Predicting ICU Mortality in Sepsis: A Retrospective Cohort Study with Nomogram Development

Predicting ICU Mortality in Sepsis: A Retrospective Cohort Study with Nomogram Development | 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 Predicting ICU Mortality in Sepsis: A Retrospective Cohort Study with Nomogram Development Hao-Hsun Liu, Hung-His Tan, Chin-Ming Chen, Willy Chou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7963988/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Sepsis remains a leading cause of death among critically ill patients, yet existing severity scores such as the APACHE II and the SOFA provide limited individualized prognostic accuracy. This study aimed to develop and internally validate a nomogram to predict ICU mortality among adult patients with sepsis. Methods We performed a retrospective cohort study including 505 adult patients with sepsis admitted to the ICUs of a tertiary medical center in Taiwan between 2017 and 2021. Clinical, laboratory, and infection-related data at ICU admission were extracted from electronic medical records. Univariate and multivariate logistic regression analyses were used to identify independent predictors of ICU mortality. Variables with statistical significance in the multivariate model were incorporated into a predictive nomogram. Model calibration and discrimination were evaluated using calibration plots and the area under the ROC curve. Results Among 505 patients, 225 (44.6%) died during ICU stay. Independent predictors of ICU mortality included male gender (adjusted odds ratio [AOR] 0.62, 95% confidence interval [CI] 0.39–0.99), higher body mass index (AOR 1.09 per kg/m², 95% CI 1.04–1.13), higher APACHE II score (AOR 1.07 per point, 95% CI 1.03–1.10), pneumonia as the primary infection source (AOR 2.45, 95% CI 1.50–3.99), lower hemoglobin level (AOR 0.96 per g/dL, 95% CI 0.92–0.99), and higher serum bilirubin (AOR 1.07 per mg/dL, 95% CI 1.01–1.14) and lactate (AOR 1.08 per mmol/L, 95% CI 1.01–1.16). The nomogram demonstrated good discrimination (area under the curve = 0.84) and satisfactory calibration between predicted and observed mortality rates. Conclusions This study developed an internally validated nomogram integrating demographic, physiologic, and biochemical parameters available at ICU admission to predict mortality in patients with sepsis. The model provides a practical, individualized bedside tool to assist early risk stratification, guide management decisions, and optimize resource allocation in critical care settings. External validation in independent cohorts is warranted. Trial registration Not applicable. Sepsis ICU mortality nomogram risk stratification critical care Figures Figure 1 Introduction Sepsis, defined as a dysregulated host response to infection leading to life-threatening organ dysfunction, remains a major global health concern and a leading cause of mortality among critically ill patients. According to recent estimates, sepsis affects nearly 50 million people worldwide each year, contributing to 11 million deaths — roughly 20% of global mortality 1 – 3 . A study by Chen et al. reported an incidence of 643 cases per 100,000 population between 2010 and 2014, with a case fatality rate of 29.2%, and an observable upward trend in both incidence and mortality 4 . Early identification and timely intervention are essential to improving outcomes in septic patients, yet sepsis presents a wide spectrum of clinical manifestations, making early risk stratification difficult 5 , 6 . Scoring systems like the SOFA and the APACHE II are widely used in the ICU to assess disease severity and predict mortality. However, these models primarily focus on physiologic dysfunction and organ failure, with limited integration of individual host characteristics such as age, comorbidities, and baseline functional status. To support precision decision-making, there is a growing need for more comprehensive, individualized prognostic tools in critical care settings. Nomograms are graphical representations of complex multivariable models that provide an intuitive means to calculate the probability of a clinical event in a specific patient. By incorporating diverse patient- and disease-related factors, nomograms can generate individualized risk estimates and have been increasingly applied across various clinical domains, including oncology, cardiovascular disease, and critical care medicine 7 . Compared with traditional scoring systems, nomograms offer several advantages: they accommodate continuous variables, allow for real-time prediction using digital tools, and are often better calibrated to specific patient populations 7 , 8 . In the context of sepsis, nomograms have shown promise in risk stratification, identifying high-risk subgroups, and guiding treatment strategies. For instance, previous studies have developed nomograms to predict outcomes such as extubation success, septic shock, or sepsis-associated encephalopathy 8 . Despite the widespread utility of such tools, concerns remain regarding their proper construction, overfitting, and lack of external validation 9 . Nevertheless, when appropriately developed and validated, nomograms can enhance clinical judgment and facilitate shared decision-making between healthcare providers and patients. In this study, we aimed to develop a novel prognostic nomogram to predict ICU mortality among septic patients, utilizing comprehensive clinical data from a five-year cohort admitted to the ICUs of a tertiary care medical center. By integrating demographic data, laboratory parameters, comorbidities, severity scores, infection sites, and pathogens, our objective was to construct an easy-to-use, evidence-based predictive model to assist in early risk identification and personalized care planning for critically ill patients with sepsis. Methods Participant selection A retrospective observational study was conducted at a tertiary care medical center between January 1, 2017, and December 31, 2021. The study included all adult patients (aged > 18 years) admitted to any of the hospital’s eight ICUs, comprising a total of 95 beds. Patients were eligible if they met the diagnostic criteria for sepsis based on the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). The study was conducted in accordance with national guidelines and the principles of the Declaration of Helsinki. The study protocol was reviewed and approved by the Institutional Review Board of Chi Mei Medical Center (No. 11001-007, date: 18 Feb 2021). As this was a retrospective study using de-identified data, the requirement for informed consent was waived by the Institutional Review Board of Chi Mei Medical Center. Data collection The following clinical and laboratory data were collected for all included patients: (1).Demographic and admission characteristics including age, gender, BMI, and origin of ICU admission (e.g., emergency department, internal medicine ward); (2),Severity of illness on ICU admission including APACHE II score, SOFA score, TISS score, presence of acute organ dysfunction, diagnosis of ARDS, based on the Berlin definition; (3).Comorbidities: pre-existing chronic conditions prior to ICU admission (e.g., diabetes, liver cirrhosis, cardiovascular disease); (4).Infectious characteristics: including primary site of infection and type of causative pathogens (e.g., gram-positive, gram-negative, fungal); (5).Laboratory data on ICU admission: including WBC, Hb, BUN, Cr, serum electrolytes (Na, K), total bilirubin and serum lactate. All data were obtained from the hospital’s electronic medical records and recorded using standardized case report forms. Data analysis All statistical analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). Continuous variables are presented as means with standard deviations and were compared using the independent samples t-test. Categorical variables are expressed as frequencies with percentages and were compared using the Pearson chi-square test. To identify potential risk factors associated with ICU mortality in septic patients, univariate logistic regression analysis was initially performed. Variables with a p-value < 0.10 in the univariate analysis were subsequently entered into a multivariate logistic regression model using a forward stepwise selection approach. AORs and corresponding 95% CIs were calculated to determine the strength of associations between predictors and mortality. Based on the final multivariable model, a nomogram was constructed to visually represent the predictive model and to facilitate individualized risk estimation for ICU mortality. The nomogram was created using the regression coefficients from the final model, with each predictor assigned a point value proportional to its relative contribution to mortality risk. The total score was then mapped to the corresponding predicted probability of death. Results Patient Characteristics During the study period, a total of 922 patients with sepsis were admitted to the ICU. After excluding patients with incomplete data, 505 patients were included in the final analysis. Among them, 280 (55.4%) survived to ICU discharge, while 225 (44.6%) died during their ICU stay. The mean age of the patients was 68.3 ± 14.0 years, and 64.8% were male (Table 1 ). No significant differences in age or age group (< 65 vs. ≥65) were observed between survivors and non-survivors. However, gender and BMI were significantly associated with ICU mortality. Male patients had a lower mortality rate than female patients (40.7% vs. 51.7%, p = 0.017). Patients with higher BMI had significantly higher mortality (mean BMI: 26.15 ± 6.51 vs. 23.34 ± 4.74; p 27 had a markedly higher mortality rate (41.8%) compared to those with BMI 18.5–27 (49.8%) or < 18.5 (8.4%). Regarding comorbidities, liver cirrhosis was significantly more prevalent among non-survivors (13.8% vs. 6.8%, p = 0.009), while other conditions such as diabetes mellitus, hypertension, cancer, ESRD, and COPD did not show significant differences between groups. Severity of Illness and ICU Course Disease severity on ICU admission was significantly associated with mortality (Table 1 ). Non-survivors had higher SOFA scores (11.42 ± 4.32 vs. 7.79 ± 3.80, p < 0.001), TISS scores (33.80 ± 10.21 vs. 28.29 ± 7.89, p < 0.001), and APACHE II scores (28.24 ± 8.70 vs. 21.13 ± 8.24, p < 0.001). Furthermore, the incidence of septic shock and ARDS was significantly higher in non-survivors (septic shock: 59.1% vs. 27.9%, p < 0.001; ARDS: 48.4% vs. 21.1%, p < 0.001). Table 1 Demographic and admission characteristics of ICU sepsis patients Overall(n = 505) ICU survivors (n = 280) ICU non-survivors (n = 225) p -value* Age, mean ± SD 68.33 ± 14.02 68.14 ± 13.65 68.58 ± 14.48 0.7250 Age group, n(%) 0.2408 <65 190(37.62) 99(35.36) 91(40.44) ≥ 65 315(62.38) 181(64.64) 134(59.56) Gender, n(%) 0.0174 Female 178(35.25) 86(30.71) 92(40.89) Male 327(64.75) 194(69.29) 133(59.11) BMI, mean ± SD 24.59 ± 5.76 23.34 ± 4.74 26.15 ± 6.51 < 0.0001 BMI group < 0.0001 27 154(30.50) 60(21.43) 94(41.78) Comorbidities, n(%) Diabetes mellitus 204(40.40) 114(40.71) 90(40.00) 0.8708 Hypertension 147(29.11) 78(27.86) 69(30.67) 0.4897 History of CAD 49(9.70) 28(10.00) 21(9.33) 0.8014 History of stroke 34(6.73) 20(7.14) 14(6.22) 0.6815 Liver cirrhosis 50(9.90) 19(6.79) 31(13.78) 0.0089 Cancer 122(24.16) 66(23.57) 56(24.89) 0.7310 ESRD 51(10.10) 30(10.71) 21(9.33) 0.6087 COPD 64(12.67) 31(11.07) 33(14.67) 0.2274 Disease severity on admission SOFA, mean ± SD 9.41 ± 4.42 7.79 ± 3.80 11.42 ± 4.32 < 0.0001 TISS, mean ± SD 30.74 ± 9.40 28.29 ± 7.89 33.80 ± 10.21 < 0.0001 APACHE II, mean ± SD 24.30 ± 9.15 21.13 ± 8.24 28.24 ± 8.70 < 0.0001 Septic shock, n(%) 211(41.78) 78(27.86) 133(59.11) < 0.0001 ARDS, n(%) 168(33.27) 59(21.07) 109(48.44) < 0.0001 *Categorical variables are calculated using Pearson’s chi-square and continuous variables are estimated using Independent Sample t test. Site of Infection and Microbiology Pneumonia was the most common infection source and was significantly more prevalent among non-survivors (71.6% vs. 60.0%, p = 0.007) (Table 2 ). Other sources of infection, such as UTI, bloodstream infection, and CLABSI, showed no significant differences. The distribution of infection organisms—including gram-negative bacteria, gram-positive cocci, fungal, viral, or mixed infections—did not significantly differ between survivors and non-survivors. Table 2 Infection site and infection organism of ICU sepsis patients Overall(n = 505) ICU survivors (n = 280) ICU non-survivors (n = 225) p -value* Pathology and infection site, n(%) Bacteremia 211(41.78) 110(39.29) 101(44.89) 0.2045 UTI 185(36.63) 105(37.50) 80(35.56) 0.6522 Pneumonia 329(65.15) 168(60.00) 161(71.56) 0.0068 CLABSI 28(5.54) 17(6.07) 11(4.89) 0.5638 Infection organism, n(%) Bacterial infection 401(79.41) 223(79.64) 178(79.11) 0.8832 GNB 376(74.46) 210(75.00) 166(73.78) 0.7543 GPC 175(34.65) 103(36.79) 72(32.00) 0.2613 Fungus 106(20.99) 57(20.36) 49(21.78) 0.6968 Candida albicans 78(15.45) 47(16.79) 31(13.78) 0.3525 Virus 26(5.15) 14(5.00) 12(5.33) 0.8662 *Categorical variables are calculated using Pearson’s chi-square. Laboratory Findings on ICU Admission Several laboratory parameters at ICU admission were associated with mortality (Table 3 ). Non-survivors had significantly higher serum lactate (5.50 ± 5.86 vs. 2.56 ± 2.65 mmol/L, p < 0.001), total bilirubin (3.17 ± 5.12 vs. 1.85 ± 2.99 mg/dL, p < 0.001), and BUN (42.25 ± 31.04 vs. 36.56 ± 28.98 mg/dL, p = 0.034). Hb levels were significantly lower among non-survivors (10.05 ± 4.87 vs. 11.29 ± 6.21 g/dL, p = 0.013). Electrolyte disturbances were also noted. Patients with serum K > 5.0 mmol/L had significantly higher mortality (5.3% vs. 1.4% among survivors; p = 0.032), though serum sodium levels showed only a borderline difference ( p = 0.055). Table 3 Laboratory data on admission of ICU sepsis patient Overall(n = 505) ICU survivors (n = 280) ICU non-survivors (n = 225) p -value* WBC, mean ± SD 15.08 ± 10.36 15.17 ± 9.78 14.97 ± 11.07 0.8297 WBC group 0.0192 11 ༊ 10^3/µL 305(60.40) 168(60.00) 137(60.89) Hemoglobin, mean ± SD 10.74 ± 5.68 11.29 ± 6.21 10.05 ± 4.87 0.0125 Hemoglobin, n(%) 0.0769 <7g/dL 97(19.21) 46(16.43) 51(22.67) ≥7g/dL 408(80.79) 234(83.57) 174(77.33) BUN(mg/dl), mean ± SD 39.10 ± 30.02 36.56 ± 28.98 42.25 ± 31.04 0.0342 Cr(mg/dl), mean ± SD 2.73 ± 8.52 2.92 ± 11.26 2.51 ± 2.25 0.5495 Na(mmol/L), mean ± SD 136.09 ± 7.11 135.73 ± 6.79 136.54 ± 7.48 0.2040 Na(mmol/L), n(%) 0.0548 145 43(8.51) 18(6.43) 25(11.11) K(mmol/L), mean ± SD 3.49 ± 0.77 3.48 ± 0.70 3.51 ± 0.85 0.6673 K(mmol/L), n(%) 0.0317 5 16(3.17) 4(1.43) 12(5.33) Bilirubin, mean ± SD 2.44 ± 4.12 1.85 ± 2.99 3.17 ± 5.12 0.0007 Lactate, mean ± SD 3.87 ± 4.61 2.56 ± 2.65 5.50 ± 5.86 < 0.0001 *Categorical variables are calculated using Pearson’s chi-square and continuous variables are estimated using Independent Sample t test. Multivariate Analysis and Predictors of ICU Mortality Multivariate logistic regression identified the following variables as independent predictors of ICU mortality (Table 4 ): male gender (AOR = 0.62, 95% CI: 0.39–0.99, p = 0.043), higher BMI (AOR = 1.09 per unit increase, 95% CI: 1.04–1.13, p = 0.0001), APACHE II score (AOR = 1.07 per unit increase, 95% CI: 1.03–1.10, p = 0.0002), pneumonia as an infection source (AOR = 2.45, 95% CI: 1.50–3.99, p = 0.0003), hemoglobin (AOR = 0.96 per g/dL, 95% CI: 0.92–0.99, p = 0.042), total bilirubin (AOR = 1.07 per mg/dL, 95% CI: 1.01–1.14, p = 0.015) and lactate level (AOR = 1.08 per mmol/L, 95% CI: 1.01–1.16, p = 0.030). These variables were subsequently used to construct a predictive nomogram for ICU mortality. Table 4 Predictors of ICU mortality Crude OR p-value AOR p-value Demographic characteristics Male 0.64(0.44,0.93) 0.0177 0.62(0.39,0.99) 0.0435 BMI 1.10(1.06,1.13) < 0.0001 1.09(1.04,1.13) 0.0001 Comorbidities Liver Cirrhosis 2.20(1.20,4.00) 0.0103 1.24(0.58,2.64) 0.5761 Disease severity on admission SOFA 1.24(1.18,1.31) < 0.0001 1.08(1.00,1.16) 0.0511 TISS 1.07(1.05,1.10) < 0.0001 1.00(0.97,1.03) 0.8748 APACHE II 1.10(1.08,1.13) < 0.0001 1.07(1.03,1.10) 0.0002 Septic shock 3.74(2.58,5.43) < 0.0001 1.50(0.90,2.48) 0.1181 ARDS 3.52(2.39,5.19) < 0.0001 1.62(0.99,2.62) 0.0515 Pathology and infection site Pneumonia 1.68(1.15,2.44) 0.0070 2.45(1.50,3.99) 0.0003 Laboratory data on admission Hemoglobin 0.96(0.93,0.99) 0.0164 0.96(0.92,0.99) 0.0424 BUN (mg/dl) 1.01(1.00,1.01) 0.0354 1.00(0.99,1.01) 0.6793 Bilirubin 1.10(1.04,1.16) 0.0013 1.07(1.01,1.14) 0.0150 Lactate 1.21(1.14,1.29) < 0.0001 1.08(1.01,1.16) 0.0297 Nomogram for Predicting ICU Mortality A nomogram was constructed using the significant predictors identified in the multivariate logistic regression model, aiming to provide an intuitive and individualized prediction tool for ICU mortality among patients with sepsis (Fig. 1 ). The variables incorporated into the model include: hemoglobin level, presence of pneumonia, ARDS, septic shock, APACHE II score, TISS score, SOFA score, liver cirrhosis, BMI, gender, serum lactate level, serum bilirubin level and serum BUN level. Each variable corresponds to a point scale at the top of the nomogram. To estimate a patient's probability of ICU mortality, a vertical line is drawn from each predictor value to the corresponding "Score" line. The individual scores are then summed to yield a total score, which is located at the bottom axis of the nomogram. This total score is mapped to the "Probability" scale to determine the patient’s predicted risk of death during the ICU stay. This graphical representation provides clinicians with a user-friendly tool to facilitate risk stratification and decision-making at the bedside. Discussion In our study, male gender was independently associated with lower ICU mortality among septic patients, a finding that contrasts with some prior reports. While earlier experimental studies suggested that females may have better immunologic responses to infection 10 , 11 , clinical data remain mixed. For example, Eachempati et al. found female gender to be an independent predictor of increased mortality in surgical ICU patients with sepsis 12 , and a large multicenter study also reported higher hospital mortality in females despite adjustment for confounders 13 . These inconsistencies may reflect biological differences in immune response as well as gender disparities in care delivery. Our findings support the need for further investigation into gender-specific risk factors and tailored sepsis management strategies. Evidence from our cohort revealed that higher BMI independently correlated with increased ICU mortality in sepsis patients, which contrasts with the “obesity paradox” described in prior literature. While numerous studies have described an inverse relationship between BMI and sepsis-related mortality—suggesting a potential protective effect of adiposity 14 , 15 —this association remains controversial. Several meta-analyses and large-scale cohort studies have observed reduced adjusted mortality in overweight and obese patients compared to those with normal BMI, possibly due to greater metabolic reserves, anti-inflammatory effects of adipokines, and altered immune responses 16 – 18 . However, other studies, including ours, have indicated that increased BMI may instead correlate with worse outcomes, particularly in the setting of severe physiological stress or coexisting metabolic dysfunction 19 , 20 . Mechanistically, chronic low-grade inflammation, immune dysregulation, and impaired tissue perfusion commonly seen in obesity may contribute to poorer outcomes in critical illness. These findings reflect the complex and sometimes contradictory role of obesity in sepsis and highlight the need for further investigation into the underlying biological mechanisms and clinical phenotypes that may mediate this relationship. Our study demonstrated that both pneumonia and ARDS were independently associated with increased ICU mortality in septic patients, reinforcing the critical role of pulmonary complications in sepsis prognosis. Pneumonia remains one of the most common sites of infection leading to sepsis and is consistently associated with higher mortality compared to other infection sources 21 , 22 . The elevated mortality risk in pulmonary sepsis may stem from profound hypoxemia, heightened inflammatory responses in lung tissue, and delayed recognition or source control compared to other infection sites 22 . Furthermore, the development of ARDS in septic patients significantly worsens outcomes. Multiple multicenter studies have shown that ARDS, particularly in its severe form, contributes independently to increased ICU and in-hospital mortality 23 , 24 . The attributable 30-day mortality of ARDS in sepsis has been estimated as high as 12% overall, with nearly 20% in severe ARDS 24 . The pathophysiology of ARDS involves profound injury to the pulmonary endothelium and epithelium, typically worsened by sepsis-induced systemic inflammation and vascular leakage. Notably, ARDS is not a homogenous entity—patients with direct (pulmonary-derived) ARDS, such as from pneumonia, exhibit distinct clinical characteristics compared to those with indirect (extrapulmonary) causes, and these subtypes may respond differently to treatment 25 . Our findings highlight the complex interplay between infection site, organ dysfunction, and host response, and further emphasize the importance of early recognition and tailored interventions in patients with pulmonary infection and ARDS during sepsis. Elevated serum bilirubin and a history of liver cirrhosis were identified in our study as independent predictors of increased ICU mortality among septic patients. These findings underscore the pivotal role of liver function in the pathophysiology and prognosis of sepsis. The liver acts as both a metabolic and immunologic organ during sepsis, responsible for clearing bacteria, endotoxins, and inflammatory mediators 26 . Liver dysfunction in sepsis can impair this clearance, exacerbate systemic inflammation, and contribute to multiple organ failure 27 . Cirrhosis, in particular, is associated with a hyperinflammatory cytokine response and immune dysregulation, which elevate the risk of organ failure and sepsis-related death 28 , 29 . Multiple studies have confirmed that patients with cirrhosis face a significantly higher risk of developing sepsis and have worse outcomes when it occurs 28 , 29 . Hyperbilirubinemia, a common marker of liver dysfunction, has also been independently associated with increased mortality in critically ill patients with sepsis 30 , 31 . Our results align with these data and highlight the need for early recognition of hepatic involvement in sepsis, as well as integrated prognostic models that account for liver-related variables. Analysis from our study revealed that higher BUN levels at ICU admission were independently linked to increased mortality in patients with sepsis. BUN reflects not only renal function but also systemic catabolic states, neurohormonal activation, and volume status, all of which are frequently altered in sepsis 32 , 33 . Several large cohort studies have demonstrated that high BUN is consistently linked to higher short- and long-term mortality in critically ill patients, including those with normal creatinine levels 34 , 35 . In septic populations specifically, BUN levels above 28–40 mg/dL have been shown to significantly increase 28- and 30-day mortality risks, even after adjusting for confounders such as APACHE and SAPS scores 33 , 35 . The underlying mechanisms may involve impaired renal perfusion, enhanced protein catabolism, and delayed nitrogen clearance, all of which reflect systemic disease severity. Given its accessibility and predictive value, BUN may serve as a practical biomarker for early risk stratification and tailored management in sepsis. Our findings reinforce the role of BUN as a clinically relevant and independent prognostic factor in septic ICU patients. Low Hb levels on ICU admission were independently associated with increased mortality in septic patients in our study, highlighting anemia as a potential prognostic marker in critical illness. Anemia is highly prevalent in sepsis due to mechanisms such as inflammation-driven suppression of erythropoiesis, hemodilution, hemolysis, and impaired erythropoietin response 36 , 37 . Several large retrospective studies have demonstrated a non-linear relationship between Hb levels and mortality in sepsis, with the highest risk observed at levels below 9–10 g/dL 38,39 . A multicenter study found that hemoglobin ≤ 8 g/dL in the first 48 hours after ICU admission was significantly associated with poorer 28-day survival 37 , and another analysis confirmed that in-hospital mortality decreased with rising hemoglobin levels up to a threshold of approximately 10.2 g/dL, beyond which mortality increased 36 . Although red blood cell transfusion can improve oxygen delivery, recent guidelines advocate a restrictive transfusion strategy—typically with a threshold of 7 g/dL—for most critically ill patients, including those with sepsis, due to concerns about transfusion-related complications 40 . Our findings support the prognostic significance of early Hb levels and suggest that while transfusions may be life-saving in severe anemia, maintaining Hb within an optimal range is crucial to improve outcomes in sepsis. In summary, our study developed and validated a novel prognostic nomogram to predict ICU mortality in patients with sepsis, incorporating a comprehensive set of clinical variables readily available upon ICU admission. Independent predictors identified through multivariate analysis included gender, BMI, APACHE II score, pneumonia, serum lactate, bilirubin, hemoglobin, BUN, and hyperkalemia. These variables reflect a multidimensional interplay between baseline host factors, severity of illness, organ dysfunction, and infection characteristics. The resulting nomogram demonstrated strong discriminatory and calibration performance, offering a practical and individualized tool for early mortality risk assessment in critically ill septic patients. Our study has several notable strengths, including the use of a well-characterized, five-year ICU cohort with robust clinical data, the integration of diverse predictors spanning demographic, physiological, and infection-specific domains, and the development of a graphical nomogram that enhances usability and bedside decision-making. However, several limitations should be acknowledged. This was a single-center retrospective study, which may limit external validity, and the nomogram was only internally validated without testing in independent cohorts. Additionally, certain emerging biomarkers such as procalcitonin and interleukin-6 were unavailable, and residual confounding inherent to retrospective designs cannot be excluded. Finally, the model should be regarded as a decision-support tool rather than a replacement for clinical judgment in the dynamic context of sepsis. Conclusion In conclusion, we developed and validated a novel nomogram incorporating key clinical, laboratory, and infection-related variables to predict ICU mortality among patients with sepsis. This tool demonstrated strong predictive performance and offers an individualized, bedside-friendly approach to early risk stratification. By integrating routinely available data, the nomogram has the potential to enhance clinical decision-making, prioritize care for high-risk patients, and support more personalized sepsis management in the ICU. Future external validation and prospective implementation studies are warranted to confirm its utility and impact across diverse clinical settings. Abbreviations AORs Adjusted Odds Ratios APACHE II Acute Physiology and Chronic Health Evaluation II ARDS Acute Respiratory Distress Syndrome BMI Body Mass Index BUN Blood Urea Nitrogen CAD Coronary Artery Disease CIs Confidence Intervals CLABSI Central Line-associated Bloodstream Infection COPD Chronic Obstructive Pulmonary Disease Cr Creatinine ESRD End-stage Renal Disease GNB Gram-negative Bacilli GPC Gram-positive Cocci Hb Hemoglobin ICU Intensive Care Unit K Potassium Na Sodium WBC White Blood Cell Count SOFA Sequential Organ Failure Assessment TISS Therapeutic Intervention Scoring System UTI Urinary Tract Infection Declarations Ethics approval and consent to participate We declare that the work described has been carried out in accordance with the Declaration of Helsinki of the World Medical Association revised in 2013 for experiments involving humans Consent for publication. The study involving human participants were approved by the Institutional Review Board of Chi Mei Medical Center No. 11001-007, date: 18 Feb 2021). Consent for publication Not applicable Competing interests The authors declare that they have no competing interests Funding This research received no external funding Author Contribution CMC: Conceptualization, methodology, data curation, formal analysis and writing—original draft preparation. WC: conceptualization, review and editing. HHT: writing—review and editing. HHL: writing—review and editing. Acknowledgement This study represents the collaborative efforts of numerous investigators and healthcare professionals, whose contributions are sincerely appreciated. Data Availability The data that support the findings of the current study may be requested from the corresponding author upon reasonable request. References Stoller J, Halpin L, Weis M, Aplin B, Qu W, Georgescu C, et al. Epidemiology of severe sepsis: 2008–2012. 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Increased body mass index and adjusted mortality in ICU patients with sepsis or SEPTIC SHOCK: A systematic review and meta-analysis. Crit Care. 2016;20(1). 10.1186/s13054-016-1360-z . Nguyen AT, Tsai C, Hwang L, Lai D, Markham C, Patel B. Obesity and mortality, length of stay and hospital cost among patients with sepsis: A nationwide inpatient retrospective cohort study. PLoS ONE. 2016;11(4). 10.1371/journal.pone.0154599 . Jagan N, Morrow LE, Walters RW, Plambeck RW, Wallen TJ, Patel TM, et al. Sepsis and the obesity paradox: Size matters in more than one way. Crit Care Med. 2020;48(9). 10.1097/ccm.0000000000004459 . Frydrych LM, Bian G, O’Lone DE, Ward PA, Delano MJ. Obesity and type 2 diabetes mellitus drive immune dysfunction, infection development, and sepsis mortality. J Leukoc Biol. 2018;104(3):525–34. 10.1002/jlb.5vmr0118-021rr . 1, Kuperman EF, Showalter JW, Lehman EB, Leib AE, Kraschnewski JL. The impact of obesity on sepsis mortality: A retrospective review. BMC Infect Dis. 2013;13(1). 10.1186/1471-2334-13-377 . Prest J, Nguyen T, Rajah T, Prest AB, Sathananthan M, Jeganathan N. Sepsis-related mortality rates and trends based on site of infection. Crit Care Explorations. 2022;4(10). 10.1097/cce.0000000000000775 . Pieroni M, Olier I, Ortega-Martorell S, Johnston BW, Welters ID. In-hospital mortality of sepsis differs depending on the origin of infection: An investigation of predisposing factors. Front Med. 2022;9. 10.3389/fmed.2022.915224 . Auriemma CL, Zhuo H, Delucchi K, Deiss T, Liu T, Jauregui A, et al. Acute respiratory distress syndrome-attributable mortality in critically ill patients with sepsis. Intensive Care Med. 2020;46(6):1222–31. 10.1007/s00134-020-06010-9 . Wang D-H, Jia H-M, Zheng X, Xi X-M, Zheng Y, Li W-X. Attributable mortality of Ards among critically ill patients with sepsis: A Multicenter, retrospective cohort study. BMC Pulm Med. 2024;24(1). 10.1186/s12890-024-02913-1 . Luo L, Shaver CM, Zhao Z, Koyama T, Calfee CS, Bastarache JA, et al. Clinical predictors of hospital mortality differ between direct and indirect Ards. Chest. 2017;151(4):755–63. 10.1016/j.chest.2016.09.004 . Yan J, Li S, Li S. The role of the liver in sepsis. Int Rev Immunol. 2014;33(6):498–510. 10.3109/08830185.2014.889129 . Gustot T, Durand F, Lebrec D, Vincent J, Moreau R. Severe sepsis in cirrhosis†. Hepatology. 2009;50(6):2022–33. 10.1002/hep.23264 . Arvaniti V, D’Amico G, Fede G, Manousou P, Tsochatzis E, Pleguezuelo M, et al. Infections in patients with cirrhosis increase mortality four-fold and should be used in determining prognosis. Gastroenterology. 2010;139(4). 10.1053/j.gastro.2010.06.019 . Foreman MG, Mannino DM, Moss M. Cirrhosis as a risk factor for sepsis and death. Chest. 2003 Sept;124(3):1016–20. 10.1378/chest.124.3.1016 . Patel JJ, Taneja A, Niccum D, Kumar G, Jacobs E, Nanchal R. The association of serum bilirubin levels on the outcomes of severe sepsis. J Intensive Care Med. 2013;30(1):23–9. 10.1177/0885066613488739 . Yang Z-X, Lv X-L, Yan J. Serum total bilirubin level is associated with hospital mortality rate in adult critically ill patients: A retrospective study. Front Med. 2021;8. 10.3389/fmed.2021.697027 . Arihan O, Wernly B, Lichtenauer M, Franz M, Kabisch B, Muessig J, et al. Blood urea nitrogen (BUN) is independently associated with mortality in critically ill patients admitted to ICU. PLoS ONE. 2018;13(1). 10.1371/journal.pone.0191697 . Harazim M, Tan K, Nalos M, Matejovic M. Blood urea nitrogen - independent marker of mortality in sepsis. Biomedical Papers. 2023;167(1):24–9. 10.5507/bp.2022.015 . Beier K, Eppanapally S, Bazick HS, Chang D, Mahadevappa K, Gibbons FK, et al. Elevation of blood urea nitrogen is predictive of long-term mortality in critically ill patients independent of normal creatinine*. Crit Care Med. 2011;39(2):305–13. 10.1097/ccm.0b013e3181ffe22a . Li X, Zheng R, Zhang T, Zeng Z, Li H, Liu J. Association between blood urea nitrogen and 30-day mortality in patients with sepsis: A retrospective analysis. Annals Palliat Med. 2021;10(11):11653–63. 10.21037/apm-21-2937 . Sheng S, Li A, Zhang C, Liu X, Zhou W, Shen T, et al. Association between hemoglobin and in-hospital mortality in critically ill patients with sepsis: Evidence from two large databases. BMC Infect Dis. 2024;24(1). 10.1186/s12879-024-10335-x . Qi D, Peng M. Early hemoglobin status as a predictor of long-term mortality for sepsis patients in Intensive Care Units. Shock. 2020;55(2):215–23. 10.1097/shk.0000000000001612 . Jung SM, Kim Y-J, Ryoo SM, Kim WY. Relationship between low hemoglobin levels and mortality in patients with septic shock. Acute Crit Care. 2019;34(2):141–7. 10.4266/acc.2019.00465 . Chen Y, Chen L, Meng Z, Li Y, Tang J, Liu S, et al. The correlation of hemoglobin and 28-day mortality in septic patients: Secondary data mining using the MIMIC-IV database. BMC Infect Dis. 2023;23(1). 10.1186/s12879-023-08384-9 . Coz Yataco AO, Soghier I, Hébert PC, Belley-Cote E, Disselkamp M, Flynn D, et al. Red blood cell transfusion in critically ill adults. Chest. 2025;167(2):477–89. 10.1016/j.chest.2024.09.016 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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08:49:02","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":143943,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7963988/v1/c9f148ce444015fa85093ca9.html"},{"id":95807541,"identity":"78ded5b4-9412-41e0-844e-5b3a15561b2c","added_by":"auto","created_at":"2025-11-13 08:48:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":93641,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting ICU mortality.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7963988/v1/3e1f80946a7c21f63a8d9b95.png"},{"id":96604964,"identity":"5db1b1d4-c9b0-4326-8a77-e179efa855a2","added_by":"auto","created_at":"2025-11-24 09:16:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1006878,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7963988/v1/dee85072-3c1e-4861-8c8f-e57c87d3f20c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting ICU Mortality in Sepsis: A Retrospective Cohort Study with Nomogram Development","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSepsis, defined as a dysregulated host response to infection leading to life-threatening organ dysfunction, remains a major global health concern and a leading cause of mortality among critically ill patients. According to recent estimates, sepsis affects nearly 50\u0026nbsp;million people worldwide each year, contributing to 11\u0026nbsp;million deaths \u0026mdash; roughly 20% of global mortality\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. A study by Chen et al. reported an incidence of 643 cases per 100,000 population between 2010 and 2014, with a case fatality rate of 29.2%, and an observable upward trend in both incidence and mortality\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eEarly identification and timely intervention are essential to improving outcomes in septic patients, yet sepsis presents a wide spectrum of clinical manifestations, making early risk stratification difficult\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Scoring systems like the SOFA and the APACHE II are widely used in the ICU to assess disease severity and predict mortality. However, these models primarily focus on physiologic dysfunction and organ failure, with limited integration of individual host characteristics such as age, comorbidities, and baseline functional status. To support precision decision-making, there is a growing need for more comprehensive, individualized prognostic tools in critical care settings.\u003c/p\u003e\u003cp\u003eNomograms are graphical representations of complex multivariable models that provide an intuitive means to calculate the probability of a clinical event in a specific patient. By incorporating diverse patient- and disease-related factors, nomograms can generate individualized risk estimates and have been increasingly applied across various clinical domains, including oncology, cardiovascular disease, and critical care medicine\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Compared with traditional scoring systems, nomograms offer several advantages: they accommodate continuous variables, allow for real-time prediction using digital tools, and are often better calibrated to specific patient populations\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn the context of sepsis, nomograms have shown promise in risk stratification, identifying high-risk subgroups, and guiding treatment strategies. For instance, previous studies have developed nomograms to predict outcomes such as extubation success, septic shock, or sepsis-associated encephalopathy\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Despite the widespread utility of such tools, concerns remain regarding their proper construction, overfitting, and lack of external validation\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Nevertheless, when appropriately developed and validated, nomograms can enhance clinical judgment and facilitate shared decision-making between healthcare providers and patients.\u003c/p\u003e\u003cp\u003eIn this study, we aimed to develop a novel prognostic nomogram to predict ICU mortality among septic patients, utilizing comprehensive clinical data from a five-year cohort admitted to the ICUs of a tertiary care medical center. By integrating demographic data, laboratory parameters, comorbidities, severity scores, infection sites, and pathogens, our objective was to construct an easy-to-use, evidence-based predictive model to assist in early risk identification and personalized care planning for critically ill patients with sepsis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipant selection\u003c/h2\u003e\u003cp\u003eA retrospective observational study was conducted at a tertiary care medical center between January 1, 2017, and December 31, 2021. The study included all adult patients (aged\u0026thinsp;\u0026gt;\u0026thinsp;18 years) admitted to any of the hospital\u0026rsquo;s eight ICUs, comprising a total of 95 beds. Patients were eligible if they met the diagnostic criteria for sepsis based on the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). The study was conducted in accordance with national guidelines and the principles of the Declaration of Helsinki. The study protocol was reviewed and approved by the Institutional Review Board of Chi Mei Medical Center (No. 11001-007, date: 18 Feb 2021). As this was a retrospective study using de-identified data, the requirement for informed consent was waived by the Institutional Review Board of Chi Mei Medical Center.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eThe following clinical and laboratory data were collected for all included patients: (1).Demographic and admission characteristics including age, gender, BMI, and origin of ICU admission (e.g., emergency department, internal medicine ward); (2),Severity of illness on ICU admission including APACHE II score, SOFA score, TISS score, presence of acute organ dysfunction, diagnosis of ARDS, based on the Berlin definition; (3).Comorbidities: pre-existing chronic conditions prior to ICU admission (e.g., diabetes, liver cirrhosis, cardiovascular disease); (4).Infectious characteristics: including primary site of infection and type of causative pathogens (e.g., gram-positive, gram-negative, fungal); (5).Laboratory data on ICU admission: including WBC, Hb, BUN, Cr, serum electrolytes (Na, K), total bilirubin and serum lactate. All data were obtained from the hospital\u0026rsquo;s electronic medical records and recorded using standardized case report forms.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). Continuous variables are presented as means with standard deviations and were compared using the independent samples t-test. Categorical variables are expressed as frequencies with percentages and were compared using the Pearson chi-square test.\u003c/p\u003e\u003cp\u003eTo identify potential risk factors associated with ICU mortality in septic patients, univariate logistic regression analysis was initially performed. Variables with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in the univariate analysis were subsequently entered into a multivariate logistic regression model using a forward stepwise selection approach. AORs and corresponding 95% CIs were calculated to determine the strength of associations between predictors and mortality.\u003c/p\u003e\u003cp\u003eBased on the final multivariable model, a nomogram was constructed to visually represent the predictive model and to facilitate individualized risk estimation for ICU mortality. The nomogram was created using the regression coefficients from the final model, with each predictor assigned a point value proportional to its relative contribution to mortality risk. The total score was then mapped to the corresponding predicted probability of death.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003ePatient Characteristics\u003c/h2\u003e\u003cp\u003eDuring the study period, a total of 922 patients with sepsis were admitted to the ICU. After excluding patients with incomplete data, 505 patients were included in the final analysis. Among them, 280 (55.4%) survived to ICU discharge, while 225 (44.6%) died during their ICU stay.\u003c/p\u003e\u003cp\u003eThe mean age of the patients was 68.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0 years, and 64.8% were male (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No significant differences in age or age group (\u0026lt;\u0026thinsp;65 vs. \u0026ge;65) were observed between survivors and non-survivors. However, gender and BMI were significantly associated with ICU mortality. Male patients had a lower mortality rate than female patients (40.7% vs. 51.7%, p\u0026thinsp;=\u0026thinsp;0.017). Patients with higher BMI had significantly higher mortality (mean BMI: 26.15\u0026thinsp;\u0026plusmn;\u0026thinsp;6.51 vs. 23.34\u0026thinsp;\u0026plusmn;\u0026thinsp;4.74; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Subgroup analysis showed that the group with BMI\u0026thinsp;\u0026gt;\u0026thinsp;27 had a markedly higher mortality rate (41.8%) compared to those with BMI 18.5\u0026ndash;27 (49.8%) or \u0026lt;\u0026thinsp;18.5 (8.4%).\u003c/p\u003e\u003cp\u003eRegarding comorbidities, liver cirrhosis was significantly more prevalent among non-survivors (13.8% vs. 6.8%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), while other conditions such as diabetes mellitus, hypertension, cancer, ESRD, and COPD did not show significant differences between groups.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSeverity of Illness and ICU Course\u003c/h2\u003e\u003cp\u003eDisease severity on ICU admission was significantly associated with mortality (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Non-survivors had higher SOFA scores (11.42\u0026thinsp;\u0026plusmn;\u0026thinsp;4.32 vs. 7.79\u0026thinsp;\u0026plusmn;\u0026thinsp;3.80, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TISS scores (33.80\u0026thinsp;\u0026plusmn;\u0026thinsp;10.21 vs. 28.29\u0026thinsp;\u0026plusmn;\u0026thinsp;7.89, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and APACHE II scores (28.24\u0026thinsp;\u0026plusmn;\u0026thinsp;8.70 vs. 21.13\u0026thinsp;\u0026plusmn;\u0026thinsp;8.24, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, the incidence of septic shock and ARDS was significantly higher in non-survivors (septic shock: 59.1% vs. 27.9%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ARDS: 48.4% vs. 21.1%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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\u003eDemographic and admission characteristics of ICU sepsis patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall(n\u0026thinsp;=\u0026thinsp;505)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eICU survivors (n\u0026thinsp;=\u0026thinsp;280)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eICU non-survivors (n\u0026thinsp;=\u0026thinsp;225)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68.33\u0026thinsp;\u0026plusmn;\u0026thinsp;14.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.14\u0026thinsp;\u0026plusmn;\u0026thinsp;13.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68.58\u0026thinsp;\u0026plusmn;\u0026thinsp;14.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.7250\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge group, 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\u003e0.2408\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e190(37.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99(35.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91(40.44)\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\u003e\u0026ge; 65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e315(62.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e181(64.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e134(59.56)\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\u003eGender, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0174\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e178(35.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86(30.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92(40.89)\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\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e327(64.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e194(69.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e133(59.11)\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\u003eBMI, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.59\u0026thinsp;\u0026plusmn;\u0026thinsp;5.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.34\u0026thinsp;\u0026plusmn;\u0026thinsp;4.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.15\u0026thinsp;\u0026plusmn;\u0026thinsp;6.51\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\u003eBMI group\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\u003e\u0026lt;18.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58(11.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39(13.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19(8.44)\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\u003e18.5\u0026ndash;27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e293(58.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e181(64.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e112(49.78)\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\u003e\u0026gt;27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e154(30.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60(21.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94(41.78)\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\u003eComorbidities, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes mellitus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e204(40.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e114(40.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90(40.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.8708\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e147(29.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78(27.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69(30.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4897\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of CAD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49(9.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28(10.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21(9.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.8014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of stroke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34(6.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20(7.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14(6.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6815\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver cirrhosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50(9.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19(6.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31(13.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0089\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e122(24.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66(23.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56(24.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.7310\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eESRD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51(10.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30(10.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21(9.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6087\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64(12.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31(11.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33(14.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.2274\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisease severity on admission\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\u003eSOFA, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.41\u0026thinsp;\u0026plusmn;\u0026thinsp;4.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.79\u0026thinsp;\u0026plusmn;\u0026thinsp;3.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.42\u0026thinsp;\u0026plusmn;\u0026thinsp;4.32\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\u003eTISS, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.74\u0026thinsp;\u0026plusmn;\u0026thinsp;9.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.29\u0026thinsp;\u0026plusmn;\u0026thinsp;7.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.80\u0026thinsp;\u0026plusmn;\u0026thinsp;10.21\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\u003eAPACHE II, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.30\u0026thinsp;\u0026plusmn;\u0026thinsp;9.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.13\u0026thinsp;\u0026plusmn;\u0026thinsp;8.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.24\u0026thinsp;\u0026plusmn;\u0026thinsp;8.70\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\u003eSeptic shock, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e211(41.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78(27.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e133(59.11)\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\u003eARDS, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e168(33.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59(21.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e109(48.44)\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\u003e*Categorical variables are calculated using Pearson\u0026rsquo;s chi-square and continuous variables are estimated using Independent Sample t test.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSite of Infection and Microbiology\u003c/h3\u003e\n\u003cp\u003ePneumonia was the most common infection source and was significantly more prevalent among non-survivors (71.6% vs. 60.0%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Other sources of infection, such as UTI, bloodstream infection, and CLABSI, showed no significant differences. The distribution of infection organisms\u0026mdash;including gram-negative bacteria, gram-positive cocci, fungal, viral, or mixed infections\u0026mdash;did not significantly differ between survivors and non-survivors.\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\u003eInfection site and infection organism of ICU sepsis patients\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(n\u0026thinsp;=\u0026thinsp;505)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eICU survivors (n\u0026thinsp;=\u0026thinsp;280)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eICU non-survivors (n\u0026thinsp;=\u0026thinsp;225)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathology and infection site, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBacteremia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e211(41.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e110(39.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e101(44.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.2045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUTI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e185(36.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e105(37.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e80(35.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6522\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePneumonia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e329(65.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e168(60.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e161(71.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0068\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCLABSI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28(5.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17(6.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11(4.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5638\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfection organism, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBacterial infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e401(79.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e223(79.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e178(79.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.8832\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGNB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e376(74.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e210(75.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e166(73.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.7543\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e175(34.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e103(36.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e72(32.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.2613\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFungus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e106(20.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57(20.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49(21.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6968\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCandida albicans\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78(15.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47(16.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31(13.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.3525\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVirus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26(5.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14(5.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12(5.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.8662\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e*Categorical variables are calculated using Pearson\u0026rsquo;s chi-square.\u003c/p\u003e\n\u003ch3\u003eLaboratory Findings on ICU Admission\u003c/h3\u003e\n\u003cp\u003eSeveral laboratory parameters at ICU admission were associated with mortality (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Non-survivors had significantly higher serum lactate (5.50\u0026thinsp;\u0026plusmn;\u0026thinsp;5.86 vs. 2.56\u0026thinsp;\u0026plusmn;\u0026thinsp;2.65 mmol/L, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), total bilirubin (3.17\u0026thinsp;\u0026plusmn;\u0026thinsp;5.12 vs. 1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.99 mg/dL, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and BUN (42.25\u0026thinsp;\u0026plusmn;\u0026thinsp;31.04 vs. 36.56\u0026thinsp;\u0026plusmn;\u0026thinsp;28.98 mg/dL, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034). Hb levels were significantly lower among non-survivors (10.05\u0026thinsp;\u0026plusmn;\u0026thinsp;4.87 vs. 11.29\u0026thinsp;\u0026plusmn;\u0026thinsp;6.21 g/dL, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013). Electrolyte disturbances were also noted. Patients with serum K\u0026thinsp;\u0026gt;\u0026thinsp;5.0 mmol/L had significantly higher mortality (5.3% vs. 1.4% among survivors; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032), though serum sodium levels showed only a borderline difference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.055).\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\u003eLaboratory data on admission of ICU sepsis patient\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall(n\u0026thinsp;=\u0026thinsp;505)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eICU survivors (n\u0026thinsp;=\u0026thinsp;280)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eICU non-survivors (n\u0026thinsp;=\u0026thinsp;225)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.08\u0026thinsp;\u0026plusmn;\u0026thinsp;10.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.17\u0026thinsp;\u0026plusmn;\u0026thinsp;9.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.97\u0026thinsp;\u0026plusmn;\u0026thinsp;11.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.8297\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC group\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\u003e0.0192\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;4\u003csup\u003e༊\u003c/sup\u003e10^3/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55(10.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22(7.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33(14.67)\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\u003e4\u0026ndash;11\u003csup\u003e༊\u003c/sup\u003e10^3/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e145(28.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90(32.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55(24.44)\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\u003e\u0026gt;11\u003csup\u003e༊\u003c/sup\u003e10^3/\u0026micro;L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e305(60.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e168(60.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e137(60.89)\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\u003eHemoglobin, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.74\u0026thinsp;\u0026plusmn;\u0026thinsp;5.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.29\u0026thinsp;\u0026plusmn;\u0026thinsp;6.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.05\u0026thinsp;\u0026plusmn;\u0026thinsp;4.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0125\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin, 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\u003e0.0769\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;7g/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97(19.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46(16.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51(22.67)\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\u003e\u0026ge;7g/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e408(80.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e234(83.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e174(77.33)\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\u003eBUN(mg/dl), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.10\u0026thinsp;\u0026plusmn;\u0026thinsp;30.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.56\u0026thinsp;\u0026plusmn;\u0026thinsp;28.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.25\u0026thinsp;\u0026plusmn;\u0026thinsp;31.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0342\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCr(mg/dl), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;8.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;11.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.5495\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNa(mmol/L), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e136.09\u0026thinsp;\u0026plusmn;\u0026thinsp;7.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e135.73\u0026thinsp;\u0026plusmn;\u0026thinsp;6.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e136.54\u0026thinsp;\u0026plusmn;\u0026thinsp;7.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.2040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNa(mmol/L), 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\u003e0.0548\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e215(42.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130(46.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e85(37.78)\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\u003e135\u0026ndash;145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e247(48.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e132(47.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e115(51.11)\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\u003e\u0026gt;145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43(8.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18(6.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25(11.11)\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\u003eK(mmol/L), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6673\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK(mmol/L), 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\u003e0.0317\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e295(58.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e162(57.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e133(59.11)\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\u003e3.5-5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e194(38.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e114(40.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80(35.56)\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\u003e\u0026gt;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16(3.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4(1.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12(5.33)\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\u003eBilirubin, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.44\u0026thinsp;\u0026plusmn;\u0026thinsp;4.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.17\u0026thinsp;\u0026plusmn;\u0026thinsp;5.12\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\u003eLactate, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.87\u0026thinsp;\u0026plusmn;\u0026thinsp;4.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.56\u0026thinsp;\u0026plusmn;\u0026thinsp;2.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.50\u0026thinsp;\u0026plusmn;\u0026thinsp;5.86\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\u003e*Categorical variables are calculated using Pearson\u0026rsquo;s chi-square and continuous variables are estimated using Independent Sample t test.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eMultivariate Analysis and Predictors of ICU Mortality\u003c/h2\u003e\u003cp\u003eMultivariate logistic regression identified the following variables as independent predictors of ICU mortality (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e): male gender (AOR\u0026thinsp;=\u0026thinsp;0.62, 95% CI: 0.39\u0026ndash;0.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043), higher BMI (AOR\u0026thinsp;=\u0026thinsp;1.09 per unit increase, 95% CI: 1.04\u0026ndash;1.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001), APACHE II score (AOR\u0026thinsp;=\u0026thinsp;1.07 per unit increase, 95% CI: 1.03\u0026ndash;1.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0002), pneumonia as an infection source (AOR\u0026thinsp;=\u0026thinsp;2.45, 95% CI: 1.50\u0026ndash;3.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0003), hemoglobin (AOR\u0026thinsp;=\u0026thinsp;0.96 per g/dL, 95% CI: 0.92\u0026ndash;0.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042), total bilirubin (AOR\u0026thinsp;=\u0026thinsp;1.07 per mg/dL, 95% CI: 1.01\u0026ndash;1.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015) and lactate level (AOR\u0026thinsp;=\u0026thinsp;1.08 per mmol/L, 95% CI: 1.01\u0026ndash;1.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030). These variables were subsequently used to construct a predictive nomogram for ICU mortality.\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\u003ePredictors of ICU mortality\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCrude OR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eDemographic characteristics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.64(0.44,0.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.62(0.39,0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0435\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.10(1.06,1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.09(1.04,1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eComorbidities\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver Cirrhosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.20(1.20,4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.24(0.58,2.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5761\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eDisease severity on admission\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOFA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.24(1.18,1.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.08(1.00,1.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0511\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTISS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.07(1.05,1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00(0.97,1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8748\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPACHE II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.10(1.08,1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.07(1.03,1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeptic shock\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.74(2.58,5.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.50(0.90,2.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1181\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARDS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.52(2.39,5.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.62(0.99,2.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0515\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003ePathology and infection site\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePneumonia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.68(1.15,2.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.45(1.50,3.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eLaboratory data on admission\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.96(0.93,0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.96(0.92,0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0424\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN (mg/dl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.01(1.00,1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00(0.99,1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6793\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBilirubin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.10(1.04,1.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.07(1.01,1.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLactate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.21(1.14,1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.08(1.01,1.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0297\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eNomogram for Predicting ICU Mortality\u003c/h2\u003e\u003cp\u003eA nomogram was constructed using the significant predictors identified in the multivariate logistic regression model, aiming to provide an intuitive and individualized prediction tool for ICU mortality among patients with sepsis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The variables incorporated into the model include: hemoglobin level, presence of pneumonia, ARDS, septic shock, APACHE II score, TISS score, SOFA score, liver cirrhosis, BMI, gender, serum lactate level, serum bilirubin level and serum BUN level.\u003c/p\u003e\u003cp\u003eEach variable corresponds to a point scale at the top of the nomogram. To estimate a patient's probability of ICU mortality, a vertical line is drawn from each predictor value to the corresponding \"Score\" line. The individual scores are then summed to yield a total score, which is located at the bottom axis of the nomogram. This total score is mapped to the \"Probability\" scale to determine the patient\u0026rsquo;s predicted risk of death during the ICU stay. This graphical representation provides clinicians with a user-friendly tool to facilitate risk stratification and decision-making at the bedside.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study, male gender was independently associated with lower ICU mortality among septic patients, a finding that contrasts with some prior reports. While earlier experimental studies suggested that females may have better immunologic responses to infection\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, clinical data remain mixed. For example, Eachempati et al. found female gender to be an independent predictor of increased mortality in surgical ICU patients with sepsis\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, and a large multicenter study also reported higher hospital mortality in females despite adjustment for confounders\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. These inconsistencies may reflect biological differences in immune response as well as gender disparities in care delivery. Our findings support the need for further investigation into gender-specific risk factors and tailored sepsis management strategies.\u003c/p\u003e\u003cp\u003eEvidence from our cohort revealed that higher BMI independently correlated with increased ICU mortality in sepsis patients, which contrasts with the \u0026ldquo;obesity paradox\u0026rdquo; described in prior literature. While numerous studies have described an inverse relationship between BMI and sepsis-related mortality\u0026mdash;suggesting a potential protective effect of adiposity\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u0026mdash;this association remains controversial. Several meta-analyses and large-scale cohort studies have observed reduced adjusted mortality in overweight and obese patients compared to those with normal BMI, possibly due to greater metabolic reserves, anti-inflammatory effects of adipokines, and altered immune responses\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. However, other studies, including ours, have indicated that increased BMI may instead correlate with worse outcomes, particularly in the setting of severe physiological stress or coexisting metabolic dysfunction\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Mechanistically, chronic low-grade inflammation, immune dysregulation, and impaired tissue perfusion commonly seen in obesity may contribute to poorer outcomes in critical illness. These findings reflect the complex and sometimes contradictory role of obesity in sepsis and highlight the need for further investigation into the underlying biological mechanisms and clinical phenotypes that may mediate this relationship.\u003c/p\u003e\u003cp\u003eOur study demonstrated that both pneumonia and ARDS were independently associated with increased ICU mortality in septic patients, reinforcing the critical role of pulmonary complications in sepsis prognosis. Pneumonia remains one of the most common sites of infection leading to sepsis and is consistently associated with higher mortality compared to other infection sources\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The elevated mortality risk in pulmonary sepsis may stem from profound hypoxemia, heightened inflammatory responses in lung tissue, and delayed recognition or source control compared to other infection sites\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Furthermore, the development of ARDS in septic patients significantly worsens outcomes. Multiple multicenter studies have shown that ARDS, particularly in its severe form, contributes independently to increased ICU and in-hospital mortality \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The attributable 30-day mortality of ARDS in sepsis has been estimated as high as 12% overall, with nearly 20% in severe ARDS\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The pathophysiology of ARDS involves profound injury to the pulmonary endothelium and epithelium, typically worsened by sepsis-induced systemic inflammation and vascular leakage. Notably, ARDS is not a homogenous entity\u0026mdash;patients with direct (pulmonary-derived) ARDS, such as from pneumonia, exhibit distinct clinical characteristics compared to those with indirect (extrapulmonary) causes, and these subtypes may respond differently to treatment\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Our findings highlight the complex interplay between infection site, organ dysfunction, and host response, and further emphasize the importance of early recognition and tailored interventions in patients with pulmonary infection and ARDS during sepsis.\u003c/p\u003e\u003cp\u003eElevated serum bilirubin and a history of liver cirrhosis were identified in our study as independent predictors of increased ICU mortality among septic patients. These findings underscore the pivotal role of liver function in the pathophysiology and prognosis of sepsis. The liver acts as both a metabolic and immunologic organ during sepsis, responsible for clearing bacteria, endotoxins, and inflammatory mediators \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Liver dysfunction in sepsis can impair this clearance, exacerbate systemic inflammation, and contribute to multiple organ failure\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Cirrhosis, in particular, is associated with a hyperinflammatory cytokine response and immune dysregulation, which elevate the risk of organ failure and sepsis-related death\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Multiple studies have confirmed that patients with cirrhosis face a significantly higher risk of developing sepsis and have worse outcomes when it occurs\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Hyperbilirubinemia, a common marker of liver dysfunction, has also been independently associated with increased mortality in critically ill patients with sepsis\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Our results align with these data and highlight the need for early recognition of hepatic involvement in sepsis, as well as integrated prognostic models that account for liver-related variables.\u003c/p\u003e\u003cp\u003eAnalysis from our study revealed that higher BUN levels at ICU admission were independently linked to increased mortality in patients with sepsis. BUN reflects not only renal function but also systemic catabolic states, neurohormonal activation, and volume status, all of which are frequently altered in sepsis \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Several large cohort studies have demonstrated that high BUN is consistently linked to higher short- and long-term mortality in critically ill patients, including those with normal creatinine levels\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. In septic populations specifically, BUN levels above 28\u0026ndash;40 mg/dL have been shown to significantly increase 28- and 30-day mortality risks, even after adjusting for confounders such as APACHE and SAPS scores\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The underlying mechanisms may involve impaired renal perfusion, enhanced protein catabolism, and delayed nitrogen clearance, all of which reflect systemic disease severity. Given its accessibility and predictive value, BUN may serve as a practical biomarker for early risk stratification and tailored management in sepsis. Our findings reinforce the role of BUN as a clinically relevant and independent prognostic factor in septic ICU patients.\u003c/p\u003e\u003cp\u003eLow Hb levels on ICU admission were independently associated with increased mortality in septic patients in our study, highlighting anemia as a potential prognostic marker in critical illness. Anemia is highly prevalent in sepsis due to mechanisms such as inflammation-driven suppression of erythropoiesis, hemodilution, hemolysis, and impaired erythropoietin response\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Several large retrospective studies have demonstrated a non-linear relationship between Hb levels and mortality in sepsis, with the highest risk observed at levels below 9\u0026ndash;10 g/dL\u003csup\u003e38,39\u003c/sup\u003e. A multicenter study found that hemoglobin\u0026thinsp;\u0026le;\u0026thinsp;8 g/dL in the first 48 hours after ICU admission was significantly associated with poorer 28-day survival\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, and another analysis confirmed that in-hospital mortality decreased with rising hemoglobin levels up to a threshold of approximately 10.2 g/dL, beyond which mortality increased\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Although red blood cell transfusion can improve oxygen delivery, recent guidelines advocate a restrictive transfusion strategy\u0026mdash;typically with a threshold of 7 g/dL\u0026mdash;for most critically ill patients, including those with sepsis, due to concerns about transfusion-related complications\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Our findings support the prognostic significance of early Hb levels and suggest that while transfusions may be life-saving in severe anemia, maintaining Hb within an optimal range is crucial to improve outcomes in sepsis.\u003c/p\u003e\u003cp\u003eIn summary, our study developed and validated a novel prognostic nomogram to predict ICU mortality in patients with sepsis, incorporating a comprehensive set of clinical variables readily available upon ICU admission. Independent predictors identified through multivariate analysis included gender, BMI, APACHE II score, pneumonia, serum lactate, bilirubin, hemoglobin, BUN, and hyperkalemia. These variables reflect a multidimensional interplay between baseline host factors, severity of illness, organ dysfunction, and infection characteristics. The resulting nomogram demonstrated strong discriminatory and calibration performance, offering a practical and individualized tool for early mortality risk assessment in critically ill septic patients.\u003c/p\u003e\u003cp\u003eOur study has several notable strengths, including the use of a well-characterized, five-year ICU cohort with robust clinical data, the integration of diverse predictors spanning demographic, physiological, and infection-specific domains, and the development of a graphical nomogram that enhances usability and bedside decision-making. However, several limitations should be acknowledged. This was a single-center retrospective study, which may limit external validity, and the nomogram was only internally validated without testing in independent cohorts. Additionally, certain emerging biomarkers such as procalcitonin and interleukin-6 were unavailable, and residual confounding inherent to retrospective designs cannot be excluded. Finally, the model should be regarded as a decision-support tool rather than a replacement for clinical judgment in the dynamic context of sepsis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, we developed and validated a novel nomogram incorporating key clinical, laboratory, and infection-related variables to predict ICU mortality among patients with sepsis. This tool demonstrated strong predictive performance and offers an individualized, bedside-friendly approach to early risk stratification. By integrating routinely available data, the nomogram has the potential to enhance clinical decision-making, prioritize care for high-risk patients, and support more personalized sepsis management in the ICU. Future external validation and prospective implementation studies are warranted to confirm its utility and impact across diverse clinical settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAORs Adjusted Odds Ratios\u003c/p\u003e\u003cp\u003eAPACHE II Acute Physiology and Chronic Health Evaluation II\u003c/p\u003e\u003cp\u003eARDS Acute Respiratory Distress Syndrome\u003c/p\u003e\u003cp\u003eBMI Body Mass Index\u003c/p\u003e\u003cp\u003eBUN Blood Urea Nitrogen\u003c/p\u003e\u003cp\u003eCAD Coronary Artery Disease\u003c/p\u003e\u003cp\u003eCIs Confidence Intervals\u003c/p\u003e\u003cp\u003eCLABSI Central Line-associated Bloodstream Infection\u003c/p\u003e\u003cp\u003eCOPD Chronic Obstructive Pulmonary Disease\u003c/p\u003e\u003cp\u003eCr Creatinine\u003c/p\u003e\u003cp\u003eESRD End-stage Renal Disease\u003c/p\u003e\u003cp\u003eGNB Gram-negative Bacilli\u003c/p\u003e\u003cp\u003eGPC Gram-positive Cocci\u003c/p\u003e\u003cp\u003eHb Hemoglobin\u003c/p\u003e\u003cp\u003eICU Intensive Care Unit\u003c/p\u003e\u003cp\u003eK Potassium\u003c/p\u003e\u003cp\u003eNa Sodium\u003c/p\u003e\u003cp\u003eWBC White Blood Cell Count\u003c/p\u003e\u003cp\u003eSOFA Sequential Organ Failure Assessment\u003c/p\u003e\u003cp\u003eTISS Therapeutic Intervention Scoring System\u003c/p\u003e\u003cp\u003eUTI Urinary Tract Infection\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare that the work described has been carried out in accordance with the Declaration of Helsinki of the World Medical Association revised in 2013 for experiments involving humans Consent for publication. The study involving human participants were approved by the Institutional Review Board of Chi Mei Medical Center No. 11001-007, date: 18 Feb 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research received no external funding\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eCMC: Conceptualization, methodology, data curation, formal analysis and writing\u0026mdash;original draft preparation. WC: conceptualization, review and editing. HHT: writing\u0026mdash;review and editing. HHL: writing\u0026mdash;review and editing.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThis study represents the collaborative efforts of numerous investigators and healthcare professionals, whose contributions are sincerely appreciated.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of the current study may be requested from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStoller J, Halpin L, Weis M, Aplin B, Qu W, Georgescu C, et al. Epidemiology of severe sepsis: 2008\u0026ndash;2012. 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Chest. 2025;167(2):477\u0026ndash;89. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chest.2024.09.016\u003c/span\u003e\u003cspan address=\"10.1016/j.chest.2024.09.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sepsis, ICU mortality, nomogram, risk stratification, critical care","lastPublishedDoi":"10.21203/rs.3.rs-7963988/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7963988/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eSepsis remains a leading cause of death among critically ill patients, yet existing severity scores such as the APACHE II and the SOFA provide limited individualized prognostic accuracy. This study aimed to develop and internally validate a nomogram to predict ICU mortality among adult patients with sepsis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe performed a retrospective cohort study including 505 adult patients with sepsis admitted to the ICUs of a tertiary medical center in Taiwan between 2017 and 2021. Clinical, laboratory, and infection-related data at ICU admission were extracted from electronic medical records. Univariate and multivariate logistic regression analyses were used to identify independent predictors of ICU mortality. Variables with statistical significance in the multivariate model were incorporated into a predictive nomogram. Model calibration and discrimination were evaluated using calibration plots and the area under the ROC curve.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 505 patients, 225 (44.6%) died during ICU stay. Independent predictors of ICU mortality included male gender (adjusted odds ratio [AOR] 0.62, 95% confidence interval [CI] 0.39\u0026ndash;0.99), higher body mass index (AOR 1.09 per kg/m\u0026sup2;, 95% CI 1.04\u0026ndash;1.13), higher APACHE II score (AOR 1.07 per point, 95% CI 1.03\u0026ndash;1.10), pneumonia as the primary infection source (AOR 2.45, 95% CI 1.50\u0026ndash;3.99), lower hemoglobin level (AOR 0.96 per g/dL, 95% CI 0.92\u0026ndash;0.99), and higher serum bilirubin (AOR 1.07 per mg/dL, 95% CI 1.01\u0026ndash;1.14) and lactate (AOR 1.08 per mmol/L, 95% CI 1.01\u0026ndash;1.16). The nomogram demonstrated good discrimination (area under the curve\u0026thinsp;=\u0026thinsp;0.84) and satisfactory calibration between predicted and observed mortality rates.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study developed an internally validated nomogram integrating demographic, physiologic, and biochemical parameters available at ICU admission to predict mortality in patients with sepsis. The model provides a practical, individualized bedside tool to assist early risk stratification, guide management decisions, and optimize resource allocation in critical care settings. External validation in independent cohorts is warranted.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"Predicting ICU Mortality in Sepsis: A Retrospective Cohort Study with Nomogram Development","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 08:12:55","doi":"10.21203/rs.3.rs-7963988/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cac5955d-1096-4e9a-8498-832deff96ae0","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T07:54:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 08:12:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7963988","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7963988","identity":"rs-7963988","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}