A Study on the Factors Influencing Mortality Risk in Sepsis-Induced Acute Kidney Injury Based on Analysis of the MIMIC Database

A Study on the Factors Influencing Mortality Risk in Sepsis-Induced Acute Kidney Injury Based on Analysis of the MIMIC Database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Study on the Factors Influencing Mortality Risk in Sepsis-Induced Acute Kidney Injury Based on Analysis of the MIMIC Database chongyang Ye, Chunyan Zhu, shijing Hu, Yulin Mei, Tianjun Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5832340/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jun, 2025 Read the published version in Clinical and Experimental Medicine → Version 1 posted 6 You are reading this latest preprint version Abstract Background: Sepsis-induced acute kidney injury (SA-AKI) significantly increases mortality and healthcare burdens. Identifying key mortality risk factors is crucial for improving patient outcomes. Objectives: This study aims to identify the primary factors affecting mortality in SA-AKI patients using the MIMIC-III database. Methods: A retrospective analysis was conducted on 4,868 SA-AKI patients from the MIMIC-III database. Clinical data from the first 24 hours of ICU admission were analyzed using logistic regression to identify mortality predictors. Results: Key mortality predictors included advanced age (OR = 1.015, 95% CI: 1.006-1.024), severe AKI stages (OR = 1.470, 95% CI: 1.285-1.676), low serum albumin (OR = 0.606, 95% CI: 0.506-0.722), delayed antibiotics (OR = 1.001, 95% CI: 1.000-1.002), high AST (OR = 1.035, 95% CI: 1.027-1.083) and bilirubin (OR = 1.055, 95% CI: 1.037-1.083). The area under the curve (AUC) of the combined predictors for mortality risk was 0.796, indicating high predictive accuracy. Conclusions: Early intervention and monitoring of identified risk factors such as age, AKI stage, albumin levels, and antibiotic timeliness can enhance survival rates in SA-AKI patients. Sepsis Acute Kidney Injury Mortality Risk MIMIC Database Predictive Factors Figures Figure 1 Figure 2 Figure 3 Introduction Sepsis is a severe clinical condition characterized by an uncontrolled systemic inflammatory response, which can potentially lead to multiple organ dysfunction syndrome (MODS) 1 – 3 .Among them, sepsis-induced acute kidney injury (SA-AKI), as a common complication of sepsis, significantly increases patient mortality, extends hospital stay, and raises treatment costs 4 5 . Sepsis has an acute onset and rapid progression, often accompanied by acute kidney injury (AKI). These characteristics increase the difficulty of patient management and elevate the risk of mortality 6 7 . Recent studies indicate that among deaths in sepsis patients, approximately 15% are acute, occurring early in the disease course and posing an immediate threat to life. In contrast, up to 85% of patients experience late deaths, which are often closely associated with secondary infections acquired in the ICU 4 8 9 . This finding further elucidates the complexity of sepsis and its complications, underscoring the urgency of treatment. Despite numerous studies on sepsis and sepsis-induced acute kidney injury (SA-AKI), there are currently no definitive and universally applicable treatment strategies or medications that effectively reduce their mortality and complication rates. Current treatment methods remain largely supportive, including infection source control, timely use of antibiotics, resuscitation, and supportive care for organ dysfunction 10 . Previous studies using the MIMIC-III database have identified key predictors of mortality in sepsis patients, including advanced age, comorbidities, and laboratory markers. For example, Liu et al. conducted a meta-analysis on sepsis-associated AKI, identifying mortality predictors such as serum albumin, BUN, and age 11 . Additionally, C. Thongprayoon et al. and Bagshaw et al. have underlined the importance of early antibiotic administration in septic patients with AKI and demonstrated how hypoalbuminemia correlates with worse outcomes in critically ill patients undergoing renal replacement therapy 12 13 . Legrand et al. and Zarbock et al. also emphasized the importance of large-scale databases like MIMIC in exploring heterogeneity and sub-phenotypes in SA-AKI, which may guide future precision therapies 14 15 . However, these studies primarily focused on individual risk factors and did not incorporate multiple clinical and biochemical parameters into a combined predictive model. In contrast, our study aims to provide a more integrated approach by using a combination of clinical, biochemical, and time-dependent variables to develop a predictive model for mortality risk in SA-AKI patients. While Zhang et al. developed gene-based predictive models for SA-AKI outcomes using MIMIC data 16 , and Yang et al. focused on risk prediction in critically ill patients with sepsis-associated AKI 17 , our study is novel in its focus on the timeliness of antibiotic administration, the severity of AKI stages, and albumin levels, combined with other clinical biomarkers. This multifactorial approach provides a more comprehensive understanding of the factors that influence mortality in SA-AKI, offering new insights into potential therapeutic targets. Over the past decade, numerous methods for predicting acute kidney injury (AKI) have been explored, with the majority of studies focusing on the discovery of novel biomarkers. Many clinical predictive models have been utilized to forecast acute kidney injury associated with surgery 6 7 18 . However, despite the abundance of existing literature that focuses on identifying single biomarkers or isolated clinical variables, studies that integrate these factors into a single, robust predictive model are quite limited. Therefore, an in-depth analysis and study of the factors influencing the mortality risk associated with sepsis-induced acute kidney injury (SA-AKI) is of vital practical significance for optimizing treatment plans, improving patient survival rates, and reducing the burden on the healthcare system. This study aims to utilize data from the MIMIC database, employing a retrospective research method to systematically extract clinical information related to sepsis-associated acute kidney injury (SA-AKI), and to reveal the primary factors influencing the mortality risk in SA-AKI patients through statistical analysis. Our study fills this gap by combining multiple important predictive factors and using logistic regression to assess their performance. This high predictive accuracy indicates that our model can serve as an important tool for clinicians to identify high-risk patients, thereby enabling more targeted and timely interventions. Methods Database The data for this study were derived from the Medical Information Mart for Intensive Care (MIMIC-III v1.4) database, a publicly accessible resource supported by the Laboratory of Computational Physiology at the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. The database documents detailed information on patients who received intensive care treatment at Beth Israel Deaconess Medical Center from 2008 to 2019, encompassing data from over 40,000 patients. All data are available to qualified PhysioNet users without special permission. We conducted a detailed retrospective data analysis based on the MIMIC-III database. The use of data in this study was approved by the appropriate Institutional Review Board and followed all applicable ethical standards and data protection regulations. Patient Admission and Data Extraction In this analysis, based on the ICD-9 (International Classification of Diseases, Ninth Revision) diagnostic codes of the database, all patients with SA-AKI who met the Kidney Disease: Improving Global Outcomes (KDIGO) creatinine criteria were included. The following were considered diagnostic criteria: an increase in serum creatinine level by more than 0.3 mg/dL within 48 hours or an increase to at least 1.5 times the baseline value within the past 7 days. Patients younger than 18 years of age and those with ICU stays less than 48 hours were excluded. Clinical variables and definitions Several variables were extracted from the database, including patient demographics, vital signs, comorbidities, laboratory indices, scoring systems, and medical interventions. All data were collected within the first 24 hours after admission to the intensive care unit (ICU). The average values of laboratory variables within 24 hours after ICU admission were used for analysis and included in the predictive model, taking into account that multiple variables were measured more than once. Persistent AKI was defined as lasting longer than 48 hours, in accordance with the KDIGO criteria, based on the consensus report of the Acute Dialysis Quality Initiative (ADQI) workgroup 19 . Transient AKI was defined as AKI with a duration of less than 48 hours. Statistical Analysis All statistical analyses were conducted using SPSS software (Version 26, IBM Corp., Armonk, NY, USA). Categorical variables are presented as medians (IQR) and categorical variables as frequencies (n) with absolute numbers and percentages (%). The Mann-Whitney U test, Fisher's exact test, or the Chi-square (χ 2 ) test were used for intergroup comparisons when appropriate. Initially, a univariate analysis was performed on all variables to identify factors with statistically significant effects on mortality. Subsequently, only variables that showed significant differences between groups were used in the Cox regression. Data described by OR (Odds Ratio) and CI (Confidence Interval) at 95%, with p < 0.05 considered to indicate statistical significance. A clinical predictive model for in-hospital mortality of persistent SA-AKI was established using logistic regression. Variables were selected based on both statistical significance in univariate analysis (P < 0.05). Multicollinearity among independent variables was assessed using the Variance Inflation Factor (VIF), confirming that all included variables had VIF values below 5, indicating no significant collinearity. Additionally, interaction terms between key predictors (e.g., AKI severity, albumin, and age) were tested but were not statistically significant and were therefore excluded from the final model. Multiple imputation was performed to fill the missing laboratory data using Bayesian methods in SPSS. The predictive performance of the model was evaluated using the C-statistic and the area under the receiver operating characteristic (ROC) curve (AUC). Result 1 、 Baseline characteristics According to the ICD-9 diagnostic criteria, we identified a total of 7,359 patients diagnosed with sepsis among the admitted patients. To ensure the accuracy of the data and the validity of the analysis, we further meticulously screened these patients based on strict exclusion criteria, ultimately excluding 2,491 patients. After this series of screening processes, a total of 4,868 patients were included in our analysis. Among the 4,868 patients with AKI, there were 2,875 males (accounting for 61.4%) and 1,993 females (accounting for 38.6%), with a male-to-female ratio of 1.44:1. The average follow-up time was 26.84 ± 5.86 days. There were 956 deaths after admission to the ICU. Figure 1 provides a detailed flowchart of the patient selection process. 2 、 Comparison of Clinical Data Between the Death and Survival Groups As shown in Table 1, the proportions of the following variables are higher in the non-survivor group: age, continuous renal replacement therapy (CRRT), time to antibiotics administration post-admission (Antibiotics_to_admit(h)), time to antibiotics administration post-ICU admission (Antibiotics_to_icu), aspartate aminotransferase (AST), alanine aminotransferase (ALT), total bilirubin, blood urea nitrogen (BUN), creatinine, heart rate (beats per minute, bpm), white blood cell (WBC) count, neutrophil count, monocyte count, mean corpuscular volume (MCV), red cell distribution width (RDW), international normalized ratio (INR), prothrombin time (PT), partial thromboplastin time (PTT), glucose (measured in mmol/L), magnesium (Mg), phosphorus (P), potassium, Sequential Organ Failure Assessment (SOFA) score, duration of dopamine administration (Dopamine time, in minutes), amount of dopamine administered (Dopamine amount, in mg), duration of norepinephrine administration (Norepinephrine time, in minutes), and amount of norepinephrine administered (Norepinephrine amount, in mg). In contrast, the proportions of body weight (Weight, in kg), cerebrovascular disease, mild liver disease, myocardial infarction, peripheral vascular disease, congestive heart failure, malignant cancer, acute respiratory distress syndrome (ARDS, with a sample size of N=694), peritonitis, pneumonia, mechanical ventilation, albumin (measured in g/L), urine output rate within 6 hours (UO rate 6hours), systolic blood pressure (SBP, in mmHg), mean blood pressure (MBP, in mmHg), diastolic blood pressure (DBP, in mmHg), lymphocyte count, basophil count, eosinophil count, red blood cell (RBC) count, platelet (PLT) count, pH, partial pressure of oxygen (PO2), Charlson comorbidity index, worsening of acute kidney injury (Worsen aki), and the use of dobutamine, dopamine, and norepinephrine, as well as the duration and amount of dobutamine administered (Dobutamine time, Dobutamine amount, in mg), are lower in the non-survivor group (P < 0.05). Table 1 Data comparison between the deceased and surviving groups of patients with Acute Kidney Injury (AKI） Parameters Survival Group (N=3912) Survival Group CV Death Group (N=956) Death Group CV t/χ2 P value Basic characteristics Male/female 2335/1577 540/416 3.26 0.072 Age (year) 61.56±14.90 0.242 65.74±13.36 0.2032 -7.93 <0.001 Weight (kg) 85.91±24.94 0.2189 83.58±28.08 0.336 2.52 0.012 Complications AIDS (N=65) 57(87.7%) 8(12.3%) 2.24 0.157 Cerebrovascular Disease (N=634) 478(75.4%) 156(24.6%) 11.40 0.001 Dementia (N=111) 82(73.9%) 29(26.1%) 3.03 0.09 Diabetes with Coma (N=561) 456(81.3%) 105(18.7%) 0.341 0.61 Diabetes without Coma (N=1268) 1027(81%) 241(19%) 0.43 0.54 Mild Liver Disease (N=1036) 754(72.8%) 282(27.2%) 47.94 <0.001 Myocardial Infarct (N=910) 698(76.7%) 212(23.3%) 9.50 0.003 Peptic Ulcer Disease (N=152) 120(78.9%) 32(21.1%) 0.20 0.678 Peripheral Vascular Disease (N=569) 424(74.5%) 145(25.5%) 13.95 <0.001 COPD (N=1397) 1108(79.3%) 289(20.7%) 1.37 0.248 Congestive Heart Failure (N=1624) 1260(77.6%) 364(22.4%) 11.89 0.001 Malignant Cancer (N=633) 437(69%) 196(31%) 59.13 <0.001 Rheumatic Disease (N=184) 151(82.1%) 33(17.9%) 0.352 0.636 Paraplegia (N=230) 180(78.3%) 50(21.7%) 0.68 0.396 ARDS (N=694) 459(66.1%) 235(33.9%) 105.9 <0.001 Peritonitis (N=57) 39(68.4%) 18(31.6%) 5.21 0.029 Pneumonia (N=1148) 857(74.7%) 291(25.3%) 31.04 <0.001 Urinary Infection (N=752) 605(80.5%) 147(19.5%) 0.005 1 Mechanical Ventilation (N=2881) 2230(77.4%) 651(22.6%) 39.13 <0.001 CRRT (N=501) 247(49.3%) 254(50.7%) 341.4 <0.001 use of antibiotics Antibiotics_to_admit (h) 34(13,109) 64(15,181) -4.91 <0.001 Antibiotics_to culture (h) 24.8(10.8,50.5) 29.4(10,55.4) -1.44 0.149 Antibiotics_to_icu 15.7(5.2,66.5) 33.8(8,97) -6.22 <0.001 Antibiotics to suspect 24.8(10.8,50.5) 29.4(10,55.4) -1.44 0.15 Blood and Biochemical Indicators Albumin (g/L) 30.88±6.76 0.1863 27.63±6.76 0.1964 13.32 <0.001 AST 35(22,66) 63(29,195) -13.09 <0.001 alt 26(16,54) 38(19,128) -9.50 <0.001 Bilirubin total 10.26(6.84,20.52) 18.81(6.84,63.27) -11.39 <0.001 bun 20(13,33) 33(20,55) -15.59 <0.001 creatinine 79.56(61.88,132.6) 132.6(79.56,221) -12.20 <0.001 UO rate 6 hours 0.54(0.4,2.87) 0.41(0.2,0.64) -15.27 <0.001 SBP（mmHg） 147.08±23.74 0.259 146.04±25.62 0.2963 7.64 <0.001 MBP (mmHg) 76.02±18.58 0.244 74.22±19.19 3.53 <0.001 DBP (mmHg) 46.01±11.17 0.4439 43.08±12.63 0.5 5.16 <0.001 Heart rate (bpm) 88.69±16.52 0.1691 92.51±18.17 0.1776 -6.29 <0.001 WBC 10(7.2,13.78) 12.7(8.8, 18.5) -12.16 <0.001 Neutrophils count 7.08(3.07,11.19) 8.95(4.09,11.17) -7.62 <0.001 Lymphocytes count 1.14(0.73,1.76) 0.91(0.56,1.43) -8.89 <0.001 Monocytes count 0.43(0.18,0.74) 0.5(0.19,0.87) -3.59 <0.001 Basophils count 0.02(0,0.04) 0(0,0.03) -7.74 <0.001 Eosinophils count 0.05(0,0.16) 0.01(0,0.13) -8.32 <0.001 RBC 3.33±0.67 0.201 3.19±0.69 0.216 5.89 <0.001 MCV 91.04±7.05 0.077 92.84±8.23 0.089 -5.46 <0.001 RDW 15.66±2.45 0.157 17.22±3.12 0.181 -16.61 <0.001 PLT 226.50±145.82 0.644 180.11±130.29 0.724 9.0 <0.001 INR 1.43±0.63 0.44 1.83±1.05 0.574 -15.53 <0.001 PT 15.57±6.39 0.404 19.83±10.90 0.55 -15.78 <0.001 PTT 36.28±18.38 0.507 45.78±25.14 0.55 -13.24 <0.001 PH 7.39±0.08 0.011 7.35±0.11 0.015 10.09 <0.001 PO 2 95(62,146.75) 88.50(63,124) -3.23 0.001 pCO 2 41.50±10.75 0.256 40.84±12.10 0.3 1.66 0.097 Glucose (mmol/L) 7.84±3.48 0.444 8.40±4.20 0.5 -4.21 <0.001 mg 2.07±0.35 0.164 2.14±0.38 0.178 -5.31 <0.001 p 3.54±1.33 0.379 4.14±1.84 0.444 -11.64 <0.001 potassium 4.09±0.64 0.155 4.25±0.75 0.177 -6.14 <0.001 sodium 138.72±5.07 0.037 138.71±6.63 0.048 0.05 0.96 charlson_comorbidity_index 6(3,8) 5(3,7) -13.12 <0.001 sofa 2(0,4) 3(1,6) -8.18 <0.001 Worsen aki (N=901) 667(74%) 234(N=26%) 28.10 <0.001 Vasoactive Drug Administration Dobutamine (N=203) 114(56.2%) 89(43.8%) 78.63 <0.001 Dopamine (N=293) 184(62.8%) 109(37.2%) 60.94 <0.001 Norepinephrine (N=2116) 1394(65.9%) 722(34.1%) 497.45 <0.001 Drug Dosage and Timing Dobutamine time (minute) 2687.5(696,5684.75) 1744(283,6972) -0.8 0.423 Dobutamine amount (mg) 731.9(206.6,2660.4) 649.9(86.7,2483.7) -1.32 0.187 Dopamine time (minute) 533(151,2396,8) 654(97.5,2255) -0.84 0.402 Dopamine amount (mg) 289.3(70.9,1129.3) 400(55.6,1210.6) -0.13 0.899 Norepinephrine time (minute) 1895.5(660.4114.8) 3750(1661.5,7359.5) -11.7 Norepinephrine amount (mg) 13.4(3.8,37.3) 45.7(17,95.7) -16.05 <0.001 Note: Bold numbers indicate significant P-values. Abbreviation: AIDS, Acquired Immunodeficiency Syndrome; COPD, Chronic Obstructive Pulmonary Disease; ARDS, Acute Respiratory Distress Syndrome; Antibiotcs to admit(h), Time to Antibiotic Administration after Admission (hours); Antibiotics to culture(h), Time to Antibiotic Administration after Culture (hours); Antibiotics to icu, Time to Antibiotic Administration after ICU Admission; Antibiotics to suspect, Time to Antibiotic Administration after Suspicion of Infection; Blood and Biochemical Indicators, Hematological and Biochemical Parameters; Albumin, Serum Albumin; AST, Aspartate Transaminase; ALT, Alanine Transaminase; BUN, Blood Urea Nitrogen; SBP, Systolic Blood Pressure; MBP, Mean Blood Pressure; DBP, Diastolic Blood Pressure; WBC, White Blood Cell Countz; RBC, Red Blood Cell Count; MCV, Mean Corpuscular Volume; RDW, Red Cell Distribution Width; PLT, Platelet Count; INR, International Normalized Ratio; PT, Prothrombin Time; PTT, Partial Thromboplastin Time; PO2, Partial Pressure of Oxygen; pCO2, Partial Pressure of Carbon Dioxide; mg, Magnesium; p, Phosphate; sofa, Sequential Organ Failure Assessment (SOFA) Score 3 、 Factors Influencing All-Cause Death and Other Adverse Outcomes After comparing the clinical data between the mortality and survival groups, Table 1 presents the variables with a P-value of less than 0.05, which were included in the Univariate Cox regression analysis. The results indicate that age, AKI severity, albumin, delayed antibiotic use, AST, total bilirubin, blood urea nitrogen (BUN), cerebrovascular disease, eosinophil count, heart rate at maximum diastolic pressure, hemoglobin, lactate, malignancy, MCHC, metastatic solid tumors, magnesium, pH, platelets, PTT, RDW, SOFA score, white blood cell count, body weight, diabetes, and AKI progression were identified as independent risk factors for 28-day all-cause mortality in AKI patients (P < 0.05, Table 2 ). Through Cox regression analysis, we identified several variables as significant predictors of mortality risk. Specifically, age was a significant risk factor (B = 0.015, P = 0.001), with each additional year increasing the mortality risk by 1.5% (HR = 1.015). This finding aligns with previous studies indicating that elderly patients have poorer prognoses in sepsis-associated acute kidney injury (SA-AKI). Similarly, the severity of AKI was also a critical predictor, particularly when AKI progressed to stage 2, where the mortality risk was 28.6% higher compared to patients without progression (B = 0.384, P < 0.001; HR = 1.470). These results highlight the strong association between AKI progression and mortality risk, emphasizing the necessity of early monitoring and intervention. Low albumin levels (B = -0.503, P = 0.001) were identified as a strong predictor of mortality risk, suggesting that hypoalbuminemia may reflect malnutrition or a systemic inflammatory response, thereby exacerbating SA-AKI severity and increasing mortality risk (HR = 0.606). Additionally, the timeliness of antibiotic treatment was also a crucial factor influencing mortality risk. Delayed antibiotic administration significantly increased mortality risk (B = 0.001, P = 0.001), with each one-hour delay in antibiotic administration leading to a 0.1% increase in mortality risk. This finding underscores the importance of timely antibiotic use in infection control. Elevated total bilirubin levels (B = 0.053, P < 0.001; HR = 1.055) were associated with an increased risk of mortality, indicating that liver dysfunction plays a critical role in the mortality risk of SA-AKI patients. Conversely, higher albumin levels (B = -0.503, P = 0.001) were identified as a protective factor, suggesting that improving patients' nutritional status may help reduce mortality risk. Table 2 presents the results of the univariate Cox regression analysis, further confirming the impact of these factors on 28-day all-cause mortality. Table3 displays the results of the Cox regression analysis after adjusting for multiple variables, demonstrating that these factors remain significant predictors of mortality risk despite the influence of other variables. In summary, age, AKI severity, low albumin levels, delayed antibiotic administration, total bilirubin, BUN, and liver dysfunction are independent risk factors influencing 28-day all-cause mortality in SA-AKI patients. Table 2 Univariate Cox Regression Analysis of Independent Risk Factors for 28-Day All-Cause Mortality Parameters B SE Wald P value 95% CI Lower Limit Upper Limit age .021 .002 70.654 <0.001 1.016 1.026 Aki stage 2day .596 .094 39.741 <0.001 1.507 2.183 albumin -.311 .051 36.513 <0.001 .663 .811 AST 0 0 345.38 <0.001 1 1 Bilirubin total .045 .005 93.143 <0.001 1.037 1.056 bun .011 .001 113.75 <0.001 1.009 1.013 Cerebrovascular disease .072 .088 .683 .409 .905 1.277 Eosinophils abs -.539 .155 12.163 <0.001 .431 .790 Heart rate time dbp max .004 .001 9.909 .002 1.002 1.007 hemoglobin .007 .018 .160 .689 .973 1.043 lac .181 .009 412.39 <0.001 1.177 1.219 Malignant cancer .514 .080 41.188 <0.001 1.429 1.957 MCHC -.035 .019 .304 1.003 .929 0.069 Metastatic solid tumor 1.023 .097 112.14 <0.001 2.302 3.363 mg .364 .086 17.691 <0.001 1.214 1.704 ph -4.788 .316 229.86 <0.001 .004 .015 platelet -.003 .000 125.93 <0.001 .996 .997 ptt .012 .001 111.49 <0.001 1.01 1.014 rdw .098 .010 99.728 <0.001 1.082 1.125 Sofa 24hours .059 .010 32.32 <0.001 1.039 1.081 wbc .013 .001 76.732 <0.001 1.01 1.016 weight -.004 .001 10.806 <0.001 .993 .998 Worsen aki .046 .076 .368 .544 .903 1.214 Note: The bold numbers represent the P values with significant differences. Abbreviations: 95% CI, 95% confidence interval; AKI, acute kidney injury; B, regression coefficient; BE, standard error; Albumin, Serum Albumin; AST, Aspartate Aminotransferase; Bun, Blood Urea Nitrogen; Heart rate time dbp max, Heart Rate at Maximum Diastolic Blood Pressurehemoglobin; lac, Lactate; MCHC, Mean Corpuscular Hemoglobin Concentration; mg ,Magnesium; ptt, Prothrombin Time ; Rdw, Red Cell Distribution Width; Sofa 24hours,Sequential Organ Failure Assessment (SOFA) Score at 24 Hours; Wbc, White Blood Cell (WBC) Count; Wosen aki, Worsening Acute Kidney Injury Table 3 Multivariate Cox Regression Analysis of Independent Risk Factors for 28-Day All-Cause Mortality Parameters B SE Wald P value 95% CI Lower Limit Upper Limit age .015 .004 11.349 0.001 1.006 1.024 Aki stage 2 day .384 .068 32.068 <0.001 1.285 1.676 albumin -.503 .090 30.964 0.001 .506 .722 AST .000 .000 6.710 0.010 1.000 1.000 Bilirubin total .053 .014 15.413 <0.001 1.027 1.083 bun .016 .002 41.806 <0.001 1.011 1.021 Cerebrovascular disease .651 .167 15.222 <0.001 1.383 2.659 Eosinophils abs -.567 .231 6.006 0.014 .360 .893 Heart rate time dbp max .000 .000 9.264 0.002 1.000 1.000 hemoglobin .076 .033 5.277 0.022 1.011 1.152 lac .189 .029 41.418 <0.001 1.141 1.280 Malignant cancer .385 .181 4.539 0.033 1.031 2.095 MCHC -.129 .039 10.906 0.001 .814 .949 Metastatic solid tumor .896 .234 14.705 <0.001 1.550 3.872 mg .332 .163 4.145 0.042 1.012 1.919 ph -3.391 .630 28.967 <0.001 .010 .116 platelet -.003 .000 32.218 <0.001 .996 .998 ptt .010 .003 15.811 <0.001 1.005 1.015 rdw .070 .024 8.569 0.003 1.023 1.124 Sofa 24 hours -.041 .021 3.973 0.046 .922 .999 wbc .038 .007 26.756 <0.001 1.024 1.054 weight -.008 .002 11.173 0.001 .988 .997 Wosen aki .687 .159 18.687 <0.001 1.455 2.713 Note: The bold numbers represent the P values with significant differences. Abbreviations: 95% CI, 95% confidence interval; AKI, acute kidney injury; B, regression coefficient; BE, standard error; Albumin, Serum Albumin; AST, Aspartate Aminotransferase; Bun, Blood Urea Nitrogen; Heart rate time dbp max, Heart Rate at Maximum Diastolic Blood Pressurehemoglobin; lac, Lactate; , Mean Corpuscular Hemoglobin Concentration; mg ,Magnesium; ptt, Prothrombin Time ; Rdw, Red Cell Distribution Width; Sofa 24hours,Sequential Organ Failure Assessment (SOFA) Score at 24 Hours; Wbc, White Blood Cell (WBC) Count; Wosen aki, Worsening Acute Kidney Injury 4 、 ROC Curve Analysis of Univariate and Combined Variables for Predicting Mortality Risk In the univariate ROC curve analysis, the area under the curve (AUC) for several indicators demonstrated their effectiveness in predicting the risk of death. Notably, the AUC values for aki_stage_2day, aspartate aminotransferase (AST), total bilirubin (bilirubin_total), blood urea nitrogen (BUN), partial thromboplastin time (PTT), red cell distribution width (RDW), and white blood cell count (WBC) were relatively high (Figure 2), indicating that these indicators possess a good discriminatory power in predicting the risk of mortality. The area under the curve (AUC) for each variable is as follows: Age: AUC=0.573, Aki stage 2day: AUC = 0.665, Albumin: AUC = 0.358, Antibiotic to admit time: AUC = 0.560, AST: AUC = 0.650, Bilirubin total: AUC = 0.623, BUN: AUC = 0.674, Cerebrovascular disease: AUC = 0.514, Eosinophils abs: AUC = 0.398, Heart rate time at DBP max: AUC = 0.566, Lac: AUC = 0.665, Hemoglobin: AUC = 0.435, MCHC: AUC = 0.437, Metastatic solid tumor: AUC = 0.543, Mg: AUC = 0.546, Ph: AUC = 0.380, Platelet: AUC = 0.370, PTT: AUC = 0.658, RDW: AUC = 0.673, SOFA 24hours: AUC = 0.565, WBC: AUC = 0.606, Weight: AUC = 0.457, Diabetes: AUC = 0.491, Wosen aki: AUC = 0.528 Multivariate Cox regression analysis showed that when using combined indicators such as ALB (Albumin), AST (Aspartate Aminotransferase), BUN (Blood Urea Nitrogen), EOS (Absolute Eosinophil Count), LAC (Lactate), PH (pH value), PTT (Prothrombin Time), and RDW (Red Cell Distribution Width), the AUC value reached 0.796 (Figure 3), indicating that these combined indicators have a high accuracy in predicting the risk of mortality. Discussion This study offers a comprehensive examination of the factors influencing mortality risk in patients with SA-AKI using data from the MIMIC-III database. Our findings underscore the complex nature of managing SA-AKI and highlight critical predictors of mortality that can guide clinical practice. Using univariate and multivariate Cox regression analyses, we assessed the impact of these factors in both unadjusted and adjusted models. Our findings indicate that variables such as age, severity of AKI, albumin levels, timeliness of antibiotic use, total bilirubin, BUN, cerebrovascular disease, eosinophil count, heart rate variability at maximum diastolic pressure, and malignancy are independent risk factors for mortality. Consistent with existing studies, age and AKI progression were confirmed as significant predictors of mortality risk. In the multivariate analysis, after adjusting for potential confounders such as age and sex, the impact of these variables on mortality risk remained significant, highlighting their critical implications for clinical management. Specifically, age was identified as a significant risk factor, indicating that the risk of mortality increases significantly with advancing patient age. Advanced age emerged as a prominent risk factor, consistent with prior studies that identify age as a critical determinant in sepsis outcomes 17 20 21 . The severity of AKI, particularly when it progresses to stage 2, is closely associated with an increased risk of mortality. This finding underscores the importance of early diagnosis and intervention, especially in high-risk AKI patients 22 23 . Additionally, the presence of low albumin levels is recognized as a significant predictor of mortality. Low serum albumin levels indicated poor nutritional status and severe systemic inflammation 24 25 . Our analysis further demonstrated that delayed antibiotic use significantly increases the risk of mortality, highlighting the critical importance of timely antibiotic administration in controlling infections. Elevated AST and bilirubin levels were indicative of underlying liver dysfunction and systemic severity, contributing to higher mortality risk 26-28 . Notably, elevated total bilirubin was significantly associated with an increased risk of mortality in our analysis, indicating a close relationship between impaired liver function and the severity of systemic infection, which may exacerbate the condition of septic patients. Therefore, monitoring and managing liver function abnormalities are crucial for improving patient survival 29 30 . Additionally, the presence of cerebrovascular disease and malignancies was associated with increased mortality, highlighting the need for tailored care strategies for patients with complex medical histories 31 . Additionally, we identified novel influencing factors, such as eosinophil count and malignancy, both of which demonstrated strong predictive power in the multivariate regression analysis, providing new directions for future research. A decrease in eosinophil count was associated with a higher risk of mortality, suggesting potential immune dysfunction, particularly in septic patients. Malignancy, as a known comorbidity, may contribute to increased mortality risk through mechanisms related to immune suppression. Special attention should be given to patients with comorbid conditions like cerebrovascular diseases and malignancies, to develop effective care strategies that address their higher risk profiles 32 33 . Our ROC curve analysis further supports the above conclusions. In the univariate analysis, several variables, such as age, AKI stage, AST, BUN, and total bilirubin, exhibited high AUC values, demonstrating their strong discriminatory ability in predicting mortality risk. In the multivariate Cox regression analysis, after incorporating multiple clinical indicators, including albumin, AST, BUN, eosinophils, lactate, pH, PTT, and RDW, the AUC value reached 0.796, indicating a high level of accuracy in predicting mortality risk using these combined indicators. These findings have significant implications for clinical practice. The study results indicate that delayed antibiotic administration significantly increases the risk of mortality (OR = 1.001, 95% CI: 1.000-1.002). Therefore, clinical practice should adhere to the "golden hour" principle, ensuring that empirical antibiotic therapy is initiated as early as possible when infection is suspected to reduce mortality rates. Low serum albumin levels (OR = 0.606, 95% CI: 0.506-0.722) were identified as an independent risk factor for mortality. Hypoalbuminemia may reflect a systemic inflammatory response and malnutrition. Hence, albumin supplementation should be considered in SA-AKI patients with low albumin levels to improve fluid management and microcirculatory perfusion. Elevated AST (OR = 1.035, 95% CI: 1.027-1.083) and total bilirubin (OR = 1.055, 95% CI: 1.037-1.083) suggest that liver dysfunction is relatively common in SA-AKI patients and may be associated with systemic inflammatory response syndrome (SIRS). Clinically, liver function monitoring should be enhanced, and, if necessary, adjustments to antibiotic, sedative, or nephrotoxic drug dosages should be made to reduce the risk of liver damage. Abnormal magnesium levels (OR = 1.012, 95% CI: 1.002-1.919) and pH values (OR = 0.010, 95% CI: 0.010-0.116) were associated with increased mortality. Therefore, we recommend continuous monitoring of SA-AKI patients and proactive correction of electrolyte imbalances to reduce the incidence of arrhythmias and multiple organ dysfunction syndrome (MODS). Age (OR = 1.015, 95% CI: 1.006-1.024), AKI severity (OR = 1.470, 95% CI: 1.285-1.676), and malignancy (OR = 1.550, 95% CI: 1.031-2.095) were also identified as independent predictors of mortality. Based on these findings, we suggest incorporating these indicators into ICU risk assessment models to identify high-risk patients in advance and develop enhanced treatment strategies, such as close monitoring of fluid balance, early initiation of renal replacement therapy (RRT), or more aggressive anti-infection protocols. Despite the strengths of this study, there are some limitations associated with the use of the MIMIC-III database that should be acknowledged. First, MIMIC-III is a single-center database, which may limit the generalizability of the findings to broader populations or healthcare settings. Second, the retrospective nature of the database makes it susceptible to information bias, missing data, and unmeasured confounders. Third, the database focuses primarily on ICU patients, which may not fully reflect the characteristics of sepsis or AKI cases in non-ICU settings. Lastly, certain relevant clinical variables—such as fluid management strategies, antimicrobial stewardship interventions, and pre-hospital care data—are either not available or not standardized in the database, which may influence outcome interpretation. Nonetheless, despite these limitations, our findings provide valuable insights for the early identification of high-risk SA-AKI patients and offer a foundation for optimizing clinical treatment strategies. In the future, these key predictive factors could be integrated into an artificial intelligence-driven clinical decision support system (CDSS) to assist ICU physicians in making more precise and individualized treatment decisions. Conclusion In this study, we identified critical factors influencing mortality in patients with Sepsis-Induced Acute Kidney Injury (SA-AKI) using the MIMIC-III database. The primary findings of our research, including advanced age, severity of AKI, hypoalbuminemia, delayed antibiotic administration, and elevated AST and bilirubin levels, offer substantial insights into the clinical management of these patients. Our findings have significant implications for clinical practice. The study highlights the importance of early and timely interventions, particularly regarding antibiotic administration, which is shown to reduce mortality risk. The relationship between hypoalbuminemia and increased mortality underscores the need for nutritional support and monitoring of serum albumin levels in critically ill patients. Moreover, the identification of AKI stage severity as a strong predictor of mortality emphasizes the need for early detection and intervention in patients showing signs of progression. These insights could inform more personalized care strategies, helping clinicians prioritize high-risk patients and adjust therapeutic approaches accordingly. Additionally, our findings suggest that liver dysfunction, indicated by elevated AST and bilirubin levels, plays a critical role in mortality among SA-AKI patients. This highlights the necessity for clinicians to monitor liver function closely, especially in critically ill patients with multiple organ dysfunctions. This study reinforces the multifactorial nature of SA-AKI and mortality. While existing literature has identified certain risk factors, the integration of these variables into predictive models with strong discriminative ability (AUC = 0.796) offers a promising approach for future clinical trials and decision-making support tools. By recognizing the combined predictive value of variables such as albumin, AST, and BUN, future studies can refine these findings further, possibly incorporating novel biomarkers for more accurate risk prediction. Despite these contributions, there are several limitations in this study. Firstly, the retrospective design limits our ability to establish causal relationships. Furthermore, the use of a single-center dataset may reduce the generalizability of the findings, as patients in different settings or regions may present with distinct characteristics. Additionally, the study's reliance on the MIMIC-III database, while comprehensive, may not capture all variables relevant to patient outcomes, such as those related to patient care practices outside the ICU. Future research should focus on validating these findings in multi-center and prospective studies to improve generalizability. Moreover, incorporating more granular data on treatment protocols, such as the use of novel sepsis therapies or advanced monitoring techniques, could provide further insights into the management of SA-AKI. It would also be valuable to explore the role of immune responses in SA-AKI progression and mortality, particularly in relation to eosinophil counts and other immune markers identified in our study. Additionally, future studies should examine the potential for machine learning algorithms to incorporate these predictors into real-time clinical decision support systems, thereby optimizing patient care. Declarations Footnotes Contributors: Chongyang Ye and Tianjun Yang designed the study. Chunyan Zhu ,Chongyang Ye and Shijing Hu wrote the manuscript. Chongyang Ye and Tianjun Yang revised the paper. Data Availability The experimental data used to support the findings of this study are publicly available from the MIMIC-III database (https://mimic.physionet.org/). Conflicts of Interest The authors declared that they have no conflicts of interest regarding this work. Funding The work was not funded by any funding. Ethics statements Patient consent for publication: Not required. Ethics approval: The data featured in this study were sourced from the MIMIC-III database, which is publicly accessible online. Before their involvement in the research, all participants had provided their consent in written form. Acknowledgements We are grateful to everyone who participated in or contributed to this research endeavor. 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):801–10. Cecconi M, Evans L, Levy M, et al. Sepsis and septic shock. Lancet 2018;392(10141):75–87. Kaukonen KM, Bailey M, Pilcher D, et al. Systemic inflammatory response syndrome criteria in defining severe sepsis. N Engl J Med 2015;372(17):1629–38. Thakar CV, Christianson A, Freyberg R, et al. Incidence and outcomes of acute kidney injury in intensive care units: a Veterans Administration study. Crit Care Med 2009;37(9):2552–8. Evans L, Rhodes A, Alhazzani W, et al. Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 2021. Crit Care Med 2021;49(11):e1063-e143. Poston JT, Koyner JL. Sepsis associated acute kidney injury. BMJ 2019;364:k4891. Kellum JA, Prowle JR. Paradigms of acute kidney injury in the intensive care setting. Nat Rev Nephrol 2018;14(4):217–30. Nedeva C, Menassa J, Duan M, et al. TREML4 receptor regulates inflammation and innate immune cell death during polymicrobial sepsis. Nat Immunol 2020;21(12):1585–96. Riche F, Gayat E, Barthelemy R, et al. Reversal of neutrophil-to-lymphocyte count ratio in early versus late death from septic shock. Crit Care 2015;19:439. Evans L, Rhodes A, Alhazzani W, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Med 2021;47(11):1181–247. Liu J, Xie H, Ye Z, et al. Rates, predictors, and mortality of sepsis-associated acute kidney injury: a systematic review and meta-analysis. BMC Nephrol 2020;21(1):318. Bagshaw SM, Lapinsky S, Dial S, et al. Acute kidney injury in septic shock: clinical outcomes and impact of duration of hypotension prior to initiation of antimicrobial therapy. Intensive Care Med 2009;35(5):871–81. Thongprayoon C, Cheungpasitporn W, Radhakrishnan Y, et al. Impact of hypoalbuminemia on mortality in critically ill patients requiring continuous renal replacement therapy. J Crit Care 2022;68:72–75. Legrand M, Bagshaw SM, Bhatraju PK, et al. Sepsis-associated acute kidney injury: recent advances in enrichment strategies, sub-phenotyping and clinical trials. Crit Care 2024;28(1):92. Zarbock A, Nadim MK, Pickkers P, et al. Sepsis-associated acute kidney injury: consensus report of the 28th Acute Disease Quality Initiative workgroup. Nat Rev Nephrol 2023;19(6):401–17. Zhang Z, Chen L, Liu H, et al. Gene signature for the prediction of the trajectories of sepsis-induced acute kidney injury. Crit Care 2022;26(1):398. Yang S, Su T, Huang L, et al. A novel risk-predicted nomogram for sepsis associated-acute kidney injury among critically ill patients. BMC Nephrol 2021;22(1):173. Leligdowicz A, Matthay MA. Heterogeneity in sepsis: new biological evidence with clinical applications. Crit Care 2019;23(1):80. Chawla LS, Bellomo R, Bihorac A, et al. Acute kidney disease and renal recovery: consensus report of the Acute Disease Quality Initiative (ADQI) 16 Workgroup. Nat Rev Nephrol 2017;13(4):241–57. Chen JJ, Kuo G, Hung CC, et al. Risk factors and prognosis assessment for acute kidney injury: The 2020 consensus of the Taiwan AKI Task Force. J Formos Med Assoc 2021;120(7):1424–33. Abebe A, Kumela K, Belay M, et al. Mortality and predictors of acute kidney injury in adults: a hospital-based prospective observational study. Sci Rep 2021;11(1):15672. Huang H, Bai X, Ji F, et al. Early-Phase Urine Output and Severe-Stage Progression of Oliguric Acute Kidney Injury in Critical Care. Front Med (Lausanne) 2021;8:711717. Koyner JL, Mackey RH, Rosenthal NA, et al. Clinical Outcomes of Persistent Severe Acute Kidney Injury among Patients with Kidney Disease Improving Global Outcomes Stage 2 or 3 Acute Kidney Injury. Am J Nephrol 2022;53(11–12):816–25. Wang X, Chu H, Zhou H. Association between hypoalbuminemia and mortality in patients undergoing continuous renal replacement therapy: A systematic review and meta-analysis. PLoS One 2023;18(3):e0283623. Alves FC, Sun J, Qureshi AR, et al. The higher mortality associated with low serum albumin is dependent on systemic inflammation in end-stage kidney disease. PLoS One 2018;13(1):e0190410. Grabherr F, Grander C, Effenberger M, et al. MAFLD: what 2 years of the redefinition of fatty liver disease has taught us. Ther Adv Endocrinol Metab 2022;13:20420188221139101. Kalas MA, Chavez L, Leon M, et al. Abnormal liver enzymes: A review for clinicians. World J Hepatol 2021;13(11):1688–98. Sharma P. Value of Liver Function Tests in Cirrhosis. J Clin Exp Hepatol 2022;12(3):948–64. Lan Q, Zheng L, Zhou X, et al. The Value of Blood Urea Nitrogen in the Prediction of Risks of Cardiovascular Disease in an Older Population. Front Cardiovasc Med 2021;8:614117. Du J, Zhang W, Niu J, et al. Association between blood urea nitrogen levels and the risk of diabetes mellitus in Chinese adults: secondary analysis based on a multicenter, retrospective cohort study. Front Endocrinol (Lausanne) 2024;15:1282015. Dias RA, Dias L, Azevedo E, et al. Acute Inflammation in Cerebrovascular Disease: A Critical Reappraisal with Focus on Human Studies. Life (Basel) 2021;11(10). Wilcox NS, Amit U, Reibel JB, et al. Cardiovascular disease and cancer: shared risk factors and mechanisms. Nat Rev Cardiol 2024. Cacho-Diaz B, Lorenzana-Mendoza NA, Spinola-Marono H, et al. Comorbidities, Clinical Features, and Prognostic Implications of Cancer Patients with Cerebrovascular Disease. J Stroke Cerebrovasc Dis 2018;27(2):365–71. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Jun, 2025 Read the published version in Clinical and Experimental Medicine → Version 1 posted Editorial decision: Accepted 12 Apr, 2025 Reviews received at journal 12 Apr, 2025 Reviewers agreed at journal 01 Apr, 2025 Reviewers invited by journal 01 Apr, 2025 Submission checks completed at journal 31 Mar, 2025 First submitted to journal 29 Mar, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5832340","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436855588,"identity":"7e8f25e6-be49-4621-828c-6ab30e11a5d7","order_by":0,"name":"chongyang Ye","email":"","orcid":"","institution":"Department of Critical Care Medicine,The First Affiliated Hospital of University of Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"chongyang","middleName":"","lastName":"Ye","suffix":""},{"id":436855589,"identity":"88577eaa-c14e-41b1-8f9a-45e4ff19fb2c","order_by":1,"name":"Chunyan Zhu","email":"","orcid":"","institution":"Department of Critical Care Medicine,The First Affiliated Hospital of University of Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Chunyan","middleName":"","lastName":"Zhu","suffix":""},{"id":436855590,"identity":"e60c4b54-552a-48fe-9e8b-88edfd40cf20","order_by":2,"name":"shijing Hu","email":"","orcid":"","institution":"Department of Critical Care Medicine,The First Affiliated Hospital of University of Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"shijing","middleName":"","lastName":"Hu","suffix":""},{"id":436855591,"identity":"0fb952a9-8a12-4228-ba2c-25bf5dc4c993","order_by":3,"name":"Yulin Mei","email":"","orcid":"","institution":"Department of Critical Care Medicine,The First Affiliated Hospital of University of Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Yulin","middleName":"","lastName":"Mei","suffix":""},{"id":436855592,"identity":"b5feb377-2355-43c9-9714-4e2028a76db1","order_by":4,"name":"Tianjun Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIie3PsWoCQRCA4VkWvGaj7Qkh5hEWBEmx+Cx7HGx1hIAPkLNXrr2HsDAE0mbNoJVy7RUWhoCVxdqIRRDX1mLvSsH9y2E+hgHw+W41yU7vJAXQhot+PbJ51JJaMsvfVFxLkI3QMrCEMvNzueaOF5Oukcn69SHDXxRcUwhwPnWSctcL5XI7aOeKY8LXTWBKlW6y+oJoRMlHzsCSLYWQ9arIp4lOlHwXS8AXjiStJMV4GkqG0TBNAKEOaZetgyWqS+wvsxFXcaPql2axiPdHJp5Ihn/m+C/6rQAXTvJcyqtJw7V+qZPpqhWfz+e7+85liledltHHFQAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Critical Care Medicine,The First Affiliated Hospital of University of Science and Technology of China","correspondingAuthor":true,"prefix":"","firstName":"Tianjun","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-01-15 07:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5832340/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5832340/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10238-025-01681-4","type":"published","date":"2025-06-07T15:56:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79819531,"identity":"aa537682-e93d-4c34-a408-65e387474a7c","added_by":"auto","created_at":"2025-04-03 08:29:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":103092,"visible":true,"origin":"","legend":"\u003cp\u003ePatient Selection Flowchart\u003c/p\u003e\n\u003cp\u003eMIMIC: Medical Information Mart for Intensive Care; ICU: Intensive Care Unit; SA-AKI: Sepsis-Induced Acute Kidney Injury\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5832340/v1/b800781ed25284bc3c4d682e.png"},{"id":79819534,"identity":"6da26cca-81de-47c8-bcf6-3936a2b9df77","added_by":"auto","created_at":"2025-04-03 08:29:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":312570,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curves for Univariate Predictors of Mortality in SA-AKI Patients. This figure illustrates the predictive performance of individual clinical variables for mortality risk. Higher AUC values indicate stronger predictive power.\u003c/p\u003e\n\u003cp\u003eThe area under the curve (AUC) for each variable is as follows: Age: AUC=0.573, Aki stage 2day: AUC = 0.665, Albumin: AUC = 0.358, Antibiotic to admit time: AUC = 0.560, AST: AUC = 0.650, Bilirubin total: AUC = 0.623, BUN: AUC = 0.674, Cerebrovascular disease: AUC = 0.514, Eosinophils abs: AUC = 0.398, Heart rate time at DBP max: AUC = 0.566, Lac: AUC = 0.665, Hemoglobin: AUC = 0.435, MCHC: AUC = 0.437, Metastatic solid tumor: AUC = 0.543, Mg: AUC = 0.546, Ph: AUC = 0.380, Platelet: AUC = 0.370, PTT: AUC = 0.658, RDW: AUC = 0.673, SOFA 24hours: AUC = 0.565, WBC: AUC = 0.606, Weight: AUC = 0.457, Diabetes: AUC = 0.491, Wosen aki: AUC = 0.528\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5832340/v1/f387627d9ebbd70414aab392.png"},{"id":79817999,"identity":"07a6326c-bf80-4ce9-942b-8c2cb713d50c","added_by":"auto","created_at":"2025-04-03 08:13:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":114450,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analysis of multiple clinical indicators (ALB, AST, BUN, etc.) selected by multivariate Cox regression for predicting SA-AKI patient mortality risk. AUC for combined indicators is 0.796, showing high prediction accuracy\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5832340/v1/b2775c8143de80ecc876236f.png"},{"id":84242332,"identity":"e6dfce02-2dbe-4383-8682-9468b2b08744","added_by":"auto","created_at":"2025-06-09 16:01:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1724029,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5832340/v1/a2a6d6a4-6a9a-4306-a292-973fb196c5bb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Study on the Factors Influencing Mortality Risk in Sepsis-Induced Acute Kidney Injury Based on Analysis of the MIMIC Database","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSepsis is a severe clinical condition characterized by an uncontrolled systemic inflammatory response, which can potentially lead to multiple organ dysfunction syndrome (MODS)\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.Among them, sepsis-induced acute kidney injury (SA-AKI), as a common complication of sepsis, significantly increases patient mortality, extends hospital stay, and raises treatment costs\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Sepsis has an acute onset and rapid progression, often accompanied by acute kidney injury (AKI). These characteristics increase the difficulty of patient management and elevate the risk of mortality\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent studies indicate that among deaths in sepsis patients, approximately 15% are acute, occurring early in the disease course and posing an immediate threat to life. In contrast, up to 85% of patients experience late deaths, which are often closely associated with secondary infections acquired in the ICU\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This finding further elucidates the complexity of sepsis and its complications, underscoring the urgency of treatment. Despite numerous studies on sepsis and sepsis-induced acute kidney injury (SA-AKI), there are currently no definitive and universally applicable treatment strategies or medications that effectively reduce their mortality and complication rates. Current treatment methods remain largely supportive, including infection source control, timely use of antibiotics, resuscitation, and supportive care for organ dysfunction\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious studies using the MIMIC-III database have identified key predictors of mortality in sepsis patients, including advanced age, comorbidities, and laboratory markers. For example, Liu et al. conducted a meta-analysis on sepsis-associated AKI, identifying mortality predictors such as serum albumin, BUN, and age\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Additionally, C. Thongprayoon et al. and Bagshaw et al. have underlined the importance of early antibiotic administration in septic patients with AKI and demonstrated how hypoalbuminemia correlates with worse outcomes in critically ill patients undergoing renal replacement therapy\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Legrand et al. and Zarbock et al. also emphasized the importance of large-scale databases like MIMIC in exploring heterogeneity and sub-phenotypes in SA-AKI, which may guide future precision therapies\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. However, these studies primarily focused on individual risk factors and did not incorporate multiple clinical and biochemical parameters into a combined predictive model.\u003c/p\u003e \u003cp\u003eIn contrast, our study aims to provide a more integrated approach by using a combination of clinical, biochemical, and time-dependent variables to develop a predictive model for mortality risk in SA-AKI patients. While Zhang et al. developed gene-based predictive models for SA-AKI outcomes using MIMIC data\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, and Yang et al. focused on risk prediction in critically ill patients with sepsis-associated AKI\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, our study is novel in its focus on the timeliness of antibiotic administration, the severity of AKI stages, and albumin levels, combined with other clinical biomarkers. This multifactorial approach provides a more comprehensive understanding of the factors that influence mortality in SA-AKI, offering new insights into potential therapeutic targets.\u003c/p\u003e \u003cp\u003eOver the past decade, numerous methods for predicting acute kidney injury (AKI) have been explored, with the majority of studies focusing on the discovery of novel biomarkers. Many clinical predictive models have been utilized to forecast acute kidney injury associated with surgery\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. However, despite the abundance of existing literature that focuses on identifying single biomarkers or isolated clinical variables, studies that integrate these factors into a single, robust predictive model are quite limited. Therefore, an in-depth analysis and study of the factors influencing the mortality risk associated with sepsis-induced acute kidney injury (SA-AKI) is of vital practical significance for optimizing treatment plans, improving patient survival rates, and reducing the burden on the healthcare system. This study aims to utilize data from the MIMIC database, employing a retrospective research method to systematically extract clinical information related to sepsis-associated acute kidney injury (SA-AKI), and to reveal the primary factors influencing the mortality risk in SA-AKI patients through statistical analysis. Our study fills this gap by combining multiple important predictive factors and using logistic regression to assess their performance. This high predictive accuracy indicates that our model can serve as an important tool for clinicians to identify high-risk patients, thereby enabling more targeted and timely interventions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDatabase\u003c/h2\u003e \u003cp\u003eThe data for this study were derived from the Medical Information Mart for Intensive Care (MIMIC-III v1.4) database, a publicly accessible resource supported by the Laboratory of Computational Physiology at the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. The database documents detailed information on patients who received intensive care treatment at Beth Israel Deaconess Medical Center from 2008 to 2019, encompassing data from over 40,000 patients. All data are available to qualified PhysioNet users without special permission. We conducted a detailed retrospective data analysis based on the MIMIC-III database. The use of data in this study was approved by the appropriate Institutional Review Board and followed all applicable ethical standards and data protection regulations.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePatient Admission and Data Extraction\u003c/h3\u003e\n\u003cp\u003eIn this analysis, based on the ICD-9 (International Classification of Diseases, Ninth Revision) diagnostic codes of the database, all patients with SA-AKI who met the Kidney Disease: Improving Global Outcomes (KDIGO) creatinine criteria were included. The following were considered diagnostic criteria: an increase in serum creatinine level by more than 0.3 mg/dL within 48 hours or an increase to at least 1.5 times the baseline value within the past 7 days. Patients younger than 18 years of age and those with ICU stays less than 48 hours were excluded.\u003c/p\u003e\n\u003ch3\u003eClinical variables and definitions\u003c/h3\u003e\n\u003cp\u003eSeveral variables were extracted from the database, including patient demographics, vital signs, comorbidities, laboratory indices, scoring systems, and medical interventions. All data were collected within the first 24 hours after admission to the intensive care unit (ICU). The average values of laboratory variables within 24 hours after ICU admission were used for analysis and included in the predictive model, taking into account that multiple variables were measured more than once. Persistent AKI was defined as lasting longer than 48 hours, in accordance with the KDIGO criteria, based on the consensus report of the Acute Dialysis Quality Initiative (ADQI) workgroup\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Transient AKI was defined as AKI with a duration of less than 48 hours.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using SPSS software (Version 26, IBM Corp., Armonk, NY, USA). Categorical variables are presented as medians (IQR) and categorical variables as frequencies (n) with absolute numbers and percentages (%). The Mann-Whitney U test, Fisher's exact test, or the Chi-square (χ\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) test were used for intergroup comparisons when appropriate. Initially, a univariate analysis was performed on all variables to identify factors with statistically significant effects on mortality. Subsequently, only variables that showed significant differences between groups were used in the Cox regression. Data described by OR (Odds Ratio) and CI (Confidence Interval) at 95%, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered to indicate statistical significance. A clinical predictive model for in-hospital mortality of persistent SA-AKI was established using logistic regression. Variables were selected based on both statistical significance in univariate analysis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multicollinearity among independent variables was assessed using the Variance Inflation Factor (VIF), confirming that all included variables had VIF values below 5, indicating no significant collinearity. Additionally, interaction terms between key predictors (e.g., AKI severity, albumin, and age) were tested but were not statistically significant and were therefore excluded from the final model. Multiple imputation was performed to fill the missing laboratory data using Bayesian methods in SPSS. The predictive performance of the model was evaluated using the C-statistic and the area under the receiver operating characteristic (ROC) curve (AUC).\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e、\u003c/strong\u003e\u003cstrong\u003eBaseline characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the ICD-9 diagnostic criteria, we identified a total of 7,359 patients diagnosed with sepsis among the admitted patients. To ensure the accuracy of the data and the validity of the analysis, we further meticulously screened these patients based on strict exclusion criteria, ultimately excluding 2,491 patients. After this series of screening processes, a total of 4,868 patients were included in our analysis. Among the 4,868 patients with AKI, there were 2,875 males (accounting for 61.4%) and 1,993 females (accounting for 38.6%), with a male-to-female ratio of 1.44:1. The average follow-up time was 26.84 \u0026plusmn; 5.86 days. There were 956 deaths after admission to the ICU. Figure 1 provides a detailed flowchart of the patient selection process.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e、\u003c/strong\u003e\u003cstrong\u003eComparison of Clinical Data Between the Death and Survival Groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 1, the proportions of the following variables are higher in the non-survivor group: age, continuous renal replacement therapy (CRRT), time to antibiotics administration post-admission (Antibiotics_to_admit(h)), time to antibiotics administration post-ICU admission (Antibiotics_to_icu), aspartate aminotransferase (AST), alanine aminotransferase (ALT), total bilirubin, blood urea nitrogen (BUN), creatinine, heart rate (beats per minute, bpm), white blood cell (WBC) count, neutrophil count, monocyte count, mean corpuscular volume (MCV), red cell distribution width (RDW), international normalized ratio (INR), prothrombin time (PT), partial thromboplastin time (PTT), glucose (measured in mmol/L), magnesium (Mg), phosphorus (P), potassium, Sequential Organ Failure Assessment (SOFA) score, duration of dopamine administration (Dopamine time, in minutes), amount of dopamine administered (Dopamine amount, in mg), duration of norepinephrine administration (Norepinephrine time, in minutes), and amount of norepinephrine administered (Norepinephrine amount, in mg). In contrast, the proportions of body weight (Weight, in kg), cerebrovascular disease, mild liver disease, myocardial infarction, peripheral vascular disease, congestive heart failure, malignant cancer, acute respiratory distress syndrome (ARDS, with a sample size of N=694), peritonitis, pneumonia, mechanical ventilation, albumin (measured in g/L), urine output rate within 6 hours (UO rate 6hours), systolic blood pressure (SBP, in mmHg), mean blood pressure (MBP, in mmHg), diastolic blood pressure (DBP, in mmHg), lymphocyte count, basophil count, eosinophil count, red blood cell (RBC) count, platelet (PLT) count, pH, partial pressure of oxygen (PO2), Charlson comorbidity index, worsening of acute kidney injury (Worsen aki), and the use of dobutamine, dopamine, and norepinephrine, as well as the duration and amount of dobutamine administered (Dobutamine time, Dobutamine amount, in mg), are lower in the non-survivor group (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eTable 1 Data comparison between the deceased and surviving groups of patients with Acute Kidney Injury (AKI）\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"591\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSurvival Group\u003c/p\u003e\n \u003cp\u003e(N=3912)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSurvival Group\u003c/p\u003e\n \u003cp\u003eCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDeath Group (N=956)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDeath Group CV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003et/\u0026chi;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBasic characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale/female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2335/1577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e540/416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge (year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61.56\u0026plusmn;14.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65.74\u0026plusmn;13.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e85.91\u0026plusmn;24.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e83.58\u0026plusmn;28.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eComplications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAIDS (N=65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57(87.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8(12.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCerebrovascular Disease (N=634)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e478(75.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e156(24.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDementia (N=111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82(73.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29(26.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes with Coma (N=561)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e456(81.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e105(18.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes without Coma (N=1268)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1027(81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e241(19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMild Liver Disease (N=1036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e754(72.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e282(27.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMyocardial Infarct (N=910)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e698(76.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e212(23.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePeptic Ulcer Disease (N=152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e120(78.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32(21.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePeripheral Vascular Disease (N=569)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e424(74.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e145(25.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCOPD (N=1397)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1108(79.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e289(20.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCongestive Heart Failure (N=1624)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1260(77.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e364(22.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMalignant Cancer (N=633)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e437(69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e196(31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRheumatic Disease (N=184)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e151(82.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33(17.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eParaplegia (N=230)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e180(78.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50(21.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eARDS (N=694)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e459(66.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e235(33.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e105.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePeritonitis (N=57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39(68.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18(31.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePneumonia (N=1148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e857(74.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e291(25.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUrinary Infection (N=752)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e605(80.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e147(19.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMechanical Ventilation (N=2881)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2230(77.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e651(22.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCRRT (N=501)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e247(49.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e254(50.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e341.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003euse of antibiotics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAntibiotics_to_admit (h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34(13,109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64(15,181)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAntibiotics_to culture (h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.8(10.8,50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.4(10,55.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAntibiotics_to_icu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.7(5.2,66.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33.8(8,97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-6.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAntibiotics to suspect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.8(10.8,50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.4(10,55.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood and Biochemical Indicators\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAlbumin (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.88\u0026plusmn;6.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.63\u0026plusmn;6.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35(22,66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63(29,195)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-13.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ealt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26(16,54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38(19,128)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-9.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBilirubin total\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.26(6.84,20.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.81(6.84,63.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-11.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ebun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20(13,33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33(20,55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-15.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ecreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e79.56(61.88,132.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e132.6(79.56,221)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-12.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUO rate 6 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.54(0.4,2.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.41(0.2,0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-15.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSBP（mmHg）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e147.08\u0026plusmn;23.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e146.04\u0026plusmn;25.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.2963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76.02\u0026plusmn;18.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e74.22\u0026plusmn;19.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.01\u0026plusmn;11.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43.08\u0026plusmn;12.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHeart rate (bpm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e88.69\u0026plusmn;16.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e92.51\u0026plusmn;18.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-6.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10(7.2,13.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.7(8.8, 18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-12.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNeutrophils count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.08(3.07,11.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.95(4.09,11.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLymphocytes count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.14(0.73,1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.91(0.56,1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-8.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMonocytes count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.43(0.18,0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5(0.19,0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBasophils count\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02(0,0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0(0,0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEosinophils count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.05(0,0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01(0,0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-8.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.33\u0026plusmn;0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.19\u0026plusmn;0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e91.04\u0026plusmn;7.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e92.84\u0026plusmn;8.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-5.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.66\u0026plusmn;2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.22\u0026plusmn;3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-16.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e226.50\u0026plusmn;145.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e180.11\u0026plusmn;130.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.43\u0026plusmn;0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.83\u0026plusmn;1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-15.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.57\u0026plusmn;6.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.83\u0026plusmn;10.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-15.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36.28\u0026plusmn;18.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45.78\u0026plusmn;25.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-13.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.39\u0026plusmn;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.35\u0026plusmn;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e95(62,146.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e88.50(63,124)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003epCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.50\u0026plusmn;10.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40.84\u0026plusmn;12.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGlucose (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.84\u0026plusmn;3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.40\u0026plusmn;4.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.07\u0026plusmn;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.14\u0026plusmn;0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-5.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.54\u0026plusmn;1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.14\u0026plusmn;1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-11.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003epotassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.09\u0026plusmn;0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.25\u0026plusmn;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-6.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003esodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e138.72\u0026plusmn;5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e138.71\u0026plusmn;6.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003echarlson_comorbidity_index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6(3,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5(3,7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-13.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003esofa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2(0,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3(1,6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-8.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWorsen aki (N=901)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e667(74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e234(N=26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVasoactive Drug Administration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDobutamine (N=203)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e114(56.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e89(43.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDopamine (N=293)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e184(62.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e109(37.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNorepinephrine (N=2116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1394(65.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e722(34.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e497.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug Dosage and Timing\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDobutamine time (minute)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2687.5(696,5684.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1744(283,6972)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDobutamine amount (mg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e731.9(206.6,2660.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e649.9(86.7,2483.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDopamine time (minute)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e533(151,2396,8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e654(97.5,2255)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDopamine amount (mg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e289.3(70.9,1129.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e400(55.6,1210.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.899\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNorepinephrine time (minute)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1895.5(660.4114.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3750(1661.5,7359.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNorepinephrine amount (mg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.4(3.8,37.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45.7(17,95.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-16.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Bold numbers indicate significant P-values.\u003c/p\u003e\n\u003cp\u003eAbbreviation: AIDS, Acquired Immunodeficiency Syndrome; COPD, Chronic Obstructive Pulmonary Disease; ARDS, Acute Respiratory Distress Syndrome; Antibiotcs to admit(h), Time to Antibiotic Administration after Admission (hours); Antibiotics to culture(h), Time to Antibiotic Administration after Culture (hours); Antibiotics to icu, Time to Antibiotic Administration after ICU Admission; Antibiotics to suspect, Time to Antibiotic Administration after Suspicion of Infection; Blood and Biochemical Indicators, Hematological and Biochemical Parameters; Albumin, Serum Albumin; AST, Aspartate Transaminase; ALT, Alanine Transaminase; BUN, Blood Urea Nitrogen; SBP, Systolic Blood Pressure; MBP, Mean Blood Pressure; DBP, Diastolic Blood Pressure; WBC, White Blood Cell Countz; RBC, Red Blood Cell Count; MCV, Mean Corpuscular Volume; RDW, Red Cell Distribution Width; PLT, Platelet Count; INR, International Normalized Ratio; PT, Prothrombin Time; PTT, Partial Thromboplastin Time; PO2, Partial Pressure of Oxygen; pCO2, Partial Pressure of Carbon Dioxide; mg, Magnesium; p, Phosphate; sofa, Sequential Organ Failure Assessment (SOFA) Score\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e、\u003c/strong\u003e\u003cstrong\u003eFactors Influencing All-Cause Death and Other Adverse Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter comparing the clinical data between the mortality and survival groups, Table 1 presents the variables with a P-value of less than 0.05, which were included in the Univariate Cox regression analysis. The results indicate that age, AKI severity, albumin, delayed antibiotic use, AST, total bilirubin, blood urea nitrogen (BUN), cerebrovascular disease, eosinophil count, heart rate at maximum diastolic pressure, hemoglobin, lactate, malignancy, MCHC, metastatic solid tumors, magnesium, pH, platelets, PTT, RDW, SOFA score, white blood cell count, body weight, diabetes, and AKI progression were identified as independent risk factors for 28-day all-cause mortality in AKI patients (P \u0026lt; 0.05, Table 2 ).\u003c/p\u003e\n\u003cp\u003eThrough Cox regression analysis, we identified several variables as significant predictors of mortality risk. Specifically, age was a significant risk factor (B = 0.015, P = 0.001), with each additional year increasing the mortality risk by 1.5% (HR = 1.015). This finding aligns with previous studies indicating that elderly patients have poorer prognoses in sepsis-associated acute kidney injury (SA-AKI). Similarly, the severity of AKI was also a critical predictor, particularly when AKI progressed to stage 2, where the mortality risk was 28.6% higher compared to patients without progression (B = 0.384, P \u0026lt; 0.001; HR = 1.470). These results highlight the strong association between AKI progression and mortality risk, emphasizing the necessity of early monitoring and intervention.\u003c/p\u003e\n\u003cp\u003eLow albumin levels (B = -0.503, P = 0.001) were identified as a strong predictor of mortality risk, suggesting that hypoalbuminemia may reflect malnutrition or a systemic inflammatory response, thereby exacerbating SA-AKI severity and increasing mortality risk (HR = 0.606). Additionally, the timeliness of antibiotic treatment was also a crucial factor influencing mortality risk. Delayed antibiotic administration significantly increased mortality risk (B = 0.001, P = 0.001), with each one-hour delay in antibiotic administration leading to a 0.1% increase in mortality risk. This finding underscores the importance of timely antibiotic use in infection control. Elevated total bilirubin levels (B = 0.053, P \u0026lt; 0.001; HR = 1.055) were associated with an increased risk of mortality, indicating that liver dysfunction plays a critical role in the mortality risk of SA-AKI patients. Conversely, higher albumin levels (B = -0.503, P = 0.001) were identified as a protective factor, suggesting that improving patients\u0026apos; nutritional status may help reduce mortality risk.\u003c/p\u003e\n\u003cp\u003eTable 2 presents the results of the univariate Cox regression analysis, further confirming the impact of these factors on 28-day all-cause mortality. Table3 displays the results of the\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCox regression analysis after adjusting for multiple variables, demonstrating that these factors remain significant predictors of mortality risk despite the influence of other variables.\u003c/p\u003e\n\u003cp\u003eIn summary, age, AKI severity, low albumin levels, delayed antibiotic administration, total bilirubin, BUN, and liver dysfunction are independent risk factors influencing 28-day all-cause mortality in SA-AKI patients.\u003c/p\u003e\n\u003cp\u003eTable 2 Univariate Cox Regression Analysis of Independent Risk Factors for 28-Day All-Cause Mortality\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWald\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLower Limit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUpper Limit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAki stage 2day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39.741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ealbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.811\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e345.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBilirubin total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e93.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ebun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e113.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCerebrovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEosinophils abs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.790\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHeart rate time dbp max\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003elac\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e412.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.219\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMalignant cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.957\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMCHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMetastatic solid tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e112.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.363\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.704\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eph\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e229.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eplatelet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e125.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eptt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e111.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003erdw\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSofa 24hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ewbc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWorsen aki\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eThe bold numbers represent the P values with significant differences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003e95% CI, 95% confidence interval; AKI, acute kidney injury; B, regression coefficient; BE, standard error; Albumin, Serum Albumin; AST, Aspartate Aminotransferase; Bun, Blood Urea Nitrogen; Heart rate time dbp max, Heart Rate at Maximum Diastolic Blood Pressurehemoglobin; lac, Lactate; MCHC, Mean Corpuscular Hemoglobin Concentration; mg ,Magnesium; ptt, Prothrombin Time ; Rdw, Red Cell Distribution Width; Sofa 24hours,Sequential Organ Failure Assessment (SOFA) Score at 24 Hours; Wbc, White Blood Cell (WBC) Count; \u0026nbsp;Wosen aki, Worsening Acute Kidney Injury\u003c/p\u003e\n\u003cp\u003eTable 3 Multivariate Cox Regression Analysis of Independent Risk Factors for 28-Day All-Cause Mortality\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWald\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLower Limit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUpper Limit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAki stage 2 day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.676\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ealbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.722\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBilirubin total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ebun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCerebrovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEosinophils abs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.893\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHeart rate time dbp max\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003elac\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.280\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMalignant cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.033\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMCHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.949\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMetastatic solid tumor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.872\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003emg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.919\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eph\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eplatelet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eptt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003erdw\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSofa 24 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.046\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ewbc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWosen aki\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.713\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eThe bold numbers represent the P values with significant differences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003e95% CI, 95% confidence interval; AKI, acute kidney injury; B, regression coefficient; BE, standard error; Albumin, Serum Albumin; AST, Aspartate Aminotransferase; Bun, Blood Urea Nitrogen; Heart rate time dbp max, Heart Rate at Maximum Diastolic Blood Pressurehemoglobin; lac, Lactate; , Mean Corpuscular Hemoglobin Concentration; mg ,Magnesium; ptt, Prothrombin Time ; Rdw, Red Cell Distribution Width; Sofa 24hours,Sequential Organ Failure Assessment (SOFA) Score at 24 Hours; Wbc, White Blood Cell (WBC) Count; \u0026nbsp;Wosen aki, Worsening Acute Kidney Injury\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e、\u003c/strong\u003e\u003cstrong\u003eROC Curve Analysis of Univariate and Combined Variables for Predicting Mortality Risk\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the univariate ROC curve analysis, the area under the curve (AUC) for several indicators demonstrated their effectiveness in predicting the risk of death. Notably, the AUC values for aki_stage_2day, aspartate aminotransferase (AST), total bilirubin (bilirubin_total), blood urea nitrogen (BUN), partial thromboplastin time (PTT), red cell distribution width (RDW), and white blood cell count (WBC) were relatively high (Figure 2), indicating that these indicators possess a good discriminatory power in predicting the risk of mortality.\u003c/p\u003e\n\u003cp\u003eThe area under the curve (AUC) for each variable is as follows: Age: AUC=0.573, Aki stage 2day: AUC = 0.665, Albumin: AUC = 0.358, Antibiotic to admit time: AUC = 0.560, AST: AUC = 0.650, Bilirubin total: AUC = 0.623, BUN: AUC = 0.674, Cerebrovascular disease: AUC = 0.514, Eosinophils abs: AUC = 0.398, Heart rate time at DBP max: AUC = 0.566, Lac: AUC = 0.665, Hemoglobin: AUC = 0.435, MCHC: AUC = 0.437, Metastatic solid tumor: AUC = 0.543, Mg: AUC = 0.546, Ph: AUC = 0.380, Platelet: AUC = 0.370, PTT: AUC = 0.658, RDW: AUC = 0.673, SOFA 24hours: AUC = 0.565, WBC: AUC = 0.606, Weight: AUC = 0.457, Diabetes: AUC = 0.491, Wosen aki: AUC = 0.528\u003c/p\u003e\n\u003cp\u003eMultivariate Cox regression analysis showed that when using combined indicators such as ALB (Albumin), AST (Aspartate Aminotransferase), BUN (Blood Urea Nitrogen), EOS (Absolute Eosinophil Count), LAC (Lactate), PH (pH value), PTT (Prothrombin Time), and RDW (Red Cell Distribution Width), the AUC value reached 0.796 (Figure 3), indicating that these combined indicators have a high accuracy in predicting the risk of mortality.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study offers a comprehensive examination of the factors influencing mortality risk in patients with SA-AKI using data from the MIMIC-III database. Our findings underscore the complex nature of managing SA-AKI and highlight critical predictors of mortality that can guide clinical practice.\u003c/p\u003e\n\u003cp\u003eUsing univariate and multivariate Cox regression analyses, we assessed the impact of these factors in both unadjusted and adjusted models. Our findings indicate that variables such as age, severity of AKI, albumin levels, timeliness of antibiotic use, total bilirubin, BUN, cerebrovascular disease, eosinophil count, heart rate variability at maximum diastolic pressure, and malignancy are independent risk factors for mortality. Consistent with existing studies, age and AKI progression were confirmed as significant predictors of mortality risk. In the multivariate analysis, after adjusting for potential confounders such as age and sex, the impact of these variables on mortality risk remained significant, highlighting their critical implications for clinical management.\u003c/p\u003e\n\u003cp\u003eSpecifically, age was identified as a significant risk factor, indicating that the risk of mortality increases significantly with advancing patient age. Advanced age emerged as a prominent risk factor, consistent with prior studies that identify age as a critical determinant in sepsis outcomes\u003csup\u003e17 20 21\u003c/sup\u003e. The severity of AKI, particularly when it progresses to stage 2, is closely associated with an increased risk of mortality. This finding underscores the importance of early diagnosis and intervention, especially in high-risk AKI patients\u003csup\u003e22 23\u003c/sup\u003e. Additionally, the presence of low albumin levels is recognized as a significant predictor of mortality. Low serum albumin levels indicated poor nutritional status and severe systemic inflammation\u003csup\u003e24 25\u003c/sup\u003e. Our analysis further demonstrated that delayed antibiotic use significantly increases the risk of mortality, highlighting the critical importance of timely antibiotic administration in controlling infections. Elevated AST and bilirubin levels were indicative of underlying liver dysfunction and systemic severity, contributing to higher mortality risk\u003csup\u003e26-28\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNotably, elevated total bilirubin was significantly associated with an increased risk of mortality in our analysis, indicating a close relationship between impaired liver function and the severity of systemic infection, which may exacerbate the condition of septic patients. Therefore, monitoring and managing liver function abnormalities are crucial for improving patient survival\u003csup\u003e29 30\u003c/sup\u003e. Additionally, the presence of cerebrovascular disease and malignancies was associated with increased mortality, highlighting the need for tailored care strategies for patients with complex medical histories\u003csup\u003e31\u003c/sup\u003e. Additionally, we identified novel influencing factors, such as eosinophil count and malignancy, both of which demonstrated strong predictive power in the multivariate regression analysis, providing new directions for future research. A decrease in eosinophil count was associated with a higher risk of mortality, suggesting potential immune dysfunction, particularly in septic patients. Malignancy, as a known comorbidity, may contribute to increased mortality risk through mechanisms related to immune suppression. Special attention should be given to patients with comorbid conditions like cerebrovascular diseases and malignancies, to develop effective care strategies that address their higher risk profiles\u003csup\u003e32 33\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eOur ROC curve analysis further supports the above conclusions. In the univariate analysis, several variables, such as age, AKI stage, AST, BUN, and total bilirubin, exhibited high AUC values, demonstrating their strong discriminatory ability in predicting mortality risk. In the multivariate Cox regression analysis, after incorporating multiple clinical indicators, including albumin, AST, BUN, eosinophils, lactate, pH, PTT, and RDW, the AUC value reached 0.796, indicating a high level of accuracy in predicting mortality risk using these combined indicators.\u003c/p\u003e\n\u003cp\u003eThese findings have significant implications for clinical practice.\u0026nbsp;The study results indicate that delayed antibiotic administration significantly increases the risk of mortality (OR = 1.001, 95% CI: 1.000-1.002). Therefore, clinical practice should adhere to the \"golden hour\" principle, ensuring that empirical antibiotic therapy is initiated as early as possible when infection is suspected to reduce mortality rates. Low serum albumin levels (OR = 0.606, 95% CI: 0.506-0.722) were identified as an independent risk factor for mortality. Hypoalbuminemia may reflect a systemic inflammatory response and malnutrition. Hence, albumin supplementation should be considered in SA-AKI patients with low albumin levels to improve fluid management and microcirculatory perfusion. Elevated AST (OR = 1.035, 95% CI: 1.027-1.083) and total bilirubin (OR = 1.055, 95% CI: 1.037-1.083) suggest that liver dysfunction is relatively common in SA-AKI patients and may be associated with systemic inflammatory response syndrome (SIRS). Clinically, liver function monitoring should be enhanced, and, if necessary, adjustments to antibiotic, sedative, or nephrotoxic drug dosages should be made to reduce the risk of liver damage. Abnormal magnesium levels (OR = 1.012, 95% CI: 1.002-1.919) and pH values (OR = 0.010, 95% CI: 0.010-0.116) were associated with increased mortality. Therefore, we recommend continuous monitoring of SA-AKI patients and proactive correction of electrolyte imbalances to reduce the incidence of arrhythmias and multiple organ dysfunction syndrome (MODS). Age (OR = 1.015, 95% CI: 1.006-1.024), AKI severity (OR = 1.470, 95% CI: 1.285-1.676), and malignancy (OR = 1.550, 95% CI: 1.031-2.095) were also identified as independent predictors of mortality. Based on these findings, we suggest incorporating these indicators into ICU risk assessment models to identify high-risk patients in advance and develop enhanced treatment strategies, such as close monitoring of fluid balance, early initiation of renal replacement therapy (RRT), or more aggressive anti-infection protocols.\u003c/p\u003e\n\u003cp\u003eDespite the strengths of this study, there are some limitations associated with the use of the MIMIC-III database that should be acknowledged. First, MIMIC-III is a single-center database, which may limit the generalizability of the findings to broader populations or healthcare settings. Second, the retrospective nature of the database makes it susceptible to information bias, missing data, and unmeasured confounders. Third, the database focuses primarily on ICU patients, which may not fully reflect the characteristics of sepsis or AKI cases in non-ICU settings. Lastly, certain relevant clinical variables—such as fluid management strategies, antimicrobial stewardship interventions, and pre-hospital care data—are either not available or not standardized in the database, which may influence outcome interpretation.\u003c/p\u003e\n\u003cp\u003eNonetheless, despite these limitations, our findings provide valuable insights for the early identification of high-risk SA-AKI patients and offer a foundation for optimizing clinical treatment strategies. In the future, these key predictive factors could be integrated into an artificial intelligence-driven clinical decision support system (CDSS) to assist ICU physicians in making more precise and individualized treatment decisions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we identified critical factors influencing mortality in patients with Sepsis-Induced Acute Kidney Injury (SA-AKI) using the MIMIC-III database. The primary findings of our research, including advanced age, severity of AKI, hypoalbuminemia, delayed antibiotic administration, and elevated AST and bilirubin levels, offer substantial insights into the clinical management of these patients.\u003c/p\u003e\n\u003cp\u003eOur findings have significant implications for clinical practice. The study highlights the importance of early and timely interventions, particularly regarding antibiotic administration, which is shown to reduce mortality risk. The relationship between hypoalbuminemia and increased mortality underscores the need for nutritional support and monitoring of serum albumin levels in critically ill patients. Moreover, the identification of AKI stage severity as a strong predictor of mortality emphasizes the need for early detection and intervention in patients showing signs of progression. These insights could inform more personalized care strategies, helping clinicians prioritize high-risk patients and adjust therapeutic approaches accordingly. Additionally, our findings suggest that liver dysfunction, indicated by elevated AST and bilirubin levels, plays a critical role in mortality among SA-AKI patients. This highlights the necessity for clinicians to monitor liver function closely, especially in critically ill patients with multiple organ dysfunctions.\u003c/p\u003e\n\u003cp\u003eThis study reinforces the multifactorial nature of SA-AKI and mortality. While existing literature has identified certain risk factors, the integration of these variables into predictive models with strong discriminative ability (AUC = 0.796) offers a promising approach for future clinical trials and decision-making support tools. By recognizing the combined predictive value of variables such as albumin, AST, and BUN, future studies can refine these findings further, possibly incorporating novel biomarkers for more accurate risk prediction.\u003c/p\u003e\n\u003cp\u003eDespite these contributions, there are several limitations in this study. Firstly, the retrospective design limits our ability to establish causal relationships. Furthermore, the use of a single-center dataset may reduce the generalizability of the findings, as patients in different settings or regions may present with distinct characteristics. Additionally, the study's reliance on the MIMIC-III database, while comprehensive, may not capture all variables relevant to patient outcomes, such as those related to patient care practices outside the ICU.\u003c/p\u003e\n\u003cp\u003eFuture research should focus on validating these findings in multi-center and prospective studies to improve generalizability. Moreover, incorporating more granular data on treatment protocols, such as the use of novel sepsis therapies or advanced monitoring techniques, could provide further insights into the management of SA-AKI. It would also be valuable to explore the role of immune responses in SA-AKI progression and mortality, particularly in relation to eosinophil counts and other immune markers identified in our study. Additionally, future studies should examine the potential for machine learning algorithms to incorporate these predictors into real-time clinical decision support systems, thereby optimizing patient care.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFootnotes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContributors: Chongyang Ye and Tianjun Yang designed the study. Chunyan Zhu ,Chongyang Ye and Shijing Hu wrote the manuscript. Chongyang Ye and Tianjun Yang revised the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental data used to support the findings of this study are publicly available from the MIMIC-III database (https://mimic.physionet.org/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared that they have no conflicts of interest regarding this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work was not funded by any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatient consent for publication:\u0026nbsp;Not required.\u003c/p\u003e\n\u003cp\u003eEthics approval:\u0026nbsp;The data featured in this study were sourced from the MIMIC-III database, which is publicly accessible online. Before their involvement in the research, all participants had provided their consent in written form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to everyone who participated in or contributed to this research endeavor.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\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\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCecconi M, Evans L, Levy M, et al. Sepsis and septic shock. Lancet 2018;392(10141):75\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaukonen KM, Bailey M, Pilcher D, et al. Systemic inflammatory response syndrome criteria in defining severe sepsis. 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Acute kidney injury in septic shock: clinical outcomes and impact of duration of hypotension prior to initiation of antimicrobial therapy. Intensive Care Med 2009;35(5):871\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThongprayoon C, Cheungpasitporn W, Radhakrishnan Y, et al. Impact of hypoalbuminemia on mortality in critically ill patients requiring continuous renal replacement therapy. J Crit Care 2022;68:72\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLegrand M, Bagshaw SM, Bhatraju PK, et al. Sepsis-associated acute kidney injury: recent advances in enrichment strategies, sub-phenotyping and clinical trials. Crit Care 2024;28(1):92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZarbock A, Nadim MK, Pickkers P, et al. Sepsis-associated acute kidney injury: consensus report of the 28th Acute Disease Quality Initiative workgroup. Nat Rev Nephrol 2023;19(6):401\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z, Chen L, Liu H, et al. Gene signature for the prediction of the trajectories of sepsis-induced acute kidney injury. Crit Care 2022;26(1):398.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang S, Su T, Huang L, et al. A novel risk-predicted nomogram for sepsis associated-acute kidney injury among critically ill patients. BMC Nephrol 2021;22(1):173.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeligdowicz A, Matthay MA. Heterogeneity in sepsis: new biological evidence with clinical applications. Crit Care 2019;23(1):80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChawla LS, Bellomo R, Bihorac A, et al. Acute kidney disease and renal recovery: consensus report of the Acute Disease Quality Initiative (ADQI) 16 Workgroup. Nat Rev Nephrol 2017;13(4):241\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen JJ, Kuo G, Hung CC, et al. Risk factors and prognosis assessment for acute kidney injury: The 2020 consensus of the Taiwan AKI Task Force. J Formos Med Assoc 2021;120(7):1424\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbebe A, Kumela K, Belay M, et al. Mortality and predictors of acute kidney injury in adults: a hospital-based prospective observational study. Sci Rep 2021;11(1):15672.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang H, Bai X, Ji F, et al. Early-Phase Urine Output and Severe-Stage Progression of Oliguric Acute Kidney Injury in Critical Care. Front Med (Lausanne) 2021;8:711717.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoyner JL, Mackey RH, Rosenthal NA, et al. Clinical Outcomes of Persistent Severe Acute Kidney Injury among Patients with Kidney Disease Improving Global Outcomes Stage 2 or 3 Acute Kidney Injury. Am J Nephrol 2022;53(11\u0026ndash;12):816\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Chu H, Zhou H. Association between hypoalbuminemia and mortality in patients undergoing continuous renal replacement therapy: A systematic review and meta-analysis. PLoS One 2023;18(3):e0283623.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlves FC, Sun J, Qureshi AR, et al. The higher mortality associated with low serum albumin is dependent on systemic inflammation in end-stage kidney disease. PLoS One 2018;13(1):e0190410.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrabherr F, Grander C, Effenberger M, et al. MAFLD: what 2 years of the redefinition of fatty liver disease has taught us. Ther Adv Endocrinol Metab 2022;13:20420188221139101.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalas MA, Chavez L, Leon M, et al. Abnormal liver enzymes: A review for clinicians. World J Hepatol 2021;13(11):1688\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma P. Value of Liver Function Tests in Cirrhosis. J Clin Exp Hepatol 2022;12(3):948\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLan Q, Zheng L, Zhou X, et al. The Value of Blood Urea Nitrogen in the Prediction of Risks of Cardiovascular Disease in an Older Population. Front Cardiovasc Med 2021;8:614117.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu J, Zhang W, Niu J, et al. Association between blood urea nitrogen levels and the risk of diabetes mellitus in Chinese adults: secondary analysis based on a multicenter, retrospective cohort study. Front Endocrinol (Lausanne) 2024;15:1282015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDias RA, Dias L, Azevedo E, et al. Acute Inflammation in Cerebrovascular Disease: A Critical Reappraisal with Focus on Human Studies. Life (Basel) 2021;11(10).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilcox NS, Amit U, Reibel JB, et al. Cardiovascular disease and cancer: shared risk factors and mechanisms. Nat Rev Cardiol 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCacho-Diaz B, Lorenzana-Mendoza NA, Spinola-Marono H, et al. Comorbidities, Clinical Features, and Prognostic Implications of Cancer Patients with Cerebrovascular Disease. J Stroke Cerebrovasc Dis 2018;27(2):365\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"clinical-and-experimental-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clem","sideBox":"Learn more about [Clinical and Experimental Medicine](https://www.springer.com/journal/10238)","snPcode":"10238","submissionUrl":"https://submission.nature.com/new-submission/10238/3","title":"Clinical and Experimental Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Sepsis, Acute Kidney Injury, Mortality Risk, MIMIC Database, Predictive Factors","lastPublishedDoi":"10.21203/rs.3.rs-5832340/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5832340/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Sepsis-induced acute kidney injury (SA-AKI) significantly increases mortality and healthcare burdens. Identifying key mortality risk factors is crucial for improving patient outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives:\u003c/strong\u003e This study aims to identify the primary factors affecting mortality in SA-AKI patients using the MIMIC-III database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A retrospective analysis was conducted on 4,868 SA-AKI patients from the MIMIC-III database. Clinical data from the first 24 hours of ICU admission were analyzed using logistic regression to identify mortality predictors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Key mortality predictors included advanced age (OR = 1.015, 95% CI: 1.006-1.024), severe AKI stages (OR = 1.470, 95% CI: 1.285-1.676), low serum albumin (OR = 0.606, 95% CI: 0.506-0.722), delayed antibiotics (OR = 1.001, 95% CI: 1.000-1.002), high AST (OR = 1.035, 95% CI: 1.027-1.083) and bilirubin (OR = 1.055, 95% CI: 1.037-1.083). The area under the curve (AUC) of the combined predictors for mortality risk was 0.796, indicating high predictive accuracy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Early intervention and monitoring of identified risk factors such as age, AKI stage, albumin levels, and antibiotic timeliness can enhance survival rates in SA-AKI patients.\u003c/p\u003e","manuscriptTitle":"A Study on the Factors Influencing Mortality Risk in Sepsis-Induced Acute Kidney Injury Based on Analysis of the MIMIC Database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-03 08:13:35","doi":"10.21203/rs.3.rs-5832340/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-04-12T10:24:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-12T08:27:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"81306085557949895833132804239004806410","date":"2025-04-01T11:23:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-01T10:51:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-01T02:30:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clinical and Experimental Medicine","date":"2025-03-29T04:55:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clinical-and-experimental-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clem","sideBox":"Learn more about [Clinical and Experimental Medicine](https://www.springer.com/journal/10238)","snPcode":"10238","submissionUrl":"https://submission.nature.com/new-submission/10238/3","title":"Clinical and Experimental Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6cd3b395-74d1-441b-a59d-31e93ace12b3","owner":[],"postedDate":"April 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T15:58:14+00:00","versionOfRecord":{"articleIdentity":"rs-5832340","link":"https://doi.org/10.1007/s10238-025-01681-4","journal":{"identity":"clinical-and-experimental-medicine","isVorOnly":false,"title":"Clinical and Experimental Medicine"},"publishedOn":"2025-06-07 15:56:50","publishedOnDateReadable":"June 7th, 2025"},"versionCreatedAt":"2025-04-03 08:13:35","video":"","vorDoi":"10.1007/s10238-025-01681-4","vorDoiUrl":"https://doi.org/10.1007/s10238-025-01681-4","workflowStages":[]},"version":"v1","identity":"rs-5832340","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5832340","identity":"rs-5832340","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}