Relationship between the urea–creatinine ratio and mortality in septic patients with and without chronic kidney disease: A retrospective single-center Chinese intensive care unit cohort study

Relationship between the urea–creatinine ratio and mortality in septic patients with and without chronic kidney disease: A retrospective single-center Chinese intensive care unit cohort study | 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 Relationship between the urea–creatinine ratio and mortality in septic patients with and without chronic kidney disease: A retrospective single-center Chinese intensive care unit cohort study Ping Xu, Zhitao Zhong, Shihao Liu, Lukai Lv, Minyan Fan, Kefeng Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6783609/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 urea-creatinine ratio (UCR) has shown potential as an indicator for predicting mortality in sepsis. However, its utility, especially in patients with chronic kidney disease (CKD), remained inadequately explored, particularly in the Chinese population. This study aimed to evaluate the predictive value of UCR for in-hospital mortality in septic patients and to examine its relationship with CKD status. Methods This single-center retrospective Chinese intensive care unit (ICU) cohort study analyzed data from a revised intensive care database. Logistic regression models were used to assess the independent association between UCR and in-hospital mortality. Receiver operating characteristic (ROC) curves were employed to evaluate predictive accuracy, and stratified analyses examined interactions between UCR and clinical factors. Results Among 453 septic patients, 36.2% experienced in-hospital mortality. The UCR was identified as an independent risk factor for mortality (OR 1.054, 95% CI 1.034–1.076; P < 0.001) and exhibited particularly strong predictive performance in patients without CKD. The predictive accuracy of the UCR alone was comparable to that of the Sequential Organ Failure Assessment (SOFA) score alone (AUC 0.686, 95% CI 0.621–0.751 vs. AUC 0.694, 95% CI 0.629–0.760). The combination of the UCR and the SOFA score demonstrated the highest predictive accuracy for mortality in septic patients without CKD (AUC 0.806, 95% CI 0.753–0.858). Conclusions Higher UCR is an independent predictor of in-hospital mortality in septic patients, particularly in those without CKD. When combined with the SOFA score, UCR may enhance sepsis risk stratification. Further validation studies are needed to confirm these findings. urea-creatinine ratio chronic kidney disease sepsis mortality risk SOFA score Figures Figure 1 Figure 2 Figure 3 Figure 4 Background The most recent International Consensus Definition of Sepsis (Sepsis 3.0) defined that sepsis is life-threatening organ dysfunction caused by a dysregulated host response to infection[ 1 – 3 ]. An estimated 48.9 million new cases of sepsis were reported globally in 2017, with 11.0 million deaths, representing 19.7% of all global fatalities[ 4 ]. As a prevalent cause of hospital deaths, sepsis elevates healthcare costs and severely affected patient quality of life, imposing a significant strain on health systems around the world[ 1 , 5 ].Therefore, improving the management of sepsis is an urgent public health issue that needed to be addressed. Early recognition of patients at high risk for sepsis and personalized treatment for those with a high likelihood of poor outcomes were key to reducing mortality and improving prognosis[ 6 , 7 ]. In this context, the UCR can serve as an easily accessible marker of early kidney dysfunction, allowing clinicians to evaluate patient risk soon after ICU admission and modify fluid management or renal-protective strategies accordingly. Renal function markers, including blood urea nitrogen (BUN), creatinine (Cr), and the urea-creatinine ratio (UCR), were established as independent risk factors for sepsis-related mortality. Data from the multi-center eICU Collaborative Research Database v2.0 indicated that initial BUN levels could independently predict 28-day mortality in critically ill septic patients (OR 1.013; 95% CI 1.012–1.014; P < 0.001)[ 8 ]. Another retrospective study demonstrated that creatinine levels also independently influenced 28-day mortality in sepsis patients (HR 1.002; 95% CI: 1.000-1.005, P = 0.024)[ 9 ]. Moreover, a retrospective analysis of the Medical Information Mart for Intensive Care (MIMIC-III) database revealed that a higher UCR was significantly associated with increased 28-D mortality (fourth quartile vs. first quartile: HR = 1.268, 95%CI = 1.037–1.551, P = 0.021) in American patients with septic shock[ 10 ]. While the UCR has shown superior predictive performance in various conditions, such as trauma-related acute respiratory distress syndrome and severe burns associated with acute kidney injury[ 11 , 12 ], compared to measurements of BUN or creatinine alone, researches on BUN and creatinine in relation to sepsis prognosis were more prevalent[ 8 , 9 , 13 ]. Yet, studies focusing on the prognostic value of the UCR in sepsis were notably limited. Given this gap, exploring UCR’s predictive utility in sepsis was both vital and necessary. Chronic kidney disease (CKD) is a clinical syndrome defined by persistent modifications in kidney structure and function, often presenting with high blood pressure, swelling, and reduced urine output, along with increased BUN and serum creatinine levels.[ 14 – 16 ]. In patients with concurrent sepsis and CKD, the interpretation of BUN and serum creatinine fluctuations becomes particularly challenging due to the overlapping pathophysiological mechanisms. To the best of our knowledge, the relationship between the UCR and outcomes in sepsis patients with CKD was uncertain. Furthermore, the connection between the urea-creatinine ratio and sepsis in the Chinese demographic has not been investigated. As a result, we undertook a study to analyze the association between the UCR and the prognosis of sepsis patients among Chinese individuals, and to explore the UCR's predictive capability in subgroups with and without CKD. Methods Study design and population This research was a retrospective, single-center analysis of septic patients who were admitted to the ICUs at Zigong Fourth People’s Hospital from January 1, 2018, to December 31, 2023. Data from various hospital information systems, including the electronic healthcare record (EHR), hospital information system (HIS), laboratory information system (LIS), and critical care nursing chart system, were directly exported into a unified research dataset. We utilized an updated critical care database that initially recorded all patients moved to any ICU between January 2019 and December 2020[ 17 ], and later included sepsis cases from 2018 and from 2021 to 2023. Approval for the study was granted by the Ethics Committee of Zigong Fourth People's Hospital (No: 2023-005). This study required that all the following inclusion criteria be met: 1) Patients admitted to the central ICU and emergency ICU from 1 January 2018, to 31 December 2023, diagnosed with sepsis according to the sepsis 3.0 criteria; 2) Individuals aged 18 or older at admission. Exclusion criteria (patients were excluded if they met any of the following conditions): 1) Confirmed SARS-CoV-2 (COVID-19) infection; 2) Patients with several ICU admissions, with only the first admission's data preserved; 3) Patients who were missing key variable data, such as BUN and serum creatinine, within 24 hours of ICU admission. Definitions of the exposure and outcome variables Definitions of the exposure and outcome variables In this study, the primary exposure variable analyzed was UCR, defined as UCR = BUN (mg/dL) / creatinine (mg/dL). BUN and creatinine were calculated using the following formulas: BUN (mg/dL) = blood urea (mmol/L) × 2.8[ 18 ]; creatinine (mg/dL) = creatinine (µmol/L) / 88.4[ 19 ].The levels of BUN and serum creatinine were determined using a Siemens ADVIA 2400 autoanalyzer (Siemens Diagnostics, Tarrytown, NY, USA) through a colorimetric method. BUN was measured using the urease–glutamate dehydrogenase (Urease–GLDH) coupled technique, while serum creatinine was assessed with the creatininase–sarcosine oxidase–peroxidase coupled method. Study participants were organized into two groups based on whether they had CKD, diagnosed following the KDIGO 2021 clinical practice guidelines[ 20 ], with prior CKD diagnoses in their medical history serving as additional documentation. The study's outcome was the in-hospital mortality of sepsis patients in ICUs. Participants in the central and emergency ICUs were observed until they died or were discharged. Covariates Patients' demographics included both age and gender. Vital signs such as heart rate, systolic and diastolic blood pressure (SBP and DBP), temperature, respiratory rate, and pulse oxygen saturation (SpO2) were extracted. Comprehensive laboratory tests were conducted, covering hematocrit, hemoglobin, platelet count (PLT), C-reactive protein (CRP), neutrophil and lymphocyte counts, white blood cell (WBC) count, anion gap (AG), bicarbonate, BUN, serum creatinine, glucose, calcium, chloride, sodium, potassium, international normalized ratio (INR), prothrombin time (PT), and partial thromboplastin time (PTT). Additionally, the illness severity was assessed using the sequential organ failure assessment (SOFA) score to evaluate the condition of the patients. Sample size calculation Based on the rule of a minimum of ten events per variable, the sample size for the logistic regression analysis was adequate[ 21 , 22 ]. Using PASS software (version 11.0.7; PASS, NCSS, LLC), the minimum sample size for other statistical methods was calculated via power analysis, ensuring the power was at least 80% and the p-value was below 0.05[ 23 , 24 ]. Statistical analysis Numbers with percentages were used to present categorical data. Means with standard deviations were used to represent continuous normal variables, whereas medians with 25–75% interquartile ranges were used for continuous non-normal variables. Categorical variables were compared using either Fisher's exact tests or Chi-squared tests, normally distributed variables were compared using Student t tests, and continuous non-normal variables were compared using Wilcoxon rank tests. Univariate logistic regression identified variables with a P-value < 0.1 for inclusion in subsequent multivariate models. Demographic factors (age and gender) and independent variables in the multivariate models were considered as adjusted factors. Patients were categorized into high-risk and low-risk groups according to the UCR cut-off value in the receiver operating characteristic (ROC) curve[ 25 , 26 ]. To assess the risk of in-hospital mortality, we employed logistic regression models to compute odds ratios (OR) and 95% confidence intervals (CI), treating the UCR as both a continuous and a categorical variable by comparing the high-risk group to the low-risk group. In the crude model, no covariates were adjusted for, but Model I accounted for age and gender, and Model Ⅱ included these adjustments along with additional independent factors. Restrictive cubic spline analyses were used to investigate and display the potential association and dose–response relationship between the UCR and in-hospital mortality. ROC curves and areas under the curve (AUCs) were employed to evaluate the predictive power of the UCR and other variables for outcome. Stratification analyses were conducted based on gender (male, female), CKD (No, Yes), congestive heart failure (CHF) (No, Yes), chronic obstructive pulmonary disease (COPD) (No, Yes), septic shock (No, Yes), SOFA score (< optimal cut-off value predicting in-hospital mortality, ≥ optimal cut-off value), continuous renal replacement therapy (CRRT) (No, Yes), and ventilator use (No, Yes), as well as interaction analyses between various stratification factors and the UCR. A P-value of less than 0.05 was regarded as significant. The statistical analysis was performed with R software (version 4.4.2, R Foundation for Statistical Computing, Vienna, Austria). Results Patient characteristics The process of patient exclusion is demonstrated in Fig. 1 . The research included 453 sepsis patients, 282 (62.25%) of whom were male. The median age of the cohort was 75 years, with an interquartile range of 67 to 84. Out of the patients, 136 (30.02%) had CKD, and the in-hospital mortality rate was 36.20% (164 out of 453). Table 1 provides the baseline characteristics of the sepsis patients included in the study. Table 1 Baseline characteristics of the sepsis patients included in the study Overall n = 453 Without CKD n = 317 With CKD n = 136 P Demographics Male (%) 282(62.25) 202(63.72) 80(58.82) 0.324 Age, years, median (IQR) 75.0(67.0,84.0) 74.0(66.0,82.0) 78.0(69.0,85.5) 0.004 Vital signs Temperature, ℃, median (IQR) 36.3(36.1,36.6) 36.3(36.1,36.6) 36.3(36.1,36.6) 0.757 Heart rate, bpm, median (IQR) 131.9(119.0,146.0) 131.9(117.0,147.0) 131.9(119.0,144.0) 0.959 Respiratory rate, bpm, median (IQR) 28.9(25.0,35.0) 28.9(25.0,35.0) 28.9(26.0,35.0) 0.690 SBP, mmHg, median (IQR) 112.7(97.0,125.0) 112.7(97.0,128.0) 112.7(92.5,117.5) 0.153 DBP, mmHg, median (IQR) 66.1(57.0,72.0) 66.1(58.0,73.0) 66.1(55.5,68.0) 0.076 SpO 2 , %, median (IQR) 94.0(93.6,98.0) 94.0(93.6,98.0) 93.6(93.6,97.5) 0.779 Comorbidities CHF (%) 173(38.19) 101(31.86) 72(52.94) < 0.001 COPD (%) 141(31.13) 103(32.49) 38(27.94) 0.338 Laboratory tests BUN, mmol/L, median (IQR) 11.4(7.6,19.1) 10.1(7.0,15.8) 17.1(10.5,25.3) < 0.001 Creatinine, µmol/L, median (IQR) 122.2(77.6,204.9) 99.8(68.4,174.7) 175.8(121.6,289.9) < 0.001 UCR, median (IQR 23.5(17.6,32.4) 24.0(18.8,33.3) 21.9(15.4,29.9) 0.005 Cys C, mg/L, median (IQR) 2.17(1.45,3.26) 1.77(1.30,2.88) 2.86(2.16,4.24) < 0.001 UA, umol/L, median (IQR) 432.0(303.0,590.0) 397.0(279.0,559.0) 506.0(399.5,657.5) < 0.001 WBC, 10^9/L, median (IQR) 10.9(6.6,16.8) 10.8(6.6,16.8) 11.0(6.6,17.1) 0.888 Lymphocyte Count, 10^9/L, median (IQR) 0.530(0.310,0.950) 0.490(0.290,0.990) 0.560(0.380,0.930) 0.393 Neutrophil Count, 10^9/L, median (IQR) 10.4(6.5,15.4) 10.4(6.4,15.8) 10.5(6.8,14.9) 0.980 Monocyte Count, 10^9/L, median (IQR) 0.390(0.190,0.680) 0.380(0.190,0.660) 0.420(0.210,0.710) 0.623 HB, g/L, mean ± SD 109.7 ± 26.9 111.4 ± 26.2 105.6 ± 28.1 0.035 HCT, %, mean ± SD 0.341 ± 0.080 0.346 ± 0.078 0.330 ± 0.083 0.045 PLT, 10^9/L, median (IQR) 162.0(103.0,232.0) 167.0(110.0,242.0) 148.5(90.0,216.5) 0.030 D-Dimer, mg/L, median (IQR) 4.86(2.14,10.41) 5.02(2.16,10.35) 4.67(1.93,11.11) 0.770 PT, sec, median (IQR) 14.6(13.2,16.4) 14.5(13.2,16.4) 14.7(13.2,16.8) 0.804 INR, ratio, median (IQR) 1.29(1.15,1.47) 1.29(1.15,1.44) 1.31(1.15,1.49) 0.580 APTT, sec, median (IQR) 31.6(27.5,37.4) 31.6(27.5,37.2) 31.6(27.7,38.3) 0.493 FIB, g/L, median (IQR) 4.19(3.10,5.74) 4.26(3.23,5.87) 4.14(2.83,5.31) 0.272 Potassium, mmol/L, median (IQR) 3.97(3.39,4.50) 3.87(3.29,4.38) 4.13(3.61,4.99) < 0.001 Sodium, mmol/L, median (IQR) 138.2(133.7,142.9) 138.0(133.6,142.4) 138.8(134.0,143.0) 0.221 Chloride, mmol/L, median (IQR) 101.8(96.3,106.8) 101.3(96.3,106.1) 103.9(96.2,107.9) 0.031 Calcium, mmol/L, median (IQR) 2.17(2.05,2.31) 2.17(2.05,2.31) 2.17(2.06,2.32) 0.823 CRP, mg/L, median (IQR) 113.7(56.1,155.5) 113.7(56.1,155.4) 113.7(59.4,156.6) 0.666 ALT, U/L, median (IQR) 28.0(17.5,56.1) 28.0(17.5,54.2) 27.5(17.3,61.0) 0.930 AST, U/L, median (IQR) 45.2(26.3,101.2) 45.0(27.0,99.7) 48.8(25.3,111.2) 0.778 DBIL, umol/L, median (IQR) 6.5(3.7,11.8) 6.6(3.8,11.6) 6.2(3.4,13.2) 0.861 IBIL, umol/L, median (IQR) 7.8(4.9,12.8) 7.9(5.1,12.9) 7.1(4.5,12.5) 0.257 Albumin, g/L, mean ± SD 29.3 ± 6.0 29.2 ± 5.9 29.4 ± 6.1 0.767 Globulin, g/L, median (IQR) 29.3(25.1,33.1) 29.1(24.8,33.0) 29.6(25.8,33.3) 0.717 SOFA score 7.00(6.00,11.00) 7.00(6.00,11.00) 8.00(5.00,11.00) 0.605 Treatment during hospitalization CRRT (%) 42(9.27) 27(8.52) 15(11.03) 0.398 Ventilator (%) 399(88.08) 278(87.70) 121(88.97) 0.701 In-hospital mortality (%) 164(36.20) 98(30.91) 66(48.53) < 0.001 Continuous variables are expressed as Mean ± SD or Median (IQR), categorical variables are expressed as number (percent) Abbreviations: CKD, chronic kidney disease; IQR, interquartile range; SBP, systolic blood pressure; DBP, diastolic blood pressure; SpO2, pulse oxygen saturation; CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease; BUN, blood urea nitrogen; UCR, urea-creatinine ratio; Cys C, Cystatin C; UA, uric acid; WBC, white blood cell count; HB, hemoglobin; HCT, hematocrit; SD, standard deviation; PLT, platelet; PT, prothrombin time; INR, international normalized ratio; APTT, activated partial thromboplastin time; FIB, fibrinogen; CRP, C-reactive protein; ALT, alanine aminotransferase; AST, aspartate aminotransferase; DBIL, direct bilirubin; IBIL, indirect bilirubin; SOFA, sequential organ failure assessment; CRRT, continuous renal replacement therapy. The CKD group was significantly older (median age 78.0 years) compared to the non-CKD group (median age 74.0 years, P = 0.004). Congestive heart failure was significantly more common in the CKD group (52.94% vs. 31.86%, P < 0.001). Laboratory tests revealed that the CKD group had significantly higher creatinine (175.8 µmol/L vs. 99.8 µmol/L, P < 0.001), BUN (17.1 mmol/L vs. 10.1 mmol/L, P < 0.001), cystatin C (2.86 mg/L vs. 1.77 mg/L, P < 0.001), and uric acid (506.0 µmol/L vs. 397.0 µmol/L, P < 0.001) compared to the non-CKD group. The UCR was significantly lower in the CKD group (21.9 vs. 24.0, P = 0.005). Potassium levels were significantly higher in the CKD group (4.13 mmol/L vs. 3.87 mmol/L, P < 0.001), while chloride levels were also elevated (103.9 mmol/L vs. 101.3 mmol/L, P = 0.031). Association between the UCR and in-hospital mortality in septic patients In the logistic regression model, the UCR, CKD (yes or no), heart rate, and ventilator use (yes or no) were independent risk factors for in-hospital mortality in septic patients (see Tables S1 and S2). The odds ratios (ORs) and 95% confidence intervals (CIs) were 1.063 (1.040–1.086), 2.260 (1.384–3.690), 1.012 (1.004–1.020), and 6.414 (2.363–17.409), respectively. The optimal UCR thresholds for predicting in-hospital mortality were 30.388 for all septic patients, 29.117 for those without CKD, and 14.552 for those with CKD (see Table S3). For all septic patients, the low-risk group was defined as having a UCR < 30.388, while the high-risk group was defined as having a UCR ≥ 30.388. Among septic patients without CKD, a UCR under 29.117 was classified as low risk, and a UCR of 29.117 or above was classified as high risk. Among septic patients with CKD, a UCR lower than 14.552 defined the low-risk group, while a UCR equal to or exceeding 14.552 defined the high-risk group. Figure 2 illustrates the associations between the UCR and in-hospital mortality in septic patients. Figure 2 A shows a non-linear P-value of 0.364, suggesting a near-linear relationship between the UCR and in-hospital mortality in septic patients. Figure 2 B presents a non-linear P-value of 0.134 in septic patients without CKD, indicating that UCR's impact on mortality in these patients was nearly linear. Figure 2 C shows a non-linear P-value of 0.752, indicating that the relationship between UCR and in-hospital mortality was nearly linear in septic patients with CKD. Table 2 presents the relationship between the UCR and in-hospital mortality in septic patients, with subgroup analyses for all septic patients, those without CKD, and those with CKD. For all septic patients, the odds ratio (OR) of UCR for in-hospital mortality in the non-adjusted model, Model I, and Model Ⅱ was 1.048 (95% CI: 1.029–1.068; P < 0.001), 1.048 (95% CI: 1.030–1.069; P < 0.001), and 1.054 (95% CI: 1.034–1.076; P < 0.001), respectively. Compared with the low-risk group, the high-risk group had an OR of 2.999 (95% CI: 1.952–4.633; P < 0.001), 3.014 (95% CI: 1.960–4.662; P < 0.001), and 3.381 (95% CI: 2.133–5.414; P < 0.001), respectively. The trend was statistically significant with a P -value < 0.001. Table 2 Relationship between UCR and in-hospital mortality in septic patients Exposure Non-adjusted Model Ⅰ Model Ⅱ OR (95% CI) P OR (95% CI) P OR (95% CI) P Septic patients UCR 1.048(1.029,1.068) <0.001 1.048(1.03,1.069) <0.001 1.054(1.034,1.076) <0.001 low-risk group Ref Ref Ref high-risk group 2.999(1.952,4.633) <0.001 3.014(1.96,4.662) <0.001 3.381(2.133,5.414) <0.001 P for trend <0.001 <0.001 <0.001 Septic patients without CKD UCR 1.073(1.048,1.101) <0.001 1.075(1.05,1.104) <0.001 1.076(1.05,1.106) <0.001 low-risk group Ref Ref Ref high-risk group 4.124(2.451,7.004) <0.001 4.244(2.509,7.256) <0.001 4.727(2.716,8.368) <0.001 P for trend <0.001 <0.001 <0.001 Septic patients with CKD UCR 1.018(0.988,1.051) 0.253 1.018(0.988,1.052) 0.250 1.014(0.982,1.048) 0.395 low-risk group Ref Ref Ref high-risk group 1.773(0.799,4.034) 0.163 1.767(0.796,4.023) 0.166 1.616(0.704,3.797) 0.261 P for trend 0.163 0.166 0.261 Notes : Non-adjusted models adjusted for: None; Model I adjusted for: age and gender; Model II adjusted for: confounders from the minimally adjusted model (Model I) + CKD (yes or no), heart rate, and ventilator use (yes or no) for all septic patients, or heart rate and ventilator use (yes or no) for septic patients with or without CKD. Abbreviations: OR, odds ratios; CI, confidence intervals; UCR, urea-creatinine ratio; CKD, chronic kidney disease. For septic patients without CKD, the OR of UCR for in-hospital mortality was 1.073 (95% CI: 1.048–1.101; P < 0.001), 1.075 (95% CI: 1.050–1.104; P < 0.001), and 1.076 (95% CI: 1.050–1.106; P < 0.001) for the non-adjusted model, Model I, and Model Ⅱ, respectively. Compared with the low-risk group, the high-risk group had an OR of 4.124 (95% CI: 2.451–7.004; P < 0.001), 4.244 (95% CI: 2.509–7.256; P < 0.001), and 4.727 (95% CI: 2.716–8.368; P < 0.001), respectively. The trend was statistically significant with a P-value < 0.001. For septic patients with CKD, the OR of UCR for in-hospital mortality in the non-adjusted model, Model I, and Model Ⅱ was 1.018 (95% CI: 0.988–1.051; P = 0.163), 1.018 (95% CI: 0.988–1.052; P = 0.166), and 1.014 (95% CI: 0.982–1.048; P = 0.261), respectively. Compared with the low-risk group, the high-risk group had an OR of 1.773 (95% CI: 0.799–4.034; P = 0.271), 1.767 (95% CI: 0.796–4.023; P = 0.905), and 1.616 (95% CI: 0.704–3.797; P = 0.560), respectively. The trend was not statistically significant in any model ( P > 0.05). Predictive capacity comparison The receiver operating characteristic curves are plotted in Fig. 3 . The findings indicate that using SOFA combined with UCR offered the most precise prediction of in-hospital mortality for septic patients, especially those without CKD. Septic patients with CKD showed a significant drop in predictive performance, yet the combined use of SOFA and UCR still outperformed individual biomarkers. Among septic patients, the combination of SOFA and UCR exhibited the highest AUC of 0.734 (95% CI: 0.685–0.784), suggesting a strong predictive ability for in-hospital mortality. This was followed by the combination of SOFA and BUN, which had an AUC of 0.692 (95% CI: 0.640–0.743). The SOFA score alone demonstrated an AUC of 0.673 (95% CI: 0.620–0.725). Among individual biomarkers, UCR showed an AUC of 0.623 (95% CI: 0.568–0.678), while BUN had an AUC of 0.616 (95% CI: 0.562–0.670). Cr demonstrated the lowest AUC of 0.541 (95% CI: 0.485–0.596). Among septic patients without CKD, the combination of SOFA and UCR demonstrated the highest AUC of 0.806 (95% CI: 0.753–0.858), indicating a strong predictive ability for in-hospital mortality. This was followed by the combination of SOFA and BUN, which had an AUC of 0.713 (95% CI: 0.649–0.777). The SOFA score alone had an AUC of 0.694 (95% CI: 0.629–0.760). UCR showed a promising AUC of 0.686 (95% CI: 0.621–0.751), while BUN had an AUC of 0.632 (95% CI: 0.565–0.699). Cr demonstrated the weakest predictive ability with an AUC of 0.516 (95% CI: 0.446–0.585). Among septic patients with CKD, the combination of SOFA and UCR had an AUC of 0.644 (95% CI: 0.551–0.737), followed by the combination of SOFA and BUN, which had an AUC of 0.631 (95% CI: 0.537–0.726). The SOFA score alone showed an AUC of 0.634 (95% CI: 0.540–0.728). UCR had an AUC of 0.555 (95% CI: 0.458–0.653), while BUN and Cr had the lowest AUCs, 0.518 (95% CI: 0.420–0.616) and 0.496 (95% CI: 0.398–0.595), respectively. Subgroup analyses stratified by UCR Subgroup analyses are presented in Fig. 4 . In Fig. 4 A, the UCR appeared to be more prominent in septic patients without CKD (OR (95% CI): patients without CKD 1.07 (1.05–1.10) vs. patients with CKD 1.02 (0.99–1.05), P for interaction = 0.010) among all septic patients. In Figs. 4 B and 4 C, the outcomes remained consistent across various subgroups, with no significant interaction effects between septic patients with and without CKD. Discussion This retrospective observational study revealed an almost linear relationship between the UCR and in-hospital mortality across all septic patients, including those with and without CKD. The higher UCR was determined to be an independent risk factor for in-hospital mortality in septic patients, with a notable impact on those without CKD. In addition, the integration of the SOFA score and UCR resulted in the most accurate forecast of in-hospital mortality in septic patients, especially in those without CKD. BUN and serum creatinine are essential indicators for evaluating kidney function[ 27 ]. BUN passes through the renal tubules, and decreased cardiac output, combined with inadequate arterial perfusion, activates the sympathetic nervous system (SNS) and the renin-angiotensin-aldosterone system (RAAS). This results in enhanced sodium reabsorption in the proximal renal tubules, which subsequently increases urea concentration. Alternatively, serum creatinine, a byproduct of creatine metabolism with low molecular weight, is mostly filtered by the glomeruli and nearly completely excreted in urine. Usually, the UCR ranged from 10 to 15:1, and a UCR over 20:1 was often associated with prerenal azotemia[ 13 ]. However, in recent years, the UCR has been frequently used to assess the prognosis of various diseases, such as acute kidney injury[ 12 ], chronic kidney disease[ 28 ], upper gastrointestinal bleeding[ 29 ], septic shock[ 10 ], COVID-19[ 30 ], non-traumatic intracranial hemorrhage[ 31 ], acute ischemic stroke[ 32 ], trauma-related acute respiratory distress syndrome[ 11 ], acute myocardial infarction[ 33 – 35 ], acute and chronic heart failure[ 27 , 34 , 36 – 40 ], and cardiogenic shock[ 41 ]. We found that a higher UCR was an independent risk factor for in-hospital mortality in septic patients. This could be attributed to the UCR, which acted as a biomarker for neurohormonal activity and indicated the activation of the neurohormonal system related to infection, including vasopressin, the SNS, and RAAS, which inhibited the reabsorption of urea nitrogen[ 10 , 32 ]. Nevertheless, an elevated UCR did not independently predict in-hospital mortality for septic patients with CKD, possibly due to CKD's influence on baseline UCR variability. Additionally, our research showed that the UCR predicted in-hospital mortality in septic patients more effectively than BUN or Cr alone, which was consistent with studies on conditions such as acute kidney injury from severe burns[ 12 ], trauma-related acute respiratory distress syndrome[ 11 ]. The cutoff value of UCR for predicting mortality varied across different disease conditions. For non-traumatic intracranial hemorrhage, the cutoff for 1-year mortality was 16.25, indicating the long-term impact on survival[ 31 ]. For acute decompensated heart failure, the cutoff for predicting hospital death was 17.4[ 37 ]. In chronic heart failure, the UCR cutoff for predicting long-term survival was 19.37[ 27 ]. In acute ischemic stroke, a UCR cutoff of 19.63 was associated with in-hospital mortality, reflecting the activation of neurohormonal pathways[ 32 ]. In patients with non-end-stage chronic kidney disease, the cutoff for inpatient mortality was significantly higher, at 100, emphasizing the critical role of baseline kidney function[ 28 ]. These findings underscored the need for condition-specific UCR thresholds to guide prognosis. In the retrospective analysis of the MIMIC-III database, based on the quartile and quintile analyses, UCR values ≥ 27.3 (quartile cutoff) and ≥ 30.0 (quintile cutoff) were predictive of a significantly higher risk of 28-day mortality in septic shock[ 10 ]. This was consistent with our findings, where the optimal UCR thresholds for predicting in-hospital mortality were 30.4 for all septic patients and 29.1 for those without CKD. There were multiple strengths attributed to this study. To begin with, it delivered a thorough assessment of the UCR's prognostic importance in septic patients, focusing on Chinese patients and those with CKD, which is a relatively unexplored area in the literature. Furthermore, by merging UCR with the SOFA score, our study revealed a marked enhancement in forecasting in-hospital mortality, indicating UCR's potential as a beneficial complement to established clinical scoring frameworks. Finally, the inclusion of a varied group of septic patients allowed for the assessment of UCR's utility across different subgroups, ensuring the findings are relevant to a broad spectrum of clinical cases. These strengths provided important insights for future clinical practice and research, particularly in enhancing the accuracy of sepsis risk stratification. Some limitations were also present in this study. First, the retrospective nature of the study, along with the exclusion of patients missing BUN and Cr values, might have introduced bias in the sample selection. Second, BUN and Cr levels were measured only when the patient was admitted to the ICU, with no subsequent lab data available, so the dynamic changes in UCR were not reflected. Third, our research identified an almost linear correlation between UCR and in-hospital mortality among septic patients, which differed slightly from other studies. The discrepancy could be due to the small sample size of patients with UCR values less than 10 (15 out of 452), possibly not fully illustrating the connection between UCR and mortality. Fourth, the absence of UCR values before sepsis onset in CKD patients hindered our ability to analyze UCR changes post-sepsis onset. Conclusions UCR is an independent predictor of in-hospital mortality in septic patients, with higher values particularly affecting those without CKD. Combining UCR with the SOFA score improved prognostic accuracy, especially in patients without CKD. Future studies with larger cohorts and comprehensive data, particularly in CKD patients, are needed to validate these findings and refine UCR cutoff values for clinical use. Abbreviations Abbreviation Full Name UCR urea-creatinine ratio CKD chronic kidney disease ICU intensive care unit ROC Receiver operating characteristic SOFA Sequential Organ Failure Assessment BUN blood urea nitrogen Cr creatinine MIMIC-III Medical Information Mart for Intensive Care EHR electronic healthcare record HIS hospital information system LIS laboratory information system Urease–GLDH urease–glutamate dehydrogenase SBP systolic blood pressure DBP diastolic blood pressure SpO2 pulse oxygen saturation PLT platelet count CRP C-reactive protein WBC white blood cell AG anion gap INR international normalized ratio PT prothrombin time PTT partial thromboplastin time OR odds ratios CI confidence intervals AUC area under the curve CHF congestive heart failure COPD chronic obstructive pulmonary disease CRRT continuous renal replacement therapy SNS sympathetic nervous system RAAS renin-angiotensin-aldosterone system Declarations Ethics approval and Consent to participate Approval for the study was granted by the Ethics Committee of Zigong Fourth People's Hospital (No: 2023-005). Participants could withdraw from the study at any time. All collected data were anonymized, with researchers having no access to participant-identifying information during or after the research period. Consent for publication Not applicable. Availability of data and materials All data generated or analyzed during this study are included in this published article and its supplementary information files. Conflicts of interest All co-authors have no relevant financial or non-financial interests to disclose nor conflicts of interest to declare. Funding Ping Xu received support from the Project of Science & Technology Department of Sichuan Province (2024JDKP0021), the Sichuan Medical Association Scientific Research Project (S21019), the Special Project for Scientific and Technological Research of Sichuan Provincial Administration of Traditional Chinese Medicine (2023MS494), and the Research Project of Zigong City Science & Technology and Intellectual Property Right Bureau (2022ZCYGY05) to conduct this study. The funders had no role in the study design, data collection, analysis, interpretation, reporting, or the decision to submit the manuscript for publication. Authors’ contributions All authors contributed to manuscript editing and approved the final version. Each author took responsibility for their individual contributions and ensured that any issues regarding the accuracy or integrity of the work (including those unrelated to their direct involvement) were properly investigated, resolved, and documented. PX, ZTZ, and CZ conceived and designed the study; SHL, LKL, and MYF acquired the data; PX, ZTZ, SHL, and KFL performed statistical analysis; PX and CZ interpreted the results; PX, ZTZ, and SHL drafted the manuscript; PX and CZ critically revised the manuscript. PX, ZTZ, and SHL contributed equally as co-first authors. Acknowledgements We thanked the medical and nursing staff of the Emergency and ICU departments at Zigong Fourth People’s Hospital for their invaluable support in data collection and patient care. References Singer M, Deutschman CS, Seymour CW et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). Jama 2016;315(8):801-810. Kolodyazhna A, Wiersinga WJ, van der Poll T. Aiming for precision: personalized medicine through sepsis subtyping. Burns & trauma 2025;13:tkae073. Gildea A, Mulvihill C, McFarlane E et al. Recognition, diagnosis, and early management of suspected sepsis: summary of updated NICE guidance. BMJ 2024;385:q1173. Rudd KE, Johnson SC, Agesa KM et al. Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study. Lancet 2020;395(10219):200-211. Beane A, Shankar-Hari M. Long-term ill health in sepsis survivors: an ignored health-care challenge? Lancet 2024;404(10459):1178-1180. Sun T, Wang Y, Wu X et al. Prognostic Value of Syndecan-1 in the Prediction of Sepsis-Related Complications and Mortality: A Meta-Analysis. Frontiers in public health 2022;10:870065. Zhu JL, Yuan SQ, Huang T et al. Influence of systolic blood pressure trajectory on in-hospital mortality in patients with sepsis. BMC infectious diseases 2023;23(1):90. Harazim M, Tan K, Nalos M et al. Blood urea nitrogen - independent marker of mortality in sepsis. Biomedical papers of the Medical Faculty of the University Palacky, Olomouc, Czechoslovakia 2023;167(1):24-29. Xiao Y, Yan X, Shen L et al. Evaluation of qSOFA score, and conjugated bilirubin and creatinine levels for predicting 28-day mortality in patients with sepsis. Experimental and therapeutic medicine 2022;24(1):447. Han D, Zhang L, Zheng S et al. Prognostic Value of Blood Urea Nitrogen/Creatinine Ratio for Septic Shock: An Analysis of the MIMIC-III Clinical Database. BioMed research international 2021;2021:5595042. Ma H, Lin S, Xie Y et al. Association between BUN/creatinine ratio and the risk of in-hospital mortality in patients with trauma-related acute respiratory distress syndrome: a single-centre retrospective cohort from the MIMIC database. BMJ open 2023;13(4):e069345. Yoon J, Kim Y, Yim H et al. Analysis of prognostic factors for acute kidney injury with continuous renal replacement therapy in severely burned patients. Burns : journal of the International Society for Burn Injuries 2017;43(7):1418-1426. Feinfeld DA, Bargouthi H, Niaz Q et al. Massive and disproportionate elevation of blood urea nitrogen in acute azotemia. International urology and nephrology 2002;34(1):143-145. Anders HJ, Li Q, Steiger S. Asymptomatic hyperuricaemia in chronic kidney disease: mechanisms and clinical implications. Clinical kidney journal 2023;16(6):928-938. Wang J, Li J, Zhang X et al. Molecular mechanisms of histone deacetylases and inhibitors in renal fibrosis progression. Frontiers in molecular biosciences 2022;9:986405. Wu MF, Lee CH, Pai PH et al. Screening Cases of Suspected Early Stage Chronic Kidney Disease from Clinical Laboratory Data: The Comparison between Urine Conductivity and Urine Protein. Biomedicines 2023;11(2). Xu P, Chen L, Zhu Y et al. Critical Care Database Comprising Patients With Infection. Frontiers in public health 2022;10:852410. Wuttke M, Li Y, Li M et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nature genetics 2019;51(6):957-972. Zampieri FG, Machado FR, Biondi RS et al. Effect of Slower vs Faster Intravenous Fluid Bolus Rates on Mortality in Critically Ill Patients: The BaSICS Randomized Clinical Trial. Jama 2021;326(9):830-838. Group KDIGOKBPW. KDIGO 2021 Clinical Practice Guideline for the Management of Blood Pressure in Chronic Kidney Disease. Kidney international 2021;99(3s):S1-s87. Peduzzi P, Concato J, Kemper E et al. A simulation study of the number of events per variable in logistic regression analysis. Journal of clinical epidemiology 1996;49(12):1373-1379. Xu P, Ye L, Li L et al. Comparison of the prognostic value, feasibility, and reproducibility among different scoring methods of 8‑point lung ultrasonography in patients with acute heart failure. Internal and emergency medicine 2023;18(8):2321-2332. Dou Y, Li A, Liu G et al. Comparison of bioimpedance equations and dual-energy X-ray for assessment of fat free mass in a Chinese dialysis population. Renal failure 2023;45(1):2182131. Alqutub MN. Peri-implant parameters and cytokine profile among Peri-implant disease patients treated with Er Cr YSGG laser and PDT. Photodiagnosis and photodynamic therapy 2022;37:102641. Li LQ, Zhang LH, Zhang Y et al. Construction of immune-related gene pairs signature to predict the overall survival of osteosarcoma patients. Aging 2020;12(22):22906-22926. Zhao X, Wu P, Liu D et al. An Immunity-Associated lncRNA Signature for Predicting Prognosis in Gastric Adenocarcinoma. Journal of healthcare engineering 2022;2022:3035073. Wang Y, Xu X, Shi S et al. Blood urea nitrogen to creatinine ratio and long-term survival in patients with chronic heart failure. Eur J Med Res 2023;28(1):343. Brookes EM, Power DA. Elevated serum urea-to-creatinine ratio is associated with adverse inpatient clinical outcomes in non-end stage chronic kidney disease. Scientific reports 2022;12(1):20827. Wu KH, Shih HA, Hung MS et al. The association between blood urea nitrogen to creatinine ratio and mortality in patients with upper gastrointestinal bleeding. Arab journal of gastroenterology : the official publication of the Pan-Arab Association of Gastroenterology 2018;19(4):143-147. Ok F, Erdogan O, Durmus E et al. Predictive values of blood urea nitrogen/creatinine ratio and other routine blood parameters on disease severity and survival of COVID-19 patients. Journal of medical virology 2021;93(2):786-793. Chen P, Jiang Y, Cai J et al. Prediction of prognosis in patients with nontraumatic intracranial hemorrhage using blood urea nitrogen-to-creatinine ratio on admission: a retrospective cohort study based on data from the medical information Mart for intensive care-IV database. Frontiers in neurology 2023;14:1267815. Li B, Li J, Meng X et al. The association of blood urea nitrogen-to-creatinine ratio and in-hospital mortality in acute ischemic stroke patients with atrial fibrillation: data from the MIMIC-IV database. Frontiers in neurology 2024;15:1331626. Horiuchi Y, Aoki J, Tanabe K et al. A High Level of Blood Urea Nitrogen Is a Significant Predictor for In-hospital Mortality in Patients with Acute Myocardial Infarction. International heart journal 2018;59(2):263-271. Qian H, Tang C, Yan G. Predictive value of blood urea nitrogen/creatinine ratio in the long-term prognosis of patients with acute myocardial infarction complicated with acute heart failure. Medicine 2019;98(11):e14845. Huang S, Guo N, Duan X et al. Association between the blood urea nitrogen to creatinine ratio and in‑hospital mortality among patients with acute myocardial infarction: A retrospective cohort study. Experimental and therapeutic medicine 2023;25(1):36. Brisco MA, Coca SG, Chen J et al. Blood urea nitrogen/creatinine ratio identifies a high-risk but potentially reversible form of renal dysfunction in patients with decompensated heart failure. Circulation Heart failure 2013;6(2):233-239. Sakr ARM, Gomaa GFE, Wasif SME et al. The prognostic role of urea-to-creatinine ratio in patients with acute heart failure syndrome: a case-control study. Egypt Heart J 2023;75(1):78. Sujino Y, Nakano S, Tanno J et al. Clinical implications of the blood urea nitrogen/creatinine ratio in heart failure and their association with haemoconcentration. ESC heart failure 2019;6(6):1274-1282. Zhen Z, Liang W, Tan W et al. Prognostic significance of blood urea nitrogen/creatinine ratio in chronic HFpEF. European journal of clinical investigation 2022;52(7):e13761. Zhou Y, Zhao Q, Liu Z et al. Blood urea nitrogen/creatinine ratio in heart failure: Systematic review and meta-analysis. PloS one 2024;19(5):e0303870. Sun D, Wei C, Li Z. Blood urea nitrogen to creatinine ratio is associated with in-hospital mortality among critically ill patients with cardiogenic shock. BMC cardiovascular disorders 2022;22(1):258. Additional Declarations No competing interests reported. Supplementary Files Additionalfile.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6783609","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481722723,"identity":"c0803e04-50e5-40a6-a5f0-82e97404c811","order_by":0,"name":"Ping Xu","email":"","orcid":"","institution":"Zigong Fourth People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Xu","suffix":""},{"id":481722724,"identity":"a32b1af5-dc8a-49b5-8798-0e1915eb56de","order_by":1,"name":"Zhitao Zhong","email":"","orcid":"","institution":"Zigong Fourth People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhitao","middleName":"","lastName":"Zhong","suffix":""},{"id":481722725,"identity":"4ffbff58-4204-4743-94c2-3f42e365683e","order_by":2,"name":"Shihao Liu","email":"","orcid":"","institution":"Zigong Fourth People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shihao","middleName":"","lastName":"Liu","suffix":""},{"id":481722726,"identity":"a265ba6f-bd9e-434c-a0d6-a52b19fb2f9d","order_by":3,"name":"Lukai Lv","email":"","orcid":"","institution":"Zigong Fourth People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lukai","middleName":"","lastName":"Lv","suffix":""},{"id":481722728,"identity":"a4d2a25d-d995-45ff-a57d-ffd7bb9579c5","order_by":4,"name":"Minyan Fan","email":"","orcid":"","institution":"Zigong Fourth People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Minyan","middleName":"","lastName":"Fan","suffix":""},{"id":481722729,"identity":"6e4399a1-e957-4a84-a331-5881b7424be7","order_by":5,"name":"Kefeng Li","email":"","orcid":"","institution":"Macao Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Kefeng","middleName":"","lastName":"Li","suffix":""},{"id":481722732,"identity":"b086f4b5-30fa-470d-b623-efacda52299a","order_by":6,"name":"Cheng Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYDACCTB5AIiZGw4wVEjIyZOghRGo5YyFsWEDKVoYGNsqEsFsfEB+dvOzh19q7siZ8y9sPMw7TyKBsYH54aMbeLQwzjlmbixz7Jmx5YyHDYd5t0nksTOwGRvn4NHCLJFgJi3ZcDhxw42DYC3FjA08bNL4tLBJpH8DaamHaJkjkdhwgIAWHokcM8mPDYcTDM43ArU0EKFFQiKnTJrh2GHDDTcYGw7OOSZhbNhMwC/yM9K3Sf6oOSxvcP7w4Q9vaurk5NmbHz7GpwUEmHnA9iXAuASUgwDjDxDJf4AIpaNgFIyCUTAiAQDB31J+oSycAwAAAABJRU5ErkJggg==","orcid":"","institution":"Zigong Fourth People’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-05-30 10:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6783609/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6783609/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86420303,"identity":"ab2e7d09-3e0a-4535-98fd-2e0aa1b5fb48","added_by":"auto","created_at":"2025-07-10 12:43:50","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":159408,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of study patient selection.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e ICU, intensive care unit.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6783609/v1/fe022017e06ba1ca543f70f2.jpg"},{"id":86420328,"identity":"cb10c30f-d707-43dd-bd8d-6367366c8d46","added_by":"auto","created_at":"2025-07-10 12:44:16","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":424908,"visible":true,"origin":"","legend":"\u003cp\u003eRCS curve of UCR for in-hospital mortality. (A) RCS curve of UCR in septic patients;(B) RCS curve of UCR in septic patients without CKD; (C) RCS curve of UCR in septic patients with CKD. The model of UCR in septic patients was adjusted for age, sex (male or female), CKD (yes or no), heart rate, and ventilator use (yes or no); the model of UCR in septic patients with or without CKD was adjusted for age, sex (male or female), heart rate, and ventilator use (yes or no).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e RCS, restricted cubic spline; UCR, urea-creatinine ratio; urea-creatinine ratio; CKD, chronic kidney disease; OR, odd ratio.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6783609/v1/b414f6c96fc05545ad53d621.jpg"},{"id":86422362,"identity":"c224afd0-f243-40c1-8c04-5022594a6e4a","added_by":"auto","created_at":"2025-07-10 12:59:49","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":921891,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analysis of UCR for in-hospital mortality. (A) Comparison of ROC curves among septic patients; (B) Comparison of ROC curves among septic patients without CKD; (C) Comparison of ROC curves among septic patients with CKD.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e ROC, receiver operating characteristic; UCR, urea-creatinine ratio; CKD, chronic kidney disease; AUC, area under the curve; CI, confidence interval.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6783609/v1/afc771b1bc7fc81913821aa6.jpg"},{"id":86420292,"identity":"55aadcce-5581-42ee-8462-b060c637c55d","added_by":"auto","created_at":"2025-07-10 12:43:49","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":701677,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses for the association of UCR with in-hospital mortality. (A) Subgroup analyses in all septic patients; (B) Subgroup analyses in septic patients without CKD; (C) Subgroup analyses in septic patients with CKD.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e UCR, urea-creatinine ratio; OR, odd ratio; CI, confidence interval; CKD, chronic kidney disease; CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease; SOFA, sequential organ failure assessment; CRRT, continuous renal replacement therapy.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6783609/v1/d1f002de482a0c05e2104273.jpg"},{"id":90013173,"identity":"78509b5b-b9d7-4562-b7c8-b5ce81705a81","added_by":"auto","created_at":"2025-08-27 11:08:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3380081,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6783609/v1/324e8236-1b17-4c33-9d25-e1bb032221f8.pdf"},{"id":86420290,"identity":"f1cb3cfc-63db-492b-8f88-c7598c474458","added_by":"auto","created_at":"2025-07-10 12:43:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":37431,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-6783609/v1/a6abbcac8e41204f51f12417.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship between the urea–creatinine ratio and mortality in septic patients with and without chronic kidney disease: A retrospective single-center Chinese intensive care unit cohort study","fulltext":[{"header":"Background","content":"\u003cp\u003eThe most recent International Consensus Definition of Sepsis (Sepsis 3.0) defined that sepsis is life-threatening organ dysfunction caused by a dysregulated host response to infection[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. An estimated 48.9\u0026nbsp;million new cases of sepsis were reported globally in 2017, with 11.0\u0026nbsp;million deaths, representing 19.7% of all global fatalities[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As a prevalent cause of hospital deaths, sepsis elevates healthcare costs and severely affected patient quality of life, imposing a significant strain on health systems around the world[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].Therefore, improving the management of sepsis is an urgent public health issue that needed to be addressed.\u003c/p\u003e\u003cp\u003eEarly recognition of patients at high risk for sepsis and personalized treatment for those with a high likelihood of poor outcomes were key to reducing mortality and improving prognosis[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In this context, the UCR can serve as an easily accessible marker of early kidney dysfunction, allowing clinicians to evaluate patient risk soon after ICU admission and modify fluid management or renal-protective strategies accordingly. Renal function markers, including blood urea nitrogen (BUN), creatinine (Cr), and the urea-creatinine ratio (UCR), were established as independent risk factors for sepsis-related mortality. Data from the multi-center eICU Collaborative Research Database v2.0 indicated that initial BUN levels could independently predict 28-day mortality in critically ill septic patients (OR 1.013; 95% CI 1.012\u0026ndash;1.014; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Another retrospective study demonstrated that creatinine levels also independently influenced 28-day mortality in sepsis patients (HR 1.002; 95% CI: 1.000-1.005, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Moreover, a retrospective analysis of the Medical Information Mart for Intensive Care (MIMIC-III) database revealed that a higher UCR was significantly associated with increased 28-D mortality (fourth quartile vs. first quartile: HR\u0026thinsp;=\u0026thinsp;1.268, 95%CI\u0026thinsp;=\u0026thinsp;1.037\u0026ndash;1.551, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021) in American patients with septic shock[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile the UCR has shown superior predictive performance in various conditions, such as trauma-related acute respiratory distress syndrome and severe burns associated with acute kidney injury[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], compared to measurements of BUN or creatinine alone, researches on BUN and creatinine in relation to sepsis prognosis were more prevalent[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Yet, studies focusing on the prognostic value of the UCR in sepsis were notably limited. Given this gap, exploring UCR\u0026rsquo;s predictive utility in sepsis was both vital and necessary.\u003c/p\u003e\u003cp\u003eChronic kidney disease (CKD) is a clinical syndrome defined by persistent modifications in kidney structure and function, often presenting with high blood pressure, swelling, and reduced urine output, along with increased BUN and serum creatinine levels.[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In patients with concurrent sepsis and CKD, the interpretation of BUN and serum creatinine fluctuations becomes particularly challenging due to the overlapping pathophysiological mechanisms. To the best of our knowledge, the relationship between the UCR and outcomes in sepsis patients with CKD was uncertain. Furthermore, the connection between the urea-creatinine ratio and sepsis in the Chinese demographic has not been investigated. As a result, we undertook a study to analyze the association between the UCR and the prognosis of sepsis patients among Chinese individuals, and to explore the UCR's predictive capability in subgroups with and without CKD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and population\u003c/h2\u003e\u003cp\u003eThis research was a retrospective, single-center analysis of septic patients who were admitted to the ICUs at Zigong Fourth People\u0026rsquo;s Hospital from January 1, 2018, to December 31, 2023. Data from various hospital information systems, including the electronic healthcare record (EHR), hospital information system (HIS), laboratory information system (LIS), and critical care nursing chart system, were directly exported into a unified research dataset. We utilized an updated critical care database that initially recorded all patients moved to any ICU between January 2019 and December 2020[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and later included sepsis cases from 2018 and from 2021 to 2023. Approval for the study was granted by the Ethics Committee of Zigong Fourth People's Hospital (No: 2023-005).\u003c/p\u003e\u003cp\u003eThis study required that all the following inclusion criteria be met: 1) Patients admitted to the central ICU and emergency ICU from 1 January 2018, to 31 December 2023, diagnosed with sepsis according to the sepsis 3.0 criteria; 2) Individuals aged 18 or older at admission. Exclusion criteria (patients were excluded if they met any of the following conditions): 1) Confirmed SARS-CoV-2 (COVID-19) infection; 2) Patients with several ICU admissions, with only the first admission's data preserved; 3) Patients who were missing key variable data, such as BUN and serum creatinine, within 24 hours of ICU admission.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDefinitions of the exposure and outcome variables\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eDefinitions of the exposure and outcome variables\u003c/div\u003e\u003cp\u003eIn this study, the primary exposure variable analyzed was UCR, defined as UCR\u0026thinsp;=\u0026thinsp;BUN (mg/dL) / creatinine (mg/dL). BUN and creatinine were calculated using the following formulas: BUN (mg/dL)\u0026thinsp;=\u0026thinsp;blood urea (mmol/L) \u0026times; 2.8[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]; creatinine (mg/dL)\u0026thinsp;=\u0026thinsp;creatinine (\u0026micro;mol/L) / 88.4[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].The levels of BUN and serum creatinine were determined using a Siemens ADVIA 2400 autoanalyzer (Siemens Diagnostics, Tarrytown, NY, USA) through a colorimetric method. BUN was measured using the urease\u0026ndash;glutamate dehydrogenase (Urease\u0026ndash;GLDH) coupled technique, while serum creatinine was assessed with the creatininase\u0026ndash;sarcosine oxidase\u0026ndash;peroxidase coupled method. Study participants were organized into two groups based on whether they had CKD, diagnosed following the KDIGO 2021 clinical practice guidelines[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], with prior CKD diagnoses in their medical history serving as additional documentation. The study's outcome was the in-hospital mortality of sepsis patients in ICUs. Participants in the central and emergency ICUs were observed until they died or were discharged.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003ePatients' demographics included both age and gender. Vital signs such as heart rate, systolic and diastolic blood pressure (SBP and DBP), temperature, respiratory rate, and pulse oxygen saturation (SpO2) were extracted. Comprehensive laboratory tests were conducted, covering hematocrit, hemoglobin, platelet count (PLT), C-reactive protein (CRP), neutrophil and lymphocyte counts, white blood cell (WBC) count, anion gap (AG), bicarbonate, BUN, serum creatinine, glucose, calcium, chloride, sodium, potassium, international normalized ratio (INR), prothrombin time (PT), and partial thromboplastin time (PTT). Additionally, the illness severity was assessed using the sequential organ failure assessment (SOFA) score to evaluate the condition of the patients.\u003c/p\u003e\n\u003ch3\u003eSample size calculation\u003c/h3\u003e\n\u003cp\u003eBased on the rule of a minimum of ten events per variable, the sample size for the logistic regression analysis was adequate[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Using PASS software (version 11.0.7; PASS, NCSS, LLC), the minimum sample size for other statistical methods was calculated via power analysis, ensuring the power was at least 80% and the p-value was below 0.05[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eNumbers with percentages were used to present categorical data. Means with standard deviations were used to represent continuous normal variables, whereas medians with 25\u0026ndash;75% interquartile ranges were used for continuous non-normal variables. Categorical variables were compared using either Fisher's exact tests or Chi-squared tests, normally distributed variables were compared using Student t tests, and continuous non-normal variables were compared using Wilcoxon rank tests.\u003c/p\u003e\u003cp\u003eUnivariate logistic regression identified variables with a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1 for inclusion in subsequent multivariate models. Demographic factors (age and gender) and independent variables in the multivariate models were considered as adjusted factors. Patients were categorized into high-risk and low-risk groups according to the UCR cut-off value in the receiver operating characteristic (ROC) curve[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. To assess the risk of in-hospital mortality, we employed logistic regression models to compute odds ratios (OR) and 95% confidence intervals (CI), treating the UCR as both a continuous and a categorical variable by comparing the high-risk group to the low-risk group. In the crude model, no covariates were adjusted for, but Model I accounted for age and gender, and Model Ⅱ included these adjustments along with additional independent factors. Restrictive cubic spline analyses were used to investigate and display the potential association and dose\u0026ndash;response relationship between the UCR and in-hospital mortality. ROC curves and areas under the curve (AUCs) were employed to evaluate the predictive power of the UCR and other variables for outcome. Stratification analyses were conducted based on gender (male, female), CKD (No, Yes), congestive heart failure (CHF) (No, Yes), chronic obstructive pulmonary disease (COPD) (No, Yes), septic shock (No, Yes), SOFA score (\u0026lt;\u0026thinsp;optimal cut-off value predicting in-hospital mortality, \u0026ge; optimal cut-off value), continuous renal replacement therapy (CRRT) (No, Yes), and ventilator use (No, Yes), as well as interaction analyses between various stratification factors and the UCR.\u003c/p\u003e\u003cp\u003eA P-value of less than 0.05 was regarded as significant. The statistical analysis was performed with R software (version 4.4.2, R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003ePatient characteristics\u003c/h2\u003e\u003cp\u003eThe process of patient exclusion is demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The research included 453 sepsis patients, 282 (62.25%) of whom were male. The median age of the cohort was 75 years, with an interquartile range of 67 to 84. Out of the patients, 136 (30.02%) had CKD, and the in-hospital mortality rate was 36.20% (164 out of 453). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides the baseline characteristics of the sepsis patients included in the study.\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 sepsis patients included in the study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;453\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWithout CKD\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;317\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWith CKD\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;136\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e282(62.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e202(63.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80(58.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.324\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75.0(67.0,84.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.0(66.0,82.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e78.0(69.0,85.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVital signs\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature, ℃, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.3(36.1,36.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.3(36.1,36.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.3(36.1,36.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.757\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart rate, bpm, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e131.9(119.0,146.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131.9(117.0,147.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e131.9(119.0,144.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.959\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory rate, bpm, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.9(25.0,35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.9(25.0,35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.9(26.0,35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.690\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP, mmHg, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e112.7(97.0,125.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112.7(97.0,128.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e112.7(92.5,117.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP, mmHg, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66.1(57.0,72.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.1(58.0,73.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.1(55.5,68.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpO\u003csub\u003e2\u003c/sub\u003e, %, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94.0(93.6,98.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94.0(93.6,98.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93.6(93.6,97.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.779\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCHF (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e173(38.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e101(31.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72(52.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e141(31.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e103(32.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38(27.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.338\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaboratory tests\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN, mmol/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.4(7.6,19.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.1(7.0,15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.1(10.5,25.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine, \u0026micro;mol/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e122.2(77.6,204.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99.8(68.4,174.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e175.8(121.6,289.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUCR, median (IQR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.5(17.6,32.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.0(18.8,33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.9(15.4,29.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCys C, mg/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.17(1.45,3.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.77(1.30,2.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.86(2.16,4.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUA, umol/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e432.0(303.0,590.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e397.0(279.0,559.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e506.0(399.5,657.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC, 10^9/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.9(6.6,16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.8(6.6,16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.0(6.6,17.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.888\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocyte Count, 10^9/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.530(0.310,0.950)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.490(0.290,0.990)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.560(0.380,0.930)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.393\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil Count, 10^9/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.4(6.5,15.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.4(6.4,15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.5(6.8,14.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.980\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocyte Count, 10^9/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.390(0.190,0.680)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.380(0.190,0.660)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.420(0.210,0.710)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.623\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHB, g/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e109.7\u0026thinsp;\u0026plusmn;\u0026thinsp;26.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e111.4\u0026thinsp;\u0026plusmn;\u0026thinsp;26.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e105.6\u0026thinsp;\u0026plusmn;\u0026thinsp;28.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHCT, %, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.341\u0026thinsp;\u0026plusmn;\u0026thinsp;0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.346\u0026thinsp;\u0026plusmn;\u0026thinsp;0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.330\u0026thinsp;\u0026plusmn;\u0026thinsp;0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT, 10^9/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e162.0(103.0,232.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e167.0(110.0,242.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e148.5(90.0,216.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD-Dimer, mg/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.86(2.14,10.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.02(2.16,10.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.67(1.93,11.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.770\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePT, sec, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.6(13.2,16.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.5(13.2,16.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.7(13.2,16.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.804\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR, ratio, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.29(1.15,1.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.29(1.15,1.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.31(1.15,1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.580\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPTT, sec, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31.6(27.5,37.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.6(27.5,37.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.6(27.7,38.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.493\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFIB, g/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.19(3.10,5.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.26(3.23,5.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.14(2.83,5.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.272\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotassium, mmol/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.97(3.39,4.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.87(3.29,4.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.13(3.61,4.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSodium, mmol/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e138.2(133.7,142.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e138.0(133.6,142.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e138.8(134.0,143.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.221\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChloride, mmol/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101.8(96.3,106.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e101.3(96.3,106.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103.9(96.2,107.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium, mmol/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.17(2.05,2.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.17(2.05,2.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.17(2.06,2.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.823\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP, mg/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113.7(56.1,155.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e113.7(56.1,155.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e113.7(59.4,156.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.666\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT, U/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.0(17.5,56.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.0(17.5,54.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.5(17.3,61.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.930\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST, U/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.2(26.3,101.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.0(27.0,99.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.8(25.3,111.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.778\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBIL, umol/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.5(3.7,11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.6(3.8,11.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.2(3.4,13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.861\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIBIL, umol/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.8(4.9,12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.9(5.1,12.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.1(4.5,12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.257\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin, g/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.767\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlobulin, g/L, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29.3(25.1,33.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.1(24.8,33.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.6(25.8,33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.717\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSOFA score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.00(6.00,11.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.00(6.00,11.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.00(5.00,11.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.605\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTreatment during hospitalization\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRRT (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42(9.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27(8.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15(11.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.398\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVentilator (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e399(88.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e278(87.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e121(88.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.701\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIn-hospital mortality (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e164(36.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98(30.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66(48.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eContinuous variables are expressed as Mean ± SD or Median (IQR), categorical variables are expressed as number (percent)\nAbbreviations: CKD, chronic kidney disease; IQR, interquartile range; SBP, systolic blood pressure; DBP, diastolic blood pressure; SpO2, pulse oxygen saturation; CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease; BUN, blood urea nitrogen; UCR, urea-creatinine ratio; Cys C, Cystatin C; UA, uric acid; WBC, white blood cell count; HB, hemoglobin; HCT, hematocrit; SD, standard deviation; PLT, platelet; PT, prothrombin time; INR, international normalized ratio; APTT, activated partial thromboplastin time; FIB, fibrinogen; CRP, C-reactive protein; ALT, alanine aminotransferase; AST, aspartate aminotransferase; DBIL, direct bilirubin; IBIL, indirect bilirubin; SOFA, sequential organ failure assessment; CRRT, continuous renal replacement therapy. \n\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe CKD group was significantly older (median age 78.0 years) compared to the non-CKD group (median age 74.0 years, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). Congestive heart failure was significantly more common in the CKD group (52.94% vs. 31.86%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Laboratory tests revealed that the CKD group had significantly higher creatinine (175.8 \u0026micro;mol/L vs. 99.8 \u0026micro;mol/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), BUN (17.1 mmol/L vs. 10.1 mmol/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), cystatin C (2.86 mg/L vs. 1.77 mg/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and uric acid (506.0 \u0026micro;mol/L vs. 397.0 \u0026micro;mol/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to the non-CKD group. The UCR was significantly lower in the CKD group (21.9 vs. 24.0, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005). Potassium levels were significantly higher in the CKD group (4.13 mmol/L vs. 3.87 mmol/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while chloride levels were also elevated (103.9 mmol/L vs. 101.3 mmol/L, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAssociation between the UCR and in-hospital mortality in septic patients\u003c/h3\u003e\n\u003cp\u003eIn the logistic regression model, the UCR, CKD (yes or no), heart rate, and ventilator use (yes or no) were independent risk factors for in-hospital mortality in septic patients (see Tables S1 and S2). The odds ratios (ORs) and 95% confidence intervals (CIs) were 1.063 (1.040\u0026ndash;1.086), 2.260 (1.384\u0026ndash;3.690), 1.012 (1.004\u0026ndash;1.020), and 6.414 (2.363\u0026ndash;17.409), respectively. The optimal UCR thresholds for predicting in-hospital mortality were 30.388 for all septic patients, 29.117 for those without CKD, and 14.552 for those with CKD (see Table S3). For all septic patients, the low-risk group was defined as having a UCR\u0026thinsp;\u0026lt;\u0026thinsp;30.388, while the high-risk group was defined as having a UCR\u0026thinsp;\u0026ge;\u0026thinsp;30.388. Among septic patients without CKD, a UCR under 29.117 was classified as low risk, and a UCR of 29.117 or above was classified as high risk. Among septic patients with CKD, a UCR lower than 14.552 defined the low-risk group, while a UCR equal to or exceeding 14.552 defined the high-risk group.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the associations between the UCR and in-hospital mortality in septic patients. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA shows a non-linear P-value of 0.364, suggesting a near-linear relationship between the UCR and in-hospital mortality in septic patients. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB presents a non-linear P-value of 0.134 in septic patients without CKD, indicating that UCR's impact on mortality in these patients was nearly linear. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC shows a non-linear P-value of 0.752, indicating that the relationship between UCR and in-hospital mortality was nearly linear in septic patients with CKD.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the relationship between the UCR and in-hospital mortality in septic patients, with subgroup analyses for all septic patients, those without CKD, and those with CKD. For all septic patients, the odds ratio (OR) of UCR for in-hospital mortality in the non-adjusted model, Model I, and Model Ⅱ was 1.048 (95% CI: 1.029\u0026ndash;1.068; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 1.048 (95% CI: 1.030\u0026ndash;1.069; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 1.054 (95% CI: 1.034\u0026ndash;1.076; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. Compared with the low-risk group, the high-risk group had an OR of 2.999 (95% CI: 1.952\u0026ndash;4.633; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 3.014 (95% CI: 1.960\u0026ndash;4.662; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 3.381 (95% CI: 2.133\u0026ndash;5.414; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. The trend was statistically significant with a \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\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\u003eRelationship between UCR and in-hospital mortality in septic patients\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\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eExposure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eNon-adjusted\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eModel Ⅰ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eModel Ⅱ\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eSeptic patients\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\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\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\u003eUCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.048(1.029,1.068)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.048(1.03,1.069)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.054(1.034,1.076)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elow-risk group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eRef\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\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\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\u003ehigh-risk group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2.999(1.952,4.633)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.014(1.96,4.662)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.381(2.133,5.414)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eSeptic patients without CKD\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\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eUCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.073(1.048,1.101)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.075(1.05,1.104)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.076(1.05,1.106)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003elow-risk group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\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\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ehigh-risk group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.124(2.451,7.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.244(2.509,7.256)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.727(2.716,8.368)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eSeptic patients with CKD\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\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\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\u003eUCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.018(0.988,1.051)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.018(0.988,1.052)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.014(0.982,1.048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.395\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elow-risk group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eRef\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\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eRef\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\u003ehigh-risk group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.773(0.799,4.034)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.767(0.796,4.023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.616(0.704,3.797)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.261\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.163\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\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.261\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cb\u003eNotes\u003c/b\u003e: Non-adjusted models adjusted for: None; Model I adjusted for: age and gender; Model II adjusted for: confounders from the minimally adjusted model (Model I) + CKD (yes or no), heart rate, and ventilator use (yes or no) for all septic patients, or heart rate and ventilator use (yes or no) for septic patients with or without CKD.\nAbbreviations: OR, odds ratios; CI, confidence intervals; UCR, urea-creatinine ratio; CKD, chronic kidney disease.\n\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFor septic patients without CKD, the OR of UCR for in-hospital mortality was 1.073 (95% CI: 1.048\u0026ndash;1.101; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 1.075 (95% CI: 1.050\u0026ndash;1.104; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 1.076 (95% CI: 1.050\u0026ndash;1.106; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for the non-adjusted model, Model I, and Model Ⅱ, respectively. Compared with the low-risk group, the high-risk group had an OR of 4.124 (95% CI: 2.451\u0026ndash;7.004; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 4.244 (95% CI: 2.509\u0026ndash;7.256; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 4.727 (95% CI: 2.716\u0026ndash;8.368; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. The trend was statistically significant with a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003cp\u003eFor septic patients with CKD, the OR of UCR for in-hospital mortality in the non-adjusted model, Model I, and Model Ⅱ was 1.018 (95% CI: 0.988\u0026ndash;1.051; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.163), 1.018 (95% CI: 0.988\u0026ndash;1.052; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.166), and 1.014 (95% CI: 0.982\u0026ndash;1.048; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.261), respectively. Compared with the low-risk group, the high-risk group had an OR of 1.773 (95% CI: 0.799\u0026ndash;4.034; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.271), 1.767 (95% CI: 0.796\u0026ndash;4.023; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.905), and 1.616 (95% CI: 0.704\u0026ndash;3.797; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.560), respectively. The trend was not statistically significant in any model (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ePredictive capacity comparison\u003c/h2\u003e\u003cp\u003eThe receiver operating characteristic curves are plotted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The findings indicate that using SOFA combined with UCR offered the most precise prediction of in-hospital mortality for septic patients, especially those without CKD. Septic patients with CKD showed a significant drop in predictive performance, yet the combined use of SOFA and UCR still outperformed individual biomarkers.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAmong septic patients, the combination of SOFA and UCR exhibited the highest AUC of 0.734 (95% CI: 0.685\u0026ndash;0.784), suggesting a strong predictive ability for in-hospital mortality. This was followed by the combination of SOFA and BUN, which had an AUC of 0.692 (95% CI: 0.640\u0026ndash;0.743). The SOFA score alone demonstrated an AUC of 0.673 (95% CI: 0.620\u0026ndash;0.725). Among individual biomarkers, UCR showed an AUC of 0.623 (95% CI: 0.568\u0026ndash;0.678), while BUN had an AUC of 0.616 (95% CI: 0.562\u0026ndash;0.670). Cr demonstrated the lowest AUC of 0.541 (95% CI: 0.485\u0026ndash;0.596).\u003c/p\u003e\u003cp\u003eAmong septic patients without CKD, the combination of SOFA and UCR demonstrated the highest AUC of 0.806 (95% CI: 0.753\u0026ndash;0.858), indicating a strong predictive ability for in-hospital mortality. This was followed by the combination of SOFA and BUN, which had an AUC of 0.713 (95% CI: 0.649\u0026ndash;0.777). The SOFA score alone had an AUC of 0.694 (95% CI: 0.629\u0026ndash;0.760). UCR showed a promising AUC of 0.686 (95% CI: 0.621\u0026ndash;0.751), while BUN had an AUC of 0.632 (95% CI: 0.565\u0026ndash;0.699). Cr demonstrated the weakest predictive ability with an AUC of 0.516 (95% CI: 0.446\u0026ndash;0.585).\u003c/p\u003e\u003cp\u003eAmong septic patients with CKD, the combination of SOFA and UCR had an AUC of 0.644 (95% CI: 0.551\u0026ndash;0.737), followed by the combination of SOFA and BUN, which had an AUC of 0.631 (95% CI: 0.537\u0026ndash;0.726). The SOFA score alone showed an AUC of 0.634 (95% CI: 0.540\u0026ndash;0.728). UCR had an AUC of 0.555 (95% CI: 0.458\u0026ndash;0.653), while BUN and Cr had the lowest AUCs, 0.518 (95% CI: 0.420\u0026ndash;0.616) and 0.496 (95% CI: 0.398\u0026ndash;0.595), respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eSubgroup analyses stratified by UCR\u003c/h2\u003e\u003cp\u003eSubgroup analyses are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, the UCR appeared to be more prominent in septic patients without CKD (OR (95% CI): patients without CKD 1.07 (1.05\u0026ndash;1.10) vs. patients with CKD 1.02 (0.99\u0026ndash;1.05), \u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.010) among all septic patients. In Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, the outcomes remained consistent across various subgroups, with no significant interaction effects between septic patients with and without CKD.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis retrospective observational study revealed an almost linear relationship between the UCR and in-hospital mortality across all septic patients, including those with and without CKD. The higher UCR was determined to be an independent risk factor for in-hospital mortality in septic patients, with a notable impact on those without CKD. In addition, the integration of the SOFA score and UCR resulted in the most accurate forecast of in-hospital mortality in septic patients, especially in those without CKD.\u003c/p\u003e\u003cp\u003eBUN and serum creatinine are essential indicators for evaluating kidney function[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. BUN passes through the renal tubules, and decreased cardiac output, combined with inadequate arterial perfusion, activates the sympathetic nervous system (SNS) and the renin-angiotensin-aldosterone system (RAAS). This results in enhanced sodium reabsorption in the proximal renal tubules, which subsequently increases urea concentration. Alternatively, serum creatinine, a byproduct of creatine metabolism with low molecular weight, is mostly filtered by the glomeruli and nearly completely excreted in urine. Usually, the UCR ranged from 10 to 15:1, and a UCR over 20:1 was often associated with prerenal azotemia[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, in recent years, the UCR has been frequently used to assess the prognosis of various diseases, such as acute kidney injury[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], chronic kidney disease[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], upper gastrointestinal bleeding[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], septic shock[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], COVID-19[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], non-traumatic intracranial hemorrhage[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], acute ischemic stroke[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], trauma-related acute respiratory distress syndrome[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], acute myocardial infarction[\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], acute and chronic heart failure[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan additionalcitationids=\"CR37 CR38 CR39\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and cardiogenic shock[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe found that a higher UCR was an independent risk factor for in-hospital mortality in septic patients. This could be attributed to the UCR, which acted as a biomarker for neurohormonal activity and indicated the activation of the neurohormonal system related to infection, including vasopressin, the SNS, and RAAS, which inhibited the reabsorption of urea nitrogen[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Nevertheless, an elevated UCR did not independently predict in-hospital mortality for septic patients with CKD, possibly due to CKD's influence on baseline UCR variability. Additionally, our research showed that the UCR predicted in-hospital mortality in septic patients more effectively than BUN or Cr alone, which was consistent with studies on conditions such as acute kidney injury from severe burns[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], trauma-related acute respiratory distress syndrome[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe cutoff value of UCR for predicting mortality varied across different disease conditions. For non-traumatic intracranial hemorrhage, the cutoff for 1-year mortality was 16.25, indicating the long-term impact on survival[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. For acute decompensated heart failure, the cutoff for predicting hospital death was 17.4[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In chronic heart failure, the UCR cutoff for predicting long-term survival was 19.37[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In acute ischemic stroke, a UCR cutoff of 19.63 was associated with in-hospital mortality, reflecting the activation of neurohormonal pathways[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In patients with non-end-stage chronic kidney disease, the cutoff for inpatient mortality was significantly higher, at 100, emphasizing the critical role of baseline kidney function[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These findings underscored the need for condition-specific UCR thresholds to guide prognosis. In the retrospective analysis of the MIMIC-III database, based on the quartile and quintile analyses, UCR values\u0026thinsp;\u0026ge;\u0026thinsp;27.3 (quartile cutoff) and \u0026ge;\u0026thinsp;30.0 (quintile cutoff) were predictive of a significantly higher risk of 28-day mortality in septic shock[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This was consistent with our findings, where the optimal UCR thresholds for predicting in-hospital mortality were 30.4 for all septic patients and 29.1 for those without CKD.\u003c/p\u003e\u003cp\u003eThere were multiple strengths attributed to this study. To begin with, it delivered a thorough assessment of the UCR's prognostic importance in septic patients, focusing on Chinese patients and those with CKD, which is a relatively unexplored area in the literature. Furthermore, by merging UCR with the SOFA score, our study revealed a marked enhancement in forecasting in-hospital mortality, indicating UCR's potential as a beneficial complement to established clinical scoring frameworks. Finally, the inclusion of a varied group of septic patients allowed for the assessment of UCR's utility across different subgroups, ensuring the findings are relevant to a broad spectrum of clinical cases. These strengths provided important insights for future clinical practice and research, particularly in enhancing the accuracy of sepsis risk stratification.\u003c/p\u003e\u003cp\u003eSome limitations were also present in this study. First, the retrospective nature of the study, along with the exclusion of patients missing BUN and Cr values, might have introduced bias in the sample selection. Second, BUN and Cr levels were measured only when the patient was admitted to the ICU, with no subsequent lab data available, so the dynamic changes in UCR were not reflected. Third, our research identified an almost linear correlation between UCR and in-hospital mortality among septic patients, which differed slightly from other studies. The discrepancy could be due to the small sample size of patients with UCR values less than 10 (15 out of 452), possibly not fully illustrating the connection between UCR and mortality. Fourth, the absence of UCR values before sepsis onset in CKD patients hindered our ability to analyze UCR changes post-sepsis onset.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eUCR is an independent predictor of in-hospital mortality in septic patients, with higher values particularly affecting those without CKD. Combining UCR with the SOFA score improved prognostic accuracy, especially in patients without CKD. Future studies with larger cohorts and comprehensive data, particularly in CKD patients, are needed to validate these findings and refine UCR cutoff values for clinical use.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eUCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003eurea-creatinine ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003echronic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003eintensive care unit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003eReceiver operating characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eSOFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003eSequential Organ Failure Assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eBUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003eblood urea nitrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eCr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003ecreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eMIMIC-III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003eMedical Information Mart for Intensive Care\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eEHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003eelectronic healthcare record\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eHIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003ehospital information system\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eLIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003elaboratory information system\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eUrease\u0026ndash;GLDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003eurease\u0026ndash;glutamate dehydrogenase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003esystolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003ediastolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eSpO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003epulse oxygen saturation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003eplatelet count\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003eC-reactive protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003ewhite blood cell\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003eanion gap\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003einternational normalized ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003ePT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003eprothrombin time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003ePTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003epartial thromboplastin time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003eodds ratios\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003econfidence intervals\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003earea under the curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eCHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003econgestive heart failure\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003echronic obstructive pulmonary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eCRRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003econtinuous renal replacement therapy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eSNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003esympathetic nervous system\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eRAAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 489px;\"\u003e\n \u003cp\u003erenin-angiotensin-aldosterone system\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and Consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval for the study was granted by the Ethics Committee of Zigong Fourth People\u0026apos;s Hospital (No: 2023-005). Participants could withdraw from the study at any time. All collected data were anonymized, with researchers having no access to participant-identifying information during or after the research period.\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\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll co-authors have no\u0026nbsp;relevant financial or\u0026nbsp;non-financial interests to disclose nor conflicts\u0026nbsp;of\u0026nbsp;interest\u0026nbsp;to\u0026nbsp;declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePing Xu received support from the Project of Science \u0026amp; Technology Department of Sichuan Province (2024JDKP0021), the Sichuan Medical Association Scientific Research Project (S21019), the Special Project for Scientific and Technological Research of Sichuan Provincial Administration of Traditional Chinese Medicine (2023MS494), and the Research Project of Zigong City Science \u0026amp; Technology and Intellectual Property Right Bureau (2022ZCYGY05) to conduct this study. The funders had no role in the study design, data collection, analysis, interpretation, reporting, or the decision to submit the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to manuscript editing and approved the final version. Each author took responsibility for their individual contributions and ensured that any issues regarding the accuracy or integrity of the work (including those unrelated to their direct involvement) were properly investigated, resolved, and documented. PX, ZTZ, and CZ conceived and designed the study; SHL, LKL, and MYF acquired the data; PX, ZTZ, SHL, and KFL performed statistical analysis; PX and CZ interpreted the results; PX, ZTZ, and SHL drafted the manuscript; PX and CZ critically revised the manuscript. \u0026nbsp; PX, ZTZ, and SHL contributed equally as co-first authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thanked the medical and nursing staff of the Emergency and ICU departments at Zigong Fourth People\u0026rsquo;s Hospital for their invaluable support in data collection and patient care. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSinger M, Deutschman CS, Seymour CW et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). Jama 2016;315(8):801-810.\u003c/li\u003e\n\u003cli\u003eKolodyazhna A, Wiersinga WJ, van der Poll T. Aiming for precision: personalized medicine through sepsis subtyping. Burns \u0026amp; trauma 2025;13:tkae073.\u003c/li\u003e\n\u003cli\u003eGildea A, Mulvihill C, McFarlane E et al. Recognition, diagnosis, and early management of suspected sepsis: summary of updated NICE guidance. BMJ 2024;385:q1173.\u003c/li\u003e\n\u003cli\u003eRudd KE, Johnson SC, Agesa KM et al. Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study. Lancet 2020;395(10219):200-211.\u003c/li\u003e\n\u003cli\u003eBeane A, Shankar-Hari M. Long-term ill health in sepsis survivors: an ignored health-care challenge? Lancet 2024;404(10459):1178-1180.\u003c/li\u003e\n\u003cli\u003eSun T, Wang Y, Wu X et al. Prognostic Value of Syndecan-1 in the Prediction of Sepsis-Related Complications and Mortality: A Meta-Analysis. Frontiers in public health 2022;10:870065.\u003c/li\u003e\n\u003cli\u003eZhu JL, Yuan SQ, Huang T et al. Influence of systolic blood pressure trajectory on in-hospital mortality in patients with sepsis. BMC infectious diseases 2023;23(1):90.\u003c/li\u003e\n\u003cli\u003eHarazim M, Tan K, Nalos M et al. Blood urea nitrogen - independent marker of mortality in sepsis. Biomedical papers of the Medical Faculty of the University Palacky, Olomouc, Czechoslovakia 2023;167(1):24-29.\u003c/li\u003e\n\u003cli\u003eXiao Y, Yan X, Shen L et al. Evaluation of qSOFA score, and conjugated bilirubin and creatinine levels for predicting 28-day mortality in patients with sepsis. Experimental and therapeutic medicine 2022;24(1):447.\u003c/li\u003e\n\u003cli\u003eHan D, Zhang L, Zheng S et al. Prognostic Value of Blood Urea Nitrogen/Creatinine Ratio for Septic Shock: An Analysis of the MIMIC-III Clinical Database. BioMed research international 2021;2021:5595042.\u003c/li\u003e\n\u003cli\u003eMa H, Lin S, Xie Y et al. Association between BUN/creatinine ratio and the risk of in-hospital mortality in patients with trauma-related acute respiratory distress syndrome: a single-centre retrospective cohort from the MIMIC database. BMJ open 2023;13(4):e069345.\u003c/li\u003e\n\u003cli\u003eYoon J, Kim Y, Yim H et al. Analysis of prognostic factors for acute kidney injury with continuous renal replacement therapy in severely burned patients. Burns : journal of the International Society for Burn Injuries 2017;43(7):1418-1426.\u003c/li\u003e\n\u003cli\u003eFeinfeld DA, Bargouthi H, Niaz Q et al. Massive and disproportionate elevation of blood urea nitrogen in acute azotemia. International urology and nephrology 2002;34(1):143-145.\u003c/li\u003e\n\u003cli\u003eAnders HJ, Li Q, Steiger S. Asymptomatic hyperuricaemia in chronic kidney disease: mechanisms and clinical implications. Clinical kidney journal 2023;16(6):928-938.\u003c/li\u003e\n\u003cli\u003eWang J, Li J, Zhang X et al. Molecular mechanisms of histone deacetylases and inhibitors in renal fibrosis progression. Frontiers in molecular biosciences 2022;9:986405.\u003c/li\u003e\n\u003cli\u003eWu MF, Lee CH, Pai PH et al. Screening Cases of Suspected Early Stage Chronic Kidney Disease from Clinical Laboratory Data: The Comparison between Urine Conductivity and Urine Protein. Biomedicines 2023;11(2).\u003c/li\u003e\n\u003cli\u003eXu P, Chen L, Zhu Y et al. Critical Care Database Comprising Patients With Infection. Frontiers in public health 2022;10:852410.\u003c/li\u003e\n\u003cli\u003eWuttke M, Li Y, Li M et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nature genetics 2019;51(6):957-972.\u003c/li\u003e\n\u003cli\u003eZampieri FG, Machado FR, Biondi RS et al. Effect of Slower vs Faster Intravenous Fluid Bolus Rates on Mortality in Critically Ill Patients: The BaSICS Randomized Clinical Trial. Jama 2021;326(9):830-838.\u003c/li\u003e\n\u003cli\u003eGroup KDIGOKBPW. KDIGO 2021 Clinical Practice Guideline for the Management of Blood Pressure in Chronic Kidney Disease. Kidney international 2021;99(3s):S1-s87.\u003c/li\u003e\n\u003cli\u003ePeduzzi P, Concato J, Kemper E et al. A simulation study of the number of events per variable in logistic regression analysis. Journal of clinical epidemiology 1996;49(12):1373-1379.\u003c/li\u003e\n\u003cli\u003eXu P, Ye L, Li L et al. Comparison of the prognostic value, feasibility, and reproducibility among different scoring methods of 8‑point lung ultrasonography in patients with acute heart failure. Internal and emergency medicine 2023;18(8):2321-2332.\u003c/li\u003e\n\u003cli\u003eDou Y, Li A, Liu G et al. Comparison of bioimpedance equations and dual-energy X-ray for assessment of fat free mass in a Chinese dialysis population. Renal failure 2023;45(1):2182131.\u003c/li\u003e\n\u003cli\u003eAlqutub MN. Peri-implant parameters and cytokine profile among Peri-implant disease patients treated with Er Cr YSGG laser and PDT. Photodiagnosis and photodynamic therapy 2022;37:102641.\u003c/li\u003e\n\u003cli\u003eLi LQ, Zhang LH, Zhang Y et al. Construction of immune-related gene pairs signature to predict the overall survival of osteosarcoma patients. Aging 2020;12(22):22906-22926.\u003c/li\u003e\n\u003cli\u003eZhao X, Wu P, Liu D et al. An Immunity-Associated lncRNA Signature for Predicting Prognosis in Gastric Adenocarcinoma. Journal of healthcare engineering 2022;2022:3035073.\u003c/li\u003e\n\u003cli\u003eWang Y, Xu X, Shi S et al. Blood urea nitrogen to creatinine ratio and long-term survival in patients with chronic heart failure. Eur J Med Res 2023;28(1):343.\u003c/li\u003e\n\u003cli\u003eBrookes EM, Power DA. Elevated serum urea-to-creatinine ratio is associated with adverse inpatient clinical outcomes in non-end stage chronic kidney disease. Scientific reports 2022;12(1):20827.\u003c/li\u003e\n\u003cli\u003eWu KH, Shih HA, Hung MS et al. The association between blood urea nitrogen to creatinine ratio and mortality in patients with upper gastrointestinal bleeding. Arab journal of gastroenterology : the official publication of the Pan-Arab Association of Gastroenterology 2018;19(4):143-147.\u003c/li\u003e\n\u003cli\u003eOk F, Erdogan O, Durmus E et al. Predictive values of blood urea nitrogen/creatinine ratio and other routine blood parameters on disease severity and survival of COVID-19 patients. Journal of medical virology 2021;93(2):786-793.\u003c/li\u003e\n\u003cli\u003eChen P, Jiang Y, Cai J et al. Prediction of prognosis in patients with nontraumatic intracranial hemorrhage using blood urea nitrogen-to-creatinine ratio on admission: a retrospective cohort study based on data from the medical information Mart for intensive care-IV database. Frontiers in neurology 2023;14:1267815.\u003c/li\u003e\n\u003cli\u003eLi B, Li J, Meng X et al. The association of blood urea nitrogen-to-creatinine ratio and in-hospital mortality in acute ischemic stroke patients with atrial fibrillation: data from the MIMIC-IV database. Frontiers in neurology 2024;15:1331626.\u003c/li\u003e\n\u003cli\u003eHoriuchi Y, Aoki J, Tanabe K et al. A High Level of Blood Urea Nitrogen Is a Significant Predictor for In-hospital Mortality in Patients with Acute Myocardial Infarction. International heart journal 2018;59(2):263-271.\u003c/li\u003e\n\u003cli\u003eQian H, Tang C, Yan G. Predictive value of blood urea nitrogen/creatinine ratio in the long-term prognosis of patients with acute myocardial infarction complicated with acute heart failure. Medicine 2019;98(11):e14845.\u003c/li\u003e\n\u003cli\u003eHuang S, Guo N, Duan X et al. Association between the blood urea nitrogen to creatinine ratio and in‑hospital mortality among patients with acute myocardial infarction: A retrospective cohort study. Experimental and therapeutic medicine 2023;25(1):36.\u003c/li\u003e\n\u003cli\u003eBrisco MA, Coca SG, Chen J et al. Blood urea nitrogen/creatinine ratio identifies a high-risk but potentially reversible form of renal dysfunction in patients with decompensated heart failure. Circulation Heart failure 2013;6(2):233-239.\u003c/li\u003e\n\u003cli\u003eSakr ARM, Gomaa GFE, Wasif SME et al. The prognostic role of urea-to-creatinine ratio in patients with acute heart failure syndrome: a case-control study. Egypt Heart J 2023;75(1):78.\u003c/li\u003e\n\u003cli\u003eSujino Y, Nakano S, Tanno J et al. Clinical implications of the blood urea nitrogen/creatinine ratio in heart failure and their association with haemoconcentration. ESC heart failure 2019;6(6):1274-1282.\u003c/li\u003e\n\u003cli\u003eZhen Z, Liang W, Tan W et al. Prognostic significance of blood urea nitrogen/creatinine ratio in chronic HFpEF. European journal of clinical investigation 2022;52(7):e13761.\u003c/li\u003e\n\u003cli\u003eZhou Y, Zhao Q, Liu Z et al. Blood urea nitrogen/creatinine ratio in heart failure: Systematic review and meta-analysis. PloS one 2024;19(5):e0303870.\u003c/li\u003e\n\u003cli\u003eSun D, Wei C, Li Z. Blood urea nitrogen to creatinine ratio is associated with in-hospital mortality among critically ill patients with cardiogenic shock. BMC cardiovascular disorders 2022;22(1):258.\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":"urea-creatinine ratio, chronic kidney disease, sepsis, mortality risk, SOFA score","lastPublishedDoi":"10.21203/rs.3.rs-6783609/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6783609/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe urea-creatinine ratio (UCR) has shown potential as an indicator for predicting mortality in sepsis. However, its utility, especially in patients with chronic kidney disease (CKD), remained inadequately explored, particularly in the Chinese population. This study aimed to evaluate the predictive value of UCR for in-hospital mortality in septic patients and to examine its relationship with CKD status.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis single-center retrospective Chinese intensive care unit (ICU) cohort study analyzed data from a revised intensive care database. Logistic regression models were used to assess the independent association between UCR and in-hospital mortality. Receiver operating characteristic (ROC) curves were employed to evaluate predictive accuracy, and stratified analyses examined interactions between UCR and clinical factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 453 septic patients, 36.2% experienced in-hospital mortality. The UCR was identified as an independent risk factor for mortality (OR 1.054, 95% CI 1.034\u0026ndash;1.076; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and exhibited particularly strong predictive performance in patients without CKD. The predictive accuracy of the UCR alone was comparable to that of the Sequential Organ Failure Assessment (SOFA) score alone (AUC 0.686, 95% CI 0.621\u0026ndash;0.751 vs. AUC 0.694, 95% CI 0.629\u0026ndash;0.760). The combination of the UCR and the SOFA score demonstrated the highest predictive accuracy for mortality in septic patients without CKD (AUC 0.806, 95% CI 0.753\u0026ndash;0.858).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eHigher UCR is an independent predictor of in-hospital mortality in septic patients, particularly in those without CKD. When combined with the SOFA score, UCR may enhance sepsis risk stratification. Further validation studies are needed to confirm these findings.\u003c/p\u003e","manuscriptTitle":"Relationship between the urea–creatinine ratio and mortality in septic patients with and without chronic kidney disease: A retrospective single-center Chinese intensive care unit cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-10 12:43:44","doi":"10.21203/rs.3.rs-6783609/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":"11453de6-172e-4520-8dd7-0c2c4df5fe22","owner":[],"postedDate":"July 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-27T11:08:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-10 12:43:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6783609","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6783609","identity":"rs-6783609","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}