The Models for End-stage Liver Disease as prognostic assessment and risk stratification tools in sepsis: a study based on MIMIC-Ⅳ database

The Models for End-stage Liver Disease as prognostic assessment and risk stratification tools in sepsis: a study based on MIMIC-Ⅳ database | 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 The Models for End-stage Liver Disease as prognostic assessment and risk stratification tools in sepsis: a study based on MIMIC-Ⅳ database Tuo Shen, Xingping Lv, Yezhou Shen, Wei Zhou, Xiaobin Liu, Qimin Ma, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6455319/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: The Model for End-stage Liver Disease (MELD) and its modifications have been used to predict mortality and stratify risk for a variety of non-hepatic diseases with good stability. The purpose of this study was to evaluate the value of the MELD and its modifications in evaluating the prognosis of patients with sepsis. Methods: This study is based on the MIMIC-Ⅳ database. A total of 15,882 patients were included. The correlation between the three models and the mortality rate of patients with sepsis was evaluated, and the optimal cut-off values were calculated. Then, further subgroup analysis was performed to seek better stratification criteria. Finally, stratification was performed according to comorbidities to observe the predictive value of the MELDs in patients with different comorbidities. Results: MELD, MELD including Na (MELD-Na) and MELD excluding INR (MELD-XI) were all independent predictors of in-hospital mortality, and the optimal cut-off values were 22.5, 22.5 and 19.5, respectively. When grouped by cut-off values, high score groups were significantly associated with increased in-hospital mortality. Further subgroup analysis based on lactate revealed that patients with high MELD score and lactate level (> 4 mmol/L) had higher in-hospital mortality. Conclusions: The MELDs can effectively predict the in-hospital mortality of sepsis patients and stratify their risk. The MELDs combined with lactate can provide convenient risk stratification for sepsis patients, thus guiding clinicians to better intervene in sepsis patients at an early stage. Sepsis MELD Mortality Predict Lactate Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Sepsis is a series of syndromes in which infection leads to inflammation and dysregulation of immune responses in the body and secondary organ damage [ 1 – 4 ] . The sepsis population is obviously heterogeneous and the clinical situation is complex [ 5 , 6 ] , which makes us face great challenges in its diagnosis and treatment. The mortality rate of sepsis is about 15% -25%, and the hospitalization mortality rate of septic shock is as high as 30% -50%. Sepsis remains one of the leading causes of death worldwide, placing a huge burden on the global healthcare industry [ 7 , 8 ] . Sepsis not only brings patients the risk of immediate death, but it is also a long-term chronic critical disease [ 7 ] . The long-term damage it brings to patients cannot be ignored either. Therefore, in addition to early diagnosis and intervention of sepsis, risk stratification of sepsis patients is conducive to better management of sepsis, thereby improving its prognosis. Currently, there are many biomarkers used in sepsis identification, diagnosis, prognosis, risk stratification, etc., but no single biomarker has shown absolute advantages. It is often necessary to combine these markers to achieve better diagnosis and predictive effects [ 9 ] . Moreover, most biomarkers cannot be routinely collected in clinical work, which restricts their clinical efficacy to a certain extent. The Model for End-Stage Liver Disease (MELD) was initially used to evaluate the prognosis of patients receiving transjugular intrahepatic portosystemic shunt [ 10 ] , and has since been widely used for threat and risk assessment in patients with advanced liver disease [ 11 ] . The MELD mainly contains three indicators: bilirubin, creatinine and international normalized ratio (INR), which are objective indicators that are often collected repeatedly in clinical work, and can be used to reflect the liver and kidney dysfunction of patients. Liver and kidney dysfunction is also common in other serious diseases. Therefore, the MELD and its modifications have been widely used in mortality prediction and risk stratification for non-liver diseases [ 12 – 20 ] . The purpose of this study was to evaluate the feasibility of using the MELD and its modifications to predict the prognosis of sepsis and to stratify its risk. 2. Methods 2.1. Study design This is a retrospective study based on a large US database, Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) [ 21 ] . The MIMIC-Ⅳ-2.2 database contains information on hospitalized patients from 2008 to 2019. One of the authors (SYS) obtained access to the database and was responsible for data extraction (authentication number 48693098). Patient information was anonymized, thereby obviating the need for informed consent from individual patients for this study. 2.2. Selection of participants Inclusion criteria were as follows: (1) patients with sepsis in the MIMIC-Ⅳ database (defined by sepsis-3 criteria) [ 2 ] ; (2) adults admitted to the intensive intensive care unit (ICU) (≥ 18 years old). Exclusion criteria were as follows: (1) the length of stay in the ICU is less than 24 hours; (2) insufficient data (including creatinine, total bilirubin, international normalized ratio (INR), sodium). In addition, we analyzed first-time ICU admissions only for patients with multiple ICU admissions. 2.3. Data collection Structured Query Language (SQL) using PostgresSQL software (version 16.0). The following basic data were extracted within 24 hours of admission to ICU: age, sex, weight, comorbidities, mean arterial pressure (MAP), laboratory tests, and treatment. Variables with missing values greater than 30% were excluded from the analysis. Variables with missing values less than 30% were multiplexed by multiplex interpolation (MI) based on five replicates. MELD scores were calculated as follows: MELD score = 11.2 × (ln INR) + 3.78 × ln (serum bilirubin in mg/dL) + 9.57 × ln (serum creatinine in mg/dL) + 6.43; MELD-Na score = MELD + 1.32 × (137- serum sodium) - [0.033 × MELD × (137- serum sodium)]; MELD-XI score = 5.11 × ln (serum bilirubin in mg/dL) + 11.76 × ln (serum creatinine in mg/dL) + 9.44. Creatinine and bilirubin concentration below 1 were set to 1 and creatinine above 4 was set to 4 to avoid negative values. Outcomes The primary outcome was in-hospital mortality. The secondary outcome was 28-day mortality, ICU Los and Hospital Los. Statistical analysis Continuous variables are expressed as mean ± standard deviation (SD) or median and interquartile ranges (IQR) according to their different distributions, and categorical variables are expressed as numbers and percentages. The categorical variables were calculated using the chi-square test or Fisher precision test for intergroup comparisons, and the continuous variables were calculated using the Student t test or Mann-Whitney U test as appropriate. Univariate and multivariate logistic regression analyses were used to determine the association between MELD scores and in-hospital mortality in patients with sepsis. Receiver operating characteristic curves (ROC) and area under the curve (AUC) were used to assess the predictive performance of different MELDs and calculate cut-offs. Kaplan-Meier analyses were used as sensitivity analyses to explore the association between different MELDs and in-hospital mortality endpoints. Statistical analysis was performed using SPSS version 22.0 (IBM, USA) and R 4.3.2 (R Foundation). Statistical significance was defined as a two-sided P-value < 0.05. This was a retrospective study, and the patient data information was de-identified and patient identifiers were removed. Therefore, the study was not considered a human-subject study and was exempted from Institutional Review Board (IRB) review at The George Washington University. Informed patient consent was therefore not required. All methods were performed in accordance with the relevant guidelines and regulations of The George Washington University. 3. Results 3.1. Study population A total of 15,882 patients with sepsis were included in the analysis (Fig. 1 ). The in-hospital mortality and 28-day mortality were 20.05% and 24.32%, respectively. The baseline data of the non-survivor group and the survivor group were compared in Table 1 . The MELD score, MELD-Na score, and MELD-XI score in the non-survivor group were significantly higher than those in the survivor group (20 vs. 15, 22 vs. 17, 19 vs. 15, respectively). In addition to the MELDs, the Sequential Organ Failure Assessment (SOFA) score and the Charlson Comorbidity Index (CCI) Score indicating the comorbidities of the patient were also higher in the non-survivor group (P < 0.001). Other data and clinical characteristics were also compared in the non-survivor group and the survivor group in Table 1 . Table 1 Baseline characteristics of the survivor and non-survivor groups Variables Overall Survivors Non-survivors P-value (n = 15882) (n = 12020) (n = 3862) Male, [n (%)] 9201 (57.93) 7378 (58.11) 1823 (57.24) 0.384 Age, [years, M(IQR)] 67 [55, 78] 66 [55, 77] 70 [58, 80] < 0.001 Platelet, [10^9/L, M(IQR)] 157 [101, 228] 161 [105, 230] 145 [78, 221] < 0.001 Creatinine, [mg/dL, M(IQR)] 1.30 [0.90, 2.30] 1.30 [0.90, 2.10] 1.70 [1.10, 2.80] < 0.001 INR, [M(IQR)] 1.40 [1.20, 1.90] 1.40 [1.20, 1.80] 1.60 [1.30, 2.40] < 0.001 Total bilirubin, [mg/dL, M(IQR)] 0.80 [0.40, 1.70] 0.70 [0.40, 1.60] 1.00 [0.50, 2.50] < 0.001 Lactate, [mmol/L, M(IQR)] 2.10 [1.30, 3.50] 2.00 [1.30, 3.20] 3.00 [1.80, 5.70] < 0.001 MAP, [mmHg, M(IQR)] 57 [50, 63] 57 [50, 64] 54 [46, 61] < 0.001 Urine output, [mL, M(IQR)] 1400 [800, 2280] 1515 [925, 2400] 904 [400, 1615] < 0.001 CCI, [M(IQR)] 6 [4, 8] 6 [4, 8] 7 [5, 9] < 0.001 SOFA score, [M(IQR)] 3 [2, 5] 3 [2, 5] 4 [3, 6] < 0.001 MELD score, [M(IQR)] 16 [11, 23] 15 [10, 22] 20 [13, 28] < 0.001 MELD-Na score, [M(IQR)] 18 [11, 25] 17 [10, 24] 22 [14, 30] < 0.001 MELD-XI score, [M(IQR)] 15 [11, 23] 15 [11, 21] 19 [13, 26] < 0.001 Mechanical ventilation, [n (%)] 7859 (49.48) 5856 (46.12) 2003 (62.89) < 0.001 Vasopressor use, [n (%)] 7187 (45.25) 5246 (41.32) 1941 (60.94) < 0.001 RRT, [n (%)] 834 (5.25) 577 (4.54) 257 (8.07) < 0.001 INR, international normalized ratio; MAP, mean arterial pressure(mmHg); CCI, Charlson Comorbidity Index; SOFA, Sequential Organ Failure Assessment; MELD, Model for End-Stage Liver Disease; RRT, renal replacement therapy. 3.2. The MELDs and in-hospital and 28-day mortality To study the relationship between the MELDs and mortality, we designed a 3-logistic regression model to study the independent role of the MELDs in mortality prediction (Table 2 ). After adjusting for multivariate factors including age, sex, weight, disease history, Charlson score, SOFA score, vital signs, laboratory tests, and treatment methods (Model 3), the ORs and 95% confidence intervals (CIs) corresponding to in-hospital mortality for MELD score, MELD-Na score, and MELD-XI score after multivariate adjustment were 1.023 (1.012, 1.035), 1.023 (1.012, 1.033), and 1.016 (1.004, 1.028), respectively. The ORs and 95% CIs for 28-day mortality were 1.022 (1.012, 1.033), 1.024 (1.014, 1.034), and 1.010 (0.998, 1.021), respectively. Table 2 The association between the MELDs and in-hospital and 28-day mortality Exposure Model 1 Model 2 Model 3 OR (95% CI) P-value OR (95% CI) P-value OR (95% CI) P-value In-hospital mortality MELD 1.060 (1.055, 1.065) < 0.001 1.063 (1.058, 1.068) < 0.001 1.023(1.012, 1.035) < 0.001 MELD-Na 1.057 (1.052, 1.062) < 0.001 1.060 (1.055, 1.065) < 0.001 1.023(1.012, 1.033) < 0.001 MELD-XI 1.060 (1.054, 1.065) < 0.001 1.065(1.060, 1.071) < 0.001 1.016(1.004, 1.028) 0.008 28-day mortality MELD 1.056 (1.051, 1.060) < 0.001 1.061(1.056, 1.065) < 0.001 1.022(1.012, 1.033) < 0.001 MELD-Na 1.053 (1.049, 1.057) < 0.001 1.058(1.054, 1.063) < 0.001 1.024(1.014, 1.034) < 0.001 MELD-XI 1.054 (1.049, 1.059) < 0.001 1.062(1.057, 1.068) < 0.001 1.010(0.998,1.021) 0.095 Model 1: Unadjusted Model 2: Adjusted For age, sex Model 3: Adjusted For age, sex, platelets, total bilirubin, lactate, MAP, INR, CCI, SOFA score, urine, mechanical ventilation, vasopressor use, RRT The ROC curves for predicting in-hospital mortality for MELD, MELD-Na, and MELD-XI are shown in Fig. 2 . The AUCs were 0.635 (95% CI 0.624–0.646), 0.634 (95% CI 0.623–0.645), and 0.614 (95% CI 0.603–0.626), respectively. The optimal cut-off values for the Youden index were 22.5, 22.5, and 19.5. Based on the calculated cut-off values, the occurrence of outcome events in high score groups (> cut-off value) and low score groups (≤ cut-off value) were compared, respectively (Table 3 ). In high score groups, significant differences were observed in in-hospital mortality, 28-day mortality, ICU Los and Hospital Los. The Kaplan-Meier curves for 28-day and 90-day survival rates according to MELD, MELD-Na, and MELD-XI subgroups are shown in Fig. 3 and Fig. 4 . Table 3 Outcomes of participants categorized by the MELDs Variables MELD ≤ 22.5 MELD > 22.5 P-value MELD-Na ≤ 22.5 MELD-Na > 22.5 P-value MELD-XI ≤ 19.5 MELD-XI > 19.5 P-value n = 11762 n = 4120 n = 10621 n = 5261 n = 10501 n = 5381 In-hospital mortality [n (%)] 1852 (15.75) 1333 (32.35) < 0.001 1627 (15.32) 1558 (29.61) < 0.001 1668 (15.88) 1517 (28.19) < 0.001 28-day mortality [n (%)] 2329 (19.80) 1533 (37.21) < 0.001 2054 (19.34) 1808 (34.37) < 0.001 2111 (20.10) 1751 (32.54) < 0.001 ICU Los [days, M(IQR)] 3 [2, 7] 4 [2, 8] < 0.001 3 [2, 7] 4 [2, 8] < 0.001 3 [2, 7] 4 [2, 8] < 0.001 Hospital Los [day, M(IQR)] 9 [6, 16] 11 [6, 20] < 0.001 9 [6, 16] 10 [6, 19] < 0.001 9 [6, 16] 10 [6, 19] < 0.001 ICU, intensive care unit; LOS, length of stay Lactate has long been suggested as a prognostic tool [ 22 ] . It has been noted that initial lactate levels have predictive value for the prognosis of patients with sepsis [ 23 , 24 ] . A lactate-based subgroup analysis was conducted concurrently in this study. We combined lactate ≤ 4.0mmol/L and lactate > 4.0mmol/L for further subgroup analysis in different MELD score groups. The results showed significant differences in in-hospital mortality, 28-day mortality, ICU Los and Hospital Los among groups (Table 4 – 6 ). The Kaplan-Meier curves for 28-day survival derived by combining different MELD scores and lactate level subgroups are shown in Fig. 5 . Table 4 Outcomes of participants categorized by MELD and lactate MELD ≤ 22.5 MELD > 22.5 P-value lac ≤ 4 lac > 4 lac ≤ 4 lac > 4 n = 9947 n = 1815 n = 2685 n = 1435 In-hospital mortality [n (%)] 1387 (13.94) 465 (25.62) 632 (23.54) 701 (48.85) < 0.001 28-day mortality [n (%)] 1829 (18.39) 500 (27.55) 788 (29.35) 745 (51.92) < 0.001 ICU Los [days, M(IQR)] 3 [2, 6] 4 [2, 9] 4 [2, 7] 4 [2, 10] < 0.001 Hospital Los [days, M(IQR)] 9 [6, 16] 10 [6, 18.5] 11 [6, 19] 11 [4, 21] 22.5 P-value lac ≤ 4 lac > 4 lac ≤ 4 lac > 4 n = 9031 n = 1590 n = 3601 n = 1660 In-hospital mortality [n (%)] 1234 (13.66) 393 (24.72) 785 (21.80) 773 (46.57) < 0.001 28-day mortality [n (%)] 1626 (18.00) 428 (26.92) 991 (27.52) 817 (49.22) < 0.001 ICU Los [days, M(IQR)] 3 [2, 7] 4 [2, 9] 3 [2, 7] 4 [2, 10] < 0.001 Hospital Los [days, M(IQR)] 9 [6, 15] 10 [6, 18] 10 [6, 18] 11 [5, 20.3] 19.5 P-value lac ≤ 4 lac > 4 lac ≤ 4 lac > 4 n = 8838 n = 1663 n = 3794 n = 1587 In-hospital mortality [n (%)] 1222 (13.83) 446 (26.82) 797 (21.01) 720 (45.37) < 0.001 28-day mortality [n (%)] 1627 (18.41) 484 (29.10) 990 (26.09) 761 (47.95) < 0.001 ICU Los [days, M(IQR)] 3 [2, 7] 4 [2, 9] 4 [2, 7] 4 [2, 10] < 0.001 Hospital Los [days, M(IQR)] 9 [6, 15] 10 [6, 18] 10 [6, 18] 11 [5, 21] < 0.001 ICU, intensive care unit; LOS, length of stay. 3.3. The MELDs in different diagnoses In addition, to confirm the association between the MELDs and in-hospital mortality, further analysis was performed in sepsis patients with underlying conditions including liver disease, kidney disease, congestive heart failure, cerebrovascular disease, and chronic obstructive pulmonary disease (Table 7 ). In the fully adjusted model, MELD and MELD-Na were significantly associated with in-hospital mortality in patients with liver disease (OR = 1.062, 95% CI 041-1.083; OR = 1.068, 95% CI 1.047–1.088), kidney disease (OR = 1.021, 95% CI 001-1.042; OR = 1.023, 95% CI 1.003–1.044), congestive heart failure (OR = 1.020, 95% CI 1.002–1.039; OR = 1.024, 95% CI 1.007–1.041), cerebrovascular disease (OR = 1.038, 95% CI 1.000-1.061; OR = 1.049, 95% CI 1.012–1.068). Multivariate-adjusted MELD-XI was associated with in-hospital mortality in sepsis patients with liver disease (OR = 1.046, 95% CI 025-1.068) and cerebrovascular disease (OR = 1.040, 95% CI 009-1.073). The optimal cut-off values for the MELDs varied among different underlying conditions (Table 7 ). Table 7 Association of the MELDs with in-hospital mortality in distinct comorbidities n OR (95% CI) P-value AUC Optimal cut-off MELD liver disease 3766 1.062 (1.041, 1.083) < 0.001 0.706 25.5 renal disease 4320 1.021 (1.001, 1.042) 0.044 0.609 22.5 congestive heart failure 5490 1.020 (1.002, 1.039) 0.028 0.619 22.5 cerebrovascular disease 2211 1.038 (1.000, 1.061) 0.048 0.584 13.5 chronic pulmonary disease 4394 1.016 (0.995, 1.038) 0.146 MELD-Na liver disease 3766 1.068 (1.047, 1.088) < 0.001 0.706 29.5 renal disease 4320 1.023 (1.003, 1.044) 0.025 0.603 22.5 congestive heart failure 5490 1.024 (1.007, 1.041) 0.006 0.618 20.5 cerebrovascular disease 2211 1.039 (1.012, 1.068) 0.005 0.586 16.5 chronic pulmonary disease 4394 1.017 (0.998, 1.037) 0.076 MELD-XI liver disease 3766 1.046 (1.025, 1.068) < 0.001 0.68 21.5 renal disease 4320 1.009 (0.987, 1.032) 0.434 congestive heart failure 5490 1.013 (0.992, 1.034) 0.228 cerebrovascular disease 2211 1.040 (1.009, 1.073) 0.012 0.596 13.5 chronic pulmonary disease 4394 1.010 (0.986, 1.034) 0.411 Adjusted For age, sex, platelets, creatinine, INR, total bilirubin, lactate, MAP, CCI, SOFA score, urine, mechanical ventilation, vasopressor use, RRT. 4. Discussion Acute kidney injury (AKI) is a common complication in patients with sepsis [ 25 ] , and AKI is an independent risk factor for death in patients with septic shock [ 26 ] . In recent years, many novel biomarkers such as NGAL, IL-18, and KIM-1 have been used to identify AKI [ 27 ] . But even though creatinine itself has some limitations [ 28 ] , it was still an important surrogate indicator for the diagnosis and evaluation of renal function [ 27 ] . Sepsis is the second leading cause of jaundice in emergency patients [ 29 ] . The inflammatory response, ischemia and hypoxia caused by sepsis can damage the liver microcirculatory system and function, resulting in an increase in bilirubin and liver enzymes. This pathophysiological process can occur in the early stage of sepsis and is associated with the patient's condition, ICU Los and increased risk of death [ 30 ] . At present, there are few studies on the prognosis of the MELDs in infectious diseases. Limited studies mainly focus on infective endocarditis. MELD-XI has been proved to be the best predictor of in-hospital mortality in such patients [ 16 , 31 ] . A retrospective study identified elevated MELD score as an independent predictor of short-term mortality from Staphylococcus aureus bloodstream infection [ 14 ] . A small retrospective study of patients with vibrio vulnificus necrotizing skin and soft tissue infections (VNSSTIs) noted that the MELD/ΔMELD score model had good sensitivity and specificity for mortality in patients with VNSSTIs (both > 80%), and determined that the optimal cut-off value was 20 [ 15 ] . In this study, the MELD and its modifications were systematically evaluated. After multivariate adjustment, MELD, MELD-Na, and MELD-XI were all independent predictors of in-hospital mortality in sepsis patients. The optimal cut-off values were 22.5, 22.5, and 19.5, respectively. All three scores had good stability in predicting in-hospital mortality, while MELD and MELD-Na were better than MELD-XI (0.635 [95% CI 0.624–0.646] and 0.634 [95% CI 0.623–0.645] vs. 0.614 [95% CI 0.603–0.626]). When used to predict 28-day mortality, MELD and MELD-Na were still independent predictors, while MELD-XI performed poorly. Studies have pointed out that when used to predict the risk of death in patients with acute heart failure (AHF), the deterioration of MELD-XI and its prognostic significance are mainly driven by the deterioration of liver function (bilirubin increase) [ 11 ] , because not every creatinine increase is a deleterious effect, it may be a short-term result of the volume optimization process [ 32 ] . INR and bilirubin are both indicators of liver function. Since MELD-XI excludes INR, this to some extent weakens its predictive value for prognosis due to deterioration of liver function. In addition to the heterogeneity of sepsis patients, the underlying disease status of different patients varies greatly, which may lead to the poor performance of MELD-XI with reduced indicator in predictive value. As can be seen from the results of this data, there was no statistical difference in blood sodium levels between survivor group and non- survivor group, so MELD-Na with Na addition is similar to MELD. When we divided the patients into high score group (> cut-off value) and low score group (≤ cut-off value) according to the optimal cut-off value, in-hospital mortality, 28-day mortality, ICU Los and Hospital Los in the three models were significantly increased in the high score group (P > 0.001). In terms of clinical signs, the high score group had lower systolic blood pressure, mean arterial pressure, and less urine output; in terms of laboratory indicators, the high score group had higher WBC, worse biochemical indicators, higher lactate, and more severe acidosis; in terms of clinical intervention, the high score group had a higher use rate of vasoactive drugs and RRT. This also confirmed that the MELDs can well stratify the risk of sepsis patients. Biomarkers provide utility for diagnosis, prognosis, early disease identification, risk stratification, etc. in patients with sepsis or suspected sepsis [ 9 ] . There has been a large number of studies on biomarkers or combinations of biomarkers for prognosis and risk stratification of sepsis [ 33 – 43 ] . However, most biomarkers are difficult to obtain quickly and easily, and the optimal cut-off values are not given。Hyperlactatemia is the strongest outcome measure in sepsis, and the increase in lactate in sepsis patients is mainly caused by tissue oxygen supply or oxygen utilization impairment [ 44 ] . The Save Sepsis Campaign used lactate greater than 4 mmol/L as a condition for early quantitative resuscitation in the cluster treatment of sepsis [ 45 ] . Some studies have pointed out that lactate greater than 4 mmol/L is significantly associated with higher in-hospital mortality [ 46 ] . Therefore, we further performed a subgroup analysis combining the MELDs and lactate. The analysis showed that patients with high MELD scores and lactate > 4 mmol/L had higher in-hospital mortality and 28-day mortality, while patients with low MELD scores and lactate ≤ 4 mmol/L had lower in-hospital mortality and 28-day mortality. Finally, we further stratified the patients according to the admission diagnosis. We found that the cut-off values for stratification of sepsis patients using the MELDs are different in different disease spectra, and the different pathophysiological characteristics and severity of the primary disease itself may contribute to this situation. Because this study is based on the database, clinical information and data will inevitably be missing and inaccurate. However, because the MELDs require few objective indicators, to a certain extent, it makes full use of the advantages of a large sample size of the database and perfectly avoids the confounding interference of too many subjective indicators. It gives full play to the advantages of the models and the database and has greater reliability. 5. Conclusions We used this study to verify the effectiveness of the MELDs in the prognostic assessment and risk stratification of septic patients. MELD, MELD-Na, and MELD-XI are associated with in-hospital mortality in septic patients. The optimal cut-off values for predicting in-hospital mortality in septic patients are 22.5, 22.5, and 19.5, respectively. The combination of the MELDs and lactate can better screen patients with high risk of sepsis. Since lactate and the indicators of the MELDs are commonly used and repeatedly measured in clinical work and are easy to obtain, clinicians can easily use these indicators to stratify sepsis patients early and screen high-risk patients for more optimized treatment, thus improving the prognosis of such patients. Abbreviations MELD Models for End-stage Liver Disease MIMIC-Ⅳ Medical Information Mart for Intensive Care Ⅳ ICU intensive intensive care unit MAP mean arterial pressure MI multiplex interpolation SD standard deviation IQR interquartile ranges LOS length of stay ROC receiver operating characteristic curves AUC area under the curve IRB Institutional Review Board CCI Charlson Comorbidity Index SOFA Sequential Organ Failure Assessment RRT renal replacement therapy CIs confidence intervals AKI acute kidney injury VNSSTIs vibrio vulnificus necrotizing skin and soft tissue infections AHF acute heart failure Declarations Ethics approval and consent to participate The study was conducted in accordance with the World Medical Association Declaration of Helsinki guidelines. The MIMIC-IV project received approval from the Institutional Review Boards of both the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. Patient information was anonymized, thereby obviating the need for informed consent from individual patients for this study. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available due to usage restrictions of the MIMIC-Ⅳ database but are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This work was supported by Shanghai municipal health commission key supporting disciplines (Grant No. 2023ZDFC0104) and Shanghai Pudong New Area summit (emergency medicine and critical care) construction project (Grant No. PWYgf2021-03). The funders had no role in the design, collection, analysis, interpretation of the data or writing of this protocol. Authors' contributions FZ conceived and designed this study. SYS, XPL and YZS performed the data collection. TS, and SYS undertook the data analysis, results interpretation and manuscript preparation. WZ, XBL and QMM conducted background literature search and review. FZ was responsible for results interpretation and revision of the manuscript. All authors attest they meet the ICMJE criteria for authorship and have approved the final article. Acknowledgments We are grateful to the MIMIV-IV participants and staff. We also appreciate the invaluable contributions made by all the participants. References Wiersinga WJ, van der Poll T. Immunopathophysiology of human sepsis. EBioMedicine. 2022 Dec;86:104363. doi: 10.1016/j.ebiom.2022.104363. Epub 2022 Dec 2. PMID: 36470832; PMCID: PMC9783164. Singer M, Deutschman CS, Seymour CW, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016 Feb 23;315(8):801-10. doi: 10.1001/jama.2016.0287. 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Epub 2007 Mar 13. PMID: 17431582. White KC, Serpa-Neto A, Hurford R, et al. Sepsis-associated acute kidney injury in the intensive care unit: incidence, patient characteristics, timing, trajectory, treatment, and associated outcomes. A multicenter, observational study. Intensive Care Med. 2023 Sep;49(9):1079-1089. doi: 10.1007/s00134-023-07138-0. Epub 2023 Jul 11. PMID: 37432520; PMCID: PMC10499944. Barbar SD, Clere-Jehl R, Bourredjem A, et al. Timing of Renal-Replacement Therapy in Patients with Acute Kidney Injury and Sepsis. N Engl J Med. 2018 Oct 11;379(15):1431-1442. doi: 10.1056/NEJMoa1803213. PMID: 30304656. Pan HC, Yang SY, Chiou TT, et al. Comparative accuracy of biomarkers for the prediction of hospital-acquired acute kidney injury: a systematic review and meta-analysis. Crit Care. 2022 Nov 12;26(1):349. doi: 10.1186/s13054-022-04223-6. PMID: 36371256; PMCID: PMC9652605. Dennen P, Douglas IS, Anderson R. Acute kidney injury in the intensive care unit: an update and primer for the intensivist. Crit Care Med. 2010;38(1):261–275. doi: 10.1097/CCM.0b013e3181bfb0b5. PMID: 19829099. Whitehead MW, Hainsworth I, Kingham JG. The causes of obvious jaundice in South West Wales: perceptions versus reality. Gut. 2001;48(3):409-413. doi: 10.1136/gut.48.3.409. PMID: 11171834; PMCID: PMC1760136. Jenniskens M, Langouche L, Van den Berghe G. Cholestatic Alterations in the Critically Ill: Some New Light on an Old Problem. Chest. 2018 Mar;153(3):733-743. doi: 10.1016/j.chest.2017.08.018. Epub 2017 Aug 26. PMID: 28847548. Buburuz AM, Petris A, Costache II, et al. Evaluation of Laboratory Predictors for In-Hospital Mortality in Infective Endocarditis and Negative Blood Culture Pattern Characteristics. Pathogens. 2021 May 2;10(5):551. doi: 10.3390/pathogens10050551. PMID: 34063295; PMCID: PMC8147437. Testani JM, Chen J, McCauley BD, et al. Potential effects of aggressive decongestion during the treatment of decompensated heart failure on renal function and survival. Circulation 2010; 122: 265–272. Pierrakos C, Velissaris D, Bisdorff M, et al. Biomarkers of sepsis: time for a reappraisal. Crit Care. 2020 Jun 5;24(1):287. doi: 10.1186/s13054-020-02993-5. PMID: 32503670; PMCID: PMC7273821. Yende S, Kellum JA, Talisa VB, et al. Long-term host immune response trajectories among hospitalized patients with sepsis. JAMA Netw Open. 2019 Aug 2;2(8):e198686. doi: 10.1001/jamanetworkopen.2019.8686. PMID: 31390038; PMCID: PMC6686981. Song J, Moon S, Park DW, et al. Biomarker combination and SOFA score for the prediction of mortality in sepsis and septic shock: a prospective observational study according to the Sepsis-3 definitions. Medicine. 2020 May 29;99(22):e20495. doi: 10.1097/MD.0000000000020495. PMID: 32481464. Matsumoto H, Ogura H, Shimizu K, et al. The clinical importance of a cytokine network in the acute phase of sepsis. Sci Rep. 2018 Sep 18;8(1):13995. doi: 10.1038/s41598-018-32275-8. PMID: 30228372; PMCID: PMC6143513. Zhao GJ, Li D, Zhao Q, et al. Prognostic value of plasma tight-junction proteins for sepsis in emergency department: an observational study. Shock. 2016 Mar;45(3):326-32. doi: 10.1097/SHK.0000000000000524. PMID: 26863122. Skibsted S, Jones AE, Puskarich MA, et al. Biomarkers of endothelial cell activation in early sepsis. Shock. 2013 May;39(5):427-32. doi: 10.1097/SHK.0b013e3182903f0d. PMID: 23524845; PMCID: PMC3670087. Ikeda M, Matsumoto H, Ogura H, et al. Circulating syndecan-1 predicts the development of disseminated intravascular coagulation in patients with sepsis. J Crit Care. 2018 Feb;43:48-53. doi: 10.1016/j.jcrc.2017.07.049. Epub 2017 Jul 28. PMID: 28843664. Liu W, Geng F, Yu L. Long non-coding RNA MALAT1/microRNA 125a axis presents excellent value in discriminating sepsis patients and exhibits positive association with general disease severity, organ injury, inflammation level, and mortality in sepsis patients. J Clin Lab Anal. 2020 Jun;34(6):e23222. doi: 10.1002/jcla.23222. Epub 2020 Apr 20. PMID: 32309886; PMCID: PMC7307338. Yin WP, Li JB, Zheng XF, et al. Effect of neutrophil CD64 for diagnosing sepsis in emergency department. World J Emerg Med. 2020;11(2):79-86. doi: 10.5847/wjem.j.1920-8642.2020.02.003. PMID: 32076472; PMCID: PMC7010530. Kondo Y, Umemura Y, Hayashida K, et al. Diagnostic value of procalcitonin and presepsin for sepsis in critically ill adult patients: a systematic review and meta-analysis. J Intensive Care. 2019 Apr 15;7:22. doi: 10.1186/s40560-019-0374-4. PMID: 31016020; PMCID: PMC6466719. Anderson BJ, Calfee CS, Liu KD, et al. Plasma sTNFR1 and IL8 for prognostic enrichment in sepsis trials: a prospective cohort study. Crit Care. 2019 Dec 9;23(1):400. doi: 10.1186/s13054-019-2684-2. PMID: 31818332; PMCID: PMC6902425. Gattinoni L, Vasques F, Camporota L, et al. Understanding Lactatemia in Human Sepsis. Potential Impact for Early Management. Am J Respir Crit Care Med. 2019 Sep 1;200(5):582-589. doi: 10.1164/rccm.201812-2342OC. PMID: 30985210. Rhodes A, Evans LE, Alhazzani W, et al. Surviving Sepsis Campaign: international guidelines for management of sepsis and septic shock: 2016. Intensive Care Med 2017; 43(3):304–377. doi: 10.1007/s00134-017-4683-6. Epub 2017 Jan 18. PMID: 28101605. Casserly B, Phillips GS, Schorr C, et al. Lactate measurements in sepsis-induced tissue hypoperfusion: results from the Surviving Sepsis Campaign database. Crit Care Med. 2015 Mar;43(3):567-73. doi: 10.1097/CCM.0000000000000742. PMID: 25479113. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6455319","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":454721076,"identity":"8eb6e052-4443-479c-9bb2-9ec48a4fc767","order_by":0,"name":"Tuo Shen","email":"","orcid":"","institution":"Department of Critical Care Medicine, Shanghai East Hospital, School of Medicine, Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Tuo","middleName":"","lastName":"Shen","suffix":""},{"id":454721077,"identity":"790b41f0-00a0-412a-9988-f8a3900d389a","order_by":1,"name":"Xingping Lv","email":"","orcid":"","institution":"Department of Critical Care Medicine, Shanghai East Hospital, School of Medicine, Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Xingping","middleName":"","lastName":"Lv","suffix":""},{"id":454721078,"identity":"008c3dd0-43de-473a-9ed3-39697467794a","order_by":2,"name":"Yezhou Shen","email":"","orcid":"","institution":"Department of Critical Care Medicine, Shanghai East Hospital, School of Medicine, Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Yezhou","middleName":"","lastName":"Shen","suffix":""},{"id":454721079,"identity":"7498fabd-49b0-417b-9b78-d540f6f40ec2","order_by":3,"name":"Wei Zhou","email":"","orcid":"","institution":"Department of Critical Care Medicine, Shanghai East Hospital, School of Medicine, Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zhou","suffix":""},{"id":454721080,"identity":"09a7bbda-f0d9-4580-b926-283b9770b298","order_by":4,"name":"Xiaobin Liu","email":"","orcid":"","institution":"Department of Critical Care Medicine, Shanghai East Hospital, School of Medicine, Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Xiaobin","middleName":"","lastName":"Liu","suffix":""},{"id":454721082,"identity":"e9ee09a8-6965-44d4-a547-9ebfd0044ffa","order_by":5,"name":"Qimin Ma","email":"","orcid":"","institution":"Department of Critical Care Medicine, Shanghai East Hospital, School of Medicine, Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Qimin","middleName":"","lastName":"Ma","suffix":""},{"id":454721083,"identity":"f05fc9c3-fcd7-4176-b89a-7e79df6640d3","order_by":6,"name":"Shuyue Sheng","email":"","orcid":"","institution":"Department of Critical Care Medicine, Shanghai East Hospital, School of Medicine, Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Shuyue","middleName":"","lastName":"Sheng","suffix":""},{"id":454721084,"identity":"17b4c6fd-21ca-4d93-8a1c-6e29687911b8","order_by":7,"name":"Feng Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYDACdiBmbGBgBlJsDAkVB8CCBx7g08KMouXMAQYekJYEIrQwgLUwtkG0MODTwt/MfEzy547D7Aa3m589eDjvjpy92OGHQFvs5HQbsGuROMyWbCB55jCzwZ1j5gaJ254Z80inGQC1JBubHcBhzWEewweGbUAtN3LYJBK3HU7skU4AaTmQuA2HFvnD/B8OJMK1zAFpSf+AV4vBYR7GBwfhWhpAWnLw22J4mM3YsLEtnVnyRpqZRMKxw8Y8t3MKDiQY4PaL3PHmZ5I/26yT+W4kP5P8UXNYjn12+uYPHyrs5HB6HwqS0R2MXzkI2BFWMgpGwSgYBSMWAAC4U2NOkFJm/QAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Critical Care Medicine, Shanghai East Hospital, School of Medicine, Tongji University","correspondingAuthor":true,"prefix":"","firstName":"Feng","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2025-04-15 13:38:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6455319/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6455319/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82623901,"identity":"dd755a69-52b4-483f-a82e-e734c8002b3b","added_by":"auto","created_at":"2025-05-13 12:45:30","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":204122,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart showing patient selection for the study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMIMIC-Ⅳ: Medical Information Mart for Intensive Care Ⅳ; ICU: intensive care unit\u003c/p\u003e","description":"","filename":"Figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6455319/v1/b956978ae9e072902c1d1e97.jpg"},{"id":82623903,"identity":"f8f1146b-11e3-4369-af64-6c920e3284c6","added_by":"auto","created_at":"2025-05-13 12:45:30","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":774297,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve analysis for MELD (A)、MELD-Na (B)、MELD-XI (C) predicting In- hospital mortality in patients with sepsis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6455319/v1/99f17a3a5569cb90085ca563.jpg"},{"id":82625011,"identity":"a5db568b-2a65-4dcd-a15f-5f4a1b93d176","added_by":"auto","created_at":"2025-05-13 12:53:30","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":645662,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier Survival Curves for day 28 of sepsis patients depending on MELD (A), MELD-Na (B) and MELD-XI (C).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6455319/v1/956dc7636fd65040d85354d1.jpg"},{"id":82623906,"identity":"cb074615-bcc7-477c-bb15-7b8e46b3ac51","added_by":"auto","created_at":"2025-05-13 12:45:30","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":643570,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier Survival Curves for day 90 of sepsis patients depending on MELD (A), MELD-Na (B) and MELD-XI (C).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6455319/v1/c74dc7aef550adf6f0f2e597.jpg"},{"id":82623904,"identity":"32b89d83-dc3c-4633-995b-5ed16e39ed25","added_by":"auto","created_at":"2025-05-13 12:45:30","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":814776,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier Survival Curves for day 28 of sepsis patients depending on MELD and lactate(A), MELD-Na and lactate (B) and MELD-XI and lactate(C).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6455319/v1/2efcfeecb60045b52bb6472d.jpg"},{"id":105012938,"identity":"b997f1ab-3f98-4c9e-ab71-d51b6dd9ed61","added_by":"auto","created_at":"2026-03-19 21:39:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4288076,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6455319/v1/c7d4b328-40b6-47d0-b762-ade051afd42a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Models for End-stage Liver Disease as prognostic assessment and risk stratification tools in sepsis: a study based on MIMIC-Ⅳ database","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSepsis is a series of syndromes in which infection leads to inflammation and dysregulation of immune responses in the body and secondary organ damage \u003csup\u003e[\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. The sepsis population is obviously heterogeneous and the clinical situation is complex \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, which makes us face great challenges in its diagnosis and treatment. The mortality rate of sepsis is about 15% -25%, and the hospitalization mortality rate of septic shock is as high as 30% -50%. Sepsis remains one of the leading causes of death worldwide, placing a huge burden on the global healthcare industry \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\u003eSepsis not only brings patients the risk of immediate death, but it is also a long-term chronic critical disease \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. The long-term damage it brings to patients cannot be ignored either. Therefore, in addition to early diagnosis and intervention of sepsis, risk stratification of sepsis patients is conducive to better management of sepsis, thereby improving its prognosis. Currently, there are many biomarkers used in sepsis identification, diagnosis, prognosis, risk stratification, etc., but no single biomarker has shown absolute advantages. It is often necessary to combine these markers to achieve better diagnosis and predictive effects \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Moreover, most biomarkers cannot be routinely collected in clinical work, which restricts their clinical efficacy to a certain extent.\u003c/p\u003e \u003cp\u003eThe Model for End-Stage Liver Disease (MELD) was initially used to evaluate the prognosis of patients receiving transjugular intrahepatic portosystemic shunt \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, and has since been widely used for threat and risk assessment in patients with advanced liver disease \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. The MELD mainly contains three indicators: bilirubin, creatinine and international normalized ratio (INR), which are objective indicators that are often collected repeatedly in clinical work, and can be used to reflect the liver and kidney dysfunction of patients. Liver and kidney dysfunction is also common in other serious diseases. Therefore, the MELD and its modifications have been widely used in mortality prediction and risk stratification for non-liver diseases \u003csup\u003e[\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17 CR18 CR19\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe purpose of this study was to evaluate the feasibility of using the MELD and its modifications to predict the prognosis of sepsis and to stratify its risk.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study design\u003c/h2\u003e \u003cp\u003eThis is a retrospective study based on a large US database, Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. The MIMIC-Ⅳ-2.2 database contains information on hospitalized patients from 2008 to 2019. One of the authors (SYS) obtained access to the database and was responsible for data extraction (authentication number 48693098). Patient information was anonymized, thereby obviating the need for informed consent from individual patients for this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Selection of participants\u003c/h2\u003e \u003cp\u003eInclusion criteria were as follows: (1) patients with sepsis in the MIMIC-Ⅳ database (defined by sepsis-3 criteria) \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e; (2) adults admitted to the intensive intensive care unit (ICU) (\u0026ge;\u0026thinsp;18 years old). Exclusion criteria were as follows: (1) the length of stay in the ICU is less than 24 hours; (2) insufficient data (including creatinine, total bilirubin, international normalized ratio (INR), sodium). In addition, we analyzed first-time ICU admissions only for patients with multiple ICU admissions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data collection\u003c/h2\u003e \u003cp\u003eStructured Query Language (SQL) using PostgresSQL software (version 16.0). The following basic data were extracted within 24 hours of admission to ICU: age, sex, weight, comorbidities, mean arterial pressure (MAP), laboratory tests, and treatment. Variables with missing values greater than 30% were excluded from the analysis. Variables with missing values less than 30% were multiplexed by multiplex interpolation (MI) based on five replicates.\u003c/p\u003e \u003cp\u003eMELD scores were calculated as follows:\u003c/p\u003e \u003cp\u003eMELD score\u0026thinsp;=\u0026thinsp;11.2 \u0026times; (ln INR)\u0026thinsp;+\u0026thinsp;3.78 \u0026times; ln (serum bilirubin in mg/dL)\u0026thinsp;+\u0026thinsp;9.57 \u0026times; ln (serum creatinine in mg/dL)\u0026thinsp;+\u0026thinsp;6.43;\u003c/p\u003e \u003cp\u003eMELD-Na score\u0026thinsp;=\u0026thinsp;MELD\u0026thinsp;+\u0026thinsp;1.32 \u0026times; (137- serum sodium) - [0.033 \u0026times; MELD \u0026times; (137- serum sodium)];\u003c/p\u003e \u003cp\u003eMELD-XI score\u0026thinsp;=\u0026thinsp;5.11 \u0026times; ln (serum bilirubin in mg/dL)\u0026thinsp;+\u0026thinsp;11.76 \u0026times; ln (serum creatinine in mg/dL)\u0026thinsp;+\u0026thinsp;9.44.\u003c/p\u003e \u003cp\u003eCreatinine and bilirubin concentration below 1 were set to 1 and creatinine above 4 was set to 4 to avoid negative values.\u003c/p\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003cp\u003eThe primary outcome was in-hospital mortality. The secondary outcome was 28-day mortality, ICU Los and Hospital Los.\u003c/p\u003e \u003cp\u003eStatistical analysis\u003c/p\u003e \u003cp\u003eContinuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median and interquartile ranges (IQR) according to their different distributions, and categorical variables are expressed as numbers and percentages. The categorical variables were calculated using the chi-square test or Fisher precision test for intergroup comparisons, and the continuous variables were calculated using the Student t test or Mann-Whitney U test as appropriate. Univariate and multivariate logistic regression analyses were used to determine the association between MELD scores and in-hospital mortality in patients with sepsis. Receiver operating characteristic curves (ROC) and area under the curve (AUC) were used to assess the predictive performance of different MELDs and calculate cut-offs. Kaplan-Meier analyses were used as sensitivity analyses to explore the association between different MELDs and in-hospital mortality endpoints. Statistical analysis was performed using SPSS version 22.0 (IBM, USA) and R 4.3.2 (R Foundation). Statistical significance was defined as a two-sided P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eThis was a retrospective study, and the patient data information was de-identified and patient identifiers were removed. Therefore, the study was not considered a human-subject study and was exempted from Institutional Review Board (IRB) review at The George Washington University. Informed patient consent was therefore not required. All methods were performed in accordance with the relevant guidelines and regulations of The George Washington University.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Study population\u003c/h2\u003e \u003cp\u003eA total of 15,882 patients with sepsis were included in the analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The in-hospital mortality and 28-day mortality were 20.05% and 24.32%, respectively. The baseline data of the non-survivor group and the survivor group were compared in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The MELD score, MELD-Na score, and MELD-XI score in the non-survivor group were significantly higher than those in the survivor group (20 vs. 15, 22 vs. 17, 19 vs. 15, respectively). In addition to the MELDs, the Sequential Organ Failure Assessment (SOFA) score and the Charlson Comorbidity Index (CCI) Score indicating the comorbidities of the patient were also higher in the non-survivor group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Other data and clinical characteristics were also compared in the non-survivor group and the survivor group in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the survivor and non-survivor groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurvivors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNon-survivors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15882)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;12020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3862)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9201 (57.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7378 (58.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1823 (57.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, [years, M(IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 [55, 78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 [55, 77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e70 [58, 80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet, [10^9/L, M(IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157 [101, 228]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161 [105, 230]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e145 [78, 221]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, [mg/dL, M(IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.30 [0.90, 2.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30 [0.90, 2.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.70 [1.10, 2.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINR, [M(IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.40 [1.20, 1.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.40 [1.20, 1.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.60 [1.30, 2.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin, [mg/dL, M(IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80 [0.40, 1.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70 [0.40, 1.60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.00 [0.50, 2.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate, [mmol/L, M(IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.10 [1.30, 3.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00 [1.30, 3.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.00 [1.80, 5.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP, [mmHg, M(IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 [50, 63]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 [50, 64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e54 [46, 61]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrine output, [mL, M(IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1400 [800, 2280]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1515 [925, 2400]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e904 [400, 1615]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI, [M(IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 [4, 8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 [4, 8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e7 [5, 9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA score, [M(IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 [2, 5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 [2, 5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4 [3, 6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMELD score, [M(IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 [11, 23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 [10, 22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e20 [13, 28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMELD-Na score, [M(IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 [11, 25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 [10, 24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e22 [14, 30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMELD-XI score, [M(IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 [11, 23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 [11, 21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e19 [13, 26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilation, [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7859 (49.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5856 (46.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2003 (62.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVasopressor use, [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7187 (45.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5246 (41.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1941 (60.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRRT, [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e834 (5.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e577 (4.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e257 (8.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eINR, international normalized ratio; MAP, mean arterial pressure(mmHg); CCI, Charlson Comorbidity Index; SOFA, Sequential Organ Failure Assessment; MELD, Model for End-Stage Liver Disease; RRT, renal replacement therapy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. The MELDs and in-hospital and 28-day mortality\u003c/h2\u003e \u003cp\u003eTo study the relationship between the MELDs and mortality, we designed a 3-logistic regression model to study the independent role of the MELDs in mortality prediction (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). After adjusting for multivariate factors including age, sex, weight, disease history, Charlson score, SOFA score, vital signs, laboratory tests, and treatment methods (Model 3), the ORs and 95% confidence intervals (CIs) corresponding to in-hospital mortality for MELD score, MELD-Na score, and MELD-XI score after multivariate adjustment were 1.023 (1.012, 1.035), 1.023 (1.012, 1.033), and 1.016 (1.004, 1.028), respectively. The ORs and 95% CIs for 28-day mortality were 1.022 (1.012, 1.033), 1.024 (1.014, 1.034), and 1.010 (0.998, 1.021), respectively.\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\u003e\u003cb\u003eThe association between the MELDs and in-hospital and 28-day mortality\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eIn-hospital mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMELD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.060 (1.055, 1.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.063 (1.058, 1.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.023(1.012, 1.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMELD-Na\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.057 (1.052, 1.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.060 (1.055, 1.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.023(1.012, 1.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMELD-XI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.060 (1.054, 1.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.065(1.060, 1.071)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.016(1.004, 1.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e28-day mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMELD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.056 (1.051, 1.060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.061(1.056, 1.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.022(1.012, 1.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMELD-Na\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.053 (1.049, 1.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.058(1.054, 1.063)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.024(1.014, 1.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMELD-XI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.054 (1.049, 1.059)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.062(1.057, 1.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.010(0.998,1.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eModel 1: Unadjusted\u003c/p\u003e \u003cp\u003eModel 2: Adjusted For age, sex\u003c/p\u003e \u003cp\u003eModel 3: Adjusted For age, sex, platelets, total bilirubin, lactate, MAP, INR, CCI, SOFA score, urine, mechanical ventilation, vasopressor use, RRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe ROC curves for predicting in-hospital mortality for MELD, MELD-Na, and MELD-XI are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The AUCs were 0.635 (95% CI 0.624\u0026ndash;0.646), 0.634 (95% CI 0.623\u0026ndash;0.645), and 0.614 (95% CI 0.603\u0026ndash;0.626), respectively. The optimal cut-off values for the Youden index were 22.5, 22.5, and 19.5. Based on the calculated cut-off values, the occurrence of outcome events in high score groups (\u0026gt;\u0026thinsp;cut-off value) and low score groups (\u0026le;\u0026thinsp;cut-off value) were compared, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In high score groups, significant differences were observed in in-hospital mortality, 28-day mortality, ICU Los and Hospital Los. The Kaplan-Meier curves for 28-day and 90-day survival rates according to MELD, MELD-Na, and MELD-XI subgroups are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOutcomes of participants categorized by the MELDs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMELD\u0026thinsp;\u0026le;\u0026thinsp;22.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMELD\u0026thinsp;\u0026gt;\u0026thinsp;22.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMELD-Na\u0026thinsp;\u0026le;\u0026thinsp;22.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMELD-Na\u0026thinsp;\u0026gt;\u0026thinsp;22.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMELD-XI\u0026thinsp;\u0026le;\u0026thinsp;19.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMELD-XI\u0026thinsp;\u0026gt;\u0026thinsp;19.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;11762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;4120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;10621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;5261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;10501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;5381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn-hospital mortality [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1852 (15.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1333 (32.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1627 (15.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1558 (29.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1668 (15.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1517 (28.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28-day mortality [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2329 (19.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1533 (37.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2054 (19.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1808 (34.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2111 (20.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1751 (32.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU Los [days, M(IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 [2, 7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 [2, 8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 [2, 7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 [2, 8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 [2, 7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4 [2, 8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital Los [day, M(IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 [6, 16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 [6, 20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 [6, 16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 [6, 19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9 [6, 16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10 [6, 19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eICU, intensive care unit; LOS, length of stay\u003c/p\u003e \u003cp\u003eLactate has long been suggested as a prognostic tool \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. It has been noted that initial lactate levels have predictive value for the prognosis of patients with sepsis \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. A lactate-based subgroup analysis was conducted concurrently in this study. We combined lactate\u0026thinsp;\u0026le;\u0026thinsp;4.0mmol/L and lactate\u0026thinsp;\u0026gt;\u0026thinsp;4.0mmol/L for further subgroup analysis in different MELD score groups. The results showed significant differences in in-hospital mortality, 28-day mortality, ICU Los and Hospital Los among groups (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The Kaplan-Meier curves for 28-day survival derived by combining different MELD scores and lactate level subgroups are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eOutcomes of participants categorized by MELD and lactate\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMELD\u0026thinsp;\u0026le;\u0026thinsp;22.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMELD\u0026thinsp;\u0026gt;\u0026thinsp;22.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elac\u0026thinsp;\u0026le;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elac\u0026thinsp;\u0026gt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elac\u0026thinsp;\u0026le;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003elac\u0026thinsp;\u0026gt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;9947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;1815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;2685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;1435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn-hospital mortality [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1387 (13.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e465 (25.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e632 (23.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e701 (48.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28-day mortality [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1829 (18.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e500 (27.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e788 (29.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e745 (51.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU Los [days, M(IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 [2, 6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 [2, 9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 [2, 7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 [2, 10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital Los [days, M(IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 [6, 16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 [6, 18.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 [6, 19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 [4, 21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eICU, intensive care unit; LOS, length of stay.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eOutcomes of participants categorized by MELD-Na and lactate\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMELD-Na\u0026thinsp;\u0026le;\u0026thinsp;22.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMELD-Na\u0026thinsp;\u0026gt;\u0026thinsp;22.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elac\u0026thinsp;\u0026le;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elac\u0026thinsp;\u0026gt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elac\u0026thinsp;\u0026le;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003elac\u0026thinsp;\u0026gt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;9031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;1590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;3601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;1660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn-hospital mortality [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1234 (13.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e393 (24.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e785 (21.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e773 (46.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28-day mortality [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1626 (18.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e428 (26.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e991 (27.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e817 (49.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU Los [days, M(IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 [2, 7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 [2, 9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 [2, 7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 [2, 10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital Los [days, M(IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 [6, 15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 [6, 18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 [6, 18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 [5, 20.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eICU, intensive care unit; LOS, length of stay.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eOutcomes of participants categorized by MELD-XI and lactate\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMELD-XI\u0026thinsp;\u0026le;\u0026thinsp;19.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMELD-XI\u0026thinsp;\u0026gt;\u0026thinsp;19.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elac\u0026thinsp;\u0026le;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elac\u0026thinsp;\u0026gt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elac\u0026thinsp;\u0026le;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003elac\u0026thinsp;\u0026gt;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;8838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;1663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;3794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;1587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn-hospital mortality [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1222 (13.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e446 (26.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e797 (21.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e720 (45.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28-day mortality [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1627 (18.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e484 (29.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e990 (26.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e761 (47.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU Los [days, M(IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 [2, 7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 [2, 9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 [2, 7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 [2, 10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital Los [days, M(IQR)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 [6, 15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 [6, 18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 [6, 18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 [5, 21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eICU, intensive care unit; LOS, length of stay.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3. The MELDs in different diagnoses\u003c/h2\u003e \u003cp\u003eIn addition, to confirm the association between the MELDs and in-hospital mortality, further analysis was performed in sepsis patients with underlying conditions including liver disease, kidney disease, congestive heart failure, cerebrovascular disease, and chronic obstructive pulmonary disease (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In the fully adjusted model, MELD and MELD-Na were significantly associated with in-hospital mortality in patients with liver disease (OR\u0026thinsp;=\u0026thinsp;1.062, 95% CI 041-1.083; OR\u0026thinsp;=\u0026thinsp;1.068, 95% CI 1.047\u0026ndash;1.088), kidney disease (OR\u0026thinsp;=\u0026thinsp;1.021, 95% CI 001-1.042; OR\u0026thinsp;=\u0026thinsp;1.023, 95% CI 1.003\u0026ndash;1.044), congestive heart failure (OR\u0026thinsp;=\u0026thinsp;1.020, 95% CI 1.002\u0026ndash;1.039; OR\u0026thinsp;=\u0026thinsp;1.024, 95% CI 1.007\u0026ndash;1.041), cerebrovascular disease (OR\u0026thinsp;=\u0026thinsp;1.038, 95% CI 1.000-1.061; OR\u0026thinsp;=\u0026thinsp;1.049, 95% CI 1.012\u0026ndash;1.068). Multivariate-adjusted MELD-XI was associated with in-hospital mortality in sepsis patients with liver disease (OR\u0026thinsp;=\u0026thinsp;1.046, 95% CI 025-1.068) and cerebrovascular disease (OR\u0026thinsp;=\u0026thinsp;1.040, 95% CI 009-1.073). The optimal cut-off values for the MELDs varied among different underlying conditions (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of the MELDs with in-hospital mortality in distinct comorbidities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOptimal cut-off\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eMELD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eliver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.062 (1.041, 1.083)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erenal disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.021 (1.001, 1.042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econgestive heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.020 (1.002, 1.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecerebrovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.038 (1.000, 1.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echronic pulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.016 (0.995, 1.038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eMELD-Na\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eliver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.068 (1.047, 1.088)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erenal disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.023 (1.003, 1.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econgestive heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.024 (1.007, 1.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecerebrovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.039 (1.012, 1.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echronic pulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.017 (0.998, 1.037)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eMELD-XI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eliver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.046 (1.025, 1.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erenal disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.009 (0.987, 1.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econgestive heart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.013 (0.992, 1.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecerebrovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.040 (1.009, 1.073)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echronic pulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.010 (0.986, 1.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAdjusted For age, sex, platelets, creatinine, INR, total bilirubin, lactate, MAP, CCI, SOFA score, urine, mechanical ventilation, vasopressor use, RRT.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAcute kidney injury (AKI) is a common complication in patients with sepsis \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, and AKI is an independent risk factor for death in patients with septic shock \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. In recent years, many novel biomarkers such as NGAL, IL-18, and KIM-1 have been used to identify AKI \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. But even though creatinine itself has some limitations \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e, it was still an important surrogate indicator for the diagnosis and evaluation of renal function \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Sepsis is the second leading cause of jaundice in emergency patients \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. The inflammatory response, ischemia and hypoxia caused by sepsis can damage the liver microcirculatory system and function, resulting in an increase in bilirubin and liver enzymes. This pathophysiological process can occur in the early stage of sepsis and is associated with the patient's condition, ICU Los and increased risk of death \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. At present, there are few studies on the prognosis of the MELDs in infectious diseases. Limited studies mainly focus on infective endocarditis. MELD-XI has been proved to be the best predictor of in-hospital mortality in such patients \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. A retrospective study identified elevated MELD score as an independent predictor of short-term mortality from Staphylococcus aureus bloodstream infection \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. A small retrospective study of patients with vibrio vulnificus necrotizing skin and soft tissue infections (VNSSTIs) noted that the MELD/ΔMELD score model had good sensitivity and specificity for mortality in patients with VNSSTIs (both \u0026gt;\u0026thinsp;80%), and determined that the optimal cut-off value was 20 \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, the MELD and its modifications were systematically evaluated. After multivariate adjustment, MELD, MELD-Na, and MELD-XI were all independent predictors of in-hospital mortality in sepsis patients. The optimal cut-off values were 22.5, 22.5, and 19.5, respectively. All three scores had good stability in predicting in-hospital mortality, while MELD and MELD-Na were better than MELD-XI (0.635 [95% CI 0.624\u0026ndash;0.646] and 0.634 [95% CI 0.623\u0026ndash;0.645] vs. 0.614 [95% CI 0.603\u0026ndash;0.626]). When used to predict 28-day mortality, MELD and MELD-Na were still independent predictors, while MELD-XI performed poorly. Studies have pointed out that when used to predict the risk of death in patients with acute heart failure (AHF), the deterioration of MELD-XI and its prognostic significance are mainly driven by the deterioration of liver function (bilirubin increase) \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, because not every creatinine increase is a deleterious effect, it may be a short-term result of the volume optimization process \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. INR and bilirubin are both indicators of liver function. Since MELD-XI excludes INR, this to some extent weakens its predictive value for prognosis due to deterioration of liver function. In addition to the heterogeneity of sepsis patients, the underlying disease status of different patients varies greatly, which may lead to the poor performance of MELD-XI with reduced indicator in predictive value. As can be seen from the results of this data, there was no statistical difference in blood sodium levels between survivor group and non- survivor group, so MELD-Na with Na addition is similar to MELD.\u003c/p\u003e \u003cp\u003eWhen we divided the patients into high score group (\u0026gt;\u0026thinsp;cut-off value) and low score group (\u0026le;\u0026thinsp;cut-off value) according to the optimal cut-off value, in-hospital mortality, 28-day mortality, ICU Los and Hospital Los in the three models were significantly increased in the high score group (P\u0026thinsp;\u0026gt;\u0026thinsp;0.001). In terms of clinical signs, the high score group had lower systolic blood pressure, mean arterial pressure, and less urine output; in terms of laboratory indicators, the high score group had higher WBC, worse biochemical indicators, higher lactate, and more severe acidosis; in terms of clinical intervention, the high score group had a higher use rate of vasoactive drugs and RRT. This also confirmed that the MELDs can well stratify the risk of sepsis patients.\u003c/p\u003e \u003cp\u003eBiomarkers provide utility for diagnosis, prognosis, early disease identification, risk stratification, etc. in patients with sepsis or suspected sepsis \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. There has been a large number of studies on biomarkers or combinations of biomarkers for prognosis and risk stratification of sepsis \u003csup\u003e[\u003cspan additionalcitationids=\"CR34 CR35 CR36 CR37 CR38 CR39 CR40 CR41 CR42\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. However, most biomarkers are difficult to obtain quickly and easily, and the optimal cut-off values are not given。Hyperlactatemia is the strongest outcome measure in sepsis, and the increase in lactate in sepsis patients is mainly caused by tissue oxygen supply or oxygen utilization impairment \u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. The Save Sepsis Campaign used lactate greater than 4 mmol/L as a condition for early quantitative resuscitation in the cluster treatment of sepsis \u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. Some studies have pointed out that lactate greater than 4 mmol/L is significantly associated with higher in-hospital mortality \u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. Therefore, we further performed a subgroup analysis combining the MELDs and lactate. The analysis showed that patients with high MELD scores and lactate\u0026thinsp;\u0026gt;\u0026thinsp;4 mmol/L had higher in-hospital mortality and 28-day mortality, while patients with low MELD scores and lactate\u0026thinsp;\u0026le;\u0026thinsp;4 mmol/L had lower in-hospital mortality and 28-day mortality.\u003c/p\u003e \u003cp\u003eFinally, we further stratified the patients according to the admission diagnosis. We found that the cut-off values for stratification of sepsis patients using the MELDs are different in different disease spectra, and the different pathophysiological characteristics and severity of the primary disease itself may contribute to this situation.\u003c/p\u003e \u003cp\u003eBecause this study is based on the database, clinical information and data will inevitably be missing and inaccurate. However, because the MELDs require few objective indicators, to a certain extent, it makes full use of the advantages of a large sample size of the database and perfectly avoids the confounding interference of too many subjective indicators. It gives full play to the advantages of the models and the database and has greater reliability.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eWe used this study to verify the effectiveness of the MELDs in the prognostic assessment and risk stratification of septic patients. MELD, MELD-Na, and MELD-XI are associated with in-hospital mortality in septic patients. The optimal cut-off values for predicting in-hospital mortality in septic patients are 22.5, 22.5, and 19.5, respectively. The combination of the MELDs and lactate can better screen patients with high risk of sepsis. Since lactate and the indicators of the MELDs are commonly used and repeatedly measured in clinical work and are easy to obtain, clinicians can easily use these indicators to stratify sepsis patients early and screen high-risk patients for more optimized treatment, thus improving the prognosis of such patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMELD \u0026nbsp; \u0026nbsp; \u0026nbsp; Models for End-stage Liver Disease\u003c/p\u003e\n\u003cp\u003eMIMIC-Ⅳ \u0026nbsp; \u0026nbsp;Medical Information Mart for Intensive Care Ⅳ\u003c/p\u003e\n\u003cp\u003eICU \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; intensive intensive care unit\u003c/p\u003e\n\u003cp\u003eMAP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;mean arterial pressure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;multiplex interpolation\u003c/p\u003e\n\u003cp\u003eSD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;standard deviation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIQR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; interquartile ranges\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLOS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; length of stay\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; receiver operating characteristic curves\u003c/p\u003e\n\u003cp\u003eAUC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; area under the curve\u003c/p\u003e\n\u003cp\u003eIRB \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Institutional Review Board\u003c/p\u003e\n\u003cp\u003eCCI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Charlson Comorbidity Index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSOFA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sequential Organ Failure Assessment\u003c/p\u003e\n\u003cp\u003eRRT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; renal replacement therapy\u003c/p\u003e\n\u003cp\u003eCIs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;confidence intervals\u003c/p\u003e\n\u003cp\u003eAKI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; acute kidney injury\u003c/p\u003e\n\u003cp\u003eVNSSTIs \u0026nbsp; \u0026nbsp; vibrio vulnificus necrotizing skin and soft tissue infections\u003c/p\u003e\n\u003cp\u003eAHF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; acute heart failure\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the World Medical Association Declaration of Helsinki guidelines.\u0026nbsp;The MIMIC-IV project received approval from the Institutional Review Boards of both the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. Patient information was anonymized, thereby obviating the need for informed consent from individual patients for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to usage restrictions of the MIMIC-Ⅳ database but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Shanghai municipal health commission key supporting disciplines (Grant No. 2023ZDFC0104) and Shanghai Pudong New Area summit (emergency medicine and critical care) construction project (Grant No. PWYgf2021-03).\u0026nbsp;The funders had no role in the design, collection, analysis, interpretation of the data or writing of this protocol.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFZ conceived and designed this study. SYS, XPL and YZS performed the data collection. TS, and SYS undertook the data analysis, results interpretation and manuscript preparation. WZ, XBL and QMM conducted background literature search and review. FZ was responsible for results interpretation and revision of the manuscript. All authors attest they meet the ICMJE criteria for authorship and have approved the final article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the MIMIV-IV participants and staff. We also appreciate the invaluable contributions made by all the participants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWiersinga WJ, van der Poll T. Immunopathophysiology of human sepsis. EBioMedicine. 2022 Dec;86:104363. doi: 10.1016/j.ebiom.2022.104363. Epub 2022 Dec 2. PMID: 36470832; PMCID: PMC9783164.\u003c/li\u003e\n\u003cli\u003eSinger M, Deutschman CS, Seymour CW, et al. 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PMID: 30985210.\u003c/li\u003e\n\u003cli\u003eRhodes A, Evans LE, Alhazzani W, et al. Surviving Sepsis Campaign: international guidelines for management of sepsis and septic shock: 2016. Intensive Care Med 2017; 43(3):304\u0026ndash;377. doi: 10.1007/s00134-017-4683-6. Epub 2017 Jan 18. PMID: 28101605.\u003c/li\u003e\n\u003cli\u003eCasserly B, Phillips GS, Schorr C, et al. Lactate measurements in sepsis-induced tissue hypoperfusion: results from the Surviving Sepsis Campaign database. Crit Care Med. 2015 Mar;43(3):567-73. doi: 10.1097/CCM.0000000000000742. PMID: 25479113.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sepsis, MELD, Mortality, Predict, Lactate","lastPublishedDoi":"10.21203/rs.3.rs-6455319/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6455319/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: The Model for End-stage Liver Disease (MELD) and its modifications have been used to predict mortality and stratify risk for a variety of non-hepatic diseases with good stability. The purpose of this study was to evaluate the value of the MELD and its modifications in evaluating the prognosis of patients with sepsis.\u003c/p\u003e\n\u003cp\u003eMethods: This study is based on the MIMIC-Ⅳ database. A total of 15,882 patients were included. The correlation between the three models and the mortality rate of patients with sepsis was evaluated, and the optimal cut-off values were calculated. Then, further subgroup analysis was performed to seek better stratification criteria. Finally, stratification was performed according to comorbidities to observe the predictive value of the MELDs in patients with different comorbidities.\u003c/p\u003e\n\u003cp\u003eResults: MELD, MELD including Na (MELD-Na) and MELD excluding INR (MELD-XI) were all independent predictors of in-hospital mortality, and the optimal cut-off values were 22.5, 22.5 and 19.5, respectively. When grouped by cut-off values, high score groups were significantly associated with increased in-hospital mortality. Further subgroup analysis based on lactate revealed that patients with high MELD score and lactate level (\u0026gt; 4 mmol/L) had higher in-hospital mortality.\u003c/p\u003e\n\u003cp\u003eConclusions: The MELDs can effectively predict the in-hospital mortality of sepsis patients and stratify their risk. The MELDs combined with lactate can provide convenient risk stratification for sepsis patients, thus guiding clinicians to better intervene in sepsis patients at an early stage.\u003c/p\u003e","manuscriptTitle":"The Models for End-stage Liver Disease as prognostic assessment and risk stratification tools in sepsis: a study based on MIMIC-Ⅳ database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 12:45:25","doi":"10.21203/rs.3.rs-6455319/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":"4d3f9ccd-61f9-4064-b82e-31387c2dd0ad","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-19T21:39:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-13 12:45:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6455319","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6455319","identity":"rs-6455319","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}