Non-linear dose-response relationship between serum albumin and acute kidney injury in sepsis patients: a cohort study

Non-linear dose-response relationship between serum albumin and acute kidney injury in sepsis patients: a cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Non-linear dose-response relationship between serum albumin and acute kidney injury in sepsis patients: a cohort study Xiaomin Liang, Haofei Hu, Xinglin Chen, Yan Zhou, Guiyun Li, Sha Wen, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4341318/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The role of serum albumin on acute kidney injury (AKI) remains controversial. Additionally, research on this relationship in sepsis patients is sparse. Therefore, this research aimed to investigate the relationship between serum albumin level and probability of AKI in patients with sepsis. Methods This study was a retrospective cohort analysis of sepsis patients across the United States between 2014 and 2015 in the eICU Collaborative Research Database (eICU-CRD). To estimate the role of albumin on AKI by univariate, multivariate logistic regression and smooth curve fitting analysis. Results Among 5459 patients with sepsis, 32.94% (1798 patients) developed AKI. The results of the multivariate logistic regression analysis indicated that the albumin and AKI were negatively correlated (adjusted OR = 0.87, 95% CI 0.77–0.98, P = 0.0199). Moreover, a nonlinear relationship was observed between albumin level and probability of AKI with a inflection point at 2.1 g/dL. For albumin level < 2.1g/dL, each unit increase in serum albumin reduced the probability of AKI by 39% (adjusted OR = 0.61; 95% CI 0.44–0.85; P = 0.0032). However, for albumin levels above 2.1 g/dL, there was no significant association with the probability of AKI (adjusted OR = 0.99, 95% CI 0.83–1.16; P = 0.8691). Conclusion Serum albumin level below 2.1g/dL was defined as a risk factor for AKI in sepsis patients. Albumin Acute kidney injury Sepsis Non-linear eICU-CRD Figures Figure 1 Figure 2 Introduction Sepsis, a potentially fatal organ failure syndrome, is closely linked to an uncontrolled host reaction [ 1 ]. Acute kidney injury (AKI), which affects 66% of extremely sick patients and is on the rise, occurs in about 20% of non-serious septic patients[ 2 ].AKI, a prevalent and dangerous consequence in sepsis patients, is marked by inflammation within the kidney and throughout the body[ 3 ].Sepsis patients experience reduced renal microvascular function, which can lead to acute necrosis of tubular, subsequent death of tubular epithelial cell, or decreased renal blood flow[ 4 ].The elevated mortality and morbidity of AKI are associated with adverse outcomes such as chronic kidney disease (CKD) and end-stage renal disease (ESRD) [ 5 , 6 ].Hence, it’s critical to determine the causes of AKI in those suffering from sepsis. Previous studies have shown that the AKI incidence in sepsis is increased by variables such as age, serum albumin, mechanical ventilation, hypotension, diabetes, liver disease and high serum creatinine (Scr)[ 7 , 8 ].Albumin, a crucial protein produced by the liver, performs several key roles such as regulating osmotic pressure, carrying insoluble molecules, and providing anti-inflammatory and antioxidant properties [ 9 – 11 ].Research has shown a connection between albumin and AKI in several clinical uses like liver transplants, coronary bypass procedure, critical care facilities and hospitalized individuals[ 12 – 16 ].Nevertheless, no research has been done particularly on people with sepsis. Furthermore, there is debate and complexity around the relationship among albumin and AKI. Most studies investigating the link between serum albumin level and AKI risk have found an inverse relationship. Conversely, two other studies [ 17 , 18 ] have indicated that high albumin level increased the risk of AKI. Therefore, we postulated that low albumin level may be an indicator of risk for AKI in people with sepsis, but the association might not be linear. Using information from the eICU Collaborative Research Database (eICU-CRD) throughout 2014 and 2015, we planned to perform a cohort study in order to examine this hypothesis and make clear the the link among albumin level and probability of AKI in sepsis patients. Methods Data source The eICU-CRD [ 19 ] provided the data between 2014 and 2015 for this retrospective analysis. The eICU program from Philips Healthcare automatically saved and obtained data from an eICU database that included over 200,000 admissions across different facilities in the United States [ 19 ]. We were granted access to this database, completed the test, and received certification from the PhysioNet Review Board (record ID: 40859994). Study population Sepsis-diagnosed ICU participants were involved in the research upon admission. The possible or verified infection and a score increase of more than two points on the Sequential Organ Failure Assessment (SOFA) [ 1 ], as documented in the Acute Physiology and Chronic Health Evaluation (APACHE) IV dataset [ 20 ], were considered indicators of sepsis. Applying the International Classification of Diseases, 9th Edition (ICD-9) code, infections were detected by the eICU-CRD. ESRD is a final stage of CKD, diagnosed when renal replacement therapy (RRT) is needed for over 3 months, with a glomerular filtration rate (GFR) less than 15 mL/min [ 21 ]. ESRD in our study was verified by ICD-9 code. The criteria for exclusion were as follows:(1) Not a first-time admission to ICU; (2) ICU stay of shorter than 48 hours; (3) Age under 18 years; (4) Diagnosed as ESRD; (5) missing albumin level or system error; (6) missing AKI outcome. The research flowchart was referred to Fig. 1 . Variables The eICU database contained population data, bedside monitor readings, diagnoses using ICD-9 codes and lab data from regular medical treatment. Data of the participants in the eICU-CRD were collected within initial 24 hours after ICU admission. The apacheApsVar table was used to extract physiological information such as respiration rate, heart rate, mean arterial pressure (MAP)and temperature. Treatment details on vasopressor, mechanical ventilation, and RRT were also contained in the same table. Basic information was collected from the patient or apachePatientResult charts, including age, ethnicity, gender, height and weight. Weight divided by height squared (kg/m 2 ) was used to compute BMI. The lab recorded levels of white blood cell (WBC), platelet, hemoglobin, total bilirubin (TBIL), aspartate transaminase (AST), alanine transaminase (ALT), blood urea nitrogen (BUN), albumin, serum creatinine (Scr), potassium, sodium, and lactate. Comorbidities like congestive heart failure (CHF), hypertension, chronic obstructive pulmonary disease (COPD), acute myocardial infarction (AMI), diabetes, hepatic failure, cirrhosis, and cancer were taken from the admissionDx table. Admission severity was evaluated using Glasgow Coma Scale ( GCS ) score, Acute Physiology Score III, SOFA score and Apache IV score. Outcome The study’s outcome was the likelihood of AKI following ICU admission. AKI was defined as a increase in Scr of 0.3 mg/dL during 48 hours or at least 1.5 times the Scr baseline level during 7 days after entering the ICU, in accordance with kidney disease: Improving Global Outcomes (KDIGO) standards [ 22 ]. Missing data Among the 5459 patients in our study, The number of covariate missing values were 36 (0.66%) for ethnicity, 1 (0.02%) for gender, 103 (1.89%) for BMI, 264 (4.84%) for temperature,2 (0.04%) for heart rate, 20 (0.37%) for respiratory rate, 7 (0.13%) for MAP, 143 (2.62%) for WBC, 133(2.44%) for hemoglobin, 160 (2.93%) for platelet, 95 (1.74%) for ALT, 66 (1.21%) for AST, 569 (10.42%) for TBIL, 25 (0.46%) for BUN, 17 (0.31%) for Scr, 19 (0.35%) for potassium,19 (0.35%) for sodium, 1370 (25.10%) for lactate, 2 (0.04%) for SOFA score, 80 (1.47%) for GCS score, 595 (10.90%) for Acute Physiology Score III, 19(0.35%) for vasopressor, 595 (10.90%) for Apache IV score. Dummy variables indicated missing covariate values when continuous variables had over 1% missing values. Statistical analysis For continuous variables, the standard deviation (SD) or median with interquartile range (IQR) are displayed, whereas counts and percentages are used for categorical data. For categorical variables, chi-squared tests were performed to examine differences in albumin quartiles. For continuous variables, the study employed one-way analysis of variance (ANOVA). The relationship between albumin and probability of AKI was investigated by both univariate and multivariate logistic regression. Results were given as OR with their 95% CI. Confounders were chosen if their impact estimates changed by more than 10% or if they showed a link with the outcomes [ 23 ]. After assessing clinical significance of included covariates, we established three models: (1) Crude model: include no variables. (2) Model I: adjust for demographics of BMI, age, ethnicity and gender. (3) Model II : adjust for BMI, age, ethnicity, gender, MAP, WBC, hemoglobin, platelet, BUN, Scr, potassium, sodium, site of infection, SOFA score, APACHE IV score, hypertension, AMI, CHF, COPD, diabetes, hepatic failure, cirrhosis, cancer, mechanical ventilation and RRT. Generalized additive model (GAM) was employed in smooth curve fitting to find any nonlinear correlations between blood albumin and AKI risk in sepsis patients. Bootstrap resampling, likelihood ratio tests, and piecewise regression were applied to examine the threshold effect between serum albumin and probability of AKI [ 24 ]. To verify the stability of the results, we completed sensitivity analysis. To investigate any unmeasured confounding between serum albumin and probability of AKI, we computed E-values [ 25 ]. The amount of an unmeasured confounder required to eliminate the link between serum albumin and probability of AKI was shown by the E-value. For statistical significance, a two-tailed p-value of less than 0.05 was judged appropriate. EmpowerStats ( www.empowerstats.com , X&Y solutions, Inc. Boston, MA) and R statistical software tools ( http://www.r-project.org , The R Foundation) were adopted for all data analyses. Results Baseline characteristics of participants Table 1 Baseline characteristics of participants Albumin group(g/dL) Q1(1.00–2.00) Q2(2.00-2.40) Q3(2.40–2.80) Q4(2.80–4.70) P value Participants (n) 1230 1369 1345 1515 Demographics Age (year) 63.74 ± 15.38 66.15 ± 15.23 66.78 ± 15.75 65.21 ± 16.40 < 0.001 Gender 0.354 Male 615 (50.00%) 688 (50.26%) 659 (49.00%) 715 (47.23%) Female 615 (50.00%) 681 (49.74%) 686 (51.00%) 799 (52.77%) Ethnicity < 0.001 Caucasian 907 (74.28%) 1080 (79.53%) 1091 (81.42%) 1210 (80.45%) African American 102 (8.35%) 98 (7.22%) 109 (8.13%) 128 (8.51%) Hispanic 70 (5.73%) 80 (5.89%) 70 (5.22%) 87 (5.78%) Asian 84 (6.88%) 61 (4.49%) 48 (3.58%) 37 (2.46%) Native American 25 (2.05%) 17 (1.25%) 7 (0.52%) 12 (0.80%) Other/Unknown 33 (2.70%) 22 (1.62%) 15 (1.12%) 30 (1.99%) BMI (kg/m 2 ) 27.40 ± 8.01 28.91 ± 9.29 29.52 ± 8.99 30.31 ± 9.37 < 0.001 Vital signs Temperature (°C) 36.41 ± 1.41 36.53 ± 1.52 36.62 ± 1.32 36.61 ± 1.27 < 0.001 Heart rate (bpm) 119.34 ± 27.62 114.57 ± 28.29 114.06 ± 28.81 112.03 ± 29.40 < 0.001 Respiratory rate (bpm) 30.46 ± 14.29 29.99 ± 14.46 30.61 ± 14.49 31.46 ± 14.71 0.049 MAP (mmHg) 53.00 (46.00–73.00) 55.00 (47.00-111.00) 54.00 (46.00-114.00) 60.00 (49.00-127.00) < 0.001 Laboratory data WBC ( K/uL) 14.73 (9.00-22.40) 13.70 (8.60–19.80) 13.70 (9.00-19.60) 13.05 (9.00-18.50) < 0.001 Hemoglobin (g/dL) 9.69 ± 2.04 10.14 ± 2.03 10.64 ± 2.10 11.14 ± 2.23 < 0.001 Platelet ( K/uL) 182.00 (106.00-279.50) 180.00 (116.00-254.50) 179.00 (124.50–244.00) 188.00 (132.00-250.00) 0.162 ALT(IU/L) 29.00 (18.00–52.00) 29.00 (18.00–54.00) 30.00 (18.00–56.00) 27.00 (17.00–50.00) 0.019 AST(IU/L) 39.00 (23.00–76.00) 35.00 (21.00-72.75) 35.00 (22.00–74.00) 32.00 (21.00–63.00) < 0.001 TBIL (mg/dL) 0.70 (0.40–1.50) 0.70 (0.40–1.30) 0.70 (0.40–1.30) 0.70 (0.40–1.20) 0.052 Albumin(g/dL) 1.64 ± 0.23 2.16 ± 0.11 2.54 ± 0.11 3.14 ± 0.34 < 0.001 BUN (mg/dL) 32.00 (18.00–54.00) 32.00 (19.00–51.00) 30.00 (19.00–48.00) 28.00 (17.00–45.00) < 0.001 Scr(mg/dL) 1.40 (0.84–2.45) 1.41 (0.91–2.32) 1.44 (0.91–2.37) 1.38 (0.90–2.30) 0.503 Potassium(mmol/L) 3.96 ± 0.79 4.06 ± 0.78 4.13 ± 0.77 4.23 ± 0.84 < 0.001 Sodium(mmol/L) 138.36 ± 7.07 138.61 ± 6.87 138.24 ± 6.73 137.78 ± 6.25 0.009 Lactate (mmol/L) 1.90 (1.20–3.20) 1.90 (1.20–3.10) 1.90 (1.20–3.10) 1.80 (1.20–2.90) 0.487 Site of infection <0.001 pulmonary 465 (37.80%) 513 (37.47%) 578 (42.97%) 692 (45.68%) renal/UTI (including bladder) 243 (19.76%) 317 (23.16%) 309 (22.97%) 306 (20.20%) GI 227 (18.46%) 212 (15.49%) 188 (13.98%) 138 (9.11%) unknown 112 (9.11%) 152 (11.10%) 113 (8.40%) 191 (12.61%) cutaneous/soft tissue 105 (8.54%) 99 (7.23%) 87 (6.47%) 103 (6.80%) other 73 (5.93%) 72 (5.26%) 68 (5.06%) 80 (5.28%) gynecologic 5 (0.41%) 4 (0.29%) 2 (0.15%) 5 (0.33%) Severity of illness SOFA score 4.00 (1.00–6.00) 4.00 (1.00–6.00) 3.00 (1.00–6.00) 3.00 (1.00–5.00) < 0.001 GCS score 12.00 ± 3.69 11.85 ± 3.70 11.96 ± 3.67 12.10 ± 3.62 0.361 Acute Physiology Score III 74.61 ± 23.22 68.76 ± 23.19 61.62 ± 22.56 57.61 ± 22.15 < 0.001 Apache IV score 87.86 ± 24.81 82.69 ± 24.17 75.73 ± 23.80 70.80 ± 23.80 < 0.001 Comorbidities Hypertension 72 (5.85%) 85 (6.21%) 86 (6.39%) 124 (8.18%) 0.059 AMI 37 (3.01%) 38 (2.78%) 60 (4.46%) 71 (4.69%) 0.012 CHF 66 (5.37%) 106 (7.74%) 134 (9.96%) 195 (12.87%) < 0.001 COPD 89 (7.24%) 99 (7.23%) 106 (7.88%) 166 (10.96%) < 0.001 Diabetes 164 (13.33%) 195 (14.24%) 151 (11.23%) 190 (12.54%) 0.115 Hepatic failure 46 (3.74%) 34 (2.48%) 38 (2.83%) 32 (2.11%) 0.066 Cirrhosis 70 (5.69%) 57 (4.16%) 39 (2.90%) 39 (2.57%) < 0.001 Cancer 48 (3.90%) 52 (3.80%) 22 (1.64%) 27 (1.78%) < 0.001 Treatment Mechanical ventilation 434 (35.28%) 511 (37.33%) 500 (37.17%) 533 (35.18%) 0.488 RRT 36 (2.93%) 29 (2.12%) 30 (2.23%) 37 (2.44%) 0.557 Vasopressor 16 (1.31%) 15 (1.10%) 19 (1.42%) 11 (0.73%) 0.312 AKI <0.001 No 754 (61.30%) 921 (67.28%) 925 (68.77%) 1061 (70.03%) Yes 476 (38.70%) 448 (32.72%) 420 (31.23%) 454 (29.97%) The data were presented as counts (%), median (IQR), or mean ± SD. The data analysis involving 5459 patients revealed an average age of 65.50 ± 15.76, with men constituting 49.05% (2677 patients). Table 1 provided a detailed analysis of various factors such as vital signs, demographics, laboratory data, illness severity, infection site, comorbidities, and treatment, segmented by albumin quartiles. It was observed that individuals in the top albumin quartile were comparatively a bit younger, had a increased BMI, better lab results, and lower disease severity scores than those in the lowest quartile. AKI incidence in our study was 32.94% (1798/5459). AKI incidence across albumin tertiles, from the lowest (1.00–2.00) to the highest (2.80–4.70), were 38.70% (476 cases), 32.72% (448 cases), 31.23% (420 cases), and 29.97% (454 cases) respectively. Univariate analysis Table 2 The results of univariate analysis of AKI Variable OR(95% CI) P value Age (years) 1.00 (0.99, 1.00) 0.0174 Gender Male 1.0 Female 1.09 (0.97, 1.22) 0.1520 BMI (kg/m2) 1.01 (1.01, 1.02) < 0.0001 Ethnicity Caucasian 1.0 African American 1.26 (1.03, 1.55) 0.0249 Hispanic 1.39 (1.10, 1.76) 0.0067 Asian 1.52 (1.16, 2.00) 0.0022 Native American 1.50 (0.90, 2.52) 0.1194 Other/Unknown 1.07 (0.70, 1.63) 0.7622 Temperature (°C) 0.98 (0.94, 1.02) 0.3602 Heart rate (bpm) 1.01 (1.00, 1.01) < 0.0001 Respiratory rate (bpm) 1.01 (1.01, 1.01) < 0.0001 MAP (mmHg) 1.00 (1.00, 1.00) 0.8686 WBC ( K/uL) 1.00 (1.00, 1.01) 0.6894 Hemoglobin (g/dL) 1.01 (0.98, 1.03) 0.6183 Platelet ( K/uL) 1.00 (1.00, 1.00) 0.0648 ALT(IU/L) 1.00 (1.00, 1.00) < 0.0001 AST(IU/L) 1.00 (1.00, 1.00) < 0.0001 TBIL (mg/dL) 1.09 (1.06, 1.12) < 0.0001 Albumin(g/dL) 0.80 (0.72, 0.88) < 0.0001 BUN (mg/dL) 1.00 (1.00, 1.00) 0.0595 Scr (mg/dL) 1.17 (1.13, 1.21) < 0.0001 Potassium(mmol/L) 1.17 (1.09, 1.25) < 0.0001 Sodium(mmol/L) 0.98 (0.97, 0.99) < 0.0001 Lactate (mmol/L) 1.17 (1.13, 1.20) < 0.0001 Source of infection pulmonary 1.0 renal/UTI (including bladder) 0.69 (0.59, 0.81) < 0.0001 GI 1.39 (1.17, 1.64) 0.0001 unknown 0.97 (0.80, 1.18) 0.7652 cutaneous/soft tissue 1.05 (0.84, 1.32) 0.6669 other 1.11 (0.86, 1.44) 0.4082 gynecologic 0.66 (0.21, 2.07) 0.4802 SOFA score 1.14 (1.11, 1.16) < 0.0001 GCS score 0.98 (0.97, 1.00) 0.0110 Acute Physiology Score III 1.02 (1.02, 1.02) < 0.0001 Apache IV score 1.02 (1.02, 1.02) < 0.0001 Hypertension 1.03 (0.82, 1.29) 0.8071 AMI 1.51 (1.14, 2.01) 0.0040 CHF 1.06 (0.87, 1.29) 0.5503 COPD 0.72 (0.58, 0.89) 0.0023 Diabetes 1.03 (0.87, 1.21) 0.7667 Hepatic failure 1.18 (0.85, 1.66) 0.3248 Cirrhosis 1.35 (1.01, 1.79) 0.0418 Cancer 0.91 (0.64, 1.29) 0.5869 Mechanical ventilation 1.30 (1.16, 1.46) < 0.0001 RRT 4.09 (2.84, 5.88) < 0.0001 Vasopressor 2.27 (1.37, 3.76) 0.0015 Univariate analysis results (Table 2 ) showed age, BMI, heart rate, respiratory rate, ALT, AST, TBIL, albumin, Scr, potassium, sodium, lactate, SOFA score, GCS score, Acute Physiology Score III, Apache IV score, AMI, COPD, cirrhosis, mechanical ventilation, RRT and vasopressor were correlated with AKI significantly. Multivariate logistic regression analysis Table 3 The results of multivariate logistic regression analysis Variable Crude model Model I Model II Albumin 0.80 (0.72, 0.88) < 0.0001 0.79 (0.71, 0.87) < 0.0001 0.87 (0.77, 0.98) 0.0199 Albumin quartile Q1 1.0 1.0 1.0 Q2 0.77 (0.66, 0.91) 0.0015 0.77 (0.65, 0.91) 0.0020 0.82 (0.67, 0.99) 0.0350 Q3 0.72 (0.61, 0.85) < 0.0001 0.72 (0.61, 0.85) 0.0001 0.80 (0.66, 0.98) 0.0290 Q4 0.68 (0.58, 0.79) < 0.0001 0.67 (0.57, 0.78) < 0.0001 0.80 (0.65, 0.97) 0.0262 P for trend < 0.0001 < 0.0001 0.0395 Data were shown as an OR (95% CI) P value. Crude model adjust for: none. Model I adjust for: gender, age, BMI, ethnicity. Model II adjust for: gender, age, BMI, ethnicity, MAP, platelet, WBC, hemoglobin, BUN, Scr, potassium, sodium, site of infection, SOFA score, APACHE IV score, hypertension, AMI, CHF, COPD, diabetes, hepatic failure, cirrhosis, cancer, mechanical ventilation and RRT. The crude model results indicated a negative correlation between albumin and AKI. In Model I, the probability of AKI reduced by 21% for each 1 g/dL rise in albumin. After adjusting for all covariates in Model II, each 1 g/dL albumin increase decreased AKI risk by 13% (OR = 0.87, 95% CI 0.77–0.98, P = 0.0199). We further investigated albumin’s nonlinearity with AKI by categorizing it into quartiles, revealing a significant trend (Table 3 ). Identification of nonlinear relationship Table 4 Threshold effect analysis of albumin and AKI Models OR (95%CI) P value Model I One line effect 0.87 (0.77, 0.98) 0.0199 Model II Turning point (K) 2.1 Albumin < K 0.61 (0.44, 0.85) 0.0032 Albumin ≥ K 0.99 (0.83, 1.16) 0.8691 P value for LRT test 0.025 95% CI for turning point 2, 2.1 Model I: linear analysis. Model II: non-linear analysis. Adjusted for gender, age, BMI, ethnicity, MAP, platelet, WBC, hemoglobin, BUN, Scr, potassium, sodium, site of infection, SOFA score, APACHE IV score, hypertension, AMI, CHF, COPD, diabetes, hepatic failure, cirrhosis, cancer, mechanical ventilation and RRT. Logarithmic likelihood ratio test (LRT); P < 0.05 denotes a significant difference between Model I and II. Between albumin and AKI, we discovered a nonlinear dose-response association (Fig. 2 and Table 4 ). For serum albumin below 2.1, each 1 g/dL increase in albumin decreased AKI risk, with an adjusted OR of 0.61 (95% CI 0.44–0.85, P = 0.0032). However, for albumin level of 2.1 or higher, the effect of albumin on the probability of AKI was not statistically significant. Each unit of 1 g/dL increase in albumin level decreased AKI risk, with an adjusted OR of 0.99 (95% CI 0.83–1.16, P = 0.8691) (Table 4 ). For sensitivity to unquantified confounding, an E-value was assessed. Main results remained solid unless an unquantified confounder exceeded OR 1.88. Discussion This retrospective cohort research used data from the eICU-CRD to examine 208 ICUs in the United States during 2014 and 2015. Sepsis patients with low albumin levels were shown to be more likely to develop AKI. The non-linear relationship between serum albumin level and AKI risk was the main finding. AKI risk was shown to be reduced by 39% for each 1.0 g/dL rise in albumin when it was less than 2.1 g/dL. As far as we are aware, this is the first study to show a link between albumin level and their chance of developing AKI in patients with sepsis. The incidence rate of AKI in sepsis was 32.94% (1798/5459) in our cohort, which was similar to the results of Payen [ 26 ], Uhel [ 27 ]and Case [ 28 ]. As reported by Payen, 3147 patients from 198 ICUs across 24 European nations participated in a multicenter cohort research and 36% of those patients had AKI [ 26 ]. An observational study by Uhel comprised 1545 sepsis patients in 2 ICUs of Netherlands and found AKI accounted for 37.7% of sepsis patients [ 27 ]. Case reported in a review article that in the ICU, the prevalence of AKI varied from 20–50%, particularly higher in those with sepsis [ 28 ]. However, compared to Bagshaw [ 29 ]and Shum [ 30 ], the incidence rate of AKI in our research was lower. Bagshaw conducted a retrospective cohort study involving 57 ICUs across Australia and found AKI accounted for 42.1% among 33,375 septic patients [ 29 ]. A study by Shum showed 54.7% patients in ICU developed AKI among 3,687 patients and the primary cause of AKI (49.2%) was sepsis [ 30 ]. We had a lower likelihood of AKI compared to their findings, this discrepancy could be ascribed to the differences of two populations and the criteria for AKI classification. Most research examining the link between serum albumin level and AKI risk have found that it was inverse. Wiedermann performed a meta-analyse involving 17 studies with 3,917 patients and revealed a positive relation between low albumin level and the onset of AKI [ 14 ]. Specifically, the likelihood of AKI raised by 134% for each 1g/dL drop in serum albumin [ 14 ]. Hansrivijit discovered that an increased incidence rate of AKI in hospitalized participants was related to each 1.0 g/dL drop in albumin, based on a meta article of 39 studies encompassing a cohort of 168,740 patients with AKI [ 15 ]. Sreenivasan found that in patients undergoing coronary angioplasty, albumin lower than 3.85 g/dL predicted the likelihood of Contrast-Induced AKI in a retrospective observational research comprising 1319 patients [ 16 ]. Lee demonstrated in an RCT including 220 patients that those blood albumin level increased to more than 40 g/L as a result of albumin replacement immediately before the procedure had a decreased risk of AKI following bypass surgery for coronary arteries [ 12 ]. In a retrospective analysis of 998 patients receiving liver transplant from donors, Sang found that hypoalbuminemia in the early postoperative period was a risk factor for the development of AKI [ 13 ]. However, two studies had identified elevated serum albumin level can result in AKI. A multi-center retrospective study including 7802 children in the PSICU revealed an association between albumin levels exceeding 40.41 g/L and AKI [ 17 ]. A correlation was found between an increased risk of AKI and a high admission albumin level over 4.5 mg/dL in a retrospective research conducted by Thongprayoon encompassing 9552 patients [ 18 ]. Multiple clinical investigations found that albumin was a protective factor against the probability of AKI in different types of clinical diseases. Although many studies revealed the role of serum albumin in AKI, only few studies addressed the application of both serum albumin level and AKI risk in this population of septic patients. On the other hand, albumin and AKI were controversially linked in previous studies. Based on above two reasons, we initiated the present research. We found in our study that the protective effect of albumin against AKI was not significant when serum albumin was at level greater than 2.1 g/dL. This result was different from Thongprayoon reported that an increased likelihood of AKI existed in both the low albumin group (serum albumin level 4.5 g/dL)[ 18 ].Reasons for the inconsistency between their conclusions and ours may include the following: (1) The people under study were not the same. Our study focused on individuals with sepsis, whereas theirs was centered on hospitalized patients. (2) To investigate the role of serum albumin and AKI, multiple study designs and analysis techniques were used. While our investigation found a non-linear link between the two, their evaluation of association was done using multivariable logistic regression analysis. (3) In contrast to our findings, their investigations did not take into account the impact of correcting variables for BMI, MAP, WBC, hemoglobin, platelet, BUN, potassium, sodium, site of infection, SOFA score, APACHE IV score, hepatic failure, cancer and RRT on the connection between serum albumin and AKI. However, earlier research had linked these variables to serum albumin or AKI exposure [ 7 , 8 , 31 – 34 ]. (4) In contrast to their study, which found 8% of persons with albumin of 4.5 g/dL, our investigation found just 0.13% (7/5459) of such individuals. The sample size of high albumin level in our study was much smaller than theirs. In addition, 624 individuals participated in Xu’s retrospective cohort research, which found a nonlinear correlation among serum albumin and AKI [ 35 ]. Their findings suggested that in patients undergoing aortic coarctation surgery, albumin below 32 g/L before surgery increased the probability of AKI [ 35 ]. This was consistent with our findings, which indicated that there was not a linear relationship between serum albumin levels and the probability of AKI. The impact of low albumin levels leading to high AKI risk is complex and can be attributed to the physiological roles of albumin. Hypoalbuminemia is determined by decreased albumin synthesis and increased degradation. Serum albumin is broken down at vascular endothelium and has a half-life of sixteen days [ 36 ]. During acute illnesses such as sepsis, albumin transitions between intercellular and extracellular fluids because of increased vascular permeability [ 36 ]. In addition, the glomerular perfusion pressure, which is the product of serum oncotic pressure and hydrostatic pressure, has a significant impact on the GFR [ 37 ]. Changes of above pressures may have an impact on the GFR. Furthermore, an excess of reactive oxygen/nitrogen species is produced in reaction to cytokines released during a systemic inflammatory response, further damaging the renal tubules and glomeruli [ 38 ]. These observations could potentially lead to the onset of AKI. Our results provide credence to the theory that the risk of AKI and serum albumin level are inversely correlated. Our research emphasizes how crucial it is for clinicians to keep an eye on serum albumin levels in sepsis patients. Particularly when serum albumin falls below 2.1g/dL, it’s important to proactively identify the underlying cause and promptly increase serum albumin level. This can significantly reduce the risk of AKI. There were several limitations in the current research. Observational studies often have unaccounted confounders. We used a sensitivity analysis of the E-value to assess its potential impact, and it seems unlikely that an unobserved confounder could explain the entire observed association. In spite of some constraints, such as incomplete data for specific variables, we employed modern methods to reduce bias. Furthermore, our study focused on ICU sepsis patients, limiting its extrapolation and generalizability. Exclusions like patients with less than 48-hour ICU stay and those with ESRD mean our findings don’t apply to these groups. Lastly, the quantity of albumin might vary, therefore basing conclusions on a particular time point could be problematic. Future studies could consider repeated measurements to deepen the understanding of how albumin levels affect AKI in sepsis patients. Conclusion A total of 5459 septic patients from the eICU-CRD were included in our study. Our study found that albumin levels in septic patients influenced the probability of AKI through a nonlinear dose-response relationship. Specifically, serum albumin below 2.1 g/dL increased AKI risk in septic patients. This correlation needs further validation in future studies. Abbreviations AKI: acute kidney injury; ICU: intensive care unit; eICU-CRD: eICU Collaborative Research Database;ICD-9: International Classification of Diseases, 9th Edition; BMI: body mass index; MAP: mean arterial pressure; WBC: white blood cell; ALT: alanine transaminase; AST: aspartate transaminase; TBIL: total bilirubin; BUN: blood urea nitrogen; Scr: Serum creatinine; UTI: urinary tract infection; GI: gastrointestinal; SOFA: Sequential Organ Failure Assessment; GCS: Glasgow Coma Scale; Apache IV: Acute Physiology and Chronic Health Evaluation IV; AMI: acute myocardial infarction; CHF: congestive heart failure; COPD :chronic obstructive pulmonary disease; RRT: renal replacement therapy; CKD: chronic kidney disease; ESRD: end-stage renal disease; KDIGO: kidney disease: Improving Global Outcomes; GFR: glomerular filtration rate; SD: standard deviation; IQR: interquartile range; OR: Odds ratio; CI: Confidence interval. Declarations Ethics approval and consent to participate The study was conducted using data from eICU-CRD. This database was approved for use by the Institutional Review Board of the Massachusetts Institute of Technology and the PhysioNet review committee, under the data usage agreement (record ID: 40859994). The data release adhered to the Health Insurance Portability and Accountability Act (HIPAA) safe harbor standards. As the analysis was retrospective and utilized an anonymous and publicly accessible database, the requirement for informed consent was waived. The study was conducted in accordance with the ethical standards laid out in the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials Data were available at https://eicu-crd.mit.edu/. Competing interests None. Funding This work was supported by Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties (No. SZGSP006), Shenzhen Second People’s Hospital Clinical Research Fund of Shenzhen High-level Hospital Construction Project (Grant No. 20223357008, 2023xgyj3357003), Sanming Project of Medicine in Shenzhen (No. SZSM202211016). Authors’ contributions X.M.L contributed to the idea, planning, gathering, processing, and writing of the study’s text. H.F.H, X.L.C, J.M, G.Y.L, S.W, L.Z and Y.Z assisted in analyzing data. S.Q.G and Y.L funded the study and supervised progress. The final version of the manuscript was reviewed and approved by every author. Acknowledgements We express gratitude to eICU data providers. References Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801–10. https://doi.org/10.1001/jama.2016 . 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Archives Med Sci – Civiliz Dis. 2020;5(1). https://doi.org/10.5114/amscd.2020.95224 . Zhang X, Fu J, Feng Z, Li Y, Zhang L, Zhou X et al. High Serum Albumin Levels were Associated with Acute Kidney Injury in Pediatric Surgical Intensive Care Units. Journal of Pediatric Surgery. 2024;59(4):621–626. Publisher: Elsevier. https://doi.org/10.1016/j.jpedsurg.2023.12.006 . Thongprayoon C, Cheungpasitporn W, Mao MA, Sakhuja A, Kashani K. U-shape association of serum albumin level and acute kidney injury risk in hospitalized patients. PLoS ONE. 2018;13(6):e0199153. https://doi.org/10.1371/journal.pone. 0199153 . Pollard TJ, Johnson AEW, Raffa JD, Celi LA, Mark RG, Badawi O. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Sci Data. 2018;5:180178. https://doi.org/10.1038/sdata.2018.178 . Zimmerman JE, Kramer AA, McNair DS, Malila FM. Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today’s critically ill patients. Crit Care Med. 2006;34(5):1297–310. https://doi.org/10.1097/01.CCM.0000215112.84523.F0 . Ver Halen N, Cukor D, Constantiner M, Kimmel PL. Depression and Mortality in End-Stage Renal Disease. Curr Psychiatry Rep. 2012;14(1):36–44. https://doi.org/10.1007/s11920-011-0248-5 . JALN K, Aspelin PBR. Kidney disease: improving global outcomes (KDIGO) acute kidney injury work group KDIGO clinical practice guideline for acute kidney injury. Kidney Int Supplements. 2012;2(1):1–138. https://doi.org/10.1038/kisup.2012.1 . Jaddoe VWV, De Jonge LL, Hofman A, Franco OH, Steegers EAP, Gaillard R. First trimester fetal growth restriction and cardiovascular risk factors in school age children: population based cohort study. BMJ. 2014;348(jan23 1):g14–14. https://doi.org/10.1136/bmj.g14 . Yu X, Chen J, Li Y, Liu H, Hou C, Zeng Q, et al. Threshold effects of moderately excessive fluoride exposure on children’s health: A potential association between dental fluorosis and loss of excellent intelligence. Environ Int. 2018;118:116–24. https://doi.org/10.1016/j.envint.2018.05.042 . Haneuse S, VanderWeele TJ, Arterburn D. Using the E-Value to Assess the Potential Effect of Unmeasured Confounding in Observational Studies. JAMA. 2019;321(6):602–3. https://doi.org/10.1001/jama.2018.21554 . Payen D, de Pont AC, Sakr Y, Spies C, Reinhart K, Vincent JL, et al. A positive fluid balance is associated with a worse outcome in patients with acute renal failure. Crit Care (London England). 2008;12(3):R74. https://doi.org/10.1186/cc6916 . Uhel F, Peters-Sengers H, Falahi F, Scicluna BP, van Vught LA, Bonten MJ, et al. Mortality and host response aberrations associated with transient and persistent acute kidney injury in critically ill patients with sepsis: a prospective cohort study. Intensive Care Med. 2020;46(8):1576–89. https://doi.org/10.1007/s00134-020-06119-x . Case J, Khan S, Khalid R, Khan A. Epidemiology of acute kidney injury in the intensive care unit. Crit Care Res Pract. 2013;2013:479730. https://doi.org/10.1155/2013/479730 . Bagshaw SM, George C, Bellomo R, ANZICS Database Management Committee. Early acute kidney injury and sepsis: a multicentre evaluation. Crit Care (London England). 2008;12(2):R47. https://doi.org/10.1186/cc6863 . Shum HP, Kong HHY, Chan KC, Yan WW, Chan TM. Septic acute kidney injury in critically ill patients - a single-center study on its incidence, clinical characteristics, and outcome predictors. Ren Fail. 2016;38(5):706–16. https://doi.org/10.3109/0886022x.2016.1157749 . Malhotra R, Kashani KB, Macedo E, Kim J, Bouchard J, Wynn S, et al. A risk prediction score for acute kidney injury in the intensive care unit. Nephrology, Dialysis, Transplantation: Official Publication of the European Dialysis and Transplant Association -. Eur Ren Association. 2017;32(5):814–22. https://doi.org/10.1093/ndt/gfx026 . Bobbili RK, Deepanjali S, Rajesh NG, Ramesh A, Medha R. Acute kidney injury in patients hospitalized with febrile urinary tract infections: A prospective observational study. Clin Nephrol. 2021;95(3):127–35. https://doi.org/10.5414/CN110229 . Wang H, Zhao Z, Tong Z. Analysis of Risk Factors For Sepsis And Indicators of Prognosis In Patients With Sepsis: A Retrospective Observational Study. https://doi.org/10.21203/rs.3.rs-577114/v1 . Schrock JW, Glasenapp M, Drogell K. Elevated blood urea nitrogen/creatinine ratio is associated with poor outcome in patients with ischemic stroke. Clin Neurol Neurosurg. 2012;114(7):881–4. https://doi.org/10.1016/j.clineuro.2012.01.031 . Xu S, Wu Z, Liu Y, Zhu J, Gong M, Sun L, et al. Influence of Preoperative Serum Albumin on Acute Kidney Injury after Aortic Surgery for Acute Type A Aortic Dissection: A Retrospective Cohort Study. J Clin Med. 2023;12(4):1581. https://doi.org/10.3390/jcm12041581 . Haller C. Hypoalbuminemia in renal failure: pathogenesis and therapeutic considerations. Kidney Blood Press Res. 2005;28(5–6):307–10. https://doi.org/10.1159/000090185 . Johnson RJ. Have we ignored the role of oncotic pressure in the pathogenesis of glomerulosclerosis? American Journal of Kidney Diseases. Official J Natl Kidney Foundation. 1997;29(1):147–52. https://doi.org/10.1016/s0272-6386(97)90022-6 . Mihai S, Codrici E, Popescu ID, Enciu AM, Albulescu L, Necula LG et al. Inflammation-related mechanisms in chronic kidney disease prediction, progression, and outcome. Journal of immunology research. 2018;2018. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4341318","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":300911714,"identity":"42dceefd-49d9-4eea-b754-1fa67ab56ad7","order_by":0,"name":"Xiaomin Liang","email":"","orcid":"","institution":"The First Affiliated Hospital of Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Xiaomin","middleName":"","lastName":"Liang","suffix":""},{"id":300911715,"identity":"28018633-2519-4351-938c-1a5a1e168915","order_by":1,"name":"Haofei Hu","email":"","orcid":"","institution":"The First Affiliated Hospital of Shenzhen 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08:23:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4341318/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4341318/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56548778,"identity":"a955000c-95b2-4630-b3fc-7f828845b317","added_by":"auto","created_at":"2024-05-15 15:42:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61326,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of study population\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4341318/v1/9e3690b0d9ee39d1ac6dd0a0.png"},{"id":56548780,"identity":"1ae36aa1-5b7a-4758-ab95-4d3710799d0d","added_by":"auto","created_at":"2024-05-15 15:42:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52911,"visible":true,"origin":"","legend":"\u003cp\u003eNonlinear relationship between the albumin and AKI. \u0026nbsp;The smooth curve fit between the variables is shown by the solid red line. The 95% CI from the fit is shown by the blue bands. Adjusted for gender, age, BMI, ethnicity, MAP, platelet, WBC, hemoglobin, BUN, Scr, potassium, sodium, site of infection, SOFA score, APACHE IV score, hypertension, AMI, CHF, COPD, diabetes, hepatic failure, cirrhosis, cancer, mechanical ventilation and RRT.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4341318/v1/390387865f8ff29ee027e248.png"},{"id":60835221,"identity":"d01f2e16-d6a7-4904-830a-3ae5bd1f7451","added_by":"auto","created_at":"2024-07-22 15:49:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":904096,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4341318/v1/07b7b003-779f-4479-84d2-66b242ea9721.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Non-linear dose-response relationship between serum albumin and acute kidney injury in sepsis patients: a cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSepsis, a potentially fatal organ failure syndrome, is closely linked to an uncontrolled host reaction [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Acute kidney injury (AKI), which affects 66% of extremely sick patients and is on the rise, occurs in about 20% of non-serious septic patients[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].AKI, a prevalent and dangerous consequence in sepsis patients, is marked by inflammation within the kidney and throughout the body[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].Sepsis patients experience reduced renal microvascular function, which can lead to acute necrosis of tubular, subsequent death of tubular epithelial cell, or decreased renal blood flow[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].The elevated mortality and morbidity of AKI are associated with adverse outcomes such as chronic kidney disease (CKD) and end-stage renal disease (ESRD) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].Hence, it\u0026rsquo;s critical to determine the causes of AKI in those suffering from sepsis.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that the AKI incidence in sepsis is increased by variables such as age, serum albumin, mechanical ventilation, hypotension, diabetes, liver disease and high serum creatinine (Scr)[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].Albumin, a crucial protein produced by the liver, performs several key roles such as regulating osmotic pressure, carrying insoluble molecules, and providing anti-inflammatory and antioxidant properties [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].Research has shown a connection between albumin and AKI in several clinical uses like liver transplants, coronary bypass procedure, critical care facilities and hospitalized individuals[\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].Nevertheless, no research has been done particularly on people with sepsis. Furthermore, there is debate and complexity around the relationship among albumin and AKI. Most studies investigating the link between serum albumin level and AKI risk have found an inverse relationship. Conversely, two other studies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] have indicated that high albumin level increased the risk of AKI. Therefore, we postulated that low albumin level may be an indicator of risk for AKI in people with sepsis, but the association might not be linear. Using information from the eICU Collaborative Research Database (eICU-CRD) throughout 2014 and 2015, we planned to perform a cohort study in order to examine this hypothesis and make clear the the link among albumin level and probability of AKI in sepsis patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eData source\u003c/b\u003e The eICU-CRD [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] provided the data between 2014 and 2015 for this retrospective analysis. The eICU program from Philips Healthcare automatically saved and obtained data from an eICU database that included over 200,000 admissions across different facilities in the United States [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. We were granted access to this database, completed the test, and received certification from the PhysioNet Review Board (record ID: 40859994).\u003c/p\u003e \u003cp\u003e \u003cb\u003eStudy population\u003c/b\u003e Sepsis-diagnosed ICU participants were involved in the research upon admission. The possible or verified infection and a score increase of more than two points on the Sequential Organ Failure Assessment (SOFA) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], as documented in the Acute Physiology and Chronic Health Evaluation (APACHE) IV dataset [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], were considered indicators of sepsis. Applying the International Classification of Diseases, 9th Edition (ICD-9) code, infections were detected by the eICU-CRD. ESRD is a final stage of CKD, diagnosed when renal replacement therapy (RRT) is needed for over 3 months, with a glomerular filtration rate (GFR) less than 15 mL/min [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. ESRD in our study was verified by ICD-9 code. The criteria for exclusion were as follows:(1) Not a first-time admission to ICU; (2) ICU stay of shorter than 48 hours; (3) Age under 18 years; (4) Diagnosed as ESRD; (5) missing albumin level or system error; (6) missing AKI outcome. The research flowchart was referred to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eVariables\u003c/b\u003e The eICU database contained population data, bedside monitor readings, diagnoses using ICD-9 codes and lab data from regular medical treatment. Data of the participants in the eICU-CRD were collected within initial 24 hours after ICU admission. The apacheApsVar table was used to extract physiological information such as respiration rate, heart rate, mean arterial pressure (MAP)and temperature. Treatment details on vasopressor, mechanical ventilation, and RRT were also contained in the same table. Basic information was collected from the patient or apachePatientResult charts, including age, ethnicity, gender, height and weight. Weight divided by height squared (kg/m\u003csup\u003e2\u003c/sup\u003e) was used to compute BMI. The lab recorded levels of white blood cell (WBC), platelet, hemoglobin, total bilirubin (TBIL), aspartate transaminase (AST), alanine transaminase (ALT), blood urea nitrogen (BUN), albumin, serum creatinine (Scr), potassium, sodium, and lactate. Comorbidities like congestive heart failure (CHF), hypertension, chronic obstructive pulmonary disease (COPD), acute myocardial infarction (AMI), diabetes, hepatic failure, cirrhosis, and cancer were taken from the admissionDx table. Admission severity was evaluated using Glasgow Coma Scale ( GCS ) score, Acute Physiology Score III, SOFA score and Apache IV score.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOutcome\u003c/b\u003e The study\u0026rsquo;s outcome was the likelihood of AKI following ICU admission. AKI was defined as a increase in Scr of 0.3 mg/dL during 48 hours or at least 1.5 times the Scr baseline level during 7 days after entering the ICU, in accordance with kidney disease: Improving Global Outcomes (KDIGO) standards [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eMissing data\u003c/b\u003e Among the 5459 patients in our study, The number of covariate missing values were 36 (0.66%) for ethnicity, 1 (0.02%) for gender, 103 (1.89%) for BMI, 264 (4.84%) for temperature,2 (0.04%) for heart rate, 20 (0.37%) for respiratory rate, 7 (0.13%) for MAP, 143 (2.62%) for WBC, 133(2.44%) for hemoglobin, 160 (2.93%) for platelet, 95 (1.74%) for ALT, 66 (1.21%) for AST, 569 (10.42%) for TBIL, 25 (0.46%) for BUN, 17 (0.31%) for Scr, 19 (0.35%) for potassium,19 (0.35%) for sodium, 1370 (25.10%) for lactate, 2 (0.04%) for SOFA score, 80 (1.47%) for GCS score, 595 (10.90%) for Acute Physiology Score III, 19(0.35%) for vasopressor, 595 (10.90%) for Apache IV score. Dummy variables indicated missing covariate values when continuous variables had over 1% missing values.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical analysis\u003c/b\u003e For continuous variables, the standard deviation (SD) or median with interquartile range (IQR) are displayed, whereas counts and percentages are used for categorical data. For categorical variables, chi-squared tests were performed to examine differences in albumin quartiles. For continuous variables, the study employed one-way analysis of variance (ANOVA). The relationship between albumin and probability of AKI was investigated by both univariate and multivariate logistic regression. Results were given as OR with their 95% CI. Confounders were chosen if their impact estimates changed by more than 10% or if they showed a link with the outcomes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. After assessing clinical significance of included covariates, we established three models: (1) Crude model: include no variables. (2) Model I: adjust for demographics of BMI, age, ethnicity and gender. (3) Model II : adjust for BMI, age, ethnicity, gender, MAP, WBC, hemoglobin, platelet, BUN, Scr, potassium, sodium, site of infection, SOFA score, APACHE IV score, hypertension, AMI, CHF, COPD, diabetes, hepatic failure, cirrhosis, cancer, mechanical ventilation and RRT. Generalized additive model (GAM) was employed in smooth curve fitting to find any nonlinear correlations between blood albumin and AKI risk in sepsis patients. Bootstrap resampling, likelihood ratio tests, and piecewise regression were applied to examine the threshold effect between serum albumin and probability of AKI [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo verify the stability of the results, we completed sensitivity analysis. To investigate any unmeasured confounding between serum albumin and probability of AKI, we computed E-values [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The amount of an unmeasured confounder required to eliminate the link between serum albumin and probability of AKI was shown by the E-value. For statistical significance, a two-tailed p-value of less than 0.05 was judged appropriate. EmpowerStats (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.empowerstats.com\" target=\"_blank\"\u003ewww.empowerstats.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.empowerstats.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, X\u0026amp;Y solutions, Inc. Boston, MA) and R statistical software tools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, The R Foundation) were adopted for all data analyses.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of participants\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin group(g/dL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1(1.00\u0026ndash;2.00)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2(2.00-2.40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3(2.40\u0026ndash;2.80)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4(2.80\u0026ndash;4.70)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipants (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.74\u0026thinsp;\u0026plusmn;\u0026thinsp;15.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.15\u0026thinsp;\u0026plusmn;\u0026thinsp;15.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.78\u0026thinsp;\u0026plusmn;\u0026thinsp;15.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.21\u0026thinsp;\u0026plusmn;\u0026thinsp;16.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e615 (50.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e688 (50.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e659 (49.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e715 (47.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e615 (50.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e681 (49.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e686 (51.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e799 (52.77%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaucasian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e907 (74.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1080 (79.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1091 (81.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1210 (80.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"5\" rowspan=\"6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfrican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102 (8.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (7.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (8.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e128 (8.51%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (5.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (5.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (5.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87 (5.78%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (6.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (4.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (3.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 (2.46%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNative American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (2.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (1.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (0.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (0.80%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (2.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (1.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (1.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (1.99%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.40\u0026thinsp;\u0026plusmn;\u0026thinsp;8.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.91\u0026thinsp;\u0026plusmn;\u0026thinsp;9.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.52\u0026thinsp;\u0026plusmn;\u0026thinsp;8.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.31\u0026thinsp;\u0026plusmn;\u0026thinsp;9.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eVital signs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119.34\u0026thinsp;\u0026plusmn;\u0026thinsp;27.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114.57\u0026thinsp;\u0026plusmn;\u0026thinsp;28.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114.06\u0026thinsp;\u0026plusmn;\u0026thinsp;28.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112.03\u0026thinsp;\u0026plusmn;\u0026thinsp;29.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory rate (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.46\u0026thinsp;\u0026plusmn;\u0026thinsp;14.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.99\u0026thinsp;\u0026plusmn;\u0026thinsp;14.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.61\u0026thinsp;\u0026plusmn;\u0026thinsp;14.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.46\u0026thinsp;\u0026plusmn;\u0026thinsp;14.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.00 (46.00\u0026ndash;73.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.00 (47.00-111.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.00 (46.00-114.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.00 (49.00-127.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eLaboratory data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC ( K/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.73 (9.00-22.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.70 (8.60\u0026ndash;19.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.70 (9.00-19.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.05 (9.00-18.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.69\u0026thinsp;\u0026plusmn;\u0026thinsp;2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.14\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.64\u0026thinsp;\u0026plusmn;\u0026thinsp;2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.14\u0026thinsp;\u0026plusmn;\u0026thinsp;2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet ( K/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e182.00 (106.00-279.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180.00 (116.00-254.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179.00 (124.50\u0026ndash;244.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e188.00 (132.00-250.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT(IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.00 (18.00\u0026ndash;52.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.00 (18.00\u0026ndash;54.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.00 (18.00\u0026ndash;56.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.00 (17.00\u0026ndash;50.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST(IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.00 (23.00\u0026ndash;76.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.00 (21.00-72.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.00 (22.00\u0026ndash;74.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.00 (21.00\u0026ndash;63.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBIL (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70 (0.40\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70 (0.40\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70 (0.40\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.70 (0.40\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin(g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.00 (18.00\u0026ndash;54.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.00 (19.00\u0026ndash;51.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.00 (19.00\u0026ndash;48.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.00 (17.00\u0026ndash;45.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScr(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.40 (0.84\u0026ndash;2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41 (0.91\u0026ndash;2.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.44 (0.91\u0026ndash;2.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.38 (0.90\u0026ndash;2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138.36\u0026thinsp;\u0026plusmn;\u0026thinsp;7.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138.61\u0026thinsp;\u0026plusmn;\u0026thinsp;6.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138.24\u0026thinsp;\u0026plusmn;\u0026thinsp;6.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e137.78\u0026thinsp;\u0026plusmn;\u0026thinsp;6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.90 (1.20\u0026ndash;3.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.90 (1.20\u0026ndash;3.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.90 (1.20\u0026ndash;3.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.80 (1.20\u0026ndash;2.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eSite of infection \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epulmonary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e465 (37.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e513 (37.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e578 (42.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e692 (45.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"6\" rowspan=\"7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erenal/UTI (including bladder)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e243 (19.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e317 (23.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e309 (22.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e306 (20.20%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e227 (18.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e212 (15.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e188 (13.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138 (9.11%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eunknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112 (9.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152 (11.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113 (8.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e191 (12.61%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecutaneous/soft tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105 (8.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (7.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87 (6.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103 (6.80%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (5.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (5.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (5.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80 (5.28%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egynecologic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (0.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (0.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (0.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (0.33%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eSeverity of illness\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.00 (1.00\u0026ndash;6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.00 (1.00\u0026ndash;6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.00 (1.00\u0026ndash;6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.00 (1.00\u0026ndash;5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.00\u0026thinsp;\u0026plusmn;\u0026thinsp;3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.85\u0026thinsp;\u0026plusmn;\u0026thinsp;3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.96\u0026thinsp;\u0026plusmn;\u0026thinsp;3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.10\u0026thinsp;\u0026plusmn;\u0026thinsp;3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute Physiology Score III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.61\u0026thinsp;\u0026plusmn;\u0026thinsp;23.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.76\u0026thinsp;\u0026plusmn;\u0026thinsp;23.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.62\u0026thinsp;\u0026plusmn;\u0026thinsp;22.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.61\u0026thinsp;\u0026plusmn;\u0026thinsp;22.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApache IV score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87.86\u0026thinsp;\u0026plusmn;\u0026thinsp;24.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.69\u0026thinsp;\u0026plusmn;\u0026thinsp;24.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.73\u0026thinsp;\u0026plusmn;\u0026thinsp;23.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.80\u0026thinsp;\u0026plusmn;\u0026thinsp;23.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eComorbidities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (5.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (6.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 (6.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e124 (8.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (3.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (2.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (4.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71 (4.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (5.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106 (7.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e134 (9.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e195 (12.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (7.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (7.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106 (7.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e166 (10.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164 (13.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195 (14.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151 (11.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e190 (12.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatic failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (3.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (2.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (2.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (2.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCirrhosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (5.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (4.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (2.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39 (2.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (3.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (3.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (1.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 (1.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e434 (35.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e511 (37.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e500 (37.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e533 (35.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (2.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (2.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (2.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 (2.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVasopressor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (1.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (1.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (1.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (0.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eAKI \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e754 (61.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e921 (67.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e925 (68.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1061 (70.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e476 (38.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e448 (32.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e420 (31.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e454 (29.97%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe data were presented as counts (%), median (IQR), or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/p\u003e \u003cp\u003eThe data analysis involving 5459 patients revealed an average age of 65.50\u0026thinsp;\u0026plusmn;\u0026thinsp;15.76, with men constituting 49.05% (2677 patients). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provided a detailed analysis of various factors such as vital signs, demographics, laboratory data, illness severity, infection site, comorbidities, and treatment, segmented by albumin quartiles. It was observed that individuals in the top albumin quartile were comparatively a bit younger, had a increased BMI, better lab results, and lower disease severity scores than those in the lowest quartile. AKI incidence in our study was 32.94% (1798/5459). AKI incidence across albumin tertiles, from the lowest (1.00\u0026ndash;2.00) to the highest (2.80\u0026ndash;4.70), were 38.70% (476 cases), 32.72% (448 cases), 31.23% (420 cases), and 29.97% (454 cases) respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of univariate analysis of AKI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09 (0.97, 1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1520\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.01, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaucasian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfrican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.26 (1.03, 1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0249\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.39 (1.10, 1.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.52 (1.16, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNative American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.50 (0.90, 2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07 (0.70, 1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7622\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.94, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart rate (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.00, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory rate (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.01, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8686\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC ( K/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.98, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet ( K/uL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0648\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT(IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST(IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBIL (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09 (1.06, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin(g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.80 (0.72, 0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScr (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17 (1.13, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17 (1.09, 1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.97, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17 (1.13, 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epulmonary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erenal/UTI\u003c/p\u003e \u003cp\u003e(including bladder)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.69 (0.59, 0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.39 (1.17, 1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eunknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.80, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecutaneous/soft tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.05 (0.84, 1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6669\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11 (0.86, 1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egynecologic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.66 (0.21, 2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4802\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.14 (1.11, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.97, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute Physiology Score III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.02, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApache IV score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.02, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.82, 1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.51 (1.14, 2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06 (0.87, 1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.72 (0.58, 0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.87, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatic failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.18 (0.85, 1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCirrhosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.35 (1.01, 1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0418\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91 (0.64, 1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5869\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.30 (1.16, 1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.09 (2.84, 5.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVasopressor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.27 (1.37, 3.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUnivariate analysis results (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) showed age, BMI, heart rate, respiratory rate, ALT, AST, TBIL, albumin, Scr, potassium, sodium, lactate, SOFA score, GCS score, Acute Physiology Score III, Apache IV score, AMI, COPD, cirrhosis, mechanical ventilation, RRT and vasopressor were correlated with AKI significantly.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate logistic regression analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of multivariate logistic regression analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel I\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel II\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80 (0.72, 0.88)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79 (0.71, 0.87)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87 (0.77, 0.98) 0.0199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAlbumin quartile\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77 (0.66, 0.91) 0.0015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77 (0.65, 0.91) 0.0020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82 (0.67, 0.99) 0.0350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72 (0.61, 0.85)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72 (0.61, 0.85) 0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80 (0.66, 0.98) 0.0290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68 (0.58, 0.79)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67 (0.57, 0.78)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80 (0.65, 0.97) 0.0262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0395\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData were shown as an OR (95% CI) P value.\u003c/p\u003e \u003cp\u003eCrude model adjust for: none.\u003c/p\u003e \u003cp\u003eModel I adjust for: gender, age, BMI, ethnicity.\u003c/p\u003e \u003cp\u003eModel II adjust for: gender, age, BMI, ethnicity, MAP, platelet, WBC, hemoglobin, BUN, Scr, potassium, sodium, site of infection, SOFA score, APACHE IV score, hypertension, AMI, CHF, COPD, diabetes, hepatic failure, cirrhosis, cancer, mechanical ventilation and RRT.\u003c/p\u003e \u003cp\u003eThe crude model results indicated a negative correlation between albumin and AKI. In Model I, the probability of AKI reduced by 21% for each 1 g/dL rise in albumin. After adjusting for all covariates in Model II, each 1 g/dL albumin increase decreased AKI risk by 13% (OR\u0026thinsp;=\u0026thinsp;0.87, 95% CI 0.77\u0026ndash;0.98, P\u0026thinsp;=\u0026thinsp;0.0199). We further investigated albumin\u0026rsquo;s nonlinearity with AKI by categorizing it into quartiles, revealing a significant trend (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of nonlinear relationship\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThreshold effect analysis of albumin and AKI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eModel I\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOne line effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.87 (0.77, 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eModel II\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurning point (K)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u0026thinsp;\u0026lt;\u0026thinsp;K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.61 (0.44, 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u0026thinsp;\u0026ge;\u0026thinsp;K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.99 (0.83, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8691\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP value for LRT test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95% CI for turning point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e2, 2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eModel I: linear analysis.\u003c/p\u003e \u003cp\u003eModel II: non-linear analysis. Adjusted for gender, age, BMI, ethnicity, MAP, platelet, WBC, hemoglobin, BUN, Scr, potassium, sodium, site of infection, SOFA score, APACHE IV score, hypertension, AMI, CHF, COPD, diabetes, hepatic failure, cirrhosis, cancer, mechanical ventilation and RRT.\u003c/p\u003e \u003cp\u003eLogarithmic likelihood ratio test (LRT); P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 denotes a significant difference between Model I and II.\u003c/p\u003e \u003cp\u003eBetween albumin and AKI, we discovered a nonlinear dose-response association (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For serum albumin below 2.1, each 1 g/dL increase in albumin decreased AKI risk, with an adjusted OR of 0.61 (95% CI 0.44\u0026ndash;0.85, P\u0026thinsp;=\u0026thinsp;0.0032). However, for albumin level of 2.1 or higher, the effect of albumin on the probability of AKI was not statistically significant. Each unit of 1 g/dL increase in albumin level decreased AKI risk, with an adjusted OR of 0.99 (95% CI 0.83\u0026ndash;1.16, P\u0026thinsp;=\u0026thinsp;0.8691) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor sensitivity to unquantified confounding, an E-value was assessed. Main results remained solid unless an unquantified confounder exceeded OR 1.88.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis retrospective cohort research used data from the eICU-CRD to examine 208 ICUs in the United States during 2014 and 2015. Sepsis patients with low albumin levels were shown to be more likely to develop AKI. The non-linear relationship between serum albumin level and AKI risk was the main finding. AKI risk was shown to be reduced by 39% for each 1.0 g/dL rise in albumin when it was less than 2.1 g/dL. As far as we are aware, this is the first study to show a link between albumin level and their chance of developing AKI in patients with sepsis.\u003c/p\u003e \u003cp\u003eThe incidence rate of AKI in sepsis was 32.94% (1798/5459) in our cohort, which was similar to the results of Payen [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], Uhel [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]and Case [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. As reported by Payen, 3147 patients from 198 ICUs across 24 European nations participated in a multicenter cohort research and 36% of those patients had AKI [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. An observational study by Uhel comprised 1545 sepsis patients in 2 ICUs of Netherlands and found AKI accounted for 37.7% of sepsis patients [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Case reported in a review article that in the ICU, the prevalence of AKI varied from 20\u0026ndash;50%, particularly higher in those with sepsis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, compared to Bagshaw [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]and Shum [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], the incidence rate of AKI in our research was lower. Bagshaw conducted a retrospective cohort study involving 57 ICUs across Australia and found AKI accounted for 42.1% among 33,375 septic patients [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A study by Shum showed 54.7% patients in ICU developed AKI among 3,687 patients and the primary cause of AKI (49.2%) was sepsis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. We had a lower likelihood of AKI compared to their findings, this discrepancy could be ascribed to the differences of two populations and the criteria for AKI classification.\u003c/p\u003e \u003cp\u003eMost research examining the link between serum albumin level and AKI risk have found that it was inverse. Wiedermann performed a meta-analyse involving 17 studies with 3,917 patients and revealed a positive relation between low albumin level and the onset of AKI [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Specifically, the likelihood of AKI raised by 134% for each 1g/dL drop in serum albumin [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Hansrivijit discovered that an increased incidence rate of AKI in hospitalized participants was related to each 1.0 g/dL drop in albumin, based on a meta article of 39 studies encompassing a cohort of 168,740 patients with AKI [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Sreenivasan found that in patients undergoing coronary angioplasty, albumin lower than 3.85 g/dL predicted the likelihood of Contrast-Induced AKI in a retrospective observational research comprising 1319 patients [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Lee demonstrated in an RCT including 220 patients that those blood albumin level increased to more than 40 g/L as a result of albumin replacement immediately before the procedure had a decreased risk of AKI following bypass surgery for coronary arteries [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In a retrospective analysis of 998 patients receiving liver transplant from donors, Sang found that hypoalbuminemia in the early postoperative period was a risk factor for the development of AKI [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, two studies had identified elevated serum albumin level can result in AKI. A multi-center retrospective study including 7802 children in the PSICU revealed an association between albumin levels exceeding 40.41 g/L and AKI [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A correlation was found between an increased risk of AKI and a high admission albumin level over 4.5 mg/dL in a retrospective research conducted by Thongprayoon encompassing 9552 patients [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Multiple clinical investigations found that albumin was a protective factor against the probability of AKI in different types of clinical diseases. Although many studies revealed the role of serum albumin in AKI, only few studies addressed the application of both serum albumin level and AKI risk in this population of septic patients. On the other hand, albumin and AKI were controversially linked in previous studies. Based on above two reasons, we initiated the present research.\u003c/p\u003e \u003cp\u003eWe found in our study that the protective effect of albumin against AKI was not significant when serum albumin was at level greater than 2.1 g/dL. This result was different from Thongprayoon reported that an increased likelihood of AKI existed in both the low albumin group (serum albumin level\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;2.4 g/dL) and the high albumin group (serum albumin level\u0026thinsp;\u003cem\u003e\u0026gt;\u003c/em\u003e\u0026thinsp;4.5 g/dL)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].Reasons for the inconsistency between their conclusions and ours may include the following: (1) The people under study were not the same. Our study focused on individuals with sepsis, whereas theirs was centered on hospitalized patients. (2) To investigate the role of serum albumin and AKI, multiple study designs and analysis techniques were used. While our investigation found a non-linear link between the two, their evaluation of association was done using multivariable logistic regression analysis. (3) In contrast to our findings, their investigations did not take into account the impact of correcting variables for BMI, MAP, WBC, hemoglobin, platelet, BUN, potassium, sodium, site of infection, SOFA score, APACHE IV score, hepatic failure, cancer and RRT on the connection between serum albumin and AKI. However, earlier research had linked these variables to serum albumin or AKI exposure [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. (4) In contrast to their study, which found 8% of persons with albumin of 4.5 g/dL, our investigation found just 0.13% (7/5459) of such individuals. The sample size of high albumin level in our study was much smaller than theirs.\u003c/p\u003e \u003cp\u003eIn addition, 624 individuals participated in Xu\u0026rsquo;s retrospective cohort research, which found a nonlinear correlation among serum albumin and AKI [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Their findings suggested that in patients undergoing aortic coarctation surgery, albumin below 32 g/L before surgery increased the probability of AKI [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This was consistent with our findings, which indicated that there was not a linear relationship between serum albumin levels and the probability of AKI.\u003c/p\u003e \u003cp\u003eThe impact of low albumin levels leading to high AKI risk is complex and can be attributed to the physiological roles of albumin. Hypoalbuminemia is determined by decreased albumin synthesis and increased degradation. Serum albumin is broken down at vascular endothelium and has a half-life of sixteen days [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. During acute illnesses such as sepsis, albumin transitions between intercellular and extracellular fluids because of increased vascular permeability [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In addition, the glomerular perfusion pressure, which is the product of serum oncotic pressure and hydrostatic pressure, has a significant impact on the GFR [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Changes of above pressures may have an impact on the GFR. Furthermore, an excess of reactive oxygen/nitrogen species is produced in reaction to cytokines released during a systemic inflammatory response, further damaging the renal tubules and glomeruli [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These observations could potentially lead to the onset of AKI.\u003c/p\u003e \u003cp\u003eOur results provide credence to the theory that the risk of AKI and serum albumin level are inversely correlated. Our research emphasizes how crucial it is for clinicians to keep an eye on serum albumin levels in sepsis patients. Particularly when serum albumin falls below 2.1g/dL, it\u0026rsquo;s important to proactively identify the underlying cause and promptly increase serum albumin level. This can significantly reduce the risk of AKI.\u003c/p\u003e \u003cp\u003eThere were several limitations in the current research. Observational studies often have unaccounted confounders. We used a sensitivity analysis of the E-value to assess its potential impact, and it seems unlikely that an unobserved confounder could explain the entire observed association. In spite of some constraints, such as incomplete data for specific variables, we employed modern methods to reduce bias. Furthermore, our study focused on ICU sepsis patients, limiting its extrapolation and generalizability. Exclusions like patients with less than 48-hour ICU stay and those with ESRD mean our findings don\u0026rsquo;t apply to these groups. Lastly, the quantity of albumin might vary, therefore basing conclusions on a particular time point could be problematic. Future studies could consider repeated measurements to deepen the understanding of how albumin levels affect AKI in sepsis patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eA total of 5459 septic patients from the eICU-CRD were included in our study. Our study found that albumin levels in septic patients influenced the probability of AKI through a nonlinear dose-response relationship. Specifically, serum albumin below 2.1 g/dL increased AKI risk in septic patients. This correlation needs further validation in future studies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAKI: acute kidney injury; ICU: intensive care unit; eICU-CRD: eICU Collaborative Research Database;ICD-9: International Classification of Diseases, 9th Edition; BMI: body mass index; MAP: mean arterial pressure; WBC: white blood cell; ALT: alanine transaminase; AST: aspartate transaminase; TBIL: total bilirubin; BUN: blood urea nitrogen; Scr: Serum creatinine; UTI: urinary tract infection; GI: gastrointestinal; SOFA: Sequential Organ Failure Assessment; GCS: Glasgow Coma Scale; Apache IV: Acute Physiology and Chronic Health Evaluation IV; AMI: acute myocardial infarction; CHF: congestive heart failure; COPD :chronic obstructive pulmonary disease; RRT: renal replacement therapy; CKD: chronic kidney disease; ESRD: end-stage renal disease; KDIGO: kidney disease: Improving Global Outcomes; GFR: glomerular filtration rate; SD: standard deviation; IQR: interquartile range; OR: Odds ratio; CI: Confidence interval.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe study was conducted using data from eICU-CRD. This database was approved for use by the Institutional Review Board of the Massachusetts Institute of Technology and the PhysioNet review committee, under the data usage agreement (record ID: 40859994). The data release adhered to the Health Insurance Portability and Accountability Act (HIPAA) safe harbor standards. As the analysis was retrospective and utilized an anonymous and publicly accessible database, the requirement for informed consent was waived. The study was conducted in accordance with the ethical standards laid out in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eData were available at https://eicu-crd.mit.edu/.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties (No.\u0026nbsp;SZGSP006), Shenzhen Second People\u0026rsquo;s Hospital Clinical Research Fund of Shenzhen High-level Hospital Construction Project\u0026nbsp;(Grant No. 20223357008, 2023xgyj3357003),\u0026nbsp;Sanming Project of Medicine in Shenzhen (No. SZSM202211016).\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eX.M.L contributed to the idea, planning, gathering, processing, and writing of the study\u0026rsquo;s text.\u0026nbsp;\u0026nbsp;H.F.H, X.L.C, J.M,\u0026nbsp;G.Y.L,\u0026nbsp;S.W, L.Z and Y.Z assisted in analyzing data. S.Q.G and Y.L funded the study and supervised progress. The final version of the manuscript was reviewed and approved by every author.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe express gratitude to eICU data providers.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSinger M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016;315(8):801\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.2016\u003c/span\u003e\u003cspan address=\"10.1001/jama.2016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeters E, Antonelli M, Wittebole X, Nanchal R, Franc\u0026cedil;ois B, Sakr Y, et al. 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Journal of immunology research. 2018;2018.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Albumin, Acute kidney injury, Sepsis, Non-linear, eICU-CRD","lastPublishedDoi":"10.21203/rs.3.rs-4341318/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4341318/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe role of serum albumin on acute kidney injury (AKI) remains controversial. Additionally, research on this relationship in sepsis patients is sparse. Therefore, this research aimed to investigate the relationship between serum albumin level and probability of AKI in patients with sepsis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study was a retrospective cohort analysis of sepsis patients across the United States between 2014 and 2015 in the eICU Collaborative Research Database (eICU-CRD). To estimate the role of albumin on AKI by univariate, multivariate logistic regression and smooth curve fitting analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 5459 patients with sepsis, 32.94% (1798 patients) developed AKI. The results of the multivariate logistic regression analysis indicated that the albumin and AKI were negatively correlated (adjusted OR\u0026thinsp;=\u0026thinsp;0.87, 95% CI 0.77\u0026ndash;0.98, P\u0026thinsp;=\u0026thinsp;0.0199). Moreover, a nonlinear relationship was observed between albumin level and probability of AKI with a inflection point at 2.1 g/dL. For albumin level\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;2.1g/dL, each unit increase in serum albumin reduced the probability of AKI by 39% (adjusted OR\u0026thinsp;=\u0026thinsp;0.61; 95% CI 0.44\u0026ndash;0.85; P\u0026thinsp;=\u0026thinsp;0.0032). However, for albumin levels above 2.1 g/dL, there was no significant association with the probability of AKI (adjusted OR\u0026thinsp;=\u0026thinsp;0.99, 95% CI 0.83\u0026ndash;1.16; P\u0026thinsp;=\u0026thinsp;0.8691).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSerum albumin level below 2.1g/dL was defined as a risk factor for AKI in sepsis patients.\u003c/p\u003e","manuscriptTitle":"Non-linear dose-response relationship between serum albumin and acute kidney injury in sepsis patients: a cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-15 15:42:43","doi":"10.21203/rs.3.rs-4341318/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9d1e937d-39ed-4c43-ac68-75189ed54589","owner":[],"postedDate":"May 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-22T15:41:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-15 15:42:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4341318","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4341318","identity":"rs-4341318","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}