003eNIOF vs \u003csup\u003e*\u003c/sup\u003eSepsis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.746 (0.710\u0026ndash;0.781)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e910.11 (ng/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSepsis vs Septic shock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.607 (0.555\u0026ndash;0.660)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1560.53 (ng/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\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\u003eNIOF vs \u003csup\u003e*\u003c/sup\u003eSepsis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.843 (0.814\u0026ndash;0.873)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.95 (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e78.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSepsis vs Septic shock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.559 (0.505\u0026ndash;0.613)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e227.35 (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e89.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate\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\u003eNIOF vs \u003csup\u003e*\u003c/sup\u003eSepsis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.519 (0.478\u0026ndash;0.560)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.12 (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSepsis vs Septic shock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.751 (0.707\u0026ndash;0.795)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.10 (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e91.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAUC, area under the curve; Gal-9, galectin-9; sTREM-1, soluble triggering receptor expressed on myeloid cells-1; sCD25, soluble CD25; CRP, C-reactive protein; NIOF, noninfectious organ failure. \u003csup\u003e*\u003c/sup\u003e Sepsis, including septic shock.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic value of Gal-9, sTREM-1, and sCD25\u003c/h2\u003e \u003cp\u003eROC curve analyses using Gal-9, sTREM-1, and sCD25 levels to predict 30-day mortality are presented for the sepsis and septic shock groups (Figure. 2c). The optimal cut-off values to predict 30-day mortality were 14391.80 ng/L for Gal-9 (AUC, 0.642; 95% CI, 0.585\u0026ndash;0.698; sensitivity, 70%; specificity, 52.9%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 580.62 ng/L for sTREM-1 (AUC, 0.656; 95% CI, 0.589\u0026ndash;0.701; sensitivity, 69.8%; specificity, 58.4%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 1639.29 ng/L for sCD25 (AUC, 0.626; 95% CI, 0.567\u0026ndash;0.685; sensitivity, 60.3%; specificity, 63.5%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 49.5 mg/L for CRP (AUC, 0.545; 95% CI, 0.487\u0026ndash;0.602; sensitivity, 84.9%; specificity, 28.6%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.141), and 4.05 mmol/L for lactate (AUC, 0.710; 95% CI, 0.657\u0026ndash;0.764; sensitivity, 58.7%; specificity, 72.6%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively.\u003c/p\u003e \u003cp\u003eA multivariable logistic regression model was constructed to predict the 30-day mortality rate using the SOFA scores and biomarkers (Gal-9, sTREM-1, sCD25, CRP, and lactate) (Figure. 2d). Using the regression equation, the log of the probability was converted to the probability of 30-day mortality. In the ROC curve analysis, the AUC of the SOFA score was 0.639 (95% confidence interval [CI], 0.582\u0026ndash;0.696; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the model was well calibrated (Hosmer-Lemeshow test; \u003cem\u003eΧ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;5.629; d\u003cem\u003ef\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.689) for predicting 30-day mortality in patients with sepsis and septic shock. The AUC of the combination of the SOFA score and the two biomarkers (CRP and lactate) was 0.719 (95% CI, 0.666\u0026ndash;0.773, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the model was well calibrated (Hosmer-Lemeshow test, \u003cem\u003eΧ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;7.328; d\u003cem\u003ef\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.502). The AUC of the combination of the SOFA score and the five biomarkers (Gal-9, sTREM-1, sCD25, CRP, and lactate) was 0.740 (95% CI, 0.689\u0026ndash;0.792, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and model was well calibrated (Hosmer-Lemeshow test, \u003cem\u003eΧ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;10.977; d\u003cem\u003ef\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.203).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the Kaplan\u0026ndash;Meier survival curves stratified by the cutoff values for the probability of 30-day mortality. In all of the biomarkers tested, patients with biomarker levels over the cut-off value showed higher mortality than those with biomarker levels below the cut-off value (Gal-9\u0026thinsp;\u0026ge;\u0026thinsp;14391.80 ng/L, 35.9% vs 18.0%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; sTREM-1\u0026thinsp;\u0026ge;\u0026thinsp;580.62 ng/L, 38.5% vs 17.0%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; sCD25\u0026thinsp;\u0026ge;\u0026thinsp;1639.29 ng/L, 38.1% vs 19.7%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; CRP\u0026thinsp;\u0026ge;\u0026thinsp;49.5 mg/L, 31.1% vs 16.8%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004; lactate\u0026thinsp;\u0026ge;\u0026thinsp;4.05 mmol/L, 44.3% vs 18.2%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; respectively) (log-rank test).\u003c/p\u003e \u003cp\u003eUsing Gal-9, sTREM-1, sCD25, CRP, and lactate, multivariable Cox proportional hazards model analysis was performed to identify the risk factors for 30-day mortality among the overall study population, including NIOF, sepsis, and septic shock (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the overall patients, sCD25 (hazard ratio [HR], 1.000; 95% confidence interval [CI], 1.000\u0026ndash;1.000; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CRP (HR, 1.020; 95% CI, 1.008\u0026ndash;1.033; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), and lactate levels (HR, 1.126; 95% CI, 1.100\u0026ndash;1.154; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were identified as significant risk factors for 30-day mortality. In patients with NIOF, sTREM-1 (HR, 1.001; 95% CI, 1.000\u0026ndash;1.001; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), sCD25 (HR, 1.000; 95% CI, 1.000\u0026ndash;1.000; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046), and lactate (HR, 1.107; 95% CI, 1.069\u0026ndash;1.146; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were determined as significant risk factors for 30-day mortality. In patients with sepsis and septic shock, sCD25 (HR, 1.000; 95% CI, 1.000\u0026ndash;1.000; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040) and lactate levels (HR, 1.178; 95% CI, 1.131\u0026ndash;1.227; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significant risk factors for 30-day mortality.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Cox proportional hazards models of risk factors for 30-day mortality.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBiomarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAll patients (n\u0026thinsp;=\u0026thinsp;786)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNIOF (n\u0026thinsp;=\u0026thinsp;331)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003csup\u003e*\u003c/sup\u003eSepsis (n\u0026thinsp;=\u0026thinsp;455)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultivariable HR\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMultivariable HR\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMultivariable HR\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGal-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003e**\u003c/sup\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esTREM-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003cp\u003e(1.000\u0026ndash;1.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esCD25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003cp\u003e(1.000\u0026ndash;1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003cp\u003e(1.000\u0026ndash;1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003cp\u003e(1.000\u0026ndash;1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.020\u003c/p\u003e \u003cp\u003e(1.008\u0026ndash;1.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.126\u003c/p\u003e \u003cp\u003e(1.100\u0026ndash;1.154)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.107\u003c/p\u003e \u003cp\u003e(1.069\u0026ndash;1.146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.178\u003c/p\u003e \u003cp\u003e(1.131\u0026ndash;1.227)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHR, hazard ratio; CI, confidence interval; Gal-9, galectin-9; sTREM-1, soluble triggering receptor expressed on myeloid cells-1; sCD25, soluble CD25; CRP, C-reactive protein; NIOF, noninfectious organ failure. \u003csup\u003e*\u003c/sup\u003eSepsis, including septic shock; \u003csup\u003e**\u003c/sup\u003ens, not significant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this is the largest prospective observational study on the diagnostic and prognostic value of Gal-9, sTREM-1, and sCD25 in critically ill patients with organ failure. Furthermore, this is the first study on the clinical value of Gal-9 in patients diagnosed with sepsis in the ED. Our study showed that Gal-9, sTREM-1, and sCD25 can help discriminate sepsis from NIOF, and septic shock from sepsis. These three biomarkers also have a prognostic value in patients with sepsis. The combination of biomarkers and SOFA scores showed improved performance in predicting the 30-day mortality. sCD25 is an independent risk factor for 30-day mortality in patients with sepsis.\u003c/p\u003e \u003cp\u003eCRP and PCT levels are widely measured in various clinical settings. CRP provides useful information on a wide range of cardiovascular events and inflammatory conditions, including sepsis, and has analytical advantages as a \u0026ldquo;robust biomarker\u0026rdquo; that is minimally affected by sample or environmental conditions [21]. However, CRP has limited value in discriminating bacterial infections and predicting severity of sepsis [22\u0026ndash;24]. Although PCT is relatively specific for bacterial infections, it has a limited prognostic value in patients with sepsis. Serum lactate levels are used to detect septic shock [1]. Furthermore, it has a better prognostic value than qSOFA in patients with sepsis [25]. Several studies have demonstrated the prognostic value of lactate levels in patients with sepsis. However, lactate levels have limited value in discriminating sepsis from noninfectious diseases. Owing to the limitations of the established biomarkers, novel biomarkers with better performance are required. This study investigated Gal-9, sTREM-1, and sCD25 as potential auxiliary biomarkers of sepsis and septic shock.\u003c/p\u003e \u003cp\u003eGal-9 is released from various organs during an immunologic crisis [8]. Gal-9 reflects the status of organ dysfunction; however, its clinical value has never been assessed in patients with sepsis or septic shock. In the present study, Gal-9 discriminated between sepsis and NIOF. It can also distinguish between septic shock and sepsis. Our study showed that Gal-9 could discriminate between sepsis severities. Furthermore, the 30-day mortality differed between the two sepsis groups stratified by the cut-off value. Although Gal-9 was not superior to CRP in discriminating sepsis from NIOF, it performed better than CPR in discriminating septic shock from sepsis. Previous experimental animal studies have shown that Gal-9 has therapeutic effects in sepsis and sepsis-like models [26, 27]. Moreover, T cell immunoglobulin and mucin domain 3 (Tim-3) on the T helper 1 cell surface are closely correlated with Gal-9 and form the Tim-3/Gal-9 signaling cascade [28, 29]. Similarly, the binding of Gal-9 to Tim-3 showed protective effects in CD4 T cells against HIV infection [30]. Thus, Gal-9 may be a suitable auxiliary biomarker for identifying septic shock. As Gal-9 works as a powerful therapeutic mediator in the immune cascade to alleviate disease severity at the same time, elevated levels of Gal-9 alone in septic conditions should not be simply interpreted as the pathological severity of sepsis.\u003c/p\u003e \u003cp\u003esTREM-1 could discriminate sepsis severity, and the 30-day survival curves differed between the two sepsis groups stratified by their cutoff values. sTREM-1 levels increase in the early phase of sepsis and decrease after adequate treatment [31]. Another study showed that sTREM-1 could effectively predict 28-day mortality in patients with sepsis, severe sepsis, and septic shock [16]. However, in that study, the prognostic value of sTREM-1 was not superior to that of the SOFA and APACHE II scores. sTREM-1 did not show better performance as a diagnostic biomarker for severe sepsis and septic shock compared to CRP and IL-6 [32]. Another prospective observational study suggested that sTREM-1 is a better predictor of 90-day mortality than CRP and PCT in septic shock [33]. These discrepancies might be partly caused by the different disease severities of the study populations or the different control group settings. A prospective cohort study suggested that sTREM-1 levels in patients with sepsis admitted to the ICUs could reflect infectious conditions more accurately than CRP and PCT levels [34]. Despite these controversial results, sTREM-1 appears to have significant diagnostic and prognostic value in sepsis.\u003c/p\u003e \u003cp\u003eSeveral studies have assessed the clinical value of sCD25 as a sepsis-related biomarker. A previous study on serum protein markers suggested sCD25 as a complementary tool for diagnosing sepsis [35]. Increased plasma sCD25 levels and Treg percentages are associated with sepsis [36]. Similar to these studies, our study showed that sCD25 could discriminate sepsis from NIOF, and septic shock from sepsis. Although sCD25 was superior to sTREM-1 and sCD25 in discriminating sepsis from NIOF, it was not superior to either sTREM-1 or sCD25 in discriminating septic shock from sepsis. This may be explained by decreased sCD25 levels before death caused by sepsis, which reflects immune suppression and exhaustion of activated T-lymphocytes [18]. Among Gal-9, sTREM-1, and sCD25, only sCD25 was found to be an independent risk factor for 30-day mortality in patients with sepsis in the Cox proportional hazards model. Although sCD25 levels did not correlate with disease severity, sCD25 could effectively predict mortality in patients with sepsis in ICUs [37]. Another study suggested sIL-2Rα (i.e. sCD25) for the prediction of sepsis occurrence in multiple trauma patients [38]. Overall, sCD25 appears to have both diagnostic and prognostic value in patients with sepsis and septic shock. Furthermore, our study provides supportive evidence for the robust prognostic value of sCD25 levels.\u003c/p\u003e \u003cp\u003eThere are several limitations in the current study. First, this was a single-center, ED-based study, which has limited external validity. Therefore, further multi-center, ICUs- or other EDs-based studies are recommended to support our results. Second, only the initial levels of individual biomarkers were measured in the ED, and subsequent changes were not determined. Dynamic monitoring of biomarkers can help diagnose and prognosticate patients with sepsis; thus, further studies, including follow-up changes in these markers, are needed. Third, because our study included only patients with organ failure screened in the ED, this might have resulted in a selection bias. Although NIOF was used as a control group, there may have been patients who had an infection but no organ failure (infection without organ failure) during the study period. There may also have been patients without infection or organ failure. Because we did not enroll these patients, our results may have underestimated the discriminating power of the biomarkers. Lastly, as PCT levels were not measured in approximately 15% of our study population, we did not compare the clinical value of Gal-9, sTREM-1, and sCD25 with those of PCT.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eGal-9, sTREM-1, and sCD25 have diagnostic and prognostic value in critically ill patients with organ failure. Among these, sCD25 showed the best performance in distinguishing sepsis from NIOF. Furthermore, sCD25 level was an independent risk factor for 30-day mortality among patients with sepsis. Overall, Gal-9, sTREM-1, and sCD25 could be used as potential auxiliary biomarkers for supporting clinical decision in critically ill patients, including those with sepsis and septic shock.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAKI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute kidney injury\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\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the curve\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\"\u003eCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eED\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEmergency department\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGal-9\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGalectin-9\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\"\u003eICUs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntensive care units\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ei-SMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntelligent Sepsis Management System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNIOF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNon-infectious organ failure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ens\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProcalcitonin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eqSOFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuick sepsis-related organ failure assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003esCD25\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esoluble CD25\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\u003eSepsis-related organ failure assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSpO\u003csub\u003e2\u003c/sub\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSaturation of percutaneous oxygen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSurviving sepsis campaign\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003esTREM-1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSoluble triggering receptor expressed on myeloid cells-1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTim-3\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eT cell immunoglobulin and mucin domain 3\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTREM-1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriggering receptor expressed on myeloid cells-1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWhite blood cell\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eNone.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003eJS and UK were major contributors in writing the manuscript. SL (Sijin Lee), KSH, SJK and SL (Sungwoo Lee) helped with data analysis and curation. JS, DWP and UK provided the clinical data and collected samples. JS and DWP provided study design, technical support, and consultation. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch funding:\u0026nbsp;\u003c/strong\u003eThis work was funded in part by a National Research Foundation (NRF) grant from the Korean government (MSIT) (No. RS-2023-00208807), National Research Foundation (NRF) grant funded by the Korean government (MSIT) (No. 2020R1C1C1010362), and Korea University Ansan Hospital Grant (No. K2211961) (all were received from JS). This work was also funded in part by a National Research Foundation (NRF) grant from the Korean government (MSIT) (No. 2020R1F1A1071620) (received from DWP). However, the funding organization did not play any role in the collection, management, analysis, or interpretation of the data; the preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Statement and Informed Consent:\u0026nbsp;\u003c/strong\u003eThis study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (IRB) of Korea University Medical Center (IRB no. 2022AS0313). Verbal information about the study was provided to all study participants or their legal representatives in advance and written informed consent was obtained.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u0026nbsp;\u003c/strong\u003eThe authors state no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent of publication:\u0026nbsp;\u003c/strong\u003eNot application\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eSinger M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM \u003cem\u003eet al\u003c/em\u003e: \u003cstrong\u003eThe Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)\u003c/strong\u003e. \u003cem\u003eJama\u003c/em\u003e 2016, \u003cstrong\u003e315\u003c/strong\u003e(8):801\u0026ndash;810.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eEvans L, Rhodes A, 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M, Schmoch T, Schmitt F, Schmidt K, Zivkovic AR, Bruckner T, Weigand MA \u003cem\u003eet al\u003c/em\u003e: \u003cstrong\u003eSoluble TREM-1 as a diagnostic and prognostic biomarker in patients with septic shock: an observational clinical study\u003c/strong\u003e. \u003cem\u003eBiomarkers\u003c/em\u003e 2017, \u003cstrong\u003e22\u003c/strong\u003e(1):63\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSu L, Han B, Liu C, Liang L, Jiang Z, Deng J, Yan P, Jia Y, Feng D, Xie L: \u003cstrong\u003eValue of soluble TREM-1, procalcitonin, and C-reactive protein serum levels as biomarkers for detecting bacteremia among sepsis patients with new fever in intensive care units: a prospective cohort study\u003c/strong\u003e. \u003cem\u003eBMC Infect Dis\u003c/em\u003e 2012, \u003cstrong\u003e12\u003c/strong\u003e:157.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGarcia-Obregon S, Azkargorta M, Seijas I, Pilar-Orive J, Borrego F, Elortza F, Boyano 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\u003cstrong\u003eSepsis biomarkers in unselected patients on admission to intensive or high-dependency care\u003c/strong\u003e. \u003cem\u003eCrit Care\u003c/em\u003e 2013, \u003cstrong\u003e17\u003c/strong\u003e(2):R60.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eTrancă S, Oever JT, Ciuce C, Netea M, Slavcovici A, Petrișor C, Hagău N: \u003cstrong\u003esTREM-1, sIL-2R\u0026alpha;, and IL-6, but not sCD163, might predict sepsis in polytrauma patients: a prospective cohort study\u003c/strong\u003e. \u003cem\u003eEur J Trauma Emerg Surg\u003c/em\u003e 2017, \u003cstrong\u003e43\u003c/strong\u003e(3):363\u0026ndash;370.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Galectin-9, Mortality, Organ failure, Sepsis, Soluble CD25, Soluble TREM-1","lastPublishedDoi":"10.21203/rs.3.rs-6534330/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6534330/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigated clinical value of galectin-9 (Gal-9), a soluble triggering receptor expressed on myeloid cells-1 (sTREM-1), and soluble CD25 (sCD25) among critically ill patients with organ failure in the emergency department. Overall, 786 patients were enrolled and classified into: non-infectious organ failure (NIOF, n\u0026thinsp;=\u0026thinsp;331), sepsis (n\u0026thinsp;=\u0026thinsp;266), and septic shock (n\u0026thinsp;=\u0026thinsp;189). The diagnostic value of Gal-9, sTREM-1, and sCD25 were evaluated by receiver operating characteristic curve analysis. The prognostic value of the biomarkers was evaluated using Kaplan\u0026ndash;Meier survival curve and Cox proportional hazard model analyses. Gal-9, sTREM-1, and sCD25 could discriminate sepsis from NIOF (Gal-9, area under the curve [AUC], 0.599\u0026ndash;0.678; sTREM-1, AUC, 0.616\u0026ndash;0.695; sCD25, AUC, 0.710\u0026ndash;0.781) and septic shock from sepsis (Gal-9, AUC, 0.562\u0026ndash;0.667; sTREM-1, AUC, 0.572\u0026ndash;0.676; sCD25, AUC, 0.555\u0026ndash;0.660), respectively. Sepsis patients with higher levels of biomarkers over their cut-off value showed higher 30-day mortality compared to those with lower levels below the cut-off value (Gal-9\u0026thinsp;\u0026ge;\u0026thinsp;14391.80 ng/L, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; sTREM-1\u0026thinsp;\u0026ge;\u0026thinsp;580.62 ng/L, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; sCD25\u0026thinsp;\u0026ge;\u0026thinsp;1639.29 ng/L, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; respectively) (log-rank test). sCD25 is an independent risk factor for 30-day mortality in patients with sepsis or septic shock. Gal-9, sTREM-1, and sCD25 showed diagnostic and prognostic value in critically ill patients with organ failure. sCD25 can predict the 30-day mortality in patients with sepsis. Gal-9, sTREM-1, and sCD25 could serve as auxiliary biomarkers to support clinicians in effective sepsis management.\u003c/p\u003e","manuscriptTitle":"Clinical value of galectin-9, soluble TREM-1, and soluble CD25 among critically ill patients with organ failure in the emergency department: a prospective observational study ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 10:51:01","doi":"10.21203/rs.3.rs-6534330/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":"93295af0-8b40-4a5c-b618-c8bad4c7d2d1","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-19T06:09:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 10:51:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6534330","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6534330","identity":"rs-6534330","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}