Prognostic Value of Presepsin, Proadrenomedullin and Interleukin-6 in Sepsis: A Prospective Study From the Emergency Department

Prognostic Value of Presepsin, Proadrenomedullin and Interleukin-6 in Sepsis: A Prospective Study From the Emergency Department | 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 Prognostic Value of Presepsin, Proadrenomedullin and Interleukin-6 in Sepsis: A Prospective Study From the Emergency Department Seyit Ali KARAGOZ, Yonca Senem AKDENIZ, Dildar KONUKOGLU, Sevil KUSKU KIYAK, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6691861/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 Introduction: The objective of this study is to evaluate the predictive value of serum presepsin, proadrenomedullin, and interleukin-6 levels for prognosis and mortality in patients diagnosed with sepsis in the emergency department. Methods: The cross-sectional study was conducted with patients diagnosed with sepsis in the emergency department of a tertiary university hospital.The study analyzed the patients' demographic characteristics, comorbidities, source of sepsis, biomarker levels, treatments, microbial culture results, and 30-day mortality. Blood samples were collected upon the patients' admission to the emergency department. Results: Patients with septic shock had significantly higher serum levels of IL-6 (363.2 pg/mL) compared to those with sepsis (IL-6: 140.6 pg/mL). In contrast, patients with septic shock had significantly lower proadrenomedullin serum levels (0.27 pmol/mL) compared to septic patients (0.48 pmol/mL). Although presepsin levels were slightly higher in the septic shock group (178.3 ng/L) compared to the sepsis group (156.9 ng/L), the difference was not statistically significant. However, the levels of IL-6 were significantly higher in deceased patients (IL-6: 363.2 pg/mL) compared to those who survived (IL-6: 195.5 pg/mL). The ROC analysis results were listed as IL-6 (AUC: 0.701), proadrenomedullin (AUC: 0.658) and procalcitonin (AUC: 0.625) for determining septic shock. Conclusion: Based on the data obtained, interleukin-6, presepsin, and proadrenomedullin respectively are useful biomarkers for predicting and prognosticating sepsis and septic shock in the emergency department. However, none of these biomarkers are independent prognostic predictors for sepsis, septic shock or mortality. biomarkers interleukin-6 presepsin proadrenomedullin Figures Figure 1 INTRODUCTION Sepsis is a syndrome characterized by both signs of infection and manifestations of a systemic host response and is the primary cause of mortality associated with infection. [ 1 ] Life-threatening organ dysfunction is caused by a dysregulated host response to infection. [ 2 ] Compared with sepsis, septic shock is a state in which circulatory failure begins and is associated with a significantly greater risk of mortality. [ 3 ] Possible reasons for the increase in sepsis incidence include advanced age, immunosuppression, and infections resistant to multiple drugs. [ 4 – 6 ] The incidence is highest during the winter months, likely due to the increased incidence of respiratory tract infections. [ 7 ] The majority of all sepsis episodes (60–85%) occur in individuals aged ≥ 65 years, and with an increasingly aging population, it is likely that the incidence of sepsis will continue to rise in the future. [ 8 ] Clinical, laboratory, imaging, microbiology, and point-of-care rapid diagnostic tests are all collectively evaluated to establish the diagnosis of sepsis and septic shock. [ 9 ] Sepsis biomarkers may have significant diagnostic, therapeutic, and prognostic functions. Lactate is currently a frequently used biomarker in clinical practice. However, the use of additional biomarkers in addition to lactate can increase the diagnostic accuracy for sepsis. [ 10 ] These markers, which are indicative of neutrophil and monocyte activation synthesized in response to infection and inflammation, include procalcitonin (PCT) and C-reactive protein (CRP). [ 10 ] CRP and PCT are by far the most commonly used and investigated acute-phase proteins for diagnosing bacterial sepsis and guiding antibiotic therapy. [ 11 ] The associated limitations include the fact that their levels can increase under various inflammatory conditions. Therefore, the search for new biomarkers continues to be promising as a tool to assess sepsis individually or in combination with other biomarkers, aiming to improve overall sensitivity and specificity in the diagnosis and prognosis of bacterial infections. [ 11 ] Simultaneously, measuring multiple biomarkers may be beneficial for overcoming the limitations of any single biomarker. [ 12 ] Some clinical studies have demonstrated a significant increase in the levels of presepsin, proadrenomedullin (proADM), and interleukin-6 (IL-6) in sepsis and septic shock patients compared to healthy individuals. Moreover, these studies indicated that changes in the blood levels of these three biomarkers are significantly associated with the severity and prognosis of the disease. [ 13 , 14 ] Presepsin is the N-terminal fragment of the CD14-ST subtype, which is the soluble form of CD14. While there are significant studies regarding the effectiveness of presepsin in treating sepsis, research on this biomarker is still ongoing. [ 13 ] proADM is a component of adrenomedullin synthesized by smooth muscle cells and vascular endothelial cells. Due to its longer half-life than adrenomedullin, proADM is easier to detect in the blood. Recent studies have obtained significant results suggesting the use of proADM in predicting sepsis and, particularly, sepsis-related mortality. [ 15 ] IL-6 is a proinflammatory cytokine synthesized by T lymphocytes, fibroblasts, endothelial cells, and monocytes. [ 16 ] While IL-6 is a significant marker of the inflammatory response in sepsis, ongoing studies are still being conducted. [ 17 ] The aim of this study was to investigate the role of serum presepsin, proadrenomedullin, and IL-6 levels in predicting the prognosis and mortality of patients diagnosed with sepsis in the Emergency Department.This study is the first to simultaneously compare the effects of presepsin, proADM, and IL-6 on the prognosis of patients with sepsis in the emergency department. METHODS The cross-sectional study was conducted with patients diagnosed with sepsis in the emergency department of a tertiary university hospital. The study was ethically approved by the institutional ethics committee of our university (Approval Date: 22.03.2023 and number 649489). Patient Selection The study included patients 18 years of age and older. Patients were diagnosed with sepsis using the Sepsis-3 criteria. The quick Sequential Organ Failure Assessment (qSOFA) score was calculated because it is an easy and noninvasive measurement tool. Sepsis was diagnosed based on suspicion of infection and a qSOFA score ≥2 points, according to the Sepsis-3 diagnostic criteria. Septic shock was diagnosed in patients requiring vasopressors to maintain a mean arterial pressure (MAP) of ≥65 mm Hg despite adequate fluid replacement and lactate levels >2mmol/L, even with sufficient fluid resuscitation. Inclusion criteria: - Individuals aged 18 years and older - Patients diagnosed with sepsis - Patients diagnosed with septic shock Exclusion criteria: - Individuals under the age of 18 - Those experiencing nonseptic shock conditions - Individuals with hemolyzed blood samples taken prior to antibiotic therapy Patients who met the inclusion criteria were informed about the study, and their consent was obtained. The patients were then classified based on their sex (male or female), number and types of comorbidities, and source of infection (including respiratory system, urinary system, intra-abdominal infection, soft tissue infection (STI), central nervous system (CNS) infection, bacteremia, and no focus). The patient follow-up data included vital signs such as temperature, pulse rate, blood pressure, respiratory rate per minute, and SpO2 at the time of sepsis diagnosis for all included patients. Patients were categorized as having either sepsis or septic shock. Patients who met the criteria for sepsis and required vasopressors to maintain a mean arterial pressure (MAP) of at least 65 mmHg despite adequate fluid support and had a blood lactate level greater than 2mmol/L were included in the septic shock group. The patients' 30-day survival status was monitored. Processing of Blood Samples Blood samples were collected from all patients before initiating antibiotic therapy for analysis. Serum samples for presepsin, proadrenomedullin, and IL-6 levels were pipetted into Eppendorf tubes, centrifuged at 4000 revolutions per minute for 10 minutes, and stored at -80°C until examination. The Human Presepsin (PSPN) ELISA Kit from BT-LAB was used to measure serum presepsin levels. The Human Pro-ADM ELISA Kit from ELABSCIENCE was used to measure serum proadrenomedullin levels. Similarly, the Human IL-6 (Interleukin 6) ELISA Kit from ELABSCIENCE was used to measure serum IL-6 levels. Statistical analysis Data analysis was performed using IBM SPSS 20 software. The median (min–max), mean ± standard deviation (mean ± SD), frequency (n), and percentage (%) were calculated using descriptive statistics. The normality of the distribution of the data was assessed using the Kolmogorov‒Smirnov normality test. For the comparison of two groups, we used Student’s t test (independent samples t test) for normally distributed data and the Mann‒Whitney U test for no normally distributed data. We used the chi-square test and calculated the odds ratio (OR) for the analysis of categorical data. We employed receiver operating characteristic (ROC) analysis to evaluate the predictive ability of the data and determine the cutoff value. The statistical significance level was set at p<0.05. RESULTS Among the 88 enrolled patients, 53.4% (n = 47) were male, and 46.6% (n = 41) were female. The median age was 71 years, with a range from 26 to 90 years. While 88.7% (n = 78) of the patients had at least one comorbidity, 11.3% (n = 10) had no comorbidities. The most prevalent comorbidities among the patients were hypertension (61.3%), diabetes mellitus (35.2%), and solid cancers (31.8%). The infection sources of the patients were as follows: respiratory system (55.6%, n = 49%), urinary system (26.1%, n = 23%), gastrointestinal system (3.4%, n = 3%), soft tissue (3.4%, n = 3%), hepatobiliary system (3.4%, n = 3%), central nervous system (1.1%, n = 1%), joints (1.1%, n = 1), and peritoneum (1.1%, n = 1%). Bacteremia was detected in 4.5% of the patients. Septic shock developed in 29.5% (n = 26) of the patients. Among the patients, 54.5% (n = 48) were admitted to the intensive care unit (ICU), while 44.5% (n = 40) were admitted to the general ward. The 30-day mortality rate for the patients was 34% (n = 30) (Table 1 ). Table 1 Comparison of Variables, Comorbidities, VitalSigns, andLaboratoryValuesAmongAllPatients, Sepsis, andSepticShockGroups Variables All Patients(%) Sepsis n (%) Septic Shock n (%) p Gender 0.876 Male 47 (53.4) 30 (34.1) 17 (19.3) Female 41 (46.6) 32 (36.4) 9 (10.2) Age (Years) median (min – max) 71 (26–90) 71 (26–89) 71.5 (43–90) 0.537 Comorbidities Hypertension 54 (61.3) 38 (43.1) 16 (18.1) 0.983 Diabetes mellitus 31(35.1) 24 (27.2) 7 (7.9) 0.417 COPD 14(15.9) 11 (12.5) 3 (3.4) 0.543 CHF 10(11.3) 9 (10.2) 1 (1.1) 0.27 CKF 11(12.4) 9 (10.2) 2 (2.2) 0.496 Malignancy 28(31.8) 14 (15.9) 14 (15.9) 0.009 Immunosuppression 18(10.4) 9 (10.2) 9 (10.2) 0.065 Other 42(47.6) 29 (32.9) 13 (14.7) 0.782 ICU Hospitalization 48(54.5) 28 (31.8) 20 (22.7) 0.006 30-Day Mortality 30(30.4) 18 (20.4) 12 (13.6) 0.194 Vital Signs and Laboratory Values All Patients Sepsis (n = 62) Septic Shock (n = 26) p Median (minimum-maximum) Median (minimum-maximum) Fever (°C) 36.6(35.7–39) 36.6 (35.7–39) 36.6(36-38.7) 0.579 Systolic BP (mmHg) 100(57–182) 110(70–182) 80(57–102) < 0.001 Diastolic BP(mmHg) 60(30–102) 68(40–102) 47.5(30–58) < 0.001 RR/min 24(16–40) 24(16–40) 24(18–40) 0.843 Saturation 92(70–99) 92(70–99) 90(72–99) 0.557 WBC (10^3/µL) 12.15 (0.1–33.4) 12(1.2–32) 13.35(0.1–33.4) 0.464 Lymphocyte (10^3/µL) 0.85(0-9.2) 0.8(0.2–9.2) 0.85(0-3.9) 0.374 Neutrophil (10^3/µL) 9.8(0-31.3) 9.4(0.8–23.1) 10.75(0-31.3) 0.314 Hemoglobin (g/dl) 10.95 (4-16.2) 11.3(7.2–16.2) 10.5(4-14.7) 0.107 Platelet (10^3/µL) 200.15(4-1111) 214(33.2–568) 181.05(4-1111) 0.335 AST (IU/L) 28.95(8.7–1364) 29.2(355.5–11.2) 28.65(8.7–1364) 0.531 ALT (IU/L) 18.55(4-796) 18(6-382) 19.7(4-796) 0.704 Creatinine (mg/dL) 1.09(0.25–4.47) 0.99(0.25–4.29 ) 1.64(0.32–4.47) 0.004 Urea (mg/dL) 48(9-225) 43(9-189 ) 77.5(19–225) 0.008 Lactate (mmol/L) 2.15(0.6–26) 1.8(0.6–26) 3.2(0.8–14.6) 0.001 CRP (mg/L) 133.48(0.19–430) 98.28(0.19-371.43) 175.6(6.31–430) 0.072 Procalcitonin (µg/L) 0.81(0.03–317) 0.537(0.03–317) 1.58(0.03–100) 0.066 ProADM (pmol/ml) 0.4(0.08–7.33) 0.48(0.08–7.33) 0.27(0.09–2.25) 0.02 Presepsin (ng/L) 176.75(19.8-1989.9) 156.9(19.8-1989.9) 178.3(63.8-1785.5) 0.081 IL-6 (pg/ml) 213.5(43.3-15807.3) 140.6(43.3-15807.3) 363.2(43.3-15807.3) 0.003 COPD:ChronicObstructivePulmonaryDisease, CHF:CongestiveHeartFailure, CKF:ChronicKidneyFailure, ICU: IntensiveCareUnit, BP: Blood Pressure, RR: Respiratory Rate, WBC: White Blood Cells CRP: C-reactive protein, ProADM: Proadrenomedullin, IL-6: Interleukin-6 Table 2 shows that while the serum levels of lactate (p = 0.015) and IL-6 (p = 0.046) were significantly associated with mortality, the levels of presepsin (p = 0.561) and proADM (p = 0.846) were not significantly associated with mortality. Table 2 Comparison of VitalSignsandBiomarkerLevelsAccordingto 30-Day Mortality Variables Survivors (n = 58) n (%) Non-survivors (n = 30) n (%) p Gender 0.06 Male 27 (46.6) 20 (66.7) Female 31 (53.4) 10 833.3 Age (Years) 71 (26–89) 70.5 (45–90) 0.32 Comorbidities Hypertension 37 (63.8) 17 (56.7) 0.34 Diabetes mellitus 22 (37.9) 9 (30) 0.31 COPD 9 (15.5) 5 (16.7) 0.55 CHF 9 (15.5) 1 (3.3) 0.82 CKF 7 (12.1) 4 (13.3) 0.56 Malignancy 15 (25.9) 13 (43.3) 0.08 Immunosuppression 10 (17.2) 8 (26.7) 0.22 Other 28 (48.3) 14 (46.7) 0.53 ICU Hospitalization 21 (36.2) 27 (90) < 0.01 Septic Shock 14 (24.1) 12 (40) 0.1 Variables Survivors Non-survivors P Median (minimum-maximum) Median (minimum-maximum) Age (Year) 71 (26–89) 70.5 (45–90) 0.324 Fever (°C) 36.6 (35.7–39) 36.6 (36.4–38.5) 0.86 Systolic BP (mmHg) 101.5 (57–182) 95 (60–160) 0.503 Diastolic BP (mmHg) 60 (30–102) 60 (40–90) 0.839 RR/min 22.5 (18–40) 25.5 (16–40) 0.024 Saturation 94.5 (80–99) 90 (70–98) 0.004 WBC (10^3/µL) 12.9 (0.1–32) 10.4 (1.2–33.4) 0.13 Lymphocyte (10^3/µL) 1.15 (0-9.2) 0.7 (0.2–3.2) 0.018 Neutrophil (10^3/µL) 10.25 (0-25.6) 8.5 (0.8–31.3) 0.322 Hemoglobin (g/dl) 10.7 (4-16.2) 11.5 (7.2–14.7) 0.315 Platelet (10^3/µL) 216.75 (4-566) 181.05 (54-1111) 0.816 AST (IU/L) 26.6 (8.7-760.5) 30.5 (11.4–1364) 0.291 ALT (IU/L) 17 (4-382) 20.5 (7-796) 0.702 Creatinine (mg/dL) 1.07 (0.25–4.47) 1.18 (0.45–4.03) 0.271 Urea (mg/dL) 45 (9-225) 52.5 (16–201) 0.441 Lactate (mmol/L) 1.95 (0.6–26) 2.6 (1.4–14.6) 0.015 CRP (mg/L) 115.85 (0.19-371.43) 156.04(3-430) 0.765 Procalcitonin (µg/L) 0.54 (0.03–100) 1.44 (0.07–317) 0.219 ProADM (pmol/ml) 0.42 (0.09–2.63) 0.325 (0.08–7.33) 0.846 Presepsin (ng/L) 156.9 (63.8-1989.9) 178.3 (19.8-1785.5) 0.561 IL-6 (pg/ml) 195.5 (43.3-15807.3) 363.2 (63.8-15807.3) 0.046 COPD:ChronicObstructivePulmonaryDisease, CHF:CongestiveHeartFailure, CKF:ChronicKidneyFailure, ICU: IntensiveCareUnit, BP: Blood Pressure, RR: Respiratory Rate, WBC: White Blood Cells CRP: C-reactive protein, ProADM: Proadrenomedullin, IL-6: Interleukin-6 In 31.88% of the patients (n = 28), blood cultures revealed growth, while in 30.6% (n = 27), urine cultures revealed growth. No growth was detected in either blood or urine cultures in 51.1% of the patients (n = 45) (Table 3 ). Table 3 Comparison of BiomarkersAccordingtoGrowthStatus in Urineand Blood Cultures Urine Culture No Growth (n = 61) Growth Present (n = 27) P Median (minimum-maximum) Median (minimum-maximum) CRP (mg/L) 95 (0.19–430) 152.57 (6.31-371.43) 0.123 Procalcitonin (µg/L) 0.792 (0.03–317) 1.17 (0.03–100) 0.871 Lactate (mmol/L) 2.1 (0.6–26) 2.3 (0.8–4.7) 0.924 ProADM (pmol/ml) 0.4 (0.08–7.33) 0.35 (0.09–2.25) 0.373 Presepsin (ng/L) 162.9 (19.9-1989.9) 178.2 (63.8-1785.5) 0.271 IL-6 (pg/ml) 195.5 (43.3-15807.3) 213.5 (43.3-15807.3) 0.821 Blood Culture No Growth (n = 60) Growth Present (n = 28) P Median (minimum-maximum) Median (minimum-maximum) CRP (mg/L) 103.24 (0.19–430) 177.1 (3-371.4) 0.029 Procalcitonin(µg/L) 0.505 (0.03–317) 2.1 (0.03–100) 0.003 Lactate(mmol/L) 2.05 (0.6–26) 2.6 (0.8–14.6) 0.322 ProADM(pmol/ml) 0.32 (0.09–7.33) 0.5 (0.08–2.63) 0.072 Presepsin(ng/L) 161.4 (19.8-1989.9) 179.8 (63.8-1785.5) 0.261 IL-6 (pg/ml) 177.3 (43.3-15807.3) 293.3 (43.3-15807.3) 0.076 CRP: C-reactive protein, ProADM: Proadrenomedullin, IL-6: Interleukin-6 There was no significant difference in terms of sex between the septic shock and sepsis groups (p > 0.05). However, the association between patients in the septic shock group and a history of malignancy was found to be statistically significant (p = 0.009) (Table 1 ). Systolic blood pressure (p < 0.001), diastolic blood pressure (p < 0.001), creatinine level (p = 0.004), urea level (p = 0.008), and lactate level (p = 0.001) were significantly greater in patients with septic shock. Additionally, ProADM levels in patients with septic shock were significantly lower (p = 0.02). Moreover, IL-6 levels were significantly greater in patients with septic shock (p = 0.003) (Table 1 ). Although the median levels of C-reactive protein were high in the septic shock group, the difference was not statistically significant (p = 0.072) (Fig. 1 ). Furthermore, the difference in the median procalcitonin level was not statistically significant (p = 0.066). Additionally, the difference in the median level of presepsin in the septic shock group was not statistically significant (p = 0.81) (Fig. 1 ). However, the median values of ProADM were significantly lower in patients with septic shock than in those with sepsis (p = 0.020) (Fig. 1 ). Moreover, the median IL-6 concentration in patients with septic shock was significantly greater than that in patients with sepsis (p = 0.003) (Fig. 1 ). When comparing patients who survived for 30 days to those who did not, the SOFA score was significantly greater in the deceased group (p = 0.024). Additionally, oxygen saturation and lymphocyte counts were significantly lower in the deceased group than in the living group (p = 0.004 and p = 0.018, respectively). Furthermore, the lactate and IL-6 levels were significantly greater in the deceased group (p = 0.015 and p = 0.046, respectively). The study evaluated the relationship between the included biomarkers and the presence of growth in urine and blood cultures. The results showed no statistically significant relationship between the presence of growth in urine cultures and biomarkers (p > 0.05) (Table 3 ). However, CRP (p = 0.029) and procalcitonin (p = 0.003) were significantly greater in the group with growth in blood cultures (Table 3 ). Biomarkers were compared for the prediction of septic shock using ROC analysis. In the ROC analysis, the strongest biomarkers for determining septic shock were identified as lactate with an AUC value of 0.730 and IL-6 with an AUC value of 0.701. For the ROC analysis of 30-day mortality, statistically significant biomarkers were found to be lactate and IL-6 (Table 4 ). Table 4 ROC Analysis forSepticShockandMortality Septic Shock AUC p %95 GA Cut-off (≥) Sensitivity Spesificity Lactate (mmol/L) 0.730 0.001 0.61; 0.85 2.35 %76.9 %67.7 CRP (mg/L) 0.622 0.072 0.49; 0.76 136.06 %65.4 %58.1 Procalcitonin (µg/L) 0.625 0.066 0.50; 0.75 0.88 %73.1 %61.3 ProADM (pmol/ml) 0.658 0.009 0.23; 0.46 0.63 %92.3 %38.7 Presepsin (ng/L) 0.618 0.081 0.49; 0.75 149.45 %84.6 %46.8 IL-6 (pg/ml) 0.701 0.003 0.58; 0.82 149.85 %80.8 %51.6 30-Day Mortality AUC p %95 GA Cut-off (≥) Sensitivity Spesificity Lactate (mmol/L) 0.659 0.015 0.55; 0.77 1.35 %100.0 %32.8 CRP (mg/L) 0.520 0.765 0.39; 0.66 225.23 %33.3 %79.3 Procalcitonin (µg/L) 0.580 0.219 0.46; 0.70 0.21 %90.0 %34.5 ProADM (pmol/ml) 0.513 0.855 0.35; 0.62 0.243 %43.3 %72.4 Presepsin (ng/L) 0.538 0.561 0.41; 0.67 169.05 %63.3 %55.2 IL-6 (pg/ml) 0.630 0.046 0.50; 0.76 337.05 %53.3 %77.6 CRP: C-reactive protein, ProADM: Proadrenomedullin, IL-6: Interleukin-6 DISCUSSION Sepsis is a disease with high mortality and morbidity, emphasizing the importance of prompt diagnosis and early treatment. In the emergency department, a qSOFA score of 2 or higher is required for the diagnosis of sepsis. Biomarkers in patients diagnosed with sepsis can provide information about the prognosis of the disease. In addition to currently used biomarkers, numerous studies are underway on new biomarkers. In this study, we examined presepsin, proadrenomedullin, and IL-6 in addition to established biomarkers.This study is the first to simultaneously compare the effects of presepsin, proADM, and IL-6 on the prognosis of patients with sepsis in the emergency department. Vincent et al. [ 18 ] conducted observational studies reporting the frequency and mortality of septic shock in MEDLINE, Embase, and the Cochrane Library from January 1, 2005, to February 20, 2018. In their study, the intensive care unit (ICU) mortality rate was 37.3%, the hospital mortality rate was 39%, and the 28-/30-day mortality rate was 36.7%. Bauer et al. [ 19 ] conducted a meta-analysis of articles published in English on PubMed or the Cochrane Library Database between 2009 and 2019. The meta-analysis revealed an average 30-day mortality of 34.7% for patients with septic shock and 24.4% for patients with sepsis. In this study, the 30-day mortality rate was 34% for all the patients. The mortality rate in sepsis patients was 29.03%, while in septic shock patients, it was 46.15%. Hospitalized and no hospitalized deaths were included, and similar results were obtained to those of other studies. Sepsis is associated with high mortality rates due to comorbidities and multiple organ dysfunctions. As it progresses toward septic shock, the mortality rate increases. In sepsis, high lactate levels are observed, primarily affecting the heart and brain. Several studies [ 11 , 20 , 21 ] have shown higher lactate levels in sepsis patients than in healthy controls and in septic shock patients than in sepsis patients. Similarly, in these studies, lactate levels were greater in those who died than in those who survived. In our study, serum lactate levels were strongly associated with mortality and septic shock, consistent with the literature. CRP and PCT are among the most commonly used biomarkers for the management of sepsis. In studies [ 20 , 22 ] , CRP levels were found to be greater in the septic shock group than in the sepsis group. Similarly, in this study, CRP levels were greater in the septic shock group than in the sepsis group, although this difference did not reach statistical significance. CRP levels were greater in patients who died than in those who survived for 30 days; however, the difference was not statistically significant. The results of this study are consistent with those of other studies. In studies [ 20 , 23 ] on procalcitonin, the level of procalcitonin was greater in the septic shock group than in the sepsis group. However, in our study, the procalcitonin level in the septic shock group was lower than that in the sepsis group, and no significant difference was observed. This study revealed that patients with septic shock had lower PCT levels. This may be due to the inclusion of both bacterial and other infectious agents, a higher prevalence of viral agents, and the increased presence of comorbidities such as chronic kidney disease in patients without septic shock. Several studies in the literature have reported higher levels of presepsin in patients with septic shock than in those with sepsis. [ 22 , 24 – 26 ] While some studies [ 24 – 26 ] have detected a statistically significant difference, others [ 22 ] have not found a significant difference in the levels of presepsin between septic shock patients and sepsis patients. In our study, presepsin levels were greater in the septic shock group than in the sepsis group; however, this difference was not statistically significant. Moreover, in our study, presepsin levels were greater in those who died within 30 days than in survivors, but the difference was not statistically significant. The limited number of patients included in the study may be the reason for the lack of statistical significance. Age and kidney function can influence presepsin levels. However, all the factors that affect presepsin levels remain unknown. [ 27 ] An increase in the presepsin level may provide guidance for the diagnosis and prognosis of sepsis; however, utilizing the presepsin level in conjunction with other biomarkers will likely yield more meaningful results. Schuetz et al. [ 28 ] reported that average proADM levels in 48 septic patients (15 of whom had septic shock) were less than 1 nmol/L in nonseptic critically ill patients, 2.6 nmol/L in sepsis patients, and 8 nmol/L in patients with septic shock. In their study, the increase in proADM levels was significant. Similarly, in our study, there was a significant difference in proADM levels between the septic shock and sepsis groups. However, proADM levels in the sepsis group were lower than those in the septic shock group. ProADM levels were greater in deceased patients than in survivors, but the difference was not statistically significant. In a study conducted by Elke et al. [ 29 ] proADM levels were found to be significantly greater in deceased individuals than in survivors. The majority of studies in the literature have demonstrated that proADMcan be used as an early indicator of high mortality risk. ProADM levels may reflect the severity of organ dysfunction in the progression of the systemic inflammatory response, the transition from sepsis to septic shock, and the risk of mortality. [ 14 , 30 – 32 ] However, a clear threshold has not yet been identified for the identification of septic patients at high risk of mortality. [ 14 ] The lack of consistent findings in our study with the literature could be attributed to factors such as the presence of outliers in some patients, a limited number of participants, and the lack of standardization in proADM levels among different study methodologies. In our study, the levels of IL-6 in patients with septic shock were significantly greater than those in patients with sepsis. Moreover, for 30-day mortality, the IL-6 levels of deceased patients were significantly greater than those of survivors. These findings align with previous studies [ 32 – 34 ] in which IL-6 levels were found to be significantly greater in deceased individuals than in survivors and in individuals in the septic shock group than in those in the sepsis group. IL-6 is a proinflammatory cytokine synthesized by T lymphocytes, fibroblasts, endothelial cells, and monocytes. In response to inflammation, the IL-6 concentration dramatically increases. Acting as an acute-phase reactant in the inflammatory response in sepsis, IL-6 levels increase with a deteriorating prognosis. [ 34 ] In the study conducted by Angeletti et al. [ 35 ] , the areas under the curve (AUCs) for procalcitonin and proADM for septic shock were 0.921 and 0.977, respectively. In another study [ 20 ] , the ROC analysis for septic shock reported AUC values of 0.650 for presepsin, 0.678 for procalcitonin, 0.636 for CRP, and 0.618 for lactate, each of which was found to be statistically significant. Behnes et al. [ 27 ] reported that serum IL-6 levels had greater diagnostic value for septic shock than did procalcitonin, presepsin, and CRP levels. In our study, the strongest biomarker for predicting septic shock was lactate (AUC = 0.730), followed by IL-6 (0.701) and proADM (0.658). However, in our study, presepsin and PCT did not significantly differ when used alone as biomarkers, possibly due to their susceptibility to comorbidities and the variety of pathogens. Nevertheless, the combined use of biomarkers provided more meaningful results. Combinations of lactate with other biomarkers were statistically significant, and the use of lactate in conjunction with proADMwas found to be more robust in predicting septic shock than the use of other biomarkers. In our study, ROC analysis for 30-day mortality revealed statistically significant AUC values of 0.659 for lactate and 0.630 for IL-6. Moreover, the combination of lactate with procalcitonin and IL-6 was also a significant predictor of mortality. In the study by Ozkan et al. [ 20 ] , ROC analysis for 30-day mortality revealed statistically significant AUC values of 0.666 for procalcitonin and 0.655 for lactate. Spanuth et al. [ 25 ] reported a statistically significant AUC of 0.878 for 30-day mortality in patients treated with presepsin, followed by procalcitonin, with an AUC of 0.668. In the study by Philipp et al. [ 28 ] , ROC analysis for mortality revealed statistically significant results for CRP, while the results for proADM and procalcitonin were not statistically significant. The variability in results across studies may be attributed to differences in patient standardization and sepsis definitions. Biomarker levels are influenced by various factors, and the use of different methods for biomarker analysis is another contributing factor. Study Limitations This study has several limitations, including its single-center design, small sample size, and exclusion of patients in the nonseptic infection group. CONCLUSION Based on the obtained data, interleukin-6, presepsin, and proadrenomedullin are beneficial biomarkers for predicting sepsis and septic shock in the emergency department. However, none of these biomarkers are independent prognostic predictors for sepsis, septic shock or mortality. Furthermore, a combination of these biomarkers may be more effective in predicting septic shock and mortality. Standardization of cutoff levels is another important issue, especially for proADM. Further research is necessary to provide a more comprehensive understanding of this topic. Declarations Funding: Financial support for the study was provided by the Scientific Research Projects Unit of Istanbul University-Cerrahpasa under grant number TTU-2023-37218, with the date of provision being May 11, 2023. The article has not been previously presented at any assembly or organization. Ethical approval: The study was ethically approved by the institutional ethics committee of our university (Approval Date: 22.03.2023 and number 649489). Declaration of interests: The authors declare that there are no conflicts of interest.All authorsapproved the final version of the manuscript for publication. Author contributions The authors contributed equally to this study. Constructing an idea or hypothesis for research: S. ̈Ozkan, S.A. Karagöz, D. Konukoğlu, F.Çakmak, İ.İkizceli Planning methodology: S. ̈Ozkan, S.A. Karagöz, Y.S. Akdeniz, S.K. Kıyak, S. Biberoğlu Data collection: S.A. Karagöz, A. B. Oztürk. Biochemical analysis: D. Konukoğlu. Statistical analysis: S. ̈Ozkan, A. Ipekci. Writing: S.A. Karagöz, Y.S. Akdeniz, Acknowledgments None. References Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM, Hotchkiss RS, Levy MM, Marshall JC, Martin GS, Opal SM, Rubenfeld GD, van der Poll T, Vincent JL, Angus DC. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315:801–10. https://doi.org/10.1001/jama.2016.0287 . Vincent JL, Moreno R, Takala J, Willatts S, De Mendonça A, Bruining H, Reinhart CK, Suter PM, Thijs LG. Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. 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Aduen J, Bernstein WK, Khastgir T, Miller J, Kerzner R, Bhatiani A, Lustgarten J, Bassin AS, Davison L, Chernow B. The use and clinical importance of a substrate-specific electrode for rapid determination of blood lactate concentrations. JAMA. 1994;272:1678–85. Kahveci U, Ozkan S, Melekoglu A, Usul E, Ozturk G, Cetin E, Abatay K, Sahin A. The role of plasma presepsin levels in determining the incidence of septic shock and mortality in patients with sepsis. J Infect Dev Ctries. 2021;15:123–30. https://doi.org/10.3855/jidc.12963 . Wardi G, Brice J, Correia M, Liu D, Self M, Tainter C. Demystifying Lactate in the Emergency Department. Ann Emerg Med. 2020. https://doi.org/10.1016/j.annemergmed.2019.06.027 . 75:287 – 98. Shozushima T, Takahashi G, Matsumoto N, Kojika M, Okamura Y, Endo S. Usefulness of presepsin (sCD14-ST) measurements as a marker for the diagnosis and severity of sepsis that satisfied diagnostic criteria of systemic inflammatory response syndrome. J Infect Chemother. 2011;17:764–69. https://doi.org/10.1007/s10156-011-0254-x . Spanuth E, Ebelt H, Ivandic B, Werdan K. Diagnosticandprognosticvalue of presepsin (soluble CD14 subtype) in emergencypatientswithearlysepsisusingthenewassay PATHFAST presepsin. In: Renz H, Tauber R, editors. editorsAdvances in ClinicalChemistryandLaboratory Medicine. Berlin, Boston: De Gruyter; 2012. pp. 128–33. https://doi.org/10.1515/9783110224641.128 . Ulla M, Pizzolato E, Lucchiari M, Loiacono M, Soardo F, Forno D, Morello F, Lupia E, Moiraghi C, Mengozzi G, Battista S. Diagnostic and prognostic value of presepsin in the management of sepsis in the emergency department: a multicenter prospective study. Crit Care. 2013;17:R168. https://doi.org/10.1186/cc12847 . Behnes M, Bertsch T, Lepiorz D, Lang S, Trinkmann F, Brueckmann M, Borggrefe M, Hoffmann U. Diagnosticandprognosticutility of soluble CD 14 subtype (presepsin) for severe sepsisandsepticshockduringthefirstweek of intensivecaretreatment. CritCare. 2014. 18:507.https://doi.org/10.1186/s13054-014-0507-z . Schuetz P, Christ-Crain M, Morgenthaler NG, Struck J, Bergmann A, Müller B. (2007)Circulating precursor levels of endothelin-1 and adrenomedullin, two endothelium-derived, counteracting substances in sepsis.Endothelium14:345–51 https://doi.org/10.1080/10623320701678326 Elke G, Bloos F, Wilson DC, Brunkhorst FM, Briegel J, Reinhart K, Loeffler M, Kluge S, Nierhaus A, Jaschinski U, Moerer O, Weyland A, Meybohm P, SepNet Critical Care Trials Group. The use of mid-regional proadrenomedullin to identify disease severity and treatment response to sepsis - a secondary analysis of a large randomised controlled trial. Crit Care. 2018;22:79. https://doi.org/10.1186/s13054-018-2001-5 . Enguix-Armada A, Escobar-Conesa R, García-De LaTorre A, De La Torre-Prados MV. Usefulness of severalbiomarkers in themanagement of septicpatients: C-reactive protein, procalcitonin, presepsin andmid-regionalpro-adrenomedullin. ClinChemLabMed. 2016;54:163–8. https://doi.org/10.1515/cclm-2015-0243 . Spoto S, Fogolari M, De Florio L, Minieri M, Vicino G, Legramante J, Lia MS, Terrinoni A, Caputo D, Costantino S, Bernardini S, Ciccozzi M, Angeletti S. Procalcitoninand MR-proAdrenomedullincombination in theetiologicaldiagnosisandprognosis of sepsisandsepticshock. MicrobPathog. 2019;137:103763. https://doi.org/10.1016/j.micpath.2019.103763 . Spittler A, Razenberger M, Kupper H, Kaul M, Hackl W, Boltz-Nitulescu G, Függer R, Roth E. Relationshipbetween interleukin-6 plasmaconcentration in patientswithsepsis, monocytephenotype, monocytephagocyticproperties, andcytokineproduction. J InfectDis. 2000;31:1338–42. https://doi.org/10.1086/317499 . Oberhoffer M, Karzai W, Meier-Hellmann A, Bögel D, Fassbinder J, Reinhart K. Sensitivityandspecificity of variousmarkers of inflammationfortheprediction of tumornecrosisfactor-alphaand interleukin-6 in patientswithsepsis. Crit CareMed. 1999;27:1814–18. https://doi.org/10.1097/00003246-199909000-00018 . Song J, Park DW, Moon S, Cho HJ, Park JH, Seok H, Choi WS. Diagnosticandprognosticvalue of interleukin-6, pentraxin 3, andprocalcitoninlevelsamongsepsisandsepticshockpatients: a prospectivecontrolledstudyaccordingtothe Sepsis-3 definitions. BMC Infect Dis. 2019;19:968. https://doi.org/10.1186/s12879-019-4618-7 . Angeletti S, Battistoni F, Fioravanti M, Bernardini S, Dicuonzo G. Procalcitonin and mid-regional pro-adrenomedullin test combination in sepsis diagnosis. ClinChem Lab Med. 2013;51:1059–67. https://doi.org/10.1515/cclm-2012-0595 . Additional Declarations No competing interests reported. Supplementary Files AuthorChecklist.docx 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. 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Department\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSepsis is a syndrome characterized by both signs of infection and manifestations of a systemic host response and is the primary cause of mortality associated with infection.\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003eLife-threatening organ dysfunction is caused by a dysregulated host response to infection.\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003eCompared with sepsis, septic shock is a state in which circulatory failure begins and is associated with a significantly greater risk of mortality.\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePossible reasons for the increase in sepsis incidence include advanced age, immunosuppression, and infections resistant to multiple drugs.\u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003eThe incidence is highest during the winter months, likely due to the increased incidence of respiratory tract infections.\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003eThe majority of all sepsis episodes (60\u0026ndash;85%) occur in individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years, and with an increasingly aging population, it is likely that the incidence of sepsis will continue to rise in the future.\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eClinical, laboratory, imaging, microbiology, and point-of-care rapid diagnostic tests are all collectively evaluated to establish the diagnosis of sepsis and septic shock.\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003eSepsis biomarkers may have significant diagnostic, therapeutic, and prognostic functions. Lactate is currently a frequently used biomarker in clinical practice. However, the use of additional biomarkers in addition to lactate can increase the diagnostic accuracy for sepsis.\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e These markers, which are indicative of neutrophil and monocyte activation synthesized in response to infection and inflammation, include procalcitonin (PCT) and C-reactive protein (CRP).\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eCRP and PCT are by far the most commonly used and investigated acute-phase proteins for diagnosing bacterial sepsis and guiding antibiotic therapy.\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003eThe associated limitations include the fact that their levels can increase under various inflammatory conditions. Therefore, the search for new biomarkers continues to be promising as a tool to assess sepsis individually or in combination with other biomarkers, aiming to improve overall sensitivity and specificity in the diagnosis and prognosis of bacterial infections.\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e Simultaneously, measuring multiple biomarkers may be beneficial for overcoming the limitations of any single biomarker.\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSome clinical studies have demonstrated a significant increase in the levels of presepsin, proadrenomedullin (proADM), and interleukin-6 (IL-6) in sepsis and septic shock patients compared to healthy individuals. Moreover, these studies indicated that changes in the blood levels of these three biomarkers are significantly associated with the severity and prognosis of the disease.\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e Presepsin is the N-terminal fragment of the CD14-ST subtype, which is the soluble form of CD14. While there are significant studies regarding the effectiveness of presepsin in treating sepsis, research on this biomarker is still ongoing.\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003eproADM is a component of adrenomedullin synthesized by smooth muscle cells and vascular endothelial cells. Due to its longer half-life than adrenomedullin, proADM is easier to detect in the blood. Recent studies have obtained significant results suggesting the use of proADM in predicting sepsis and, particularly, sepsis-related mortality.\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003eIL-6 is a proinflammatory cytokine synthesized by T lymphocytes, fibroblasts, endothelial cells, and monocytes.\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e While IL-6 is a significant marker of the inflammatory response in sepsis, ongoing studies are still being conducted.\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe aim of this study was to investigate the role of serum presepsin, proadrenomedullin, and IL-6 levels in predicting the prognosis and mortality of patients diagnosed with sepsis in the Emergency Department.This study is the first to simultaneously compare the effects of presepsin, proADM, and IL-6 on the prognosis of patients with sepsis in the emergency department.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThe cross-sectional study was conducted with patients diagnosed with sepsis in the emergency department of a tertiary university hospital. The study was ethically approved by the institutional ethics committee of our university (Approval Date: 22.03.2023 and number 649489).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study included patients 18 years of age and older. Patients were diagnosed with sepsis using the Sepsis-3 criteria. The quick Sequential Organ Failure Assessment (qSOFA) score was calculated because it is an easy and noninvasive measurement tool. Sepsis was diagnosed based on suspicion of infection and a qSOFA score \u0026ge;2 points, according to the Sepsis-3 diagnostic criteria. Septic shock was diagnosed in patients requiring vasopressors to maintain a mean arterial pressure (MAP) of \u0026ge;65 mm Hg despite adequate fluid replacement and lactate levels \u0026gt;2mmol/L, even with sufficient fluid resuscitation.\u003c/p\u003e\n\u003cp\u003eInclusion criteria:\u003c/p\u003e\n\u003cp\u003e- Individuals aged 18 years and older\u003c/p\u003e\n\u003cp\u003e- Patients diagnosed with sepsis\u003c/p\u003e\n\u003cp\u003e- Patients diagnosed with septic shock\u003c/p\u003e\n\u003cp\u003eExclusion criteria:\u003c/p\u003e\n\u003cp\u003e- Individuals under the age of 18\u003c/p\u003e\n\u003cp\u003e- Those experiencing nonseptic shock conditions\u003c/p\u003e\n\u003cp\u003e- Individuals with hemolyzed blood samples taken prior to antibiotic therapy\u003c/p\u003e\n\u003cp\u003ePatients who met the inclusion criteria were informed about the study, and their consent was obtained. The patients were then classified based on their sex (male or female), number and types of comorbidities, and source of infection (including respiratory system, urinary system, intra-abdominal infection, soft tissue infection (STI), central nervous system (CNS) infection, bacteremia, and no focus). The patient follow-up data included vital signs such as temperature, pulse rate, blood pressure, respiratory rate per minute, and SpO2 at the time of sepsis diagnosis for all included patients. Patients were categorized as having either sepsis or septic shock. Patients who met the criteria for sepsis and required vasopressors to maintain a mean arterial pressure (MAP) of at least 65 mmHg despite adequate fluid support and had a blood lactate level greater than 2mmol/L were included in the septic shock group. The patients\u0026apos; 30-day survival status was monitored.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcessing of Blood Samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBlood samples were collected from all patients before initiating antibiotic therapy for analysis. Serum samples for presepsin, proadrenomedullin, and IL-6 levels were pipetted into Eppendorf tubes, centrifuged at 4000 revolutions per minute for 10 minutes, and stored at -80\u0026deg;C until examination. The Human Presepsin (PSPN) ELISA Kit from BT-LAB was used to measure serum presepsin levels. The Human Pro-ADM ELISA Kit from ELABSCIENCE was used to measure serum proadrenomedullin levels. Similarly, the Human IL-6 (Interleukin 6) ELISA Kit from ELABSCIENCE was used to measure serum IL-6 levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData analysis was performed using IBM SPSS 20 software. The median (min\u0026ndash;max), mean \u0026plusmn; standard deviation (mean \u0026plusmn; SD), frequency (n), and percentage (%) were calculated using descriptive statistics. The normality of the distribution of the data was assessed using the Kolmogorov‒Smirnov normality test. For the comparison of two groups, we used Student\u0026rsquo;s t test (independent samples t test) for normally distributed data and the Mann‒Whitney U test for no normally distributed data. We used the chi-square test and calculated the odds ratio (OR) for the analysis of categorical data. We employed receiver operating characteristic (ROC) analysis to evaluate the predictive ability of the data and determine the cutoff value. The statistical significance level was set at p\u0026lt;0.05.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eAmong the 88 enrolled patients, 53.4% (n\u0026thinsp;=\u0026thinsp;47) were male, and 46.6% (n\u0026thinsp;=\u0026thinsp;41) were female. The median age was 71 years, with a range from 26 to 90 years. While 88.7% (n\u0026thinsp;=\u0026thinsp;78) of the patients had at least one comorbidity, 11.3% (n\u0026thinsp;=\u0026thinsp;10) had no comorbidities. The most prevalent comorbidities among the patients were hypertension (61.3%), diabetes mellitus (35.2%), and solid cancers (31.8%).\u003c/p\u003e \u003cp\u003eThe infection sources of the patients were as follows: respiratory system (55.6%, n\u0026thinsp;=\u0026thinsp;49%), urinary system (26.1%, n\u0026thinsp;=\u0026thinsp;23%), gastrointestinal system (3.4%, n\u0026thinsp;=\u0026thinsp;3%), soft tissue (3.4%, n\u0026thinsp;=\u0026thinsp;3%), hepatobiliary system (3.4%, n\u0026thinsp;=\u0026thinsp;3%), central nervous system (1.1%, n\u0026thinsp;=\u0026thinsp;1%), joints (1.1%, n\u0026thinsp;=\u0026thinsp;1), and peritoneum (1.1%, n\u0026thinsp;=\u0026thinsp;1%). Bacteremia was detected in 4.5% of the patients.\u003c/p\u003e \u003cp\u003eSeptic shock developed in 29.5% (n\u0026thinsp;=\u0026thinsp;26) of the patients. Among the patients, 54.5% (n\u0026thinsp;=\u0026thinsp;48) were admitted to the intensive care unit (ICU), while 44.5% (n\u0026thinsp;=\u0026thinsp;40) were admitted to the general ward. The 30-day mortality rate for the patients was 34% (n\u0026thinsp;=\u0026thinsp;30) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eComparison of Variables, Comorbidities, VitalSigns, andLaboratoryValuesAmongAllPatients, Sepsis, andSepticShockGroups\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll Patients(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSepsis n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSeptic Shock\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (53.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (19.3)\u003c/p\u003e \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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (46.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (10.2)\u003c/p\u003e \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\u003eAge (Years) median (min \u0026ndash; max)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (26\u0026ndash;90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (26\u0026ndash;89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.5 (43\u0026ndash;90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31(35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (27.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.417\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\u003e14(15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.496\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\u003e28(31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunosuppression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42(47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU Hospitalization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48(54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30-Day Mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eVital Signs and Laboratory Values\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAll Patients\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSepsis\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;62)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSeptic Shock\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;26)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMedian (minimum-maximum)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMedian (minimum-maximum)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFever (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.6(35.7\u0026ndash;39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.6 (35.7\u0026ndash;39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.6(36-38.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100(57\u0026ndash;182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110(70\u0026ndash;182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80(57\u0026ndash;102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60(30\u0026ndash;102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68(40\u0026ndash;102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.5(30\u0026ndash;58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRR/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(16\u0026ndash;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(16\u0026ndash;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24(18\u0026ndash;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaturation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92(70\u0026ndash;99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92(70\u0026ndash;99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90(72\u0026ndash;99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (10^3/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.15 (0.1\u0026ndash;33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(1.2\u0026ndash;32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.35(0.1\u0026ndash;33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte (10^3/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85(0-9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8(0.2\u0026ndash;9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85(0-3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil (10^3/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.8(0-31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.4(0.8\u0026ndash;23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.75(0-31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.95 (4-16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.3(7.2\u0026ndash;16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.5(4-14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet (10^3/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200.15(4-1111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214(33.2\u0026ndash;568)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e181.05(4-1111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.95(8.7\u0026ndash;1364)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.2(355.5\u0026ndash;11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.65(8.7\u0026ndash;1364)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.55(4-796)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(6-382)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.7(4-796)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09(0.25\u0026ndash;4.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99(0.25\u0026ndash;4.29 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.64(0.32\u0026ndash;4.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48(9-225)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(9-189 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.5(19\u0026ndash;225)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.15(0.6\u0026ndash;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8(0.6\u0026ndash;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.2(0.8\u0026ndash;14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133.48(0.19\u0026ndash;430)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.28(0.19-371.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e175.6(6.31\u0026ndash;430)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcalcitonin (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.81(0.03\u0026ndash;317)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.537(0.03\u0026ndash;317)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.58(0.03\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProADM (pmol/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4(0.08\u0026ndash;7.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48(0.08\u0026ndash;7.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27(0.09\u0026ndash;2.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresepsin (ng/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176.75(19.8-1989.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156.9(19.8-1989.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e178.3(63.8-1785.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6 (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e213.5(43.3-15807.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140.6(43.3-15807.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e363.2(43.3-15807.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eCOPD:ChronicObstructivePulmonaryDisease, CHF:CongestiveHeartFailure, CKF:ChronicKidneyFailure, ICU: IntensiveCareUnit, BP: Blood Pressure, RR: Respiratory Rate, WBC: White Blood Cells CRP: C-reactive protein, ProADM: Proadrenomedullin, IL-6: Interleukin-6\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that while the serum levels of lactate (p\u0026thinsp;=\u0026thinsp;0.015) and IL-6 (p\u0026thinsp;=\u0026thinsp;0.046) were significantly associated with mortality, the levels of presepsin (p\u0026thinsp;=\u0026thinsp;0.561) and proADM (p\u0026thinsp;=\u0026thinsp;0.846) were not significantly associated with mortality.\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\u003eComparison of VitalSignsandBiomarkerLevelsAccordingto 30-Day Mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurvivors (n\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-survivors (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (46.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (53.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 833.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (26\u0026ndash;89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.5 (45\u0026ndash;90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (63.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (56.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\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\u003e9 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56\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\u003e15 (25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (43.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunosuppression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU Hospitalization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptic Shock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eVariables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSurvivors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNon-survivors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMedian (minimum-maximum)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMedian (minimum-maximum)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (26\u0026ndash;89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.5 (45\u0026ndash;90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFever (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.6 (35.7\u0026ndash;39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.6 (36.4\u0026ndash;38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.5 (57\u0026ndash;182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 (60\u0026ndash;160)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (30\u0026ndash;102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (40\u0026ndash;90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRR/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.5 (18\u0026ndash;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.5 (16\u0026ndash;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaturation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.5 (80\u0026ndash;99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (70\u0026ndash;98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (10^3/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.9 (0.1\u0026ndash;32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.4 (1.2\u0026ndash;33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte (10^3/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15 (0-9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7 (0.2\u0026ndash;3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil (10^3/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.25 (0-25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.5 (0.8\u0026ndash;31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.7 (4-16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.5 (7.2\u0026ndash;14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet (10^3/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e216.75 (4-566)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181.05 (54-1111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.6 (8.7-760.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.5 (11.4\u0026ndash;1364)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (4-382)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.5 (7-796)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07 (0.25\u0026ndash;4.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18 (0.45\u0026ndash;4.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (9-225)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.5 (16\u0026ndash;201)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.95 (0.6\u0026ndash;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6 (1.4\u0026ndash;14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115.85 (0.19-371.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156.04(3-430)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcalcitonin (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.54 (0.03\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.44 (0.07\u0026ndash;317)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProADM (pmol/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.42 (0.09\u0026ndash;2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.325 (0.08\u0026ndash;7.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresepsin (ng/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156.9 (63.8-1989.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178.3 (19.8-1785.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6 (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195.5 (43.3-15807.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e363.2 (63.8-15807.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eCOPD:ChronicObstructivePulmonaryDisease, CHF:CongestiveHeartFailure, CKF:ChronicKidneyFailure, ICU: IntensiveCareUnit, BP: Blood Pressure, RR: Respiratory Rate, WBC: White Blood Cells CRP: C-reactive protein, ProADM: Proadrenomedullin, IL-6: Interleukin-6\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn 31.88% of the patients (n\u0026thinsp;=\u0026thinsp;28), blood cultures revealed growth, while in 30.6% (n\u0026thinsp;=\u0026thinsp;27), urine cultures revealed growth. No growth was detected in either blood or urine cultures in 51.1% of the patients (n\u0026thinsp;=\u0026thinsp;45) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eComparison of BiomarkersAccordingtoGrowthStatus in Urineand Blood Cultures\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUrine Culture\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Growth (n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrowth Present (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (minimum-maximum)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (minimum-maximum)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95 (0.19\u0026ndash;430)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152.57 (6.31-371.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcalcitonin (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.792 (0.03\u0026ndash;317)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17 (0.03\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.1 (0.6\u0026ndash;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3 (0.8\u0026ndash;4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProADM (pmol/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4 (0.08\u0026ndash;7.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35 (0.09\u0026ndash;2.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresepsin (ng/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162.9 (19.9-1989.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e178.2 (63.8-1785.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6 (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195.5 (43.3-15807.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e213.5 (43.3-15807.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eBlood Culture\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNo Growth (n\u0026thinsp;=\u0026thinsp;60)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eGrowth Present (n\u0026thinsp;=\u0026thinsp;28)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMedian (minimum-maximum)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMedian (minimum-maximum)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103.24 (0.19\u0026ndash;430)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177.1 (3-371.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcalcitonin(\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.505 (0.03\u0026ndash;317)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1 (0.03\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.05 (0.6\u0026ndash;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6 (0.8\u0026ndash;14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProADM(pmol/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.32 (0.09\u0026ndash;7.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5 (0.08\u0026ndash;2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresepsin(ng/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161.4 (19.8-1989.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e179.8 (63.8-1785.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6 (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e177.3 (43.3-15807.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e293.3 (43.3-15807.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eCRP: C-reactive protein, ProADM: Proadrenomedullin, IL-6: Interleukin-6\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThere was no significant difference in terms of sex between the septic shock and sepsis groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, the association between patients in the septic shock group and a history of malignancy was found to be statistically significant (p\u0026thinsp;=\u0026thinsp;0.009) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Systolic blood pressure (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), diastolic blood pressure (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), creatinine level (p\u0026thinsp;=\u0026thinsp;0.004), urea level (p\u0026thinsp;=\u0026thinsp;0.008), and lactate level (p\u0026thinsp;=\u0026thinsp;0.001) were significantly greater in patients with septic shock. Additionally, ProADM levels in patients with septic shock were significantly lower (p\u0026thinsp;=\u0026thinsp;0.02). Moreover, IL-6 levels were significantly greater in patients with septic shock (p\u0026thinsp;=\u0026thinsp;0.003) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough the median levels of C-reactive protein were high in the septic shock group, the difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.072) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Furthermore, the difference in the median procalcitonin level was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.066). Additionally, the difference in the median level of presepsin in the septic shock group was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.81) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, the median values of ProADM were significantly lower in patients with septic shock than in those with sepsis (p\u0026thinsp;=\u0026thinsp;0.020) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Moreover, the median IL-6 concentration in patients with septic shock was significantly greater than that in patients with sepsis (p\u0026thinsp;=\u0026thinsp;0.003) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen comparing patients who survived for 30 days to those who did not, the SOFA score was significantly greater in the deceased group (p\u0026thinsp;=\u0026thinsp;0.024). Additionally, oxygen saturation and lymphocyte counts were significantly lower in the deceased group than in the living group (p\u0026thinsp;=\u0026thinsp;0.004 and p\u0026thinsp;=\u0026thinsp;0.018, respectively). Furthermore, the lactate and IL-6 levels were significantly greater in the deceased group (p\u0026thinsp;=\u0026thinsp;0.015 and p\u0026thinsp;=\u0026thinsp;0.046, respectively).\u003c/p\u003e \u003cp\u003eThe study evaluated the relationship between the included biomarkers and the presence of growth in urine and blood cultures. The results showed no statistically significant relationship between the presence of growth in urine cultures and biomarkers (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, CRP (p\u0026thinsp;=\u0026thinsp;0.029) and procalcitonin (p\u0026thinsp;=\u0026thinsp;0.003) were significantly greater in the group with growth in blood cultures (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBiomarkers were compared for the prediction of septic shock using ROC analysis. In the ROC analysis, the strongest biomarkers for determining septic shock were identified as lactate with an AUC value of 0.730 and IL-6 with an AUC value of 0.701. For the ROC analysis of 30-day mortality, statistically significant biomarkers were found to be lactate and IL-6 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eROC Analysis forSepticShockandMortality\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\"\u003e \u003cp\u003eSeptic Shock\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%95 GA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCut-off (\u0026ge;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpesificity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.61; 0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%76.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%67.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49; 0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e136.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%65.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%58.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcalcitonin (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50; 0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%73.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%61.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProADM (pmol/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23; 0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%92.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%38.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresepsin (ng/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49; 0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e149.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%84.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%46.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6 (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58; 0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e149.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%80.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%51.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e30-Day Mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAUC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e%95 GA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eCut-off (\u0026ge;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eSensitivity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eSpesificity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.55; 0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%32.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39; 0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e225.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%79.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcalcitonin (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.46; 0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%90.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%34.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProADM (pmol/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.35; 0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%43.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%72.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresepsin (ng/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41; 0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e169.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%63.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%55.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6 (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50; 0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e337.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%53.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%77.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eCRP: C-reactive protein, ProADM: Proadrenomedullin, IL-6: Interleukin-6\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eSepsis is a disease with high mortality and morbidity, emphasizing the importance of prompt diagnosis and early treatment. In the emergency department, a qSOFA score of 2 or higher is required for the diagnosis of sepsis. Biomarkers in patients diagnosed with sepsis can provide information about the prognosis of the disease. In addition to currently used biomarkers, numerous studies are underway on new biomarkers. In this study, we examined presepsin, proadrenomedullin, and IL-6 in addition to established biomarkers.This study is the first to simultaneously compare the effects of presepsin, proADM, and IL-6 on the prognosis of patients with sepsis in the emergency department.\u003c/p\u003e \u003cp\u003eVincent et al.\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e conducted observational studies reporting the frequency and mortality of septic shock in MEDLINE, Embase, and the Cochrane Library from January 1, 2005, to February 20, 2018. In their study, the intensive care unit (ICU) mortality rate was 37.3%, the hospital mortality rate was 39%, and the 28-/30-day mortality rate was 36.7%. Bauer et al.\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e conducted a meta-analysis of articles published in English on PubMed or the Cochrane Library Database between 2009 and 2019. The meta-analysis revealed an average 30-day mortality of 34.7% for patients with septic shock and 24.4% for patients with sepsis. In this study, the 30-day mortality rate was 34% for all the patients. The mortality rate in sepsis patients was 29.03%, while in septic shock patients, it was 46.15%. Hospitalized and no hospitalized deaths were included, and similar results were obtained to those of other studies. Sepsis is associated with high mortality rates due to comorbidities and multiple organ dysfunctions. As it progresses toward septic shock, the mortality rate increases.\u003c/p\u003e \u003cp\u003eIn sepsis, high lactate levels are observed, primarily affecting the heart and brain. Several studies\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e have shown higher lactate levels in sepsis patients than in healthy controls and in septic shock patients than in sepsis patients. Similarly, in these studies, lactate levels were greater in those who died than in those who survived. In our study, serum lactate levels were strongly associated with mortality and septic shock, consistent with the literature.\u003c/p\u003e \u003cp\u003eCRP and PCT are among the most commonly used biomarkers for the management of sepsis. In studies\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, CRP levels were found to be greater in the septic shock group than in the sepsis group. Similarly, in this study, CRP levels were greater in the septic shock group than in the sepsis group, although this difference did not reach statistical significance. CRP levels were greater in patients who died than in those who survived for 30 days; however, the difference was not statistically significant. The results of this study are consistent with those of other studies.\u003c/p\u003e \u003cp\u003eIn studies\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e on procalcitonin, the level of procalcitonin was greater in the septic shock group than in the sepsis group. However, in our study, the procalcitonin level in the septic shock group was lower than that in the sepsis group, and no significant difference was observed. This study revealed that patients with septic shock had lower PCT levels. This may be due to the inclusion of both bacterial and other infectious agents, a higher prevalence of viral agents, and the increased presence of comorbidities such as chronic kidney disease in patients without septic shock.\u003c/p\u003e \u003cp\u003eSeveral studies in the literature have reported higher levels of presepsin in patients with septic shock than in those with sepsis.\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e While some studies\u003csup\u003e[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e have detected a statistically significant difference, others\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e have not found a significant difference in the levels of presepsin between septic shock patients and sepsis patients. In our study, presepsin levels were greater in the septic shock group than in the sepsis group; however, this difference was not statistically significant. Moreover, in our study, presepsin levels were greater in those who died within 30 days than in survivors, but the difference was not statistically significant. The limited number of patients included in the study may be the reason for the lack of statistical significance. Age and kidney function can influence presepsin levels. However, all the factors that affect presepsin levels remain unknown.\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003eAn increase in the presepsin level may provide guidance for the diagnosis and prognosis of sepsis; however, utilizing the presepsin level in conjunction with other biomarkers will likely yield more meaningful results.\u003c/p\u003e \u003cp\u003eSchuetz et al.\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e reported that average proADM levels in 48 septic patients (15 of whom had septic shock) were less than 1 nmol/L in nonseptic critically ill patients, 2.6 nmol/L in sepsis patients, and 8 nmol/L in patients with septic shock. In their study, the increase in proADM levels was significant. Similarly, in our study, there was a significant difference in proADM levels between the septic shock and sepsis groups. However, proADM levels in the sepsis group were lower than those in the septic shock group. ProADM levels were greater in deceased patients than in survivors, but the difference was not statistically significant. In a study conducted by Elke et al.\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003eproADM levels were found to be significantly greater in deceased individuals than in survivors. The majority of studies in the literature have demonstrated that proADMcan be used as an early indicator of high mortality risk. ProADM levels may reflect the severity of organ dysfunction in the progression of the systemic inflammatory response, the transition from sepsis to septic shock, and the risk of mortality.\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003eHowever, a clear threshold has not yet been identified for the identification of septic patients at high risk of mortality.\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003eThe lack of consistent findings in our study with the literature could be attributed to factors such as the presence of outliers in some patients, a limited number of participants, and the lack of standardization in proADM levels among different study methodologies.\u003c/p\u003e \u003cp\u003eIn our study, the levels of IL-6 in patients with septic shock were significantly greater than those in patients with sepsis. Moreover, for 30-day mortality, the IL-6 levels of deceased patients were significantly greater than those of survivors. These findings align with previous studies\u003csup\u003e[\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e in which IL-6 levels were found to be significantly greater in deceased individuals than in survivors and in individuals in the septic shock group than in those in the sepsis group. IL-6 is a proinflammatory cytokine synthesized by T lymphocytes, fibroblasts, endothelial cells, and monocytes. In response to inflammation, the IL-6 concentration dramatically increases. Acting as an acute-phase reactant in the inflammatory response in sepsis, IL-6 levels increase with a deteriorating prognosis.\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn the study conducted by Angeletti et al.\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e, the areas under the curve (AUCs) for procalcitonin and proADM for septic shock were 0.921 and 0.977, respectively. In another study\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, the ROC analysis for septic shock reported AUC values of 0.650 for presepsin, 0.678 for procalcitonin, 0.636 for CRP, and 0.618 for lactate, each of which was found to be statistically significant. Behnes et al.\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e reported that serum IL-6 levels had greater diagnostic value for septic shock than did procalcitonin, presepsin, and CRP levels. In our study, the strongest biomarker for predicting septic shock was lactate (AUC\u0026thinsp;=\u0026thinsp;0.730), followed by IL-6 (0.701) and proADM (0.658). However, in our study, presepsin and PCT did not significantly differ when used alone as biomarkers, possibly due to their susceptibility to comorbidities and the variety of pathogens. Nevertheless, the combined use of biomarkers provided more meaningful results. Combinations of lactate with other biomarkers were statistically significant, and the use of lactate in conjunction with proADMwas found to be more robust in predicting septic shock than the use of other biomarkers.\u003c/p\u003e \u003cp\u003eIn our study, ROC analysis for 30-day mortality revealed statistically significant AUC values of 0.659 for lactate and 0.630 for IL-6. Moreover, the combination of lactate with procalcitonin and IL-6 was also a significant predictor of mortality. In the study by Ozkan et al.\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, ROC analysis for 30-day mortality revealed statistically significant AUC values of 0.666 for procalcitonin and 0.655 for lactate. Spanuth et al.\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e reported a statistically significant AUC of 0.878 for 30-day mortality in patients treated with presepsin, followed by procalcitonin, with an AUC of 0.668. In the study by Philipp et al.\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, ROC analysis for mortality revealed statistically significant results for CRP, while the results for proADM and procalcitonin were not statistically significant.\u003c/p\u003e \u003cp\u003eThe variability in results across studies may be attributed to differences in patient standardization and sepsis definitions. Biomarker levels are influenced by various factors, and the use of different methods for biomarker analysis is another contributing factor.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy Limitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations, including its single-center design, small sample size, and exclusion of patients in the nonseptic infection group.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eBased on the obtained data, interleukin-6, presepsin, and proadrenomedullin are beneficial biomarkers for predicting sepsis and septic shock in the emergency department. However, none of these biomarkers are independent prognostic predictors for sepsis, septic shock or mortality. Furthermore, a combination of these biomarkers may be more effective in predicting septic shock and mortality. Standardization of cutoff levels is another important issue, especially for proADM. Further research is necessary to provide a more comprehensive understanding of this topic.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding: \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinancial support for the study was provided by the Scientific Research Projects Unit of Istanbul University-Cerrahpasa under grant number TTU-2023-37218, with the date of provision being May 11, 2023.\u003c/p\u003e\n\u003cp\u003eThe article has not been previously presented at any assembly or organization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was ethically approved by the institutional ethics committee of our university (Approval Date: 22.03.2023 and number 649489).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest.All authorsapproved the final version of the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors contributed equally to this study. Constructing an idea or hypothesis for research: S. ̈Ozkan, S.A. Karag\u0026ouml;z, D. Konukoğlu, F.\u0026Ccedil;akmak, İ.İkizceli Planning methodology: S. ̈Ozkan, S.A. Karag\u0026ouml;z, Y.S. Akdeniz, S.K. Kıyak, S. Biberoğlu Data collection: S.A. Karag\u0026ouml;z, A. B. Ozt\u0026uuml;rk. Biochemical analysis: D. Konukoğlu. \u0026nbsp;Statistical analysis: S. ̈Ozkan, A. Ipekci. Writing: S.A. Karag\u0026ouml;z, Y.S. Akdeniz,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSinger M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM, Hotchkiss RS, Levy MM, Marshall JC, Martin GS, Opal SM, Rubenfeld GD, van der Poll T, Vincent JL, Angus DC. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). 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ClinChem Lab Med. 2013;51:1059\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1515/cclm-2012-0595\u003c/span\u003e\u003cspan address=\"10.1515/cclm-2012-0595\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"biomarkers, interleukin-6, presepsin, proadrenomedullin","lastPublishedDoi":"10.21203/rs.3.rs-6691861/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6691861/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction: \u003c/strong\u003eThe objective of this study is to evaluate the predictive value of serum presepsin, proadrenomedullin, and interleukin-6 levels for prognosis and mortality in patients diagnosed with sepsis in the emergency department.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe cross-sectional study was conducted with patients diagnosed with sepsis in the emergency department of a tertiary university hospital.The study analyzed the patients' demographic characteristics, comorbidities, source of sepsis, biomarker levels, treatments, microbial culture results, and 30-day mortality. Blood samples were collected upon the patients' admission to the emergency department.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003ePatients with septic shock had significantly higher serum levels of IL-6 (363.2 pg/mL) compared to those with sepsis (IL-6: 140.6 pg/mL). In contrast, patients with septic shock had significantly lower proadrenomedullin serum levels (0.27 pmol/mL) compared to septic patients (0.48 pmol/mL). Although presepsin levels were slightly higher in the septic shock group (178.3 ng/L) compared to the sepsis group (156.9 ng/L), the difference was not statistically significant. However, the levels of IL-6 were significantly higher in deceased patients (IL-6: 363.2 pg/mL) compared to those who survived (IL-6: 195.5 pg/mL). The ROC analysis results were listed as IL-6 (AUC: 0.701), proadrenomedullin (AUC: 0.658) and procalcitonin (AUC: 0.625) for determining septic shock.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eBased on the data obtained, interleukin-6, presepsin, and proadrenomedullin respectively are useful biomarkers for predicting and prognosticating sepsis and septic shock in the emergency department. However, none of these biomarkers are independent prognostic predictors for sepsis, septic shock or mortality.\u003c/p\u003e","manuscriptTitle":"Prognostic Value of Presepsin, Proadrenomedullin and Interleukin-6 in Sepsis: A Prospective Study From the Emergency Department","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-24 06:55:03","doi":"10.21203/rs.3.rs-6691861/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":"e2f4bbf9-fed4-41ca-8af0-d3b0ecc5d29a","owner":[],"postedDate":"June 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-16T05:08:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-24 06:55:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6691861","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6691861","identity":"rs-6691861","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}