Association between Hemoglobin Turnover Rate and Mortality in Septic Shock Patients

Association between Hemoglobin Turnover Rate and Mortality in Septic Shock Patients | 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 Association between Hemoglobin Turnover Rate and Mortality in Septic Shock Patients Dongzhi Zheng, Huan Jiang, Zihao Zheng, Jingquan Liu, Run Zhang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8689595/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Septic shock is associated with high incidence and mortality in intensive care units (ICUs). This study aims to investigate whether Hemoglobin Turnover Rate (HTR) can serve as an early predictor of mortality in patients with septic shock. Methods Patients diagnosed with septic shock within 48 hours of ICU admission were enrolled, and sequential biochemical data from the first 7 days were collected. Restricted cubic splines (RCS) were applied to explore the association between single time-point measurements and outcomes. Group-based trajectory modeling (GBTM) was used to identify latent subgroups of patients with similar progression patterns. The primary outcome was all-cause mortality. Results This study integrated cross-sectional and longitudinal analyses to assess the prognostic significance of HTR in septic shock. Cross-sectionally, elevated HTR-both at admission and within 7 days thereafter-was consistently associated with higher 7-day mortality, with the most pronounced predictive effect observed for measurements taken within the 7 days preceding death (Q4 vs Q1 OR reaching 3.52). Longitudinal trajectory modeling further identified two distinct temporal patterns of HTR, among which a steadily rising trajectory was strongly predictive of fatal outcome. The robustness of these associations was confirmed through sensitivity analyses that excluded patients with severe liver disease, as well as via propensity score–based sensitivity analyses. Conclusion: HTR reflects integrated red blood cell metabolism and bilirubin clearance, capturing aspects of both oxygen transport and metabolic balance. It shows promise as an early prognostic marker in septic shock, though further validation is required before clinical adoption. Septic shock Hemodynamics Evaluation indicators Mortality Figures Figure 1 Figure 2 Figure 3 1. Introduction Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. It accounts for nearly 10% of intensive care unit (ICU) admissions and represents a major global public health challenge and economic burden [ 1 – 3 ] . Although its incidence and mortality have declined over the past decades, sepsis remains associated with a high fatality risk, contributing to approximately one in five deaths worldwide [ 4 – 6 ] . Given its severity and poor prognosis, sepsis necessitates early recognition and prompt intervention, with fluid resuscitation, vasopressors, and organ support (e.g., mechanical ventilation) serving as cornerstone management strategies [ 6 , 7 ] . However, early diagnosis is hindered by nonspecific symptoms, the absence of a single gold-standard biomarker, the complexity of the Sequential Organ Failure Assessment (SOFA) score, challenges in differentiating sepsis from non-infectious systemic inflammatory responses, and the low positivity rate of blood cultures. Although scoring systems such as SOFA [ 8 ] and the Acute Physiology and Chronic Health Evaluation (APACHE) [ 9 ] are widely used to evaluate severity and prognosis, their accuracy is affected by multiple factors—including age, comorbidities, immunosuppression, infection site, pathogen type, antimicrobial resistance, fluid balance, and nutritional status—and their calculation requires extensive testing, which increases costs [ 10 – 12 ] . Therefore, there is an urgent need for simple, effective, and inexpensive markers to assess severity and prognosis, thereby reducing the global disease burden. In this context, our study investigates the interplay between bilirubin and hemoglobin and introduces a novel indicator, the Hemoglobin turnover rate (HTR). We aim to explore the dynamics of HTR in patients with sepsis and septic shock and its association with mortality. We hypothesize that elevated HTR at ICU admission indicates greater disease severity, and that persistently high HTR is closely associated with increased risk of death. 2. Study Design and Methods 2.1. Model Development and Variable Construction In our established physiological model (Appendix), red blood cells (RBCs) constitute the central element. Erythropoiesis depends on sufficient availability of proteins, iron, folate, and vitamin B12—where proteins and iron provide the fundamental substrates for hemoglobin synthesis, while folate and vitamin B12 are crucial for RBC maturation [ 13 ] . Under the synergistic regulation of erythropoietin (EPO) and multiple growth factors (e.g., Stem Cell Factor, Interleukin-3) [ 14 ] , RBCs progressively differentiate and mature, eventually expelling their nuclei before entering the bloodstream to perform oxygen transport and other physiological roles. Normal RBCs have an average lifespan of approximately 120 days. Hemoglobin derived from senescent RBCs contributes 80–85% of total bilirubin, with 10–15% originating from immature erythroid cells and 1–5% from hepatic free heme and hemoproteins [ 15 ] . Following RBC turnover, hemoglobin is catabolized to indirect bilirubin, converted in the liver to direct bilirubin, and excreted via bile. Together, these fractions comprise total bilirubin [ 16 ] . The senescence and clearance of RBCs mark the complete loss of their oxygen-carrying capacity. To maintain homeostasis of the circulating RBC pool, continuous erythropoiesis and maturation are essential. This process relies on adequate provision of proteins, iron, folate, and vitamin B12, along with precise regulatory input from EPO and growth factors. Accordingly, we propose HTR, defined as the dynamic rate encompassing hemoglobin synthesis, degradation, and conversion to bilirubin within a specified period (see sExplicate 5; Fig. 1 ): $$\:\text{H}\text{T}\text{R}=\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{B}\text{i}\text{l}\text{i}\text{r}\text{u}\text{b}\text{i}\text{n}⁄\text{H}\text{e}\text{m}\text{o}\text{g}\text{l}\text{o}\text{b}\text{i}\text{n}$$ 2.2. Data Sources The MIMIC-IV database (version 3.1, https://physionet.org/content/mimiciv/3.1/ ) (Metavision system) contains de-identified health information from intensive care unit (ICU) admissions at Beth Israel Deaconess Medical Center (BIDMC) between 2008 and 2022. The YiduCloud Database (Hospital Information System, HIS) contains data from patients admitted to the Department of Critical Care Medicine at Zhejiang Provincial People’s Hospital between January 2018 and March 2021. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines and adhered to the principles of the Declaration of Helsinki [ 17 ] . All data were anonymized to protect patient confidentiality. Database access was obtained by a certified author, Huan Jiang (Record ID: 66212733), with data use permissions granted by Beth Israel Deaconess Medical Center–Women’s Health Care. The Ethics Committee of Zhejiang Provincial People’s Hospital waived the requirement for informed consent. 2.3. Eligibility Criteria Adult patients diagnosed with severe sepsis or septic shock were included. For patients with multiple admissions, only the first ICU admission was considered. Eligible patients were those with septic shock who stayed in the ICU for at least three days. Exclusion criteria were: (1) patients with fewer than three available data points at baseline or within the first 7 days of ICU stay, and (2) patients who underwent surgery during ICU hospitalization (sFigure 1). Sepsis was defined as life-threatening organ dysfunction caused by infection at ICU admission or within the first 48 hours after admission. Septic shock was defined as sepsis with persistent hypotension requiring vasopressors to maintain a mean arterial pressure ≥ 65 mmHg and a serum lactate level > 2 mmol/L despite adequate fluid resuscitation [ 1 ] . Baseline variables included demographics (age, gender, height, weight, BMI, population categories, and treatment center), comorbidities [Charlson Comorbidity Index (CCI) score, diabetes, severe liver disease, chronic pulmonary disease, cancer, renal disease, and congestive heart failure]. Clinical and laboratory variables were collected to calculate disease severity and SOFA scores, and all hemoglobin and bilirubin results were recorded until discharge (sTable 1). Comorbidities were defined using International Classification of Diseases, Tenth Revision (ICD-10) codes. Details of covariate definitions and the study timeline are provided in the Supplementary Methods (sFigure 2). The primary endpoint was in-hospital mortality, defined as survival status prior to hospital discharge within 7 days after ICU admission (sTable 2). 2.4. Statistical Analysis Restricted cubic splines (RCS) were used to examine the association between HTR at ICU admission and the last measurement within 7 days with 7-day mortality. Univariate and multivariate logistic regression analyses were employed to assess associations between HTR and outcomes, using a significance level of 0.05 for variable inclusion. The multivariate model was adjusted for age, gender, height, weight, BMI, SOFA score, CCI score, comorbidities (diabetes, severe liver disease, chronic pulmonary disease, cancer, renal disease, and congestive heart failure), population categories, and treatment center. Paired t-tests compared HTR values between the first and last measurements, with results reported as odds ratios (ORs) and 95% confidence intervals (CIs). Group-based trajectory modeling (GBTM) was applied to identify latent subgroups with distinct HTR trajectories over the first 7 days, stratified by treatment duration (3–5 vs. 5–7 days). As a finite mixture modeling technique, GBTM assumes the population consists of discrete subgroups [ 18 ] . Models were selected based on the Bayesian Information Criterion (BIC) to determine the number of trajectories and optimal polynomial order (linear, quadratic, or cubic) [ 18 , 19 ] . Adequate fit was defined as each group comprising ≥ 5% of participants with average posterior probabilities ≥ 0.70 [ 20 ] . Sensitivity analyses excluding patients with pre-existing liver disease yielded results consistent with the primary analysis. For further robustness, multilevel propensity score–matched analyses were performed using multivariable logistic regression to estimate propensity scores, adjusting for both individual- and hospital-level confounders (Supplementary Methods). Subgroup analyses were conducted to examine effect heterogeneity by age (< 60 vs. ≥60 years), gender, BMI (18.5–24.9, ≥ 24.9 kg/m²), population categories, treatment center, and major comorbidities (diabetes, severe liver disease, chronic pulmonary disease, cancer, renal disease, and congestive heart failure). A generalized additive mixed model was also applied to analyze HTR growth trajectories and rates of change, capturing heterogeneity at both individual and population levels. Continuous variables are presented as mean ± standard deviation (SD) or median with interquartile range, and categorical variables as frequencies (percentages). Group comparisons used one-way ANOVA, Wilcoxon rank-sum, or chi-square tests, as appropriate. Multiple comparisons were corrected using the Bonferroni method, and Cohen’s d was calculated to report standardized effect sizes. All analyses were performed using R version 4.1.0 (R Foundation for Statistical Computing, Vienna, Austria; https://www.r-project.org/ ). 3. Results 3.1. Patient characteristics After screening, a total of 8,183 patients were included in the analysis, among whom 948 (11.59%) died within 7 days of admission. Compared with survivors, non-survivors were older (median age 68 vs. 62 years), had higher SOFA scores (9.43 vs. 7.19), and presented with more comorbidities, including severe liver disease (20.25% vs. 15.85%), chronic pulmonary disease (22.26% vs. 14.75%), diabetes (34.70% vs. 26.92%), renal disease (27.11% vs. 18.48%), and cancer (27.11% vs. 19.63%), with all differences being statistically significant (P < 0.0001). In addition, both dataset source and surgical type were significantly associated with mortality, with the highest mortality observed in the MIMIC cohort (77.53%) and in non-surgical patients (79.75%). In contrast, no significant differences were found with respect to gender or height (Table 1 ). Table 1 Baseline characteristics and outcomes (survival vs. death) Overall N = 8,183 Survival N = 7,235 Death N = 948 P Dataset, n (%) < 0.0001 HIS 3370 (41.18) 3157 (43.64) 213 (22.47) MIMIC 4813 (58.82) 4078 (56.36) 735 (77.53) Population categories, n (%) < 0.0001 Cardiac Surgery 1690 (20.65) 1680 (23.22) 10 (1.05) Non-Cardiac Surgery 1810 (22.12) 1628 (22.50) 182 (19.20) No Surgery 4683 (57.23) 3927 (54.28) 756 (79.75) Baseline Characteristics Gender, Male (%) 5035 (61.53) 4475 (61.85) 560 (59.07) 0.1054 Age, years 63.13 [52.55, 73.00] 62.22 [52.00, 72.52] 68.23 [57.86, 77. 0] < 0.0001 Height, cm 168.00 [160.00, 175.00] 168.00 [160.00, 175.00] 168.09 [160.39, 175.00] 0.4934 Weight, kg 72.10 [60.00, 87.70] 72.00 [60.00, 87.00] 75.66 [60.05, 92.03] 0.0001 BMI, kg/m 2 25.57 [22.06, 29.95] 25.47 [22.04, 29.73] 26.53 [22.09, 31.64] 0.0002 Median Sofa score 7.45 (3.85) 7.19 (3.75) 9.425 (4.06) < 0.0001 Comorbidities† Median CCI score 4.50 (2.96) 4.31 (2.89) 5.95 (3.07) < 0.0001 Severe liver disease (%) 1339 (16.36) 1147 (15.85) 192 (20.25) 0.0007 Chronic pulmonary disease (%) 1278 (15.62) 1067 (14.75) 211 (22.26) < 0.0001 Congestive heart failure (%) 3047 (37.24) 2672 (36.93) 375 (39.56) 0.1244 Diabetes (%) 2277 (27.83) 1948 (26.92) 329 (34.70) < 0.0001 Renal disease (%) 1594 (19.48) 1337 (18.48) 257 (27.11) < 0.0001 Cancer (%) 1677 (20.49) 1420 (19.63) 257 (27.11) < 0.0001 Outcome First HTR 0.11 [0.06, 0.23] 0.107 [0.07, 0.22] 0.14 [0.06, 0.39] < 0.0001 First Total Bilirubin, mg/dL 1.10 [0.66, 2.22] 1.10 [0.66, 2.16] 1.30 [0.60, 3.42] 0.0001 First Hemoglobin, g/dL 10.10 [8.50, 11.80] 10.20 [8.60, 11.80] 9.60 [8.00, 11.50] < 0.0001 Within 7d last HTR 0.11 [0.06, 0.28] 0.10 [0.06, 0.24] 0.23 [0.08, 0.69] < 0.0001 Within 7d last Total Bilirubin, mg/dL 1.02 [0.60, 2.50] 0.99 [0.60, 2.13] 1.96 [0.80, 5.90] < 0.0001 Within 7d last Hemoglobin, g/dL 9.20 [8.10, 10.70] 9.30 [8.20, 10.80] 8.70 [7.70, 10.20] < 0.0001 BMI = Body Mass Index; CCI = Charlson Comorbidity Index; SOFA= Sequential Organ Failure Assessment; HTR = Hemoglobin Turnover Rate. Notes: Data are presented as median [interquartile range, IQR] or frequency (%). P < 0.05 indicates statistical significance (Kruskal-Wallis test for continuous variables; χ² test for categorical variables). † Defined using International Classification of Diseases, 10th revision codes 3.2. Main Results This study employed both cross-sectional and longitudinal trajectory analyses. The cross-sectional approach examined associations at specific time points, while the longitudinal analysis assessed dynamic changes in indicators over multiple time periods in relation to clinical outcomes. As shown in Fig. 2 , HTR was associated with 7-day mortality across time points, and Table 2 summarizes these relationships stratified by HTR quartiles in three statistical models at three distinct intervals: at admission, within 7 days after admission, and overall. In the unadjusted model (Model 1), each one-unit increase in HTR was consistently associated with elevated 7-day mortality, regardless of timing. At admission, patients in the highest HTR quartile (Q4) had significantly increased mortality risk compared to the lowest quartile (Q1) (OR = 1.45, 95% CI: 1.22–1.74, p < 0.0001), with a 23% higher risk per standard deviation increase (OR per SD = 1.23). Although effect sizes were attenuated in Models 2 and 3, the association for Q4 remained marginally significant (OR ≈ 1.23). The association strengthened within 7 days post-admission: in Model 1, Q4 was associated with a substantially elevated mortality risk (OR = 3.52, 95% CI: 3.00-4.13, p < 0.0001), corresponding to a 59% increase per SD (OR = 1.59). Models 2 and 3 also showed significant, though reduced, effects. Collectively, these findings indicate that elevated HTR is consistently associated with higher 7-day mortality, with the strongest predictive value observed when measured within the 7 days preceding death. Table 2 Effect of hemoglobin turnover rate (HTR) at Different Time Points on 7-Day Mortality Across Quartiles. Quartiles of Hemoglobin Turnover Rate P For Trend Per SD Increase Q1 Q2 Q3 Q4 At Admission ≥ 0.01&<0.06 ≥ 0.06&<0.11 ≥ 0.11&<0.23 ≥ 0.23&≤6.63 Case/Person 246/2047 158/2046 205/2045 339/2045 Model1 1(refrence) 0.61(0.50–0.76) 0.82(0.67–0.99) 1.45(1.22–1.74) < 0.0001 1.23(1.17–1.30) Model2 1(refrence) 0.86(0.69–1.07) 0.95(0.77–1.18) 1.23(0.99–1.52) 0.0669 1.16(1.08–1.23) Model3 1(refrence) 0.86(0.69–1.07) 0.96(0.78–1.18) 1.23(0.99–1.52) 0.0654 1.16(1.08–1.23) Last Within 7d ≥ 0.01&<0.06 ≥ 0.06&<0.11 ≥ 0.11&<0.28 ≥ 0.28&≤10.91 Case/Person 262/2049 200/2043 362/2045 696/2046 Model1 1(refrence) 0.74(0.61–0.90) 1.47(1.24–1.74) 3.52(3.00-4.13) < 0.0001 1.59(1.49–1.65) Model2 1(refrence) 0.88(0.72–1.09) 1.29(1.07–1.55) 2.76(2.28–3.34) < 0.0001 1.49(1.40–1.59) Model3 1(refrence) 0.89(0.72–1.09) 1.30(1.08–1.57) 2.60(2.14–3.17) < 0.0001 1.95(1.75–2.18) Overall ≥ 0.01&<0.06 ≥ 0.06&<0.11 ≥ 0.11&<0.28 ≥ 0.28&≤10.91 Case/Person 262/2049 200/2043 362/2045 696/2046 Model1 1(refrence) 0.74(0.61–0.90) 1.47(1.24–1.74) 3.52(3.00-4.13) < 0.0001 1.57(1.49–1.65) Model2 1(refrence) 0.88(0.72–1.09) 1.29(1.07–1.55) 2.76(2.28–3.34) < 0.0001 1.49(1.40–1.59) Model3 1(refrence) 0.89(0.72–1.09) 1.30(1.08–1.57) 2.60(2.14–3.17) < 0.0001 1.95(1.75–2.18) Model 1: unadjusted; Model 2: adjusted for dataset, age, severe liver disease, chronic pulmonary disease, diabetes, renal disease, cancer, Charlson comorbidity index, SOFA score, and population categories; Model 3: additionally adjusted for gender, height, weight, BMI, and congestive heart failure on top of Model 2. Notes: All results are presented as odds ratios (ORs) with 95% confidence intervals (95% CIs).Q1–Q4: quartiles of hemoglobin turnover rate (HTR), with Q1 representing the lowest quartile and Q4 the highest; P for Trend: p-value for trend across quartiles; Per SD Increase: risk estimate per 1 standard deviation increase in HTR. Based on cross-sectional analysis, paired t-tests demonstrated that HTR values were consistently higher in non-survivors compared with survivors. The difference was modest yet significant at admission (0.44 vs. 0.28, P = 0.01) and widened considerably by the last measurement within 7 days (0.61 vs. 0.32, P < 0.0001), reflecting progressive divergence between the groups (sTable 3). To account for variability in treatment duration, longitudinal trajectory analysis focused on two critical windows (days 3–5 and days 5–7). Although models with more classes yielded lower BIC values, they produced small, unstable subgroups with poor interpretability. In contrast, two-class models offered favorable model fit (BIC = -1158.45 and − 1425.47), high entropy (0.90 and 0.95), and well-balanced classification probabilities (0.73/0.27 for days 3–5; 0.81/0.19 for days 5–7), supporting the two-class solution as the most robust and clinically meaningful (Fig. 3 ; Table 3 ). Table 3 Model Fit Statistics for HTR Trajectories Group Class K Log(L) AIC BIC Entropy Classification Probabilities Days 3–5 1 8 -1,480.85 2,977.71 3,019.60 1.00 1.00 2 14 629.88 -1,231.76 -1,158.45 0.90 0.73/ 0.27 3 20 964.15 -1,888.29 -1,783.56 0.92 0.72/ 0.03/ 0.25 4 26 1,277.46 -2,502.92 -2,366.78 0.91 0.68/ 0.25/ 0.04/ 0.03 5 32 1,551.12 -3,038.25 -2,870.69 0.92 0.66/ 0.03/ 0.05/ 0.25 Days 5–7 1 8 -1,283.36 2,582.72 2,622.03 1.00 1.00 2 14 761.13 -1,494.27 -1,425.47 0.95 0.81/ 0.19 3 20 1,382.93 -2,725.86 -2,627.58 0.96 0.78/ 0.01/ 0.21 4 26 1,693.14 -3,334.28 -3,206.53 0.96 0.76/ 0.03/ 0.21 5 32 1,985.81 -3,907.62 -3,750.38 0.96 0.02/ 0.75/ 0.20/ 0.03 Notes: K is the number of parameters estimated freely in the model; Log(L) is Maximum log-likelihood value; AIC/BIC: Akaike/Bayesian Information Criteria (lower values indicate better fit); Entropy: Classification accuracy metric (0–1, > 0.8 indicates excellent separation). Significant inter-class differences were identified (Table 4 ). During days 3–5, non-survivors in Class 1 exhibited higher HTR at time 3 than survivors (mean 0.2 vs. 0.1; Cohen’s d = -0.55, indicating a medium effect size). In Class 2, non-survivors demonstrated elevated HTR at both time 2 and time 3, with Cohen’s d values ranging from − 0.39 to -0.56, consistent with small-to-medium effect sizes. During days 5–7, non-survivors in Class 1 again showed significantly higher HTR at time 3 (Cohen’s d = -0.69, medium effect). Although mean HTR was lower overall in Class 2, non-survivors within this class still displayed a medium effect size at time 3 (Cohen’s d = -0.79). Taken together, these results indicate that HTR tends to be elevated in non-survivors, particularly at later time points in Class 2. The persistent rise in HTR was positively associated with short-term mortality, highlighting its potential utility as an early risk assessment marker in septic shock. Table 4 Clinical Characteristics and Outcomes by HTR trajectories. Time Survived Died W statistic Raw p -value Adjusted p -value Cohen's d N Mean ± SD N Mean ± SD Day 3–5 Class1(n = 1013) 1 631 0.1 ± 0.1 382 0.1 ± 0.1 134,500.50 0.00 0.01 0.27 2 631 0.1 ± 0.1 382 0.1 ± 0.1 114,418.50 0.18 1.00 -0.07 3 631 0.1 ± 0.1 382 0.2 ± 0.1 91,240.00 0.00 0.00 -0.55 Class2 (n = 376) 1 181 1.0 ± 1.0 195 1.1 ± 1.1 18,674.00 0.33 1.00 -0.05 2 181 0.9 ± 0.8 195 1.3 ± 1.1 14,121.50 0.00 0.00 -0.39 3 181 0.9 ± 0.8 195 1.5 ± 1.1 10,565.00 0.00 0.00 -0.56 Day 5–7 Class1(n = 818) 1 626 0.1 ± 0.1 192 0.1 ± 0.1 63,798.00 0.20 1.00 0.04 2 626 0.1 ± 0.1 192 0.1 ± 0.1 58,712.50 0.63 1.00 -0.20 3 626 0.1 ± 0.1 192 0.1 ± 0.1 44,013.50 0.00 0.00 -0.79 Class2(n = 188) 1 82 0.9 ± 0.9 106 1.1 ± 1.3 4,705.50 0.33 1.00 -0.11 2 82 0.8 ± 0.6 106 1.1 ± 1.0 3,905.50 0.23 1.00 -0.34 3 82 0.8 ± 0.7 106 1.5 ± 1.3 2,447.50 0.00 0.00 -0.69 Note: Cohen’s d indicates effect size: 0.0–0.2 = Negligible/Very Small, 0.2–0.5 = Small, 0.5–0.8 = Medium, > 0.8 = Large. All p -values are Bonferroni-adjusted for 6 multiple comparisons. Effect sizes interpreted using Cohen’s d (Negligible/ Small/ Medium/ Large). 3.3. Sensitivity Analyses In sensitivity analyses, we excluded 1,339 patients with severe liver disease to minimize potential confounding from impaired hepatic function. The associations of HTR with both cross-sectional outcomes and longitudinal trajectories remained consistent with the primary analysis (sTable 4; sFigure 3). Similarly, results were robust when using multilevel propensity score–matched analyses (sTable 5;sFigure 4). 3.4. Additional Analyses HTR was consistently and strongly associated with an increased risk of short-term adverse events across all patient subgroups, with the highest risk observed in the fourth quartile (Q4) (P for trend < 0.0001; OR per SD 1.4–1.6). This association was most pronounced among patients with congestive heart failure or cancer. Trajectory analyses revealed minimal differences at baseline but significant divergence over time, as non-survivors consistently exhibited elevated HTR. The effects were particularly evident in subgroups with congestive heart failure, chronic pulmonary disease, diabetes, higher BMI, and advanced age. Overall, dynamic HTR monitoring proved superior to single admission measurements, highlighting its value as a robust prognostic marker (sTable 6–10; sFigure 5–20). Given the observed dose-response relationship between HTR and outcomes, we further examined its association with survival time among non-survivors. Results indicated that survival time was significantly shorter once HTR reached a value of 3, except in patients with severe liver disease (sFigure 21). 4. Discussion Sepsis is a rapidly progressive disorder and a major contributor to global mortality. Prompt and accurate evaluation of disease severity is critical for improving patient outcomes and reducing fatality rates [ 6 , 21 ] . In this study, we proposed and validated that HTR was significantly elevated in non-survivors compared to survivors. trajectory modeling further revealed that dynamic increases in HTR were strongly correlated with adverse outcomes in sepsis. Notably, a sharp increase in 7-day mortality risk was observed once HTR exceeded 0.1, and survival time was substantially shortened when HTR approached 3. RBCs play a dual role: they must meet the functional demands of the macrocirculation while operating within the constraints of their own metabolic lifecycle. This lifecycle culminates in clearance and degradation into bilirubin. The production and release of RBCs, dependent on metabolic substrates and hematopoietic factors, represent a significant biological investment. In return, a stable functional output is expected. Short-term fluctuations may occur, but premature loss of RBC function or accelerated clearance can disrupt physiological homeostasis. At the macrocirculatory level, the principal role of RBCs is gas exchange. Clinical and experimental data indicate that tissue oxygen consumption can remain stable even when hemoglobin levels fall as low as 5g/dL [ 22 ] , implying considerable redundancy in systemic oxygen delivery. Furthermore, emerging evidence contradicts the traditional view that sepsis invariably causes tissue metabolism to become supply-dependent [ 23 , 24 ] . Studies demonstrate that in septic patients, systemic oxygen consumption does not improve significantly with increased oxygen delivery—even during hyperlactatemia. In fact, mean central venous oxygen saturation (ScvO₂) in sepsis rarely drops below 70% [ 25 ] . These observations suggest that metabolic dysregulation in sepsis stems mainly from impaired oxygen utilization rather than inadequate supply [ 26 , 27 ] . In the absence of conclusive evidence and given that microcirculatory monitoring is not yet routine clinically, the concept of hemodynamic coherence remains contentious [ 28 ] . Therefore, this study shifts focus from conventional metrics like hemoglobin concentration or oxygen delivery to emphasize the clinical relevance of dynamic hemoglobin turnover within its metabolic cycle. Bilirubin, an end product of hemoglobin catabolism, often rises in settings of hepatic dysfunction or accelerated RBC destruction. Previous research supports its role as a prognostic marker in sepsis [ 29 – 31 ] . Pierrakos et al. [ 29 ] identified hyperbilirubinemia as an independent risk factor for morbidity and mortality in ICU patients, with mortality increasing linearly across bilirubin levels of 1.1-6 mg/dL. Similarly, Patel et al. [ 30 ] reported that elevated bilirubin within 72 hours of admission correlated with higher mortality in severe sepsis and septic shock. Building on this evidence, we introduced HTR as a novel metric to assess the balance between the macrocirculatory functions of RBCs and their metabolic turnover. Although transfusions during septic shock management may confound short-term HTR measurements [ 1 , 32 – 34 ] , over extended observation, HTR remained significantly elevated in non-survivors. Bilirubin may also directly affect clinical outcomes through several mechanisms: its antioxidant properties can impair neutrophil bactericidal function [ 35 , 36 ] , and it has been associated with nephrotoxic [ 37 ] and neurotoxic [ 38 ] effects. Elevated bilirubin can result from either increased RBC destruction or reduced hepatic clearance [ 39 , 40 ] . Given the clinical importance of hepatic dysfunction in sepsis-related organ failure [ 41 ] , we conducted a sensitivity analysis that excluded patients with pre-existing severe liver disease. The results remained consistent with the primary analysis, and similar trends were observed across other subgroups, including those with severe liver disease. These findings indicate that elevated HTR may serve as an early indicator of short-term mortality in septic shock across diverse patient populations. However, previous studies on liver injury have reported inconsistent associations between hyperbilirubinemia and mortality [ 29 , 42 ] . Our subgroup analysis further revealed that patients with severe liver disease exhibited a higher HTR inflection point and an inconsistent relationship with survival, suggesting that hepatobiliary diseases may represent important confounders and should be excluded when applying HTR in clinical practice. Nevertheless, a major concern of this study lies in the intrinsic issue of “rate” implied by this concept. Laboratory indicators are essentially static measurements obtained from inherently dynamic processes. Under complex pathological conditions, such static indicators may be distorted, thereby obscuring the actual progression of disease. Beyond the intrinsic mass–quantity relationship between hemoglobin and bilirubin production, this metric also inherently encompasses factors such as macrocirculatory blood flow, microcirculatory metabolic rate, and mean corpuscular hemoglobin (Appendix). Given the limitations of current monitoring techniques, our study could only perform a preliminary analysis of this indicator. Further investigation will require more refined measurement approaches and clinical trials to elucidate its pathophysiological implications and clinical utility. 5. Conclusion HTR reflects both red blood cell metabolism and bilirubin clearance, thereby capturing aspects of systemic oxygen transport and metabolic balance. Our results suggest that HTR has potential value as an early prognostic marker in septic shock. However, given the limitations of current monitoring and the exploratory nature of this study, its clinical utility requires further validation in larger, prospective cohorts before being considered for routine application. Abbreviations ICU Intensive Care Unit HTR Hemoglobin Turnover Rate RCS Restricted Cubic Splines BMI Body Mass Index GBTM Group-Based Trajectory Modeling APACHE II Acute Physiology and Chronic Health Evaluation II SOFA Sequential Organ Failure Assessment MAP Mean Arterial Pressure RBC Red Blood Cell Count IQR Interquartile Range GAMM Generalized Additive Mixed Models EPO Erythropoietin OR Odds Ratio CI Confidence Interval BIC Bayesian Information Criterion SD Standard Deviation. Declarations Acknowledgements We thank the authors of the primary studies for their timely and helpful responses to our information requests. Authors’ contributions Dongzhi Zheng: Conceptualization; Data curation; Formal analysis; Investigation; Writing—original draft. Huan Jiang and Zihao Zheng: Data curation; Formal analysis; Writing—review & editing. Run Zhang, and Jun Hong: Conceptualization; Methodology; Supervision; Validation; Writing—review & editing. Xianghong Yang and Bai Xu: Conceptualization; Investigation; Methodology; Resources; Supervision; Validation; Writing—review & editing. Jingquan Liu: Conceptualization; Methodology; Project administration; Resources. Siyu Tang: Conceptualization; Formal analysis; Investigation; Methodology; Project administration; Software; Supervision; Validation; Writing—review & editing. Funding This work was supported by the Zhejiang Provincial Key Research and Development Plan (No. 2019C03024), the Zhejiang Medical and Health Science and Technology Plan (No. WKJ-ZJ-1811), and the Health Department of Zhejiang Province (Nos. 2022KY497, 2023KY038, 2026791893). Availability of data and materials The datasets supporting the conclusions of this article are included within the articl. Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki. The use of the database was authorized by certified investigator Huan Jiang (Record ID: 66212733) and granted by Beth Israel Deaconess Medical Center–Women’s Health Care. The requirement for informed consent was waived by the Ethics Committee of Zhejiang Provincial People’s Hospital (Approval No. 202509152037000250901) due to the retrospective nature of the study. Consent for publication Not Applicable. Competing interests The authors declare that they have no competing interests. Clinical trial number Not Applicable. Authors’ information Dongzhi Zheng, Email: [email protected] . Huan Jiang, Email: [email protected] . Zihao Zheng, Email: [email protected] . Jingquan Liu, Email: [email protected] . Run Zhang, Email: [email protected] . Jun Hong, Email: [email protected] . Bai Xu, Email: [email protected] . Xianghong Yang, Email: [email protected] . Siyu Tang, Email: [email protected] . References Evans L, Rhodes A, Alhazzani W, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021[J]. Intensive Care Med. 2021;47(11):1181–247. Angus DC, Van Der Poll T. Severe sepsis and septic shock[J]. N Engl J Med. 2013;369(9):840–51. Singer M, Deutschman CS, Seymour CW, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)[J]. JAMA. 2016;315(8):801–10. Liu V, Escobar GJ, Greene JD, et al. Hospital deaths in patients with sepsis from 2 independent cohorts[J]. JAMA. 2014;312(1):90–2. Fleischmann C, Scherag A, Adhikari NK, et al. Assessment of Global Incidence and Mortality of Hospital-treated Sepsis. Current Estimates and Limitations[J]. Am J Respir Crit Care Med. 2016;193(3):259–72. Rudd KE, Johnson SC, Agesa KM, et al. Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study[J]. Lancet. 2020;395(10219):200–11. Srzić I, Nesek Adam V, Tunjić Pejak D. SEPSIS DEFINITION: WHAT'S NEW IN THE TREATMENT GUIDELINES[J]. Acta Clin Croat. 2022;61(Suppl 1):67–72. Vincent JL, Moreno R, Takala J, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine[J]. Intensive Care Med. 1996;22(7):707–10. Knaus WA, Wagner DP, Draper EA, et al. The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults[J]. Chest. 1991;100(6):1619–36. Reddy V, Reddy H, Gemnani R, et al. Navigating the Complexity of Scoring Systems in Sepsis Management: A Comprehensive Review[J]. Cureus. 2024;16(2):e54030. Oduncu AF, Kıyan GS, Yalçınlı S. Comparison of qSOFA, SIRS, and NEWS scoring systems for diagnosis, mortality, and morbidity of sepsis in emergency department[J]. Am J Emerg Med. 2021;48:54–9. Sheikh F, Douglas W, Catenacci V, et al. Social Determinants of Health Associated With the Development of Sepsis in Adults: A Scoping Review[J]. Crit Care Explor. 2022;4(7):e0731. Auerbach M, Deloughery TG, Tirnauer JS. Iron Deficiency in Adults: A Review[J]. JAMA. 2025;333(20):1813–23. Allison SJ. Identification of erythropoietin-producing cells[J]. Nat Rev Nephrol. 2023;19(7):424. Clark MR. Senescence of red blood cells: progress and problems[J]. Physiol Rev. 1988;68(2):503–54. Thiagarajan P, Parker CJ, Prchal JT. How Do Red Blood Cells Die?[J]. Front Physiol. 2021;12:655393. Von Elm E, Altman DG, Egger M, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies[J]. BMJ. 2007;335(7624):806–8. Nagin DS, Jones BL, Elmer J. Recent Advances in Group-Based Trajectory Modeling for Clinical Research[J]. Annu Rev Clin Psychol. 2024;20(1):285–305. Twisk J, Hoekstra T. Classifying developmental trajectories over time should be done with great caution: a comparison between methods[J]. J Clin Epidemiol. 2012;65(10):1078–87. Nagin DS, Tremblay RE. Analyzing developmental trajectories of distinct but related behaviors: a group-based method[J]. Psychol Methods. 2001;6(1):18–34. Coopersmith CM, De Backer D, Deutschman CS, et al. Surviving Sepsis Campaign: Research Priorities for Sepsis and Septic Shock[J]. Crit Care Med. 2018;46(8):1334–56. Weiskopf RB, Viele MK, Feiner J, et al. Human cardiovascular and metabolic response to acute, severe isovolemic anemia[J]. JAMA. 1998;279(3):217–21. Hanique G, Dugernier T, Laterre PF, et al. Significance of pathologic oxygen supply dependency in critically ill patients: comparison between measured and calculated methods[J]. Intensive Care Med. 1994;20(1):12–8. Mira JP, Fabre JE, Baigorri F, et al. Lack of oxygen supply dependency in patients with severe sepsis. A study of oxygen delivery increased by military antishock trouser and dobutamine[J]. Chest. 1994;106(5):1524–31. Marik PE, Varon J. Early goal-directed therapy: on terminal life support?[J]. Am J Emerg Med. 2010;28(2):243–5. Vincent JL, De Backer D. Circulatory shock[J]. N Engl J Med. 2013;369(18):1726–34. Lelubre C, Vincent JL. Mechanisms and treatment of organ failure in sepsis[J]. Nat Rev Nephrol. 2018;14(7):417–27. Gruartmoner G, Mesquida J. Hemodynamic coherence: a metabolic perspective[J]. Crit Care. 2025;29(1):293. Pierrakos C, Velissaris D, Felleiter P, et al. Increased mortality in critically ill patients with mild or moderate hyperbilirubinemia[J]. J Crit Care. 2017;40:31–5. Patel JJ, Taneja A, Niccum D, et al. The association of serum bilirubin levels on the outcomes of severe sepsis[J]. J Intensive Care Med. 2015;30(1):23–9. Peng M, Deng F, Qi D, et al. The Hyperbilirubinemia and Potential Predictors Influence on Long-Term Outcomes in Sepsis: A Population-Based Propensity Score-Matched Study[J]. Front Med (Lausanne). 2021;8:713917. Cable CA, Razavi SA, Roback JD, et al. RBC Transfusion Strategies in the ICU: A Concise Review[J]. Crit Care Med. 2019;47(11):1637–44. Dupuis C, Sonneville R, Adrie C, et al. Impact of transfusion on patients with sepsis admitted in intensive care unit: a systematic review and meta-analysis[J]. Ann Intensive Care. 2017;7(1):5. Coz Yataco AO, Soghier I, Hébert PC, et al. Red Blood Cell Transfusion in Critically Ill Adults: An American College of Chest Physicians Clinical Practice Guideline[J]. Chest. 2025;167(2):477–89. Arai T, Yoshikai Y, Kamiya J, et al. Bilirubin impairs bactericidal activity of neutrophils through an antioxidant mechanism in vitro[J]. J Surg Res. 2001;96(1):107–13. Maruhashi T, Soga J, Fujimura N, et al. Hyperbilirubinemia, augmentation of endothelial function, and decrease in oxidative stress in Gilbert syndrome[J]. Circulation. 2012;126(5):598–603. Uslu A, Taşli FA, Nart A, et al. Human kidney histopathology in acute obstructive jaundice: a prospective study[J]. Eur J Gastroenterol Hepatol. 2010;22(12):1458–65. Brites D, Fernandes A. Bilirubin-induced neural impairment: a special focus on myelination, age-related windows of susceptibility and associated co-morbidities[J]. Semin Fetal Neonatal Med. 2015;20(1):14–9. Kluge M, Tacke F. Liver impairment in critical illness and sepsis: the dawn of new biomarkers?[J]. Ann Transl Med. 2019;7(Suppl 8):S258. Jensen JS, Peters L, Itenov TS, et al. Biomarker-assisted identification of sepsis-related acute liver impairment: a frequent and deadly condition in critically ill patients[J]. Clin Chem Lab Med. 2019;57(9):1422–31. Han HS, Park CM, Lee DS, et al. Evaluating mortality and recovery of extreme hyperbilirubinemia in critically ill patients by phasing the peak bilirubin level: A retrospective cohort study[J]. PLoS ONE. 2021;16(8):e0255230. Saloojee A, Skinner DL, Loots E, et al. Hepatic dysfunction: A common occurrence in severely injured patients[J]. Injury. 2017;48(1):127–32. Additional Declarations No competing interests reported. Supplementary Files Supplementpaper.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 17 Feb, 2026 Editor assigned by journal 16 Feb, 2026 Editor invited by journal 29 Jan, 2026 Submission checks completed at journal 28 Jan, 2026 First submitted to journal 28 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8689595","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592917034,"identity":"6f739c17-dccc-4648-8354-533a1d5fdc26","order_by":0,"name":"Dongzhi Zheng","email":"","orcid":"","institution":"Zhejiang Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dongzhi","middleName":"","lastName":"Zheng","suffix":""},{"id":592917036,"identity":"05daef9e-bdcd-4422-9ec0-797e6357221b","order_by":1,"name":"Huan Jiang","email":"","orcid":"","institution":"Tongde Hospital of Zhejiang Province","correspondingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"Jiang","suffix":""},{"id":592917039,"identity":"f273852e-6109-4970-b7f6-aaad753490bb","order_by":2,"name":"Zihao Zheng","email":"","orcid":"","institution":"Zhejiang Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zihao","middleName":"","lastName":"Zheng","suffix":""},{"id":592917044,"identity":"8008abd7-7fdb-4705-b66b-96908e1e4200","order_by":3,"name":"Jingquan Liu","email":"","orcid":"","institution":"Zhejiang Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jingquan","middleName":"","lastName":"Liu","suffix":""},{"id":592917045,"identity":"1f405a71-ae46-47d1-941c-3f081ddd196e","order_by":4,"name":"Run Zhang","email":"","orcid":"","institution":"Zhejiang Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Run","middleName":"","lastName":"Zhang","suffix":""},{"id":592917047,"identity":"33ad2a3e-b6b7-46e5-8c83-fa7a71378638","order_by":5,"name":"Jun Hong","email":"","orcid":"","institution":"Zhejiang Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Hong","suffix":""},{"id":592917049,"identity":"18aae3dc-41b5-4c25-a063-dd4ae1f08c81","order_by":6,"name":"Bai Xu","email":"","orcid":"","institution":"Zhejiang Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bai","middleName":"","lastName":"Xu","suffix":""},{"id":592917050,"identity":"fea1b68b-da1b-45ee-a5a9-069ab30305a9","order_by":7,"name":"Xianghong Yang","email":"","orcid":"","institution":"Zhejiang Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xianghong","middleName":"","lastName":"Yang","suffix":""},{"id":592917051,"identity":"e1228865-27f8-41db-a2cf-37be029800ee","order_by":8,"name":"Siyu Tang","email":"data:image/png;base64,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","orcid":"","institution":"Zhejiang Provincial People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Siyu","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2026-01-25 01:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8689595/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8689595/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103167120,"identity":"28d87879-372d-4652-a01d-a6c54eae3a85","added_by":"auto","created_at":"2026-02-22 12:44:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1207532,"visible":true,"origin":"","legend":"\u003cp\u003eDual Roles of Red Blood Cells: Circulatory Function and Hemoglobin Turnover. Red blood cells ensure systemic oxygen transport while being subject to continuous metabolic turnover, a dual burden that may become conflicting in pathological states.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8689595/v1/2c029d4e79c110a5fe5be47d.png"},{"id":103167118,"identity":"4cae0ed0-a189-4709-8e23-4f77e7d0db02","added_by":"auto","created_at":"2026-02-22 12:44:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":857154,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of hemoglobin turnover rate (HTR) Dynamics on 7-Day Mortality. Panels A–C: Restricted cubic spline (RCS) analyses for HTR at admission (A), last measurement within 7 days (B), and overall (C). Upper subpanels show HTR density distributions; lower subpanels show incidence of 7-day mortality. Panel D: Paired t-tests comparing HTR between survivors and non-survivors. Significant differences are indicated (*** P \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8689595/v1/ae9fa699ee34cd8542c008fc.png"},{"id":103505283,"identity":"529325f9-35d3-4c43-82c5-51d8a0b4fd8b","added_by":"auto","created_at":"2026-02-26 13:29:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":532035,"visible":true,"origin":"","legend":"\u003cp\u003eTrajectories of Hemoglobin Turnover Rate (HTR) by Treatment Duration and Outcome. Panels A-B: HTR trajectories for 3-5 days. Panel A shows different trajectory subgroups; Panel B shows trajectory changes within each subgroup by outcome. Panels C-D: HTR trajectories for 5-7 days. Cohen’s d indicates effect size: 0.0-0.2 = negligible/very small, 0.2-0.5 = small, 0.5-0.8 = medium, \u0026gt;0.8 = large. All p-values were Bonferroni-adjusted for six multiple comparisons.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8689595/v1/4a0c47ac712c19baa6205ca8.png"},{"id":103509512,"identity":"f61c950e-d209-4174-8e15-491272f29d11","added_by":"auto","created_at":"2026-02-26 13:59:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3418565,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8689595/v1/a8db8603-5e92-43e4-81ce-a2764ff16229.pdf"},{"id":103167121,"identity":"bb7044d6-5e40-4a20-99f7-30b97162e483","added_by":"auto","created_at":"2026-02-22 12:44:02","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":8591729,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementpaper.docx","url":"https://assets-eu.researchsquare.com/files/rs-8689595/v1/a1d2c2b43e7a27a76151f0aa.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Hemoglobin Turnover Rate and Mortality in Septic Shock Patients","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. It accounts for nearly 10% of intensive care unit (ICU) admissions and represents a major global public health challenge and economic burden\u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Although its incidence and mortality have declined over the past decades, sepsis remains associated with a high fatality risk, contributing to approximately one in five deaths worldwide\u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven its severity and poor prognosis, sepsis necessitates early recognition and prompt intervention, with fluid resuscitation, vasopressors, and organ support (e.g., mechanical ventilation) serving as cornerstone management strategies\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. However, early diagnosis is hindered by nonspecific symptoms, the absence of a single gold-standard biomarker, the complexity of the Sequential Organ Failure Assessment (SOFA) score, challenges in differentiating sepsis from non-infectious systemic inflammatory responses, and the low positivity rate of blood cultures. Although scoring systems such as SOFA\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e and the Acute Physiology and Chronic Health Evaluation (APACHE)\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e are widely used to evaluate severity and prognosis, their accuracy is affected by multiple factors\u0026mdash;including age, comorbidities, immunosuppression, infection site, pathogen type, antimicrobial resistance, fluid balance, and nutritional status\u0026mdash;and their calculation requires extensive testing, which increases costs\u003csup\u003e[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Therefore, there is an urgent need for simple, effective, and inexpensive markers to assess severity and prognosis, thereby reducing the global disease burden.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn this context, our study investigates the interplay between bilirubin and hemoglobin and introduces a novel indicator, the Hemoglobin turnover rate (HTR). We aim to explore the dynamics of HTR in patients with sepsis and septic shock and its association with mortality. We hypothesize that elevated HTR at ICU admission indicates greater disease severity, and that persistently high HTR is closely associated with increased risk of death.\u003c/p\u003e"},{"header":"2. Study Design and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Model Development and Variable Construction\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn our established physiological model (Appendix), red blood cells (RBCs) constitute the central element. Erythropoiesis depends on sufficient availability of proteins, iron, folate, and vitamin B12\u0026mdash;where proteins and iron provide the fundamental substrates for hemoglobin synthesis, while folate and vitamin B12 are crucial for RBC maturation \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Under the synergistic regulation of erythropoietin (EPO) and multiple growth factors (e.g., Stem Cell Factor, Interleukin-3) \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, RBCs progressively differentiate and mature, eventually expelling their nuclei before entering the bloodstream to perform oxygen transport and other physiological roles.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eNormal RBCs have an average lifespan of approximately 120 days. Hemoglobin derived from senescent RBCs contributes 80\u0026ndash;85% of total bilirubin, with 10\u0026ndash;15% originating from immature erythroid cells and 1\u0026ndash;5% from hepatic free heme and hemoproteins\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Following RBC turnover, hemoglobin is catabolized to indirect bilirubin, converted in the liver to direct bilirubin, and excreted via bile. Together, these fractions comprise total bilirubin \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe senescence and clearance of RBCs mark the complete loss of their oxygen-carrying capacity. To maintain homeostasis of the circulating RBC pool, continuous erythropoiesis and maturation are essential. This process relies on adequate provision of proteins, iron, folate, and vitamin B12, along with precise regulatory input from EPO and growth factors. Accordingly, we propose HTR, defined as the dynamic rate encompassing hemoglobin synthesis, degradation, and conversion to bilirubin within a specified period (see sExplicate 5; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{H}\\text{T}\\text{R}=\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{B}\\text{i}\\text{l}\\text{i}\\text{r}\\text{u}\\text{b}\\text{i}\\text{n}\u0026frasl;\\text{H}\\text{e}\\text{m}\\text{o}\\text{g}\\text{l}\\text{o}\\text{b}\\text{i}\\text{n}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data Sources\u003c/h2\u003e \u003cp\u003eThe MIMIC-IV database (version 3.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://physionet.org/content/mimiciv/3.1/\u003c/span\u003e\u003cspan address=\"https://physionet.org/content/mimiciv/3.1/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Metavision system) contains de-identified health information from intensive care unit (ICU) admissions at Beth Israel Deaconess Medical Center (BIDMC) between 2008 and 2022. The YiduCloud Database (Hospital Information System, HIS) contains data from patients admitted to the Department of Critical Care Medicine at Zhejiang Provincial People\u0026rsquo;s Hospital between January 2018 and March 2021.\u003c/p\u003e \u003cp\u003eThis study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines and adhered to the principles of the Declaration of Helsinki\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. All data were anonymized to protect patient confidentiality. Database access was obtained by a certified author, Huan Jiang (Record ID: 66212733), with data use permissions granted by Beth Israel Deaconess Medical Center\u0026ndash;Women\u0026rsquo;s Health Care. The Ethics Committee of Zhejiang Provincial People\u0026rsquo;s Hospital waived the requirement for informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Eligibility Criteria\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAdult patients diagnosed with severe sepsis or septic shock were included. For patients with multiple admissions, only the first ICU admission was considered. Eligible patients were those with septic shock who stayed in the ICU for at least three days. Exclusion criteria were: (1) patients with fewer than three available data points at baseline or within the first 7 days of ICU stay, and (2) patients who underwent surgery during ICU hospitalization (sFigure 1). Sepsis was defined as life-threatening organ dysfunction caused by infection at ICU admission or within the first 48 hours after admission. Septic shock was defined as sepsis with persistent hypotension requiring vasopressors to maintain a mean arterial pressure\u0026thinsp;\u0026ge;\u0026thinsp;65 mmHg and a serum lactate level\u0026thinsp;\u0026gt;\u0026thinsp;2 mmol/L despite adequate fluid resuscitation\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBaseline variables included demographics (age, gender, height, weight, BMI, population categories, and treatment center), comorbidities [Charlson Comorbidity Index (CCI) score, diabetes, severe liver disease, chronic pulmonary disease, cancer, renal disease, and congestive heart failure]. Clinical and laboratory variables were collected to calculate disease severity and SOFA scores, and all hemoglobin and bilirubin results were recorded until discharge (sTable 1). Comorbidities were defined using International Classification of Diseases, Tenth Revision (ICD-10) codes. Details of covariate definitions and the study timeline are provided in the Supplementary Methods (sFigure 2).\u003c/p\u003e \u003cp\u003eThe primary endpoint was in-hospital mortality, defined as survival status prior to hospital discharge within 7 days after ICU admission (sTable 2).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eRestricted cubic splines (RCS) were used to examine the association between HTR at ICU admission and the last measurement within 7 days with 7-day mortality. Univariate and multivariate logistic regression analyses were employed to assess associations between HTR and outcomes, using a significance level of 0.05 for variable inclusion. The multivariate model was adjusted for age, gender, height, weight, BMI, SOFA score, CCI score, comorbidities (diabetes, severe liver disease, chronic pulmonary disease, cancer, renal disease, and congestive heart failure), population categories, and treatment center. Paired t-tests compared HTR values between the first and last measurements, with results reported as odds ratios (ORs) and 95% confidence intervals (CIs).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eGroup-based trajectory modeling (GBTM) was applied to identify latent subgroups with distinct HTR trajectories over the first 7 days, stratified by treatment duration (3\u0026ndash;5 vs. 5\u0026ndash;7 days). As a finite mixture modeling technique, GBTM assumes the population consists of discrete subgroups \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Models were selected based on the Bayesian Information Criterion (BIC) to determine the number of trajectories and optimal polynomial order (linear, quadratic, or cubic)\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Adequate fit was defined as each group comprising\u0026thinsp;\u0026ge;\u0026thinsp;5% of participants with average posterior probabilities\u0026thinsp;\u0026ge;\u0026thinsp;0.70\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSensitivity analyses excluding patients with pre-existing liver disease yielded results consistent with the primary analysis. For further robustness, multilevel propensity score\u0026ndash;matched analyses were performed using multivariable logistic regression to estimate propensity scores, adjusting for both individual- and hospital-level confounders (Supplementary Methods).\u003c/p\u003e \u003cp\u003eSubgroup analyses were conducted to examine effect heterogeneity by age (\u0026lt;\u0026thinsp;60 vs. \u0026ge;60 years), gender, BMI (18.5\u0026ndash;24.9, \u0026ge;\u0026thinsp;24.9 kg/m\u0026sup2;), population categories, treatment center, and major comorbidities (diabetes, severe liver disease, chronic pulmonary disease, cancer, renal disease, and congestive heart failure). A generalized additive mixed model was also applied to analyze HTR growth trajectories and rates of change, capturing heterogeneity at both individual and population levels.\u003c/p\u003e \u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median with interquartile range, and categorical variables as frequencies (percentages). Group comparisons used one-way ANOVA, Wilcoxon rank-sum, or chi-square tests, as appropriate. Multiple comparisons were corrected using the Bonferroni method, and Cohen\u0026rsquo;s d was calculated to report standardized effect sizes. All analyses were performed using R version 4.1.0 (R Foundation for Statistical Computing, Vienna, Austria; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Patient characteristics\u003c/h2\u003e \u003cp\u003eAfter screening, a total of 8,183 patients were included in the analysis, among whom 948 (11.59%) died within 7 days of admission. Compared with survivors, non-survivors were older (median age 68 vs. 62 years), had higher SOFA scores (9.43 vs. 7.19), and presented with more comorbidities, including severe liver disease (20.25% vs. 15.85%), chronic pulmonary disease (22.26% vs. 14.75%), diabetes (34.70% vs. 26.92%), renal disease (27.11% vs. 18.48%), and cancer (27.11% vs. 19.63%), with all differences being statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). In addition, both dataset source and surgical type were significantly associated with mortality, with the highest mortality observed in the MIMIC cohort (77.53%) and in non-surgical patients (79.75%). In contrast, no significant differences were found with respect to gender or height (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics and outcomes (survival vs. death)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;8,183\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;7,235\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;948\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDataset, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3370 (41.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3157 (43.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e213 (22.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIMIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4813 (58.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4078 (56.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e735 (77.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation categories, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac Surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1690 (20.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1680 (23.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10 (1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Cardiac Surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1810 (22.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1628 (22.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e182 (19.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4683 (57.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3927 (54.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e756 (79.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline Characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, Male (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5035 (61.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4475 (61.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e560 (59.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63.13 [52.55, 73.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.22 [52.00, 72.52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.23 [57.86, 77. 0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e168.00 [160.00, 175.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e168.00 [160.00, 175.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e168.09 [160.39, 175.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.10 [60.00, 87.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.00 [60.00, 87.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.66 [60.05, 92.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.57 [22.06, 29.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.47 [22.04, 29.73]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.53 [22.09, 31.64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian Sofa score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.45 (3.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.19 (3.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.425 (4.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian CCI score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.50 (2.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.31 (2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.95 (3.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere liver disease (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1339 (16.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1147 (15.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e192 (20.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic pulmonary disease (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1278 (15.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1067 (14.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e211 (22.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive heart failure (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3047 (37.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2672 (36.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e375 (39.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2277 (27.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1948 (26.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e329 (34.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal disease (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1594 (19.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1337 (18.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e257 (27.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1677 (20.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1420 (19.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e257 (27.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst HTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11 [0.06, 0.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.107 [0.07, 0.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14 [0.06, 0.39]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst Total Bilirubin, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10 [0.66, 2.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10 [0.66, 2.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.30 [0.60, 3.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst Hemoglobin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.10 [8.50, 11.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.20 [8.60, 11.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.60 [8.00, 11.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin 7d last HTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11 [0.06, 0.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10 [0.06, 0.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23 [0.08, 0.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin 7d last Total Bilirubin, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 [0.60, 2.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99 [0.60, 2.13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.96 [0.80, 5.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin 7d last Hemoglobin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.20 [8.10, 10.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.30 [8.20, 10.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.70 [7.70, 10.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBMI\u0026thinsp;=\u0026thinsp;Body Mass Index; CCI\u0026thinsp;=\u0026thinsp;Charlson Comorbidity Index; SOFA= Sequential Organ Failure Assessment; HTR\u0026thinsp;=\u0026thinsp;Hemoglobin Turnover Rate.\u003c/p\u003e \u003cp\u003eNotes: Data are presented as median [interquartile range, IQR] or frequency (%). \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates statistical significance (Kruskal-Wallis test for continuous variables; χ\u0026sup2; test for categorical variables).\u003c/p\u003e \u003cp\u003e\u0026dagger; Defined using International Classification of Diseases, 10th revision codes\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Main Results\u003c/h2\u003e \u003cp\u003eThis study employed both cross-sectional and longitudinal trajectory analyses. The cross-sectional approach examined associations at specific time points, while the longitudinal analysis assessed dynamic changes in indicators over multiple time periods in relation to clinical outcomes.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, HTR was associated with 7-day mortality across time points, and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes these relationships stratified by HTR quartiles in three statistical models at three distinct intervals: at admission, within 7 days after admission, and overall. In the unadjusted model (Model 1), each one-unit increase in HTR was consistently associated with elevated 7-day mortality, regardless of timing. At admission, patients in the highest HTR quartile (Q4) had significantly increased mortality risk compared to the lowest quartile (Q1) (OR\u0026thinsp;=\u0026thinsp;1.45, 95% CI: 1.22\u0026ndash;1.74, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with a 23% higher risk per standard deviation increase (OR per SD\u0026thinsp;=\u0026thinsp;1.23). Although effect sizes were attenuated in Models 2 and 3, the association for Q4 remained marginally significant (OR\u0026thinsp;\u0026asymp;\u0026thinsp;1.23). The association strengthened within 7 days post-admission: in Model 1, Q4 was associated with a substantially elevated mortality risk (OR\u0026thinsp;=\u0026thinsp;3.52, 95% CI: 3.00-4.13, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), corresponding to a 59% increase per SD (OR\u0026thinsp;=\u0026thinsp;1.59). Models 2 and 3 also showed significant, though reduced, effects.\u003c/p\u003e \u003cp\u003eCollectively, these findings indicate that elevated HTR is consistently associated with higher 7-day mortality, with the strongest predictive value observed when measured within the 7 days preceding death.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of hemoglobin turnover rate (HTR) at Different Time Points on 7-Day Mortality Across Quartiles.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eQuartiles of Hemoglobin Turnover Rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e For Trend\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePer SD Increase\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt Admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.01\u0026amp;\u0026lt;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.06\u0026amp;\u0026lt;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.11\u0026amp;\u0026lt;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.23\u0026amp;\u0026le;6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase/Person\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e246/2047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158/2046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e205/2045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e339/2045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(refrence)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61(0.50\u0026ndash;0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82(0.67\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.45(1.22\u0026ndash;1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.23(1.17\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(refrence)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86(0.69\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95(0.77\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.23(0.99\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.16(1.08\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(refrence)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86(0.69\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96(0.78\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.23(0.99\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.16(1.08\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLast Within 7d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.01\u0026amp;\u0026lt;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.06\u0026amp;\u0026lt;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.11\u0026amp;\u0026lt;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.28\u0026amp;\u0026le;10.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase/Person\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e262/2049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200/2043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e362/2045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e696/2046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(refrence)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74(0.61\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.47(1.24\u0026ndash;1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.52(3.00-4.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.59(1.49\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(refrence)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88(0.72\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.29(1.07\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.76(2.28\u0026ndash;3.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.49(1.40\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(refrence)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89(0.72\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30(1.08\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.60(2.14\u0026ndash;3.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.95(1.75\u0026ndash;2.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.01\u0026amp;\u0026lt;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.06\u0026amp;\u0026lt;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.11\u0026amp;\u0026lt;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.28\u0026amp;\u0026le;10.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase/Person\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e262/2049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200/2043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e362/2045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e696/2046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(refrence)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74(0.61\u0026ndash;0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.47(1.24\u0026ndash;1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.52(3.00-4.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.57(1.49\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(refrence)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88(0.72\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.29(1.07\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.76(2.28\u0026ndash;3.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.49(1.40\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(refrence)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89(0.72\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30(1.08\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.60(2.14\u0026ndash;3.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.95(1.75\u0026ndash;2.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModel 1: unadjusted; Model 2: adjusted for dataset, age, severe liver disease, chronic pulmonary disease, diabetes, renal disease, cancer, Charlson comorbidity index, SOFA score, and population categories; Model 3: additionally adjusted for gender, height, weight, BMI, and congestive heart failure on top of Model 2.\u003c/p\u003e \u003cp\u003eNotes: All results are presented as odds ratios (ORs) with 95% confidence intervals (95% CIs).Q1\u0026ndash;Q4: quartiles of hemoglobin turnover rate (HTR), with Q1 representing the lowest quartile and Q4 the highest; P for Trend: p-value for trend across quartiles; Per SD Increase: risk estimate per 1 standard deviation increase in HTR.\u003c/p\u003e \u003cp\u003eBased on cross-sectional analysis, paired t-tests demonstrated that HTR values were consistently higher in non-survivors compared with survivors. The difference was modest yet significant at admission (0.44 vs. 0.28, P\u0026thinsp;=\u0026thinsp;0.01) and widened considerably by the last measurement within 7 days (0.61 vs. 0.32, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), reflecting progressive divergence between the groups (sTable 3).\u003c/p\u003e \u003cp\u003eTo account for variability in treatment duration, longitudinal trajectory analysis focused on two critical windows (days 3\u0026ndash;5 and days 5\u0026ndash;7). Although models with more classes yielded lower BIC values, they produced small, unstable subgroups with poor interpretability. In contrast, two-class models offered favorable model fit (BIC = -1158.45 and \u0026minus;\u0026thinsp;1425.47), high entropy (0.90 and 0.95), and well-balanced classification probabilities (0.73/0.27 for days 3\u0026ndash;5; 0.81/0.19 for days 5\u0026ndash;7), supporting the two-class solution as the most robust and clinically meaningful (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Fit Statistics for HTR Trajectories\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLog(L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEntropy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eClassification Probabilities\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays 3\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1,480.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,977.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3,019.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e629.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1,231.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1,158.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.73/ 0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e964.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1,888.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1,783.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.72/ 0.03/ 0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,277.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2,502.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2,366.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.68/ 0.25/ 0.04/ 0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,551.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3,038.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2,870.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.66/ 0.03/ 0.05/ 0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays 5\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1,283.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,582.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2,622.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e761.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1,494.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1,425.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.81/ 0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,382.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2,725.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2,627.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.78/ 0.01/ 0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,693.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3,334.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3,206.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.76/ 0.03/ 0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,985.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-3,907.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3,750.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.02/ 0.75/ 0.20/ 0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNotes: K is the number of parameters estimated freely in the model; Log(L) is Maximum log-likelihood value; AIC/BIC: Akaike/Bayesian Information Criteria (lower values indicate better fit); Entropy: Classification accuracy metric (0\u0026ndash;1, \u0026gt;\u0026thinsp;0.8 indicates excellent separation).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSignificant inter-class differences were identified (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). During days 3\u0026ndash;5, non-survivors in Class 1 exhibited higher HTR at time 3 than survivors (mean 0.2 vs. 0.1; Cohen\u0026rsquo;s d = -0.55, indicating a medium effect size). In Class 2, non-survivors demonstrated elevated HTR at both time 2 and time 3, with Cohen\u0026rsquo;s d values ranging from \u0026minus;\u0026thinsp;0.39 to -0.56, consistent with small-to-medium effect sizes. During days 5\u0026ndash;7, non-survivors in Class 1 again showed significantly higher HTR at time 3 (Cohen\u0026rsquo;s d = -0.69, medium effect). Although mean HTR was lower overall in Class 2, non-survivors within this class still displayed a medium effect size at time 3 (Cohen\u0026rsquo;s d = -0.79).\u003c/p\u003e \u003cp\u003eTaken together, these results indicate that HTR tends to be elevated in non-survivors, particularly at later time points in Class 2. The persistent rise in HTR was positively associated with short-term mortality, highlighting its potential utility as an early risk assessment marker in septic shock.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical Characteristics and Outcomes by HTR trajectories.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSurvived\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eDied\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eW statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRaw \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCohen's d\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 3\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass1(n\u0026thinsp;=\u0026thinsp;1013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e134,500.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e114,418.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e91,240.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass2 (n\u0026thinsp;=\u0026thinsp;376)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18,674.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14,121.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10,565.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay 5\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass1(n\u0026thinsp;=\u0026thinsp;818)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63,798.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58,712.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44,013.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass2(n\u0026thinsp;=\u0026thinsp;188)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,705.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3,905.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2,447.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: Cohen\u0026rsquo;s d indicates effect size: 0.0\u0026ndash;0.2\u0026thinsp;=\u0026thinsp;Negligible/Very Small, 0.2\u0026ndash;0.5\u0026thinsp;=\u0026thinsp;Small, 0.5\u0026ndash;0.8\u0026thinsp;=\u0026thinsp;Medium, \u0026gt;\u0026thinsp;0.8\u0026thinsp;=\u0026thinsp;Large. All \u003cem\u003ep\u003c/em\u003e-values are Bonferroni-adjusted for 6 multiple comparisons. Effect sizes interpreted using Cohen\u0026rsquo;s d (Negligible/ Small/ Medium/ Large).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Sensitivity Analyses\u003c/h2\u003e \u003cp\u003eIn sensitivity analyses, we excluded 1,339 patients with severe liver disease to minimize potential confounding from impaired hepatic function. The associations of HTR with both cross-sectional outcomes and longitudinal trajectories remained consistent with the primary analysis (sTable 4; sFigure 3). Similarly, results were robust when using multilevel propensity score\u0026ndash;matched analyses (sTable 5;sFigure 4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Additional Analyses\u003c/h2\u003e \u003cp\u003eHTR was consistently and strongly associated with an increased risk of short-term adverse events across all patient subgroups, with the highest risk observed in the fourth quartile (Q4) (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; OR per SD 1.4\u0026ndash;1.6). This association was most pronounced among patients with congestive heart failure or cancer. Trajectory analyses revealed minimal differences at baseline but significant divergence over time, as non-survivors consistently exhibited elevated HTR. The effects were particularly evident in subgroups with congestive heart failure, chronic pulmonary disease, diabetes, higher BMI, and advanced age. Overall, dynamic HTR monitoring proved superior to single admission measurements, highlighting its value as a robust prognostic marker (sTable 6\u0026ndash;10; sFigure 5\u0026ndash;20).\u003c/p\u003e \u003cp\u003eGiven the observed dose-response relationship between HTR and outcomes, we further examined its association with survival time among non-survivors. Results indicated that survival time was significantly shorter once HTR reached a value of 3, except in patients with severe liver disease (sFigure 21).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eSepsis is a rapidly progressive disorder and a major contributor to global mortality. Prompt and accurate evaluation of disease severity is critical for improving patient outcomes and reducing fatality rates\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. In this study, we proposed and validated that HTR was significantly elevated in non-survivors compared to survivors. trajectory modeling further revealed that dynamic increases in HTR were strongly correlated with adverse outcomes in sepsis. Notably, a sharp increase in 7-day mortality risk was observed once HTR exceeded 0.1, and survival time was substantially shortened when HTR approached 3.\u003c/p\u003e \u003cp\u003eRBCs play a dual role: they must meet the functional demands of the macrocirculation while operating within the constraints of their own metabolic lifecycle. This lifecycle culminates in clearance and degradation into bilirubin. The production and release of RBCs, dependent on metabolic substrates and hematopoietic factors, represent a significant biological investment. In return, a stable functional output is expected. Short-term fluctuations may occur, but premature loss of RBC function or accelerated clearance can disrupt physiological homeostasis.\u003c/p\u003e \u003cp\u003eAt the macrocirculatory level, the principal role of RBCs is gas exchange. Clinical and experimental data indicate that tissue oxygen consumption can remain stable even when hemoglobin levels fall as low as 5g/dL\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, implying considerable redundancy in systemic oxygen delivery. Furthermore, emerging evidence contradicts the traditional view that sepsis invariably causes tissue metabolism to become supply-dependent\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Studies demonstrate that in septic patients, systemic oxygen consumption does not improve significantly with increased oxygen delivery\u0026mdash;even during hyperlactatemia. In fact, mean central venous oxygen saturation (ScvO₂) in sepsis rarely drops below 70%\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. These observations suggest that metabolic dysregulation in sepsis stems mainly from impaired oxygen utilization rather than inadequate supply\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. In the absence of conclusive evidence and given that microcirculatory monitoring is not yet routine clinically, the concept of hemodynamic coherence remains contentious\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Therefore, this study shifts focus from conventional metrics like hemoglobin concentration or oxygen delivery to emphasize the clinical relevance of dynamic hemoglobin turnover within its metabolic cycle.\u003c/p\u003e \u003cp\u003eBilirubin, an end product of hemoglobin catabolism, often rises in settings of hepatic dysfunction or accelerated RBC destruction. Previous research supports its role as a prognostic marker in sepsis\u003csup\u003e[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Pierrakos et al.\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e identified hyperbilirubinemia as an independent risk factor for morbidity and mortality in ICU patients, with mortality increasing linearly across bilirubin levels of 1.1-6 mg/dL. Similarly, Patel et al.\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e reported that elevated bilirubin within 72 hours of admission correlated with higher mortality in severe sepsis and septic shock. Building on this evidence, we introduced HTR as a novel metric to assess the balance between the macrocirculatory functions of RBCs and their metabolic turnover. Although transfusions during septic shock management may confound short-term HTR measurements\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, over extended observation, HTR remained significantly elevated in non-survivors.\u003c/p\u003e \u003cp\u003eBilirubin may also directly affect clinical outcomes through several mechanisms: its antioxidant properties can impair neutrophil bactericidal function\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e, and it has been associated with nephrotoxic\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e and neurotoxic\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e effects. Elevated bilirubin can result from either increased RBC destruction or reduced hepatic clearance\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Given the clinical importance of hepatic dysfunction in sepsis-related organ failure\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e, we conducted a sensitivity analysis that excluded patients with pre-existing severe liver disease. The results remained consistent with the primary analysis, and similar trends were observed across other subgroups, including those with severe liver disease. These findings indicate that elevated HTR may serve as an early indicator of short-term mortality in septic shock across diverse patient populations. However, previous studies on liver injury have reported inconsistent associations between hyperbilirubinemia and mortality\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Our subgroup analysis further revealed that patients with severe liver disease exhibited a higher HTR inflection point and an inconsistent relationship with survival, suggesting that hepatobiliary diseases may represent important confounders and should be excluded when applying HTR in clinical practice.\u003c/p\u003e \u003cp\u003eNevertheless, a major concern of this study lies in the intrinsic issue of \u0026ldquo;rate\u0026rdquo; implied by this concept. Laboratory indicators are essentially static measurements obtained from inherently dynamic processes. Under complex pathological conditions, such static indicators may be distorted, thereby obscuring the actual progression of disease. Beyond the intrinsic mass\u0026ndash;quantity relationship between hemoglobin and bilirubin production, this metric also inherently encompasses factors such as macrocirculatory blood flow, microcirculatory metabolic rate, and mean corpuscular hemoglobin (Appendix). Given the limitations of current monitoring techniques, our study could only perform a preliminary analysis of this indicator. Further investigation will require more refined measurement approaches and clinical trials to elucidate its pathophysiological implications and clinical utility.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eHTR reflects both red blood cell metabolism and bilirubin clearance, thereby capturing aspects of systemic oxygen transport and metabolic balance. Our results suggest that HTR has potential value as an early prognostic marker in septic shock. However, given the limitations of current monitoring and the exploratory nature of this study, its clinical utility requires further validation in larger, prospective cohorts before being considered for routine application.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntensive Care Unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHTR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHemoglobin Turnover Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRestricted Cubic Splines\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGBTM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGroup-Based Trajectory Modeling\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPACHE II\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute Physiology and Chronic Health Evaluation II\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSOFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSequential Organ Failure Assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMean Arterial Pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRed Blood Cell Count\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile Range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGAMM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneralized Additive Mixed Models\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEPO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eErythropoietin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBayesian Information Criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the authors of the primary studies for their timely and helpful responses to our information requests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDongzhi Zheng: Conceptualization; Data curation; Formal analysis; Investigation; Writing\u0026mdash;original draft. Huan Jiang and Zihao Zheng: Data curation; Formal analysis; Writing\u0026mdash;review \u0026amp; editing. Run Zhang, and Jun Hong: Conceptualization; Methodology; Supervision; Validation; Writing\u0026mdash;review \u0026amp; editing. Xianghong Yang and Bai Xu: Conceptualization; Investigation; Methodology; Resources; Supervision; Validation; Writing\u0026mdash;review \u0026amp; editing. Jingquan Liu: Conceptualization; Methodology; Project administration; Resources. Siyu Tang: Conceptualization; Formal analysis; Investigation; Methodology; Project administration; Software; Supervision; Validation; Writing\u0026mdash;review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Zhejiang Provincial Key Research and Development Plan (No. 2019C03024), the Zhejiang Medical and Health Science and Technology Plan (No. WKJ-ZJ-1811), and the Health Department of Zhejiang Province (Nos. 2022KY497, 2023KY038, 2026791893).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are included within the articl.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki. The use of the database was authorized by certified investigator Huan Jiang (Record ID: 66212733) and granted by Beth Israel Deaconess Medical Center\u0026ndash;Women\u0026rsquo;s Health Care. The requirement for informed consent was waived by the Ethics Committee of Zhejiang Provincial People\u0026rsquo;s Hospital (Approval No. 202509152037000250901) due to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDongzhi Zheng, Email: [email protected]. Huan Jiang, Email: [email protected]. Zihao Zheng, Email: [email protected]. Jingquan Liu, Email: [email protected]. Run Zhang, Email: [email protected]. Jun Hong, Email: [email protected]. Bai Xu, Email: [email protected]. Xianghong Yang, Email: [email protected]. Siyu Tang, Email: [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEvans L, Rhodes A, Alhazzani W, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021[J]. Intensive Care Med. 2021;47(11):1181\u0026ndash;247.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAngus DC, Van Der Poll T. Severe sepsis and septic shock[J]. N Engl J Med. 2013;369(9):840\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinger M, Deutschman CS, Seymour CW, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)[J]. JAMA. 2016;315(8):801\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu V, Escobar GJ, Greene JD, et al. Hospital deaths in patients with sepsis from 2 independent cohorts[J]. 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Injury. 2017;48(1):127\u0026ndash;32.\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Septic shock, Hemodynamics, Evaluation indicators, Mortality","lastPublishedDoi":"10.21203/rs.3.rs-8689595/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8689595/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSeptic shock is associated with high incidence and mortality in intensive care units (ICUs). This study aims to investigate whether Hemoglobin Turnover Rate (HTR) can serve as an early predictor of mortality in patients with septic shock.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePatients diagnosed with septic shock within 48 hours of ICU admission were enrolled, and sequential biochemical data from the first 7 days were collected. Restricted cubic splines (RCS) were applied to explore the association between single time-point measurements and outcomes. Group-based trajectory modeling (GBTM) was used to identify latent subgroups of patients with similar progression patterns. The primary outcome was all-cause mortality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThis study integrated cross-sectional and longitudinal analyses to assess the prognostic significance of HTR in septic shock. Cross-sectionally, elevated HTR-both at admission and within 7 days thereafter-was consistently associated with higher 7-day mortality, with the most pronounced predictive effect observed for measurements taken within the 7 days preceding death (Q4 vs Q1 OR reaching 3.52). Longitudinal trajectory modeling further identified two distinct temporal patterns of HTR, among which a steadily rising trajectory was strongly predictive of fatal outcome. The robustness of these associations was confirmed through sensitivity analyses that excluded patients with severe liver disease, as well as via propensity score\u0026ndash;based sensitivity analyses.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eHTR reflects integrated red blood cell metabolism and bilirubin clearance, capturing aspects of both oxygen transport and metabolic balance. It shows promise as an early prognostic marker in septic shock, though further validation is required before clinical adoption.\u003c/p\u003e","manuscriptTitle":"Association between Hemoglobin Turnover Rate and Mortality in Septic Shock Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-22 12:43:57","doi":"10.21203/rs.3.rs-8689595/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-02-17T09:04:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-16T12:30:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-29T05:46:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-29T04:13:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-01-29T04:06:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"29bcaf7c-cbea-4536-b799-a64b31883001","owner":[],"postedDate":"February 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-22T12:43:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-22 12:43:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8689595","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8689595","identity":"rs-8689595","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}