Serial Assessment of Serum HMGB1 and △SOFA for Predicting 28-Day Mortality in Sepsis Patients: A Prospective Cohort Study in the Emergency Department

Serial Assessment of Serum HMGB1 and △SOFA for Predicting 28-Day Mortality in Sepsis Patients: A Prospective Cohort Study in 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 Serial Assessment of Serum HMGB1 and △SOFA for Predicting 28-Day Mortality in Sepsis Patients: A Prospective Cohort Study in the Emergency Department Qian Su, Jie Yu, Shuangjun He, Chenyu Fan, Yi Chen, Wei Zhou, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7599267/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Mar, 2026 Read the published version in BMC Emergency Medicine → Version 1 posted 10 You are reading this latest preprint version Abstract Background : Prognostic assessment in sepsis relies on evaluating the dynamic progression of organ dysfunction and the inflammatory response. This study compared the predictive value of serially measured serum HMGB1, a key late mediator of sepsis, with the △SOFA score for 28-day mortality. Methods : This prospective cohort study enrolled 250 sepsis patients admitted to the emergency department of a tertiary hospital from January 2022 to August 2024. Serum HMGB1 levels and SOFA scores were dynamically assessed on days 1, 4, and 7. Receiver operating characteristic (ROC) curve analysis and Kaplan-Meier survival analysis were employed to evaluate their prognostic performance. The primary endpoint was 28-day all-cause mortality. Results : A total of 232 patients (median age 71.5 years, 56% male) with a 28-day mortality rate of 13.8% (32/232) were included. Non-survivors had a higher prevalence of autoimmune diseases (43.8% vs. 11.5%) and lung infections (81.3% vs. 47.0%). Serum HMGB1 levels peaked on day 4 and were significantly higher in non-survivors and septic shock patients (P < 0.05). D1-HMGB1 levels showed significant positive correlations with SOFA scores and inflammatory cytokines (IL-6, IL-8, IL-10, IL-17a, TNF-α). The area under the ROC curve (AUC) for predicting 28-day mortality was 0.856 (95% CI: 0.751-0.921) for day 4 HMGB1 (D4-HMGB1) and 0.893 (95% CI: 0.845-0.941) for D7-△SOFA. The optimal cut-off value for D4-HMGB1 was 6.4 ng/mL. Kaplan-Meier analysis confirmed that patients with D4-HMGB1 ≥ 6.4 ng/mL had significantly higher mortality (log-rank, P = 0.001). This prognostic value remained consistent across key patient subgroups, including those with acute kidney injury, autoimmune diseases, or respiratory comorbidities. Conclusion : Dynamic monitoring of serum HMGB1 provides valuable prognostic information. Specifically, the day 4 HMGB1 level demonstrates excellent and earlier predictive performance for 28-day mortality, even comparable to the D7-△SOFA score, highlighting its potential as a prognostic biomarker in sepsis. Clinical trial number : Not applicable. sepsis HMGB1 SOFA score prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection, representing a major global health burden with mortality rates ranging from 30% to 50% according to recent epidemiological studies [ 1 , 2 ]. Accurate prognostic assessment is equally crucial as early diagnosis in sepsis management, as it enables appropriate resource allocation and guides therapeutic interventions. The Sequential Organ Failure Assessment (SOFA) score has been established as a key tool for diagnosing sepsis and assessing its severity according to international guidelines [ 1 – 3 ]. While the initial SOFA score provides valuable information, evidence increasingly supports that dynamic monitoring through ΔSOFA (change in SOFA score over time) offers superior prognostic capability [ 4 – 7 ]. The evolving pattern of organ dysfunction, as captured by serial SOFA measurements, more accurately reflects disease progression and therapeutic response than a single assessment. In parallel, research continues to identify biomarkers that reflect the complex pathophysiology of sepsis. High Mobility Group Box 1 protein (HMGB1) has emerged as a significant late mediator of sepsis pathogenesis [ 8 ]. Unlike early-response cytokines, HMGB1 exhibits delayed release kinetics and participates in sustained inflammatory responses that drive organ injury [ 9 , 10 ]. Its extracellular functions include activation of innate immune pathways through Toll-like receptors (TLRs) and receptor for advanced glycation end products (RAGE), amplification of pro-inflammatory cytokine production, and promotion of endothelial dysfunction and pyroptosis [ 11 – 13 ]. Clinical studies have demonstrated correlations between elevated plasma HMGB1 levels and both organ dysfunction severity and mortality in septic shock [ 14 , 15 ], suggesting HMGB1 as a potential novel therapeutic target. However, the comparative prognostic performance between serially monitored HMGB1 levels and dynamically assessed ΔSOFA scores remains insufficiently investigated, particularly in the emergency department setting where early risk stratification is most critical. This prospective cohort study aimed to determine the expression dynamics of serum HMGB1 in septic patients, investigate its correlation with SOFA scores at multiple time points, and directly compare the predictive performance of HMGB1 and ΔSOFA for 28-day mortality. The findings seek to provide evidence for incorporating biomarker monitoring alongside clinical scores to enhance prognostic accuracy in sepsis management. Material and methods Study design and patients This prospective observational cohort study was conducted in the emergency department (ED) of a tertiary care hospital in Shanghai, China, between January 2022 and August 2024. A total of 250 consecutive adult patients (age ≥ 18 years) meeting the Sepsis-3.0 diagnostic criteria [ 2 ] were initially screened for eligibility. The inclusion criteria required: 1) presentation to the ED within 72 hours of symptom onset; and 2) fulfillment of Sepsis-3.0 criteria. Exclusion criteria comprised: 1) diagnosis of active malignancy; 2) missing essential clinical data or loss to follow-up; or 3) declined to provide informed consent. Ultimately, 232 patients completed the 28-day follow-up and were included in the final analysis (Fig. 1 ). All enrolled patients underwent prospective serial monitoring of serum HMGB1 levels and SOFA scores at three predefined time points: day 1 (admission), day 4, and day 7. The primary endpoint for prognostic evaluation was 28-day all-cause mortality. Diagnostic criteria adhered to the Sepsis-3.0 definitions jointly published by the Society of Critical Care Medicine (SCCM) and the European Society of Intensive Care Medicine (ESICM) [ 2 ]. Septic shock was identified as a subset of sepsis characterized by profound circulatory, cellular, and metabolic abnormalities, clinically defined by the requirement of vasopressors to maintain a mean arterial pressure ≥ 65 mmHg and serum lactate level > 2 mmol/L (> 18 mg/dL) in the absence of hypovolemia. Treatment protocols followed the 2018 Chinese Guidelines for Emergency Management of Sepsis/Septic Shock, which emphasize 1-hour bundle therapy (including broad-spectrum antibiotics, fluid resuscitation, and source control) alongside dynamic monitoring of organ function. The study protocol was approved by the Institutional Review Board of Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Approval No: RA-2021-487). Written informed consent, detailing the study objectives, procedures, and potential risks, was obtained from all participants or their legally authorized representatives. Data collection Upon enrollment, blood samples were collected from each patient for baseline analysis. Subsequent sampling for serum HMGB1 quantification was performed on day 4 and day 7. It is noted that not all patients provided samples on day 4 (n = 221) and day 7 (n = 159) due to death, discharge, or other clinical reasons. The following data were systematically recorded for each participant upon ED admission: demographic characteristics (age, gender), comorbidities, primary infection site, laboratory parameters, SOFA scores, and clinical outcomes. Laboratory assessments included complete blood count, electrolyte levels, coagulation profile, hepatic and renal function tests, C-reactive protein, procalcitonin, lactate, and cytokine levels (including IL-6, IL-8, IL-10, IL-17a, and TNF-α), all performed according to standardized manufacturer protocols. Data on critical interventions and complications were prospectively collected, including: the requirement for mechanical ventilation (endotracheal intubation), continuous renal replacement therapy (CRRT), and the diagnosis of disseminated intravascular coagulation (DIC). DIC was diagnosed according to the International Society on Thrombosis and Haemostasis (ISTH) scoring system [ 16 ]. Microbiological identification was conducted through a comprehensive diagnostic approach. Blood cultures were obtained for all patients, while cultures from other sites—including tracheal aspirate, cerebrospinal fluid (CSF), urine, and central venous catheter (CVC) tips—were collected when clinically indicated. Furthermore, pathogen detection was supplemented with advanced methodologies including next-generation sequencing (NGS), serological immunoassays, and polymerase chain reaction (PCR) when required, to ensure comprehensive microbiological assessment. All patients underwent SOFA scoring at admission (day 1), day 4, and day 7. The ΔSOFA was calculated as follows: Δ4-SOFA = SOFA (day 4) - SOFA (day 1); Δ7-SOFA = SOFA (day 7) - SOFA (day 1), representing the change in organ function from baseline. Sample collection and measurement of serum HMGB1 Blood samples for serum HMGB1 analysis were collected at three time points: day 1 (upon enrollment), day 4, and day 7. The sample collection procedure was standardized as follows: three milliliters of venous blood were drawn from each patient and transferred into anticoagulant-containing tubes. The samples were subsequently centrifuged at 3,000 revolutions per minute for 15 minutes to separate serum from cellular components. The supernatant serum was carefully aliquoted and stored at -80°C until further analysis. Serum HMGB1 concentrations were quantified using a commercially available double-antibody sandwich enzyme-linked immunosorbent assay (ELISA) kit (Arigo, AREX Biosciences Ltd., China), strictly following the manufacturer's instructions. To minimize measurement bias, all samples were re-coded prior to analysis, and researchers performing the assays were blinded to clinical information and group assignments. Each sample was tested in duplicate, and the average of the two measurements was used for statistical analysis. The lower limit of detection for the assay was 0.3125 ng/mL. Cytokine concentrations Serum concentrations of interleukin (IL)-6, IL-8, IL-10, IL-17a, and tumor necrosis factor-alpha (TNF-α) were quantitatively determined using Luminex multiplex cytokine assays. Measurements were performed with the Fluorokine® MAP Multiplex Kit (R&D Systems, Minneapolis, MN) on a Luminex® 100/200™ instrument (Luminex Corporation, Austin, TX), in accordance with the manufacturer's protocols. The assay detection range for all cytokines was 18–10,000 pg/mL. All samples were analyzed in duplicate to ensure technical reproducibility. Samples exceeding the upper limit of detection were appropriately diluted and reanalyzed to obtain values within the quantitative range. Statistical analysis Data are presented as number (percentage) for categorical variables and as mean ± standard deviation or median (interquartile range) for normally and non-normally distributed continuous variables, respectively. The normality of distribution was assessed using the Shapiro-Wilk test. Comparisons of continuous variables between groups were performed using the independent Student's t-test or the Mann-Whitney U test, as appropriate. Categorical variables were compared using Pearson's chi-square test or Fisher's exact test. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the predictive performance of HMGB1 and SOFA scores for 28-day mortality. The optimal cut-off values were determined by maximizing Youden's index. Areas under the ROC curve (AUCs), along with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were calculated. Correlations between HMGB1 levels and SOFA scores or inflammatory cytokines were analyzed using Spearman’s rank correlation. Survival analysis was performed using the Kaplan-Meier method, with patients stratified according to the optimal cut-off value of D4-HMGB1. Group differences in survival were assessed with the log-rank test. The prognostic value of D4-HMGB1 was further evaluated in subgroup analyses of patients with acute kidney injury, autoimmune diseases, or respiratory comorbidities. A two-sided p-value < 0.05 was considered statistically significant for all analyses. All statistical analyses were performed using GraphPad Prism version 8.0 (GraphPad Software, San Diego, CA, USA) and SigmaPlot version 14.0 (Systat Software, San Jose, CA, USA). Results Demographic and clinical characteristics of patients with sepsis A total of 232 patients with sepsis were included in this cohort study, comprising 130 males and 102 females. All patients were followed up for 28 days and categorized according to their outcomes into survivor (n = 200) and non-survivor (n = 32), based on disease severity, patients were further stratified into sepsis (n = 154) and septic shock (n = 78). The baseline characteristics were presented in Table 1 . The 28-day mortality rate for the overall cohort was 16% (32/200). Non-survivors had a higher incidence of autoimmune diseases (43.8% vs. 11.5%) and lung infection (81.3% vs. 43%). The dynamic SOFA at measured timing (day1, day 4, day 7) and HMGB1 at measured timing (day 4, day 7) were significantly higher in non-survivors and septic shock group compared with survivor and sepsis group. Non-survivors and septic shock group were also more likely to be on mechanical ventilation and developing disseminated intravascular coagulation. Table 1 Baseline characteristics of patients with sepsis. Variable Total (n = 232) Survivors (n = 200) Deceased (n = 32) P Value Sepsis (n = 154) Sepsis shock(n = 78) P Value Gender, male, n (%) 130(56.0) 102(56.0) 18(56.3) 0.979 89(68.4) 41(31.5) 0.449 Age (years) 71.5(58.5–77.8) 72.0(59.0-78.8) 70.0(59.0-78.5) 0.125 72.0(59.8–79.0) 67.0(55.8–76.2) 0.053 BMI (kg/m2) 23.4 ± 4.3 23.5 ± 4.3 22.3 ± 5.2 0.512 23.4 ± 4.3 23.7 ± 4.2 0.512 Diabetes mellitus, n (%) 64(27.5) 58(29.0) 6(18.8) 0.228 44(28.6) 20(25.6) 0.637 Hypertension, n (%) 91(39.2) 83(41.5) 8(25.0) 0.076 68(44.2) 23(29.5) 0.030 Coronary artery disease, n (%) 36(15.5) 28(14.0) 8(25.0) 0.111 24(15.6) 12(15.4) 0.968 Cerebrovascular disease, n (%) 29(12.5) 23(11.5) 6(18.8) 0.250 20(13.0) 9(11.5) 0.753 Autoimmune Diseases, n (%) 37(15.9) 23(11.5) 14(43.8) < 0.001 19(12.3) 18(23.1) 0.031 Pulmonary diseases, n (%) 10(4.3) 7(3.5) 3(9.4) 0.129 4(2.6) 6(7.7) 0.071 Primary site of infection, n (%) Lung 120(51.7) 94(47) 26(81.3) < 0.001 69(44.8) 51(65.4) 0.004 Digestive tract 18(7.7) 16(8) 2(6.3) 0.731 15(9.7) 3(3.8) 0.640 Urinary tract 45(19.3) 43(21.5) 2(6.3) 0.051 38(24.7) 7(9.0) 0.004 Biliary tract 30(12.9) 27(13.5) 3(9.4) 0.409 16(10.4) 4(5.1) 0.420 CNS 4(1.7) 4(2) 0(0) 0.184 3(1.9) 1(1.3) 0.515 Skin and soft tissue 7(3.0) 7(3.5) 0(0) 0.366 7(4.5) 0(0) 0.648 Unknown/others 8(3.4) 8(4) 0(0) 0.400 7(4.5) 1(1.3) 0.351 Pathogen(s),n (%) bacteria 54(23.2) 48(24.0) 6(18.8) 0.514 38(24.7) 16(20.5) 0.479 Fungi 12(5.2) 11(5.5) 1(3.1) 0.573 10(6.4) 2(2.6) 0.467 virus 2(0.9) 1(0.5) 1(3.1) 0.136 1(0.6) 1(1.3) 0.622 Mixed 15(6.5) 10(5) 5(15.6) 10× 109 /L 121(52.2) 100(50.0) 20(62.5) 0.189 76(49.4) 45(57.7) 0.246 Platelets 100 mg/L 136(58.6) 114(57.0) 15(46.9) 0.285 93(60.4) 43(55.1) 0.442 PCT > 2 ng/mL 112(48.3) 94(47.0) 16(50.0) 0.752 66(42.9) 46(59.0) 0.193 Lactate > 2 mmol/L 112(48.9) 88(44.0) 24(75) 0.001 66(42.9) 44(56.4) 0.040 IL-6 > 250 pg/mL 17(7.3) 13(6.5) 3(9.4) 0.551 10(6.5) 7(9.0) 20.6 pg/mL 82(35.3) 75(37.5) 7(21.9) < 0.001 65(42.2) 17(21.8) 4.91 pg/mL 81(34.9) 74(37.0) 7(21.9) < 0.001 64(41.6) 17(21.8) 4.6 pg/mL 1(0.4) 1(0.5) 0(0) 0.785 0(0) 1(1.3) 0.048 IL-17A > 20.6 pg/mL 1(0.4) 0(0) 1(3.1) < 0.001 0(0) 1(1.3) 0.048 SOFA score, median Day 1(n = 232 ) 5.0(3.0–9.0) 5.0(3.0–8.0) 9.0(5.0–12.0) < 0.001 4.0(3.0–6.0) 8.0(5.0–11.0) < 0.001 Day 4(n = 221 ) 4.0(2.0–7.0) 3.0(1.0–7.0) 10.0(5.0-12.5) < 0.001 3.0(1.0–5.0) 7.0(4.0–12.0) < 0.001 Day 7(n = 159 ) 3.0(1.0–6.0) 2.0(1.0–5.0) 11.0(6.0-12.5) < 0.001 1.0(0–4.0) 6.0(3.0–11.0) < 0.001 ΔSOFA, Day 4 - Day 1 -1(-2-1) -1(-2.0-1.0) 1(0.0–1.0) < 0.001 -1.0(-2.0-0) 0(-1.0-1.0) < 0.001 ΔSOFA, Day 7 - Day 1 -2(-3-0) -2(-4–1) 1(0–2.0) < 0.001 -2(-3.0-1.3) -1(-4.0-1.0) 0.056 HMGB1 (ng /ml), median Day 1(n = 232 ) 5.0(4.2–5.4) 5.1(4.3–5.5) 4.2(3.4–5.1) 0.001 4.9(4.2–5.3) 5.2(4.2–5.6) 0.002 Day 4(n = 221 ) 5.8(5.4–6.34) 5.6(5.3-6.0) 6.8(6.2-7.0) < 0.001 5.5(5.2–5.9) 6.2(5.8–6.9) < 0.001 Day 7(n = 159 ) 3.5(3.3–3.9) 3.5(3.3–3.8) 5.1(3.8–6.6) 0.013 3.5(3.2–3.8) 3.8(3.4–4.3) 0.001 MV, n (%) 51(21.9) 30(15) 21(65.6) < 0.001 11(7.1) 40(51.3) < 0.001 Need for CRRT, n (%) 24(10.3) 19(9.5) 5(15.6) 0.291 11(7.1) 13(16.7) 0.024 DIC, n (%) 20(8.6) 9(4.5) 11(34.4) < 0.001 4(2.6) 16(20.5) < 0.001 Values are presented as the mean ± standard deviation, median (IQR) or number(%) CNS: Central nervous system; BMI: Body mass index; CRP: C-reactive protein; PCT: procalcitonin; SOFA: sequential organ failure assessment score; HMGB1: high-mobility group protein B1; MV: Mechanical Ventilation; CRRT: continuous renal replacement therapy; DIC: disseminated intravascular coagulation Serum HMGB1 levels in sepsis patients The serum HMGB1 levels on day 1, day 4 and day 7 in every group were demonstrated in Fig. 2 . Serum HMGB1 levels were highest on day 4, followed by a decreasing trend. Non-survivors exhibited markedly elevated HMGB1 levels on day 4 compared with the survivors group . Additionally, patients with increased HMGB1 levels in inital three days had higher mortality than those patients with decreased HMGB1 levels in inital three days, espcially those maintained higher HMGB1 in second three days. The dynamic change trends of SOFA were basically consistent with that of HMGB1, as showed in Fig. s1 . Spearman’s rank correlation analysis revealed that serum HMGB1 was positively associated with SOFA and inflammatory factors (IL-6, IL-8, IL-10, IL-17a, TNF-α) in patients with sepsis(Fig. s2 and s3). HMGB1 for the prediction of 28-day mortality in sepsis patients Table 2 and Fig. 3 illustrated ROC analysis for accessing the value of serum HMGB1 levels and SOFA scores in predicting 28-day mortality in patients with sepsis. The serum HMGB1 levels on day (D4-HMGB1; AUC 0.856, sensitivity 76.9%, specificity 88.1%) and D7-ΔSOFA (AUC 0.893, sensitivity 97.4%, specificity 75.8%) both showed a great profermance in predicting 28-day mortality. When combined with SOFA, HMGB1 had a better predictive efficacy. Table 2 Pairwise comparison of receiver operating characteristic curves. Variables D1-HMGB1 D4-HMGB1 D7-HMGB1 D4-△SOFA D7-△SOFA D4-HMGB1 + D4-SOFA AUC(%) 69.5 85.6 81.1 75.8 89.3 87.4 Best Cut-off Value 4.4 6.4 4.6 -0.5 -1.5 - Sensitivity(%) 64.5 76.9 70.7 80.0 97.4 84.0 Specificity(%) 78.5 88.1 98.5 69.6 75.8 84.0 Negative Predictive Value(%) 93.5 96.6 95.5 96.4 98.7 97.6 Positive Predictive Value(%) 31.7 46.5 95.5 25.3 33.9 40.4 Youden Index 143.0 165.1 169.2 149.6 173.2 168.0 AUC: area under the curve The cut-off value for D4-HMGB1 was (Youden’s index = 165.1). Kaplan–Meier survival curves were used to evaluate D4-HMGB1 as a predictor of 28-day mortality in sepsis patients, as presented in Fig. 4 . The 28-day mortality rate in the high D4-HMGB1 group (≥ 6.4ng/mL) was significantly higher than in the low D4-HMGB1 group (< 6.4ng/mL; log-rank, P = 0.001). The D4-HMGB1 levels retained predictive accuracy for sepsis patients complicated with acute kidney injury, autoimmune or respiratory diseases (Fig. s4). Discussion The incentive to the present research was to evaluate whether the serum HMGB1 expression levels could help to clarify the clinical outcomes of sepsis, while also compare the predictive performance of HMGB1 and SOFA for sepsis prognosis. HMGB1 is a damage-associated molecular pattern (DAMP) molecule released from necrotic cells or secreted by activated macrophages or monocytes during the delayed phase of sepsis[ 17 ]. Our key findings revealed HMGB1 levels peaked significantly at day 4, contrasting with the decline of early-phase cytokines such as C-reactive protiens, TNF-α and IL-6, aligning with its role as a late-phase mediator of sustained inflammation. Similar to previous studies, Sun et al. [ 18 ] demonstrated that in LPS-induced septic animal models, serum HMGB1 levels significantly increased 16–32 hours post-LPS injection later than other early inflammatory cytokines. More importantly, serum HMGB1 levels on day 4 outperformed simultaneous SOFA scores (AUC 0.82 vs. 0.79) in mortality discrimination, suggesting it captures ongoing pathological processes beyond organ dysfunction quantified by SOFA. This timing might suggests HMGB1 may amplify "second-hit" organ injury, explaining its superior prognostic value over initial biomarkers. Given that extracellular HMGB1 binds to Toll-like receptors (TLRs) and the receptor for advanced glycation end products (RAGE), activating intracellular signaling pathways including p38 mitogen-activated protein kinase (MAPK), extracellular signal-regulated kinase 1/2 (ERK1/2), NF-κB and others. This cascade promotes the secretion of transforming growth factor-beta 1 (TGF-β1) and platelet-derived growth factor (PDGF), exacerbating tissue damage. HMGB1 also activates the NLRP3 inflammasome, inducing gasdermin-D-mediated pyroptosis, and creates a feed-forward loop of DAMP release[ 13 , 19 – 21 ]. The subsequent pro-inflammatory cytokine release, which further stimulates HMGB1 release, establishes a positive feedback loop that continuously triggering and sustaining downstream inflammatory responses, amplifing the inflammatory cascade, thereby driving sepsis progression[ 10 ]. The SOFA scoring system has been commonly employed to foretell the clinical aftermath of critically ill cases. Dynamic and serial SOFA, particularly ΔSOFA, provides a more direct method for evaluating patient prognosis compared to initial SOFA[ 5 , 6 ]. In an international study[ 22 ], the rate of change in SOFA by day 7 (D7-ΔSOFA) relative to the admission SOFA was an effective predictor of 28-day mortality (AUC 0.84, sensitivity 78%, specificity 80%). Mortality increased approximately 15-fold in patients with a reduction in admission SOFA of less than 25% by day 7. This finding might effectively identify patients at higher risk of mortality, indicating that ΔSOFA on day 7 may hold significant clinical value in assessing the prognosis and mortality risk in sepsis patients. A review by Grooth et al. [ 7 ] similarly concluded that ΔSOFA, rather than a fixed-day SOFA, demonstrates a stable and persistent association with sepsis mortality, strongly recommending its prioritization as a surrogate endpoint in RCTs. Our results agree with previous studies in which D7-ΔSOFA showed a great profermance in predicting 28-day mortality (AUC 0.87, sensitivity 77%, specificity 87%). However, the requirement to wait until day 7 to determine D7-ΔSOFA represents a significant delay, hindering early sepsis outcome evaluation. It was noting that serum HMGB1 levels on day 4 demonstrated earlier and comparative performance even compared with D7-ΔSOFA in terms of predicting 28-day mortality in present study. Since serum HMGB1 levels on day 4 could be obtained eailer and more directly, it could better predict the prognosis of patients with sepsis than D7-ΔSOFA. While procalcitonin and C-reactive protein aid early diagnosis, they lack prognostic specificity beyond 72 hours. HMGB1’s delayed peak provides a unique temporal advantage for monitoring treatment response. Notably, its prognostic power might surpasse lactate (AUC typically 0.63–0.73), which reflecting direct linkage to cellular injury rather than hypoperfusion alone. The day 4 peak might also coincide with sepsis’s transition from hyperinflammation to immunosuppression. HMGB1 exacerbates organ injury by sustaining NF-κB activation and pyroptosis, impairing mitochondrial function and promoting endothelial glycocalyx shedding, worsening capillary leak. This explains why non-survivors maintained higher HMGB1 through day 7 compared to survivors, reflecting persistent tissue damage. In this study, 15.9% of patients with sepsis were complicated with autoimmune diseases. The day 4 peak HMGB1 still retained predictive accuracy expect of baseline interference. This conclusion also applied to sepsis patients with or without actue renal injury, which suggested renal function did not attenuate HMGB1’s prognostic value. Our study has some limitations that must be taken into consideration. First, it should be reminded that the current study was an observational study, it was difficult to account for all confounders, therefore could only show a statistical association to the mortality. Second, the fixed timing on day 1, day 4 and day 7, rather than a continuous dynamic detection, might miss individual peak variation and affect the precision of assessing serum HMGB1. The effect of the value of ΔHMGB1 on predicting prognosis in sepsis was also not evaluated. Third, the elevation of HMGB1 was not specific for infection and may be observed in various other conditions such as chronic inflammatory disorders[ 23 ], tissue damage[ 24 ], and neoplasia[ 8 ]. Furthermore, it was single-center design which limited its generalizability, and more data and external validation were needed to confirm the accuracy of its results. Conclusion This prospective single-center study demonstrated that serum HMGB1 had good performance in predicting 28-day mortality in patients with sepsis even compared with SOFA, especially a distinct peak HMGB1 on day 4. The day 4 peak might represents a therapeutic opportunity, early blockade may impair host defense. Abbreviations HMGB1: high-mobility group protein B1 SOFA: sequential organ failure assessment score ROC: receiver operating characteristic AUC: area under the curve NF-κB: nuclear factor kappa B CRRT: continuous renal replacement treatment DIC: disseminated intravascular coagulation CSF: cerebrospinal fluid CVC: central venous catheter AKI: acute kindey injury Declarations Ethics approval and consent to participate Ethical approval was obtained from Mardin Artuklu University NonInterventional Clinical Research Ethics Committee (Date: 06/11/2023, REF: 2023/11−7). In addition, the necessary permissions were obtained from the Mardin Directorate of Health (Date: 27/12/2023, REF: E-68051626-770- 232730112). Also, informed consent was obtained from all participants. This study was carried out in accordance with the principles of the Declaration of Helsinki. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Data availability The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request. Funding The study was funded by Shanghai Municipal Health Commission, China (201940105, 202240018). Acknowledgements The authors thank all study participants and acknowledge the dedication of the study teams. Author contributions SQ and YJ contributed in methodology, statistical analysis, manuscript drafting and revising; HS and FC participated in data collection and assembly, statistical analysis; CY participated in study design and manuscript review; QM and ZW contributed in the study conception, design and coordination, and manuscript review. All authors read and approved the final manuscript. References Rudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, et al. Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the global burden of disease study. 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1","display":"","copyAsset":false,"role":"figure","size":90528,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study population.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7599267/v1/31571343d1054690f383dcbb.png"},{"id":92739767,"identity":"5844eab7-8325-4bf4-8a1a-0de816f99a48","added_by":"auto","created_at":"2025-10-03 17:10:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":122229,"visible":true,"origin":"","legend":"\u003cp\u003eDynamic serum HMGB1 levels in patients with sepsis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA)\u003c/strong\u003e Serum HMGB1 levels stratified by 28d all-cause mortality and severity in patients with sepsis on admission day 1,4 and 7;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB)\u003c/strong\u003e Dynamic serum HMGB1 levels in survivors and deceased group;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC)\u003c/strong\u003e Differntial expression levels of serum HMGB1 based on SOFA score in patients with sepsis;\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7599267/v1/825d6e6d753b2e9b6cae3920.png"},{"id":92737744,"identity":"d3afb4a3-6fee-4992-be40-3f6825e67a93","added_by":"auto","created_at":"2025-10-03 16:46:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":128981,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnosis values of serum HMGB1 and SOFA score for predicting 28-Day Survival in sepsis patients.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7599267/v1/d66d06ec1ac36a8d9cd73b2c.png"},{"id":92739207,"identity":"6f13e725-9282-46e8-8ed9-b4f94c453089","added_by":"auto","created_at":"2025-10-03 17:02:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":78432,"visible":true,"origin":"","legend":"\u003cp\u003eValues of serum HMGB1 levels on day 4 for predicting 28d all-cause mortality of sepsis patients.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7599267/v1/5134b271ac72ef59a7d28259.png"},{"id":105755899,"identity":"2e998130-5313-45bc-b4d0-e12c317af198","added_by":"auto","created_at":"2026-03-30 16:32:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1428480,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7599267/v1/4ec99bc4-4b7e-4988-ab0c-2831dc0d3a50.pdf"},{"id":92737740,"identity":"62d8e8fa-3b91-42ed-907c-d80e173e635d","added_by":"auto","created_at":"2025-10-03 16:46:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":447186,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymatrial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7599267/v1/ffc42ce73cfbab3308016dc8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Serial Assessment of Serum HMGB1 and △SOFA for Predicting 28-Day Mortality in Sepsis Patients: A Prospective Cohort Study in the Emergency Department","fulltext":[{"header":"Background","content":"\u003cp\u003eSepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection, representing a major global health burden with mortality rates ranging from 30% to 50% according to recent epidemiological studies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Accurate prognostic assessment is equally crucial as early diagnosis in sepsis management, as it enables appropriate resource allocation and guides therapeutic interventions.\u003c/p\u003e\u003cp\u003eThe Sequential Organ Failure Assessment (SOFA) score has been established as a key tool for diagnosing sepsis and assessing its severity according to international guidelines [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. While the initial SOFA score provides valuable information, evidence increasingly supports that dynamic monitoring through ΔSOFA (change in SOFA score over time) offers superior prognostic capability [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The evolving pattern of organ dysfunction, as captured by serial SOFA measurements, more accurately reflects disease progression and therapeutic response than a single assessment.\u003c/p\u003e\u003cp\u003eIn parallel, research continues to identify biomarkers that reflect the complex pathophysiology of sepsis. High Mobility Group Box 1 protein (HMGB1) has emerged as a significant late mediator of sepsis pathogenesis [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Unlike early-response cytokines, HMGB1 exhibits delayed release kinetics and participates in sustained inflammatory responses that drive organ injury [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Its extracellular functions include activation of innate immune pathways through Toll-like receptors (TLRs) and receptor for advanced glycation end products (RAGE), amplification of pro-inflammatory cytokine production, and promotion of endothelial dysfunction and pyroptosis [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Clinical studies have demonstrated correlations between elevated plasma HMGB1 levels and both organ dysfunction severity and mortality in septic shock [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], suggesting HMGB1 as a potential novel therapeutic target. However, the comparative prognostic performance between serially monitored HMGB1 levels and dynamically assessed ΔSOFA scores remains insufficiently investigated, particularly in the emergency department setting where early risk stratification is most critical.\u003c/p\u003e\u003cp\u003eThis prospective cohort study aimed to determine the expression dynamics of serum HMGB1 in septic patients, investigate its correlation with SOFA scores at multiple time points, and directly compare the predictive performance of HMGB1 and ΔSOFA for 28-day mortality. The findings seek to provide evidence for incorporating biomarker monitoring alongside clinical scores to enhance prognostic accuracy in sepsis management.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and patients\u003c/h2\u003e\u003cp\u003eThis prospective observational cohort study was conducted in the emergency department (ED) of a tertiary care hospital in Shanghai, China, between January 2022 and August 2024. A total of 250 consecutive adult patients (age\u0026thinsp;\u0026ge;\u0026thinsp;18 years) meeting the Sepsis-3.0 diagnostic criteria [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] were initially screened for eligibility. The inclusion criteria required: 1) presentation to the ED within 72 hours of symptom onset; and 2) fulfillment of Sepsis-3.0 criteria. Exclusion criteria comprised: 1) diagnosis of active malignancy; 2) missing essential clinical data or loss to follow-up; or 3) declined to provide informed consent. Ultimately, 232 patients completed the 28-day follow-up and were included in the final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All enrolled patients underwent prospective serial monitoring of serum HMGB1 levels and SOFA scores at three predefined time points: day 1 (admission), day 4, and day 7. The primary endpoint for prognostic evaluation was 28-day all-cause mortality.\u003c/p\u003e\u003cp\u003eDiagnostic criteria adhered to the Sepsis-3.0 definitions jointly published by the Society of Critical Care Medicine (SCCM) and the European Society of Intensive Care Medicine (ESICM) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Septic shock was identified as a subset of sepsis characterized by profound circulatory, cellular, and metabolic abnormalities, clinically defined by the requirement of vasopressors to maintain a mean arterial pressure\u0026thinsp;\u0026ge;\u0026thinsp;65 mmHg and serum lactate level\u0026thinsp;\u0026gt;\u0026thinsp;2 mmol/L (\u0026gt;\u0026thinsp;18 mg/dL) in the absence of hypovolemia. Treatment protocols followed the 2018 Chinese Guidelines for Emergency Management of Sepsis/Septic Shock, which emphasize 1-hour bundle therapy (including broad-spectrum antibiotics, fluid resuscitation, and source control) alongside dynamic monitoring of organ function. The study protocol was approved by the Institutional Review Board of Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Approval No: RA-2021-487). Written informed consent, detailing the study objectives, procedures, and potential risks, was obtained from all participants or their legally authorized representatives.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eUpon enrollment, blood samples were collected from each patient for baseline analysis. Subsequent sampling for serum HMGB1 quantification was performed on day 4 and day 7. It is noted that not all patients provided samples on day 4 (n\u0026thinsp;=\u0026thinsp;221) and day 7 (n\u0026thinsp;=\u0026thinsp;159) due to death, discharge, or other clinical reasons.\u003c/p\u003e\u003cp\u003eThe following data were systematically recorded for each participant upon ED admission: demographic characteristics (age, gender), comorbidities, primary infection site, laboratory parameters, SOFA scores, and clinical outcomes. Laboratory assessments included complete blood count, electrolyte levels, coagulation profile, hepatic and renal function tests, C-reactive protein, procalcitonin, lactate, and cytokine levels (including IL-6, IL-8, IL-10, IL-17a, and TNF-α), all performed according to standardized manufacturer protocols. Data on critical interventions and complications were prospectively collected, including: the requirement for mechanical ventilation (endotracheal intubation), continuous renal replacement therapy (CRRT), and the diagnosis of disseminated intravascular coagulation (DIC). DIC was diagnosed according to the International Society on Thrombosis and Haemostasis (ISTH) scoring system [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMicrobiological identification was conducted through a comprehensive diagnostic approach. Blood cultures were obtained for all patients, while cultures from other sites\u0026mdash;including tracheal aspirate, cerebrospinal fluid (CSF), urine, and central venous catheter (CVC) tips\u0026mdash;were collected when clinically indicated. Furthermore, pathogen detection was supplemented with advanced methodologies including next-generation sequencing (NGS), serological immunoassays, and polymerase chain reaction (PCR) when required, to ensure comprehensive microbiological assessment.\u003c/p\u003e\u003cp\u003eAll patients underwent SOFA scoring at admission (day 1), day 4, and day 7. The ΔSOFA was calculated as follows: Δ4-SOFA\u0026thinsp;=\u0026thinsp;SOFA (day 4) - SOFA (day 1); Δ7-SOFA\u0026thinsp;=\u0026thinsp;SOFA (day 7) - SOFA (day 1), representing the change in organ function from baseline.\u003c/p\u003e\n\u003ch3\u003eSample collection and measurement of serum HMGB1\u003c/h3\u003e\n\u003cp\u003eBlood samples for serum HMGB1 analysis were collected at three time points: day 1 (upon enrollment), day 4, and day 7. The sample collection procedure was standardized as follows: three milliliters of venous blood were drawn from each patient and transferred into anticoagulant-containing tubes. The samples were subsequently centrifuged at 3,000 revolutions per minute for 15 minutes to separate serum from cellular components. The supernatant serum was carefully aliquoted and stored at -80\u0026deg;C until further analysis. Serum HMGB1 concentrations were quantified using a commercially available double-antibody sandwich enzyme-linked immunosorbent assay (ELISA) kit (Arigo, AREX Biosciences Ltd., China), strictly following the manufacturer's instructions. To minimize measurement bias, all samples were re-coded prior to analysis, and researchers performing the assays were blinded to clinical information and group assignments. Each sample was tested in duplicate, and the average of the two measurements was used for statistical analysis. The lower limit of detection for the assay was 0.3125 ng/mL.\u003c/p\u003e\n\u003ch3\u003eCytokine concentrations\u003c/h3\u003e\n\u003cp\u003eSerum concentrations of interleukin (IL)-6, IL-8, IL-10, IL-17a, and tumor necrosis factor-alpha (TNF-α) were quantitatively determined using Luminex multiplex cytokine assays. Measurements were performed with the Fluorokine\u0026reg; MAP Multiplex Kit (R\u0026amp;D Systems, Minneapolis, MN) on a Luminex\u0026reg; 100/200\u0026trade; instrument (Luminex Corporation, Austin, TX), in accordance with the manufacturer's protocols. The assay detection range for all cytokines was 18\u0026ndash;10,000 pg/mL. All samples were analyzed in duplicate to ensure technical reproducibility. Samples exceeding the upper limit of detection were appropriately diluted and reanalyzed to obtain values within the quantitative range.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eData are presented as number (percentage) for categorical variables and as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range) for normally and non-normally distributed continuous variables, respectively. The normality of distribution was assessed using the Shapiro-Wilk test. Comparisons of continuous variables between groups were performed using the independent Student's t-test or the Mann-Whitney U test, as appropriate. Categorical variables were compared using Pearson's chi-square test or Fisher's exact test. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the predictive performance of HMGB1 and SOFA scores for 28-day mortality. The optimal cut-off values were determined by maximizing Youden's index. Areas under the ROC curve (AUCs), along with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were calculated. Correlations between HMGB1 levels and SOFA scores or inflammatory cytokines were analyzed using Spearman\u0026rsquo;s rank correlation.\u003c/p\u003e\u003cp\u003eSurvival analysis was performed using the Kaplan-Meier method, with patients stratified according to the optimal cut-off value of D4-HMGB1. Group differences in survival were assessed with the log-rank test. The prognostic value of D4-HMGB1 was further evaluated in subgroup analyses of patients with acute kidney injury, autoimmune diseases, or respiratory comorbidities. A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant for all analyses. All statistical analyses were performed using GraphPad Prism version 8.0 (GraphPad Software, San Diego, CA, USA) and SigmaPlot version 14.0 (Systat Software, San Jose, CA, USA).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eDemographic and clinical characteristics of patients with sepsis\u003c/h2\u003e\n \u003cp\u003eA total of 232 patients with sepsis were included in this cohort study, comprising 130 males and 102 females. All patients were followed up for 28 days and categorized according to their outcomes into survivor (n\u0026thinsp;=\u0026thinsp;200) and non-survivor (n\u0026thinsp;=\u0026thinsp;32), based on disease severity, patients were further stratified into sepsis (n\u0026thinsp;=\u0026thinsp;154) and septic shock (n\u0026thinsp;=\u0026thinsp;78). The baseline characteristics were presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe 28-day mortality rate for the overall cohort was 16% (32/200). Non-survivors had a higher incidence of autoimmune diseases (43.8% vs. 11.5%) and lung infection (81.3% vs. 43%). The dynamic SOFA at measured timing (day1, day 4, day 7) and HMGB1 at measured timing (day 4, day 7) were significantly higher in non-survivors and septic shock group compared with survivor and sepsis group. Non-survivors and septic shock group were also more likely to be on mechanical ventilation and developing disseminated intravascular coagulation.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics of patients with sepsis.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;232)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSurvivors (n\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDeceased (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSepsis (n\u0026thinsp;=\u0026thinsp;154)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSepsis shock(n\u0026thinsp;=\u0026thinsp;78)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender, male, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130(56.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102(56.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(56.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89(68.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41(31.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.449\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.5(58.5\u0026ndash;77.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.0(59.0-78.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.0(59.0-78.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.0(59.8\u0026ndash;79.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.0(55.8\u0026ndash;76.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64(27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58(29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44(28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91(39.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83(41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68(44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoronary artery disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36(15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28(14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCerebrovascular disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAutoimmune Diseases, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(43.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePulmonary diseases, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary site of infection, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLung\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120(51.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94(47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26(81.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69(44.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51(65.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDigestive tract\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrinary tract\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45(19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43(21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38(24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBiliary tract\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27(13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.420\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkin and soft tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown/others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathogen(s),n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebacteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54(23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48(24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38(24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFungi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003evirus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory parameters, n (% )\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeucocytes\u0026thinsp;\u0026gt;\u0026thinsp;10\u0026times; 109 /L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121(52.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100(50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76(49.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45(57.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlatelets\u0026thinsp;\u0026lt;\u0026thinsp;100 \u0026times; 109 /L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65(28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54(27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36(23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29(18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymphocytopenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45(19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39(19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCRP\u0026thinsp;\u0026gt;\u0026thinsp;100 mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136(58.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114(57.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(46.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93(60.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43(55.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCT\u0026thinsp;\u0026gt;\u0026thinsp;2 ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112(48.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94(47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66(42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46(59.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLactate\u0026thinsp;\u0026gt;\u0026thinsp;2 mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112(48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88(44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66(42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44(56.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-6\u0026thinsp;\u0026gt;\u0026thinsp;250 pg/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-8\u0026thinsp;\u0026gt;\u0026thinsp;20.6 pg/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82(35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75(37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65(42.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-10\u0026thinsp;\u0026gt;\u0026thinsp;4.91 pg/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81(34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74(37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64(41.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNF-\u0026alpha;\u0026thinsp;\u0026gt;\u0026thinsp;4.6 pg/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL-17A\u0026thinsp;\u0026gt;\u0026thinsp;20.6 pg/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSOFA score, median\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDay 1(n\u0026thinsp;=\u0026thinsp;232 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.0(3.0\u0026ndash;9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.0(3.0\u0026ndash;8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.0(5.0\u0026ndash;12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.0(3.0\u0026ndash;6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.0(5.0\u0026ndash;11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDay 4(n\u0026thinsp;=\u0026thinsp;221 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.0(2.0\u0026ndash;7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.0(1.0\u0026ndash;7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0(5.0-12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.0(1.0\u0026ndash;5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.0(4.0\u0026ndash;12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDay 7(n\u0026thinsp;=\u0026thinsp;159 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.0(1.0\u0026ndash;6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0(1.0\u0026ndash;5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.0(6.0-12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(0\u0026ndash;4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.0(3.0\u0026ndash;11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;SOFA, Day 4 - Day 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1(-2-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1(-2.0-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0.0\u0026ndash;1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.0(-2.0-0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(-1.0-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026Delta;SOFA, Day 7 - Day 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2(-3-0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2(-4\u0026ndash;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0\u0026ndash;2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2(-3.0-1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1(-4.0-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHMGB1 (ng\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e/ml), median\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDay 1(n\u0026thinsp;=\u0026thinsp;232 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.0(4.2\u0026ndash;5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.1(4.3\u0026ndash;5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.2(3.4\u0026ndash;5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.9(4.2\u0026ndash;5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.2(4.2\u0026ndash;5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDay 4(n\u0026thinsp;=\u0026thinsp;221 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.8(5.4\u0026ndash;6.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.6(5.3-6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.8(6.2-7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.5(5.2\u0026ndash;5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.2(5.8\u0026ndash;6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDay 7(n\u0026thinsp;=\u0026thinsp;159 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5(3.3\u0026ndash;3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5(3.3\u0026ndash;3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.1(3.8\u0026ndash;6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5(3.2\u0026ndash;3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.8(3.4\u0026ndash;4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMV, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51(21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(65.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40(51.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeed for CRRT, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDIC, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eValues are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, median (IQR) or number(%)\u003c/p\u003e\n \u003cp\u003eCNS: Central nervous system; BMI: Body mass index; CRP: C-reactive protein; PCT: procalcitonin; SOFA: sequential organ failure assessment score; HMGB1: high-mobility group protein B1; MV: Mechanical Ventilation; CRRT: continuous renal replacement therapy; DIC: disseminated intravascular coagulation\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eSerum HMGB1 levels in sepsis patients\u003c/h3\u003e\n\u003cp\u003eThe serum HMGB1 levels on day 1, day 4 and day 7 in every group were demonstrated in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Serum HMGB1 levels were highest on day 4, followed by a decreasing trend. Non-survivors exhibited markedly elevated HMGB1 levels on day 4 compared with the survivors group .\u003c/p\u003e\n\u003cp\u003eAdditionally, patients with increased HMGB1 levels in inital three days had higher mortality than those patients with decreased HMGB1 levels in inital three days, espcially those maintained higher HMGB1 in second three days. The dynamic change trends of SOFA were basically consistent with that of HMGB1, as showed in Fig. \u003cspan class=\"InternalRef\"\u003es1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eSpearman\u0026rsquo;s rank correlation analysis revealed that serum HMGB1 was positively associated with SOFA and inflammatory factors (IL-6, IL-8, IL-10, IL-17a, TNF-\u0026alpha;) in patients with sepsis(Fig. s2 and s3).\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eHMGB1 for the prediction of 28-day mortality in sepsis patients\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e illustrated ROC analysis for accessing the value of serum HMGB1 levels and SOFA scores in predicting 28-day mortality in patients with sepsis. The serum HMGB1 levels on day (D4-HMGB1; AUC 0.856, sensitivity 76.9%, specificity 88.1%) and D7-\u0026Delta;SOFA (AUC 0.893, sensitivity 97.4%, specificity 75.8%) both showed a great profermance in predicting 28-day mortality. When combined with SOFA, HMGB1 had a better predictive efficacy.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePairwise comparison of receiver operating characteristic curves.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eD1-HMGB1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eD4-HMGB1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eD7-HMGB1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eD4-△SOFA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eD7-△SOFA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eD4-HMGB1\u0026thinsp;+\u0026thinsp;D4-SOFA\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUC(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBest Cut-off Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSensitivity(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecificity(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative Predictive Value(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive Predictive Value(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYouden Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e143.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e165.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e169.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e149.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e173.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eAUC: area under the curve\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe cut-off value for D4-HMGB1 was (Youden\u0026rsquo;s index\u0026thinsp;=\u0026thinsp;165.1). Kaplan\u0026ndash;Meier survival curves were used to evaluate D4-HMGB1 as a predictor of 28-day mortality in sepsis patients, as presented in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The 28-day mortality rate in the high D4-HMGB1 group (\u0026ge;\u0026thinsp;6.4ng/mL) was significantly higher than in the low D4-HMGB1 group (\u0026lt;\u0026thinsp;6.4ng/mL; log-rank, P\u0026thinsp;=\u0026thinsp;0.001). The D4-HMGB1 levels retained predictive accuracy for sepsis patients complicated with acute kidney injury, autoimmune or respiratory diseases (Fig. s4).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe incentive to the present research was to evaluate whether the serum HMGB1 expression levels could help to clarify the clinical outcomes of sepsis, while also compare the predictive performance of HMGB1 and SOFA for sepsis prognosis.\u003c/p\u003e\u003cp\u003eHMGB1 is a damage-associated molecular pattern (DAMP) molecule released from necrotic cells or secreted by activated macrophages or monocytes during the delayed phase of sepsis[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Our key findings revealed HMGB1 levels peaked significantly at day 4, contrasting with the decline of early-phase cytokines such as C-reactive protiens, TNF-α and IL-6, aligning with its role as a late-phase mediator of sustained inflammation. Similar to previous studies, Sun et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] demonstrated that in LPS-induced septic animal models, serum HMGB1 levels significantly increased 16\u0026ndash;32 hours post-LPS injection later than other early inflammatory cytokines. More importantly, serum HMGB1 levels on day 4 outperformed simultaneous SOFA scores (AUC 0.82 vs. 0.79) in mortality discrimination, suggesting it captures ongoing pathological processes beyond organ dysfunction quantified by SOFA. This timing might suggests HMGB1 may amplify \"second-hit\" organ injury, explaining its superior prognostic value over initial biomarkers. Given that extracellular HMGB1 binds to Toll-like receptors (TLRs) and the receptor for advanced glycation end products (RAGE), activating intracellular signaling pathways including p38 mitogen-activated protein kinase (MAPK), extracellular signal-regulated kinase 1/2 (ERK1/2), NF-κB and others. This cascade promotes the secretion of transforming growth factor-beta 1 (TGF-β1) and platelet-derived growth factor (PDGF), exacerbating tissue damage. HMGB1 also activates the NLRP3 inflammasome, inducing gasdermin-D-mediated pyroptosis, and creates a feed-forward loop of DAMP release[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The subsequent pro-inflammatory cytokine release, which further stimulates HMGB1 release, establishes a positive feedback loop that continuously triggering and sustaining downstream inflammatory responses, amplifing the inflammatory cascade, thereby driving sepsis progression[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe SOFA scoring system has been commonly employed to foretell the clinical aftermath of critically ill cases. Dynamic and serial SOFA, particularly ΔSOFA, provides a more direct method for evaluating patient prognosis compared to initial SOFA[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In an international study[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], the rate of change in SOFA by day 7 (D7-ΔSOFA) relative to the admission SOFA was an effective predictor of 28-day mortality (AUC 0.84, sensitivity 78%, specificity 80%). Mortality increased approximately 15-fold in patients with a reduction in admission SOFA of less than 25% by day 7. This finding might effectively identify patients at higher risk of mortality, indicating that ΔSOFA on day 7 may hold significant clinical value in assessing the prognosis and mortality risk in sepsis patients. A review by Grooth et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] similarly concluded that ΔSOFA, rather than a fixed-day SOFA, demonstrates a stable and persistent association with sepsis mortality, strongly recommending its prioritization as a surrogate endpoint in RCTs. Our results agree with previous studies in which D7-ΔSOFA showed a great profermance in predicting 28-day mortality (AUC 0.87, sensitivity 77%, specificity 87%). However, the requirement to wait until day 7 to determine D7-ΔSOFA represents a significant delay, hindering early sepsis outcome evaluation. It was noting that serum HMGB1 levels on day 4 demonstrated earlier and comparative performance even compared with D7-ΔSOFA in terms of predicting 28-day mortality in present study. Since serum HMGB1 levels on day 4 could be obtained eailer and more directly, it could better predict the prognosis of patients with sepsis than D7-ΔSOFA.\u003c/p\u003e\u003cp\u003eWhile procalcitonin and C-reactive protein aid early diagnosis, they lack prognostic specificity beyond 72 hours. HMGB1\u0026rsquo;s delayed peak provides a unique temporal advantage for monitoring treatment response. Notably, its prognostic power might surpasse lactate (AUC typically 0.63\u0026ndash;0.73), which reflecting direct linkage to cellular injury rather than hypoperfusion alone. The day 4 peak might also coincide with sepsis\u0026rsquo;s transition from hyperinflammation to immunosuppression. HMGB1 exacerbates organ injury by sustaining NF-κB activation and pyroptosis, impairing mitochondrial function and promoting endothelial glycocalyx shedding, worsening capillary leak. This explains why non-survivors maintained higher HMGB1 through day 7 compared to survivors, reflecting persistent tissue damage.\u003c/p\u003e\u003cp\u003eIn this study, 15.9% of patients with sepsis were complicated with autoimmune diseases. The day 4 peak HMGB1 still retained predictive accuracy expect of baseline interference. This conclusion also applied to sepsis patients with or without actue renal injury, which suggested renal function did not attenuate HMGB1\u0026rsquo;s prognostic value.\u003c/p\u003e\u003cp\u003eOur study has some limitations that must be taken into consideration. First, it should be reminded that the current study was an observational study, it was difficult to account for all confounders, therefore could only show a statistical association to the mortality. Second, the fixed timing on day 1, day 4 and day 7, rather than a continuous dynamic detection, might miss individual peak variation and affect the precision of assessing serum HMGB1. The effect of the value of ΔHMGB1 on predicting prognosis in sepsis was also not evaluated. Third, the elevation of HMGB1 was not specific for infection and may be observed in various other conditions such as chronic inflammatory disorders[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], tissue damage[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and neoplasia[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Furthermore, it was single-center design which limited its generalizability, and more data and external validation were needed to confirm the accuracy of its results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis prospective single-center study demonstrated that serum HMGB1 had good performance in predicting 28-day mortality in patients with sepsis even compared with SOFA, especially a distinct peak HMGB1 on day 4. The day 4 peak might represents a therapeutic opportunity, early blockade may impair host defense.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHMGB1: high-mobility group protein B1\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSOFA: sequential organ failure assessment score\u003c/p\u003e\n\u003cp\u003eROC: receiver operating characteristic \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAUC: area under the curve\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNF-\u0026kappa;B: nuclear factor kappa B\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCRRT: continuous renal replacement treatment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDIC: disseminated intravascular coagulation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCSF: cerebrospinal fluid\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCVC: central venous catheter\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAKI: acute kindey injury\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from Mardin Artuklu University NonInterventional Clinical Research Ethics Committee (Date: 06/11/2023, REF: 2023/11−7). In addition, the necessary permissions were obtained from the Mardin Directorate of Health (Date: 27/12/2023, REF: E-68051626-770- 232730112). Also, informed consent was obtained from all participants. This study was carried out in accordance with the principles of the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded by Shanghai Municipal Health Commission, China (201940105, 202240018).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank\u0026nbsp;all study participants and acknowledge\u0026nbsp;the\u0026nbsp;dedication of\u0026nbsp;the\u0026nbsp;study teams.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u003c/strong\u003e \u003cstrong\u003econtributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSQ and YJ contributed in methodology, statistical analysis, manuscript drafting and revising; HS and FC participated in data collection and assembly, statistical analysis; CY participated in study design and manuscript review; QM and ZW contributed in the study conception, design and coordination, and manuscript review. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, et al. Global, regional, and national sepsis incidence and mortality, 1990\u0026ndash;2017: analysis for the global burden of disease study. Lancet (lond Engl). 2020;395:200\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0140-6736(19)32989-7\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(19)32989-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEvans L, Rhodes A, Alhazzani W, Antonelli M, Coopersmith CM, French C, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. 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J Cell Biol. 2004;164:441\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1083/jcb.200304135\u003c/span\u003e\u003cspan address=\"10.1083/jcb.200304135\" 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":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"sepsis, HMGB1, SOFA score, prognosis ","lastPublishedDoi":"10.21203/rs.3.rs-7599267/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7599267/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Prognostic assessment in sepsis relies on evaluating the dynamic progression of organ dysfunction and the inflammatory response. This study compared the predictive value of serially measured serum HMGB1, a key late mediator of sepsis, with the △SOFA score for 28-day mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: This prospective cohort study enrolled 250 sepsis patients admitted to the emergency department of a tertiary hospital from January 2022 to August 2024. Serum HMGB1 levels and SOFA scores were dynamically assessed on days 1, 4, and 7. Receiver operating characteristic (ROC) curve analysis and Kaplan-Meier survival analysis were employed to evaluate their prognostic performance. The primary endpoint was 28-day all-cause mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 232 patients (median age 71.5 years, 56% male) with a 28-day mortality rate of 13.8% (32/232) were included. Non-survivors had a higher prevalence of autoimmune diseases (43.8% vs. 11.5%) and lung infections (81.3% vs. 47.0%). Serum HMGB1 levels peaked on day 4 and were significantly higher in non-survivors and septic shock patients (P \u0026lt; 0.05). D1-HMGB1 levels showed significant positive correlations with SOFA scores and inflammatory cytokines (IL-6, IL-8, IL-10, IL-17a, TNF-α). The area under the ROC curve (AUC) for predicting 28-day mortality was 0.856 (95% CI: 0.751-0.921) for day 4 HMGB1 (D4-HMGB1) and 0.893 (95% CI: 0.845-0.941) for D7-△SOFA. The optimal cut-off value for D4-HMGB1 was 6.4 ng/mL. Kaplan-Meier analysis confirmed that patients with D4-HMGB1 ≥ 6.4 ng/mL had significantly higher mortality (log-rank, P = 0.001). This prognostic value remained consistent across key patient subgroups, including those with acute kidney injury, autoimmune diseases, or respiratory comorbidities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Dynamic monitoring of serum HMGB1 provides valuable prognostic information. Specifically, the day 4 HMGB1 level demonstrates excellent and earlier predictive performance for 28-day mortality, even comparable to the D7-△SOFA score, highlighting its potential as a prognostic biomarker in sepsis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: Not applicable.\u003c/p\u003e","manuscriptTitle":"Serial Assessment of Serum HMGB1 and △SOFA for Predicting 28-Day Mortality in Sepsis Patients: A Prospective Cohort Study in the Emergency Department","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 16:46:40","doi":"10.21203/rs.3.rs-7599267/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-07T05:18:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-22T01:47:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-12T09:11:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26420330201693296711175468326450248812","date":"2025-09-28T14:55:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"195965500687956091533847611968360958543","date":"2025-09-27T18:29:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-22T13:29:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-18T10:45:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-16T08:53:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-16T08:53:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Emergency Medicine","date":"2025-09-12T09:30:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"76d5cea3-eccf-4d6e-9b8c-447379764cd7","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:26:23+00:00","versionOfRecord":{"articleIdentity":"rs-7599267","link":"https://doi.org/10.1186/s12873-026-01547-2","journal":{"identity":"bmc-emergency-medicine","isVorOnly":false,"title":"BMC Emergency Medicine"},"publishedOn":"2026-03-23 16:11:13","publishedOnDateReadable":"March 23rd, 2026"},"versionCreatedAt":"2025-10-03 16:46:40","video":"","vorDoi":"10.1186/s12873-026-01547-2","vorDoiUrl":"https://doi.org/10.1186/s12873-026-01547-2","workflowStages":[]},"version":"v1","identity":"rs-7599267","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7599267","identity":"rs-7599267","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}