Hyperlactatemia in critically ill patients with acute kidney injury treated with renal replacement therapy in the intensive care unit

Hyperlactatemia in critically ill patients with acute kidney injury treated with renal replacement therapy in the intensive care unit | 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 Hyperlactatemia in critically ill patients with acute kidney injury treated with renal replacement therapy in the intensive care unit Robert Ekart, Barbara Kobal, Tea Korošec, Eva Jakopin, Franc Svenšek, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5806235/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Apr, 2025 Read the published version in BMC Nephrology → Version 1 posted 15 You are reading this latest preprint version Abstract Background Hyperlactatemia is common in intensive care unit (ICU) patients. The aim of our retrospective observational study was to analyse the impact of serum lactate on admission on mortality in patients with acute kidney injury (AKI) treated with renal replacement therapy (RRT). Methods During the study period of 4 years, 2939 patients were admitted to the ICU, 503 patients were diagnosed with AKI and 209 of them required RRT. After excluding patients on chronic dialysis and with known malignant disease, we retrospectively analysed 154 patients. Hyperlactatemia was defined as a serum lactate concentration above 4 mmol/L on admission to the ICU. Results The mean age of patients was 62.8 years, and 69.5% were men. The mean Charlson Comorbidity Index (CCI) on admission to the ICU was 3.7 and fifty-six (36.4%) patients had acute hyperlactatemia. All included patients had AKI stage 3 and were treated with RRT, 125 (81.2%) with continuous RRT and 29 (18.8%) with intermittent hemodialysis. The mean length of stay in the ICU was 15.7 ± 13 days and 118 (76.6%) patients died during the 60-day observation period. A Kaplan-Meier survival analysis showed that the survival rate was statistically significantly lower in the group of patients with hyperlactatemia (log-rank; p = 0.032). The univariate Cox regression analysis showed that serum lactate on admission to the ICU significantly predict 60-day survival (HR 1.075; 95%CI 1.015–1.140; p = 0.014). In the multivariate Cox regression analysis, which included age, gender, diabetes, hypertension, chronic kidney disease, estimated glomerular filtration rate, serum lactate, CCI and C-reactive protein, only age (HR 1.031; 95%CI 1.007–1.056; p = 0.011) and serum lactate (HR 1.067; 95%CI 1.004–1.134; p = 0.035) were independent predictors of mortality. Conclusion Our study underscores the independent association between hyperlactatemia of more than 4 mmol/L on admission to the ICU and increased 60-day mortality in patients with AKI treated with RRT. These findings, which have significant implications for the management and prognosis of critically ill patients with AKI, provide a new understanding of the role of serum lactate in patient outcomes. Trial registration: Name of the registry: ClinicalTrials.gov Trial registration number: NCT06565403 Date of registration, followed by the words 'Retrospectively registered' : August, 19,2024 URL of trial registry record: https://clinicaltrials.gov/study/NCT06565403 acute kidney injury lactate acidosis survival Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Patients with acute kidney injury (AKI) who are treated in the intensive care unit (ICU) with renal replacement therapy (RRT) are among the sickest patients in the ICU. The prevalence of patients with AKI requiring RRT in the ICU is between 5% and 6% ( 1 ). Despite technical improvements in dialysis in recent decades, mortality in critically ill patients with AKI remains high, exceeding 40–60% ( 2 ). A variety of factors have been associated with increased mortality, including male gender, race, older age, oliguria, sepsis, respiratory or liver failure, cerebrovascular events and, most importantly, overall disease severity ( 3 ). Serum lactate is widely recognised as an important biomarker for assessing the hemodynamic status of critically ill patients. It reflects the balance between lactate production and excretion ( 4 , 5 ). Lactate metabolism via the liver and kidney has a remarkable physiological reserve under resting conditions. Renal excretion normally accounts for less than 2% under resting conditions, but increases in hyperlactatemia and especially in acidemia ( 5 , 6 ). Hyperlactatemia occurs when lactate production exceeds lactate excretion in the body, resulting in a serum lactate concentration above 2 mmol/L ( 7 ). However, hyperlactatemia can progress to lactic acidosis (LA), which is characterised by elevated lactate levels (> 4 mmol/L) and subsequent acidemia (pH < 7.35) ( 7 , 8 ). Severe LA worsens the function of various organs and has a significant impact on the outcome. Although lactate is a non-toxic molecule, the increase in concentration indicates important alterations in homeostasis and is therefore associated with increased mortality ( 4 , 9 ). Some studies have suggested that serum lactate is a useful biomarker for risk stratification, particularly in septic patients (10–12). Studies on the use of serum lactate to predict outcomes in patients with AKI receiving RRT in the ICU are lacking. The aim of our study was to investigate the impact of serum lactate at ICU admission on mortality in critically ill patients with AKI treated with RRT. MATERIALS AND METHODS Study design and participants We conducted a retrospective clinical study over a 4-year period (January 2017 to December 2020) in the 12-bed medical intensive care unit at the tertiary University Medical Centre Maribor, Slovenia. We wanted to exclude the influence of the Covid pandemic and for this reason we did not include patients treated in the ICU in the years 2021–2023. Inclusion criteria were: i) age > 18 years, ii) presence of AKI, stage 3 according to KDIGO criteria ( 13 ), iii) treatment with RRT, and iv) laboratory data of serum lactate at admission. Exclusion criteria were: i) patients with chronic kidney disease (CKD) receiving chronic RRT, ii) previous known malignancy. All patients were treated with either continuous RRT (CRRT) or intermittent hemodialysis (IHD). We did not treat any patient with peritoneal dialysis in the acute stage. The indications for RRT as well as the modality, anticoagulation and all dialysis parameters, including the selected ultrafiltration, were determined by consensus between the treating intensive care physicians and the nephrologist. It should be emphasised that each patient received an appropriate form of treatment depending on the indication and clinical condition, which complied with all relevant recommendations and guidelines. Data collection Patient and clinical data, data on comorbidity and diagnostic categories were collected from the electronic hospital database “Medis”, the medical files in the intensive care unit and in the dialysis department in paper form as well as laboratory data from the laboratory database. Disease severity was assessed at the time of ICU admission using the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) score ( 14 ), organ dysfunction parameters (Sepsis-related Organ Failure Assessment - SOFA score)( 15 ) and comorbidity using the Charlson Comorbidity Index (CCI)( 16 ). All patients were followed up during their ICU stay and the primary outcome considered was ICU all-cause mortality. Mortality status was determined from the medical records. Survival time was counted from ICU admission to death or 60 days after ICU admission. The study protocol was reviewed and approved by the Ethics Committees of the University Medical Centre Maribor under the number UKC-MB-KME-65/22 and was conducted in accordance with the Declaration of Helsinki. At admission to the hospital, patients or their relatives signed an informed consent form for all treatment, including RRT. Given the nature of the research, additional patient consent was not expected. Serum lactate We analysed only data on serum lactate at ICU admission. Hyperlactatemia was defined as a serum lactate concentration above 4 mmol/L on admission to the ICU. Due to the retrospective nature of the study, it was not possible to obtain data on serial measurements of serum lactate during ICU treatment and serum lactate values immediately before the start of RRT. RRT procedure RRT was performed in the ICU according to the indications and consensus between the attending intensivists and nephrologists. All CRRT sessions were performed using the Monitor Multifiltrate (Fresenius Medical Care, Bad Homburg, Germany) equipped with a highly permeable polysulfone hemofilter (AV1000 or filter of the same series, Fresenius Medical Care). All IHD sessions were performed with the Fresenius 5008 monitor and a highly permeable polysulfone or polyamide hemofilter. Commercially available bicarbonate-containing bags for exchange/infusion fluids (Fresenius Medical Care) were used for all CRRT sessions. Dialysis sessions were performed as continuous, prolonged (> 6 h) or standard (< 6 h), hemodiafiltration or hemodialysis. The duration of sessions, prescribed dialysis dose and net fluid removal were based on patients' clinical needs and best practice recommendations. Patients received unfractionated heparin or sodium citrate for anticoagulation in the extracorporeal circuit. Statistical analysis Descriptive statistics for continuous variables were calculated using mean ± SD (normally distributed) or median (interquartile range, IQR) (non-normally distributed). Normality in continuous variables was tested with Kolmogorov-Smirnov and Shapiro-Wilk tests. For categorical variables, the frequency and proportion (n, %) were used. The Student's T-test for independent samples was used to compare two groups. The chi-square test was used to analyse categorical variables. The time-dependent receiver operating characteristic (ROC) curve was used to analyse the diagnostic accuracy of a single continuous variable measured at baseline in relation to the occurrence of an outcome. Risk factors for 60-day mortality were analysed using univariate and multivariate Cox regression models and presented as hazard ratio (HR) and the associated 95% confidence interval (CI). The estimated survival probability of the patients was analysed using the log-rank test and presented using the Kaplan-Meier curve. Statistical significance was considered as p < 0.05, and all reported probability tests were two-sided. Statistical analysis was conducted using IBM SPSS software, version 29.0.0.0 (IBM, Armonk, NY, USA). RESULTS In the period between 1 January 2017 and 31 December 2020, a total of 2939 patients were admitted to our medical intensive care unit. Of these, 503 patients had a diagnosis of AKI. After excluding patients undergoing chronic dialysis, patients with known malignancy or insufficient data for analysis, a total of 154 critically ill patients with stage 3 AKI who received RRT were included in the final study cohort. The flowchart of study participants is shown in Fig. 1 . Baseline demographic, clinical and laboratory data of the entire study population and the two groups after 60-day survival from ICU admission are shown in Table 1 . One hundred and seven patients (69.5%) were men and 47 (30.5%) women with a mean age of 62.8 ± 12.9 years. The most common indications for admission to ICU were: acute respiratory failure (56 (36.4%) patients), cardiopulmonary resuscitation (25 (16.2%) patients), shock (17 (11%9 patients), acute coronary syndrome (11 (7.1%) patients) and sepsis (7 (4.5%) patients). Regarding the main comorbidities, sixty-six (42.9%) of the participants had diabetes mellitus (DM), 105 (68.2%) had hypertension, 33 (21.4%) had chronic kidney disease (CKD), 44 (28.6%) had heart failure and 36 patients (23.4%) had a history of coronary artery disease. Sixty-six (41.6%) patients were current or former smokers. One hundred and twenty-five (81.2%) patients received CRRT and 29 (18.8%) received IHD as their first RRT modality. Fourteen (9.1%) patients had been prescribed metformin prior to ICU admission. In our cohort, six (3.9%) patients had liver cirrhosis and none of these patients were prescribed metformin. Table 1 Baseline characteristics of the study population Characteristics Total cohort N = 154 Survivors N = 36 Non-survivors N = 118 p-value Age (years); mean; (median, IQR) 62.8; (64,19) 53.4; (54,19) 65.7; (68,17) < 0.001 Male, N (%) 107 (69.5) 24 (66.7%) 83 (70.3) 0.683 BMI (kg/m 2 ), mean; (median, IQR) 30.1; (29.4,7.9) 29.9; (29.4,9.3) 30.1; (29.3,7.6) 0.845 Underlying diseases Diabetes mellitus, N (%) 66 (42.9) 10 (27.8) 56 (47.5) 0.053 Hypertension, N (%) 105 (68.2) 22 (61.1) 83 (70.3) 0.313 CKD, N (%) 33 (21.4) 6 (16.7) 27 (22.9) 0.494 Heart failure, N (%) 44 (28.6) 4 (11.1) 40 (33.9) 0.01 Coronary artery disesase, N(%) 36 (23.4) 5 (13.9) 31 (26.3) 0.177 COPD, N (%) 18 (11.7) 1 (2.8) 17 (14.4) 0.075 Liver cirrhosis, N (%) 6 (3.9) 0 (0) 6 (5.1) 0.337 Laboratory tests at ICU admission Serum creatinine (µmol/L); mean; (median, IQR) 290; (190,292) 344; (239,474) 274; (177,254) 0.150 BUN (mmol/L);mean; (median, IQR) 20; ( 16 , 18 ) 21; ( 18 , 19 ) 20; ( 15 , 17 ) 0.716 eGFR (mL/min/1.73m2); mean; (median, IQR) 36; (28,48) 34; (21,51) 37; (32,47) 0.627 Hemoglobin (g/L); mean ± SD 119 ± 28 117 ± 28 119 ± 28 0.702 CRP (mg/L); mean; (median, IQR) 121; (91,147) 147; (115,284) 114; (87,132) 0.146 Procalcitonin (µg/L); mean; (median, IQR) 12.7; (1.7,10.2) 20.7; (2,27.8) 10.3; (1.6,6.4) 0.027 pH; mean ± SD 7.24 ± 0.14 7.26 ± 0.16 7.24 ± 0.13 0.424 Bicarbonate (mmol/L); mean ± SD 19 ± 6 17 ± 6 19 ± 6 0.212 Lactate (mmol/L); mean; (median, IQR) 3.8; (2.5,3.3) 3.2; (2.2,3.1) 4; (2.6,3.6) 0.179 Sodium (mmol/L); mean; (median, IQR) 138; (139,7) 137; (136,6) 139; (139,7) 0.066 Potassium (mmol/L); mean; (median, IQR) 4.7; (4.4,1.4) 4.6; (4.4,1.8) 4.8; (4.4,1.3) 0.485 Calcium (mmol/L); mean; (median, IQR) 1.91; (1.93,0.24) 1.85; (1.87,0.32) 1.92; (1.94,0.21) 0.086 Serum albumin (g/L); mean; (median, IQR) 26; (26,7) 26; (27,9) 26; (26,7) 0.826 Disease severity APACHE II score; mean; (median, IQR)* 27; (29,14) 25; (28,20) 28; (29,13) 0.110 SOFA score; mean; (median, IQR) ** 11; ( 11 , 4 ) 11; ( 11 , 5 ) 11; ( 11 , 4 ) 0.786 CCI; mean; (median, IQR) 3.7; ( 4 , 3 ) 2.1; ( 2 , 3 ) 4.2; ( 4 , 4 ) < 0.001 Mechanical ventilation, N (%) 136 (88.3) 30 (83.3) 106 (89.8) 0.372 *N = 144 **N = 149 Abbreviations: APACHE II, Acute Physiologic Assessment and Chronic Health Evaluation II; BMI, body mass index; BUN, blood urea nitrogen; CCI, Charlton morbidity index; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; SOFA Sepsis-related Organ Failure Assessment; Of the patients studied, 36.4% (n = 56) had hyperlactatemia prior to ICU admission. Compared to those without hyperlactatemia, these patients had lower pH (7.16 vs 7.29; p < 0.001), higher serum potassium (5.05 vs 4.54 mmol/L; p = 0.012), and a shorter time from ICU admission to RRT initiation (88 vs 128 hours; p = 0.046). Age, CCI, eGFR, CRP, serum albumin, and serum creatinine showed no significant differences between groups. Clinical profile and characteristics of the RRT sessions Most patients (81.2%) were treated with CRRT, the main reason being hemodynamic (in)stability. During the ICU stay, one hundred and forty-five (94.2%) were treated with noradrenaline, seventeen (11%) with adrenaline, forty-five (29.2%) with dobutamine and five (3.2%) with dopamine. Unfortunately, we found no data on treatment with vasopressor drugs for three patients. At the start of RRT, all patients were either uremic with metabolic acidosis (mean creatinine and pH were 477 µmol/L and 7.23, respectively; Table 2 ) or hypervolemic. The time interval between ICU admission and the start of RRT was 114 hours, with a mean duration of RRT of 43 hours. Chronological time from ICU admission to starting RRT was not significantly associated with 60 days mortality in univariable Cox regression analysis (HR 0.99, 95% CI 0.997–1.00, p = 0.126). Unfractionated heparin (56 patients), sodium citrate (38 patients) and heparin (10 patients) were the most commonly used anticoagulants, with the remaining patients receiving a combination of heparin, unfractionated heparin and citrate during the RRT sessions (some details in Table 2 ). Lactate levels on ICU admission were higher in patients anticoagulated with sodium citrate than with unfractionated heparin during RRT (4.65±3.27 vs 3.14±2.5 mmol/L; p = 0.013). Table 2 Biochemical data and dialytic parameters for RRT patients in ICU Parameter Total cohort N = 154 Survivors N = 36 Non-survivors N = 118 p-value At RRT start BUN (mmol/L), mean; (median, IQR) 40; (36,28) 39; (35,28) 40; (36,29) 0.687 Serum creatinine (µmol/L); mean ± SD 477 ± 233 547 ± 261 455 ± 220 0.037 eGFR (mL/min/1.73m2); mean; (median, IQR) 15; ( 10 , 9 ) 16; ( 8 , 9 ) 14; ( 10 , 10 ) 0.511 Potassium (mmol/L); mean; (median, IQR) 4.98; (4.82,1.59) 5.01; (5.06,1.38) 4.97; (4.78,1.65) 0.874 CRP (mg/L); mean; (median, IQR) 167; (123,200) 177; (159,200) 165; (123,199) 0.638 pH; mean; (median, IQR) 7.23; (7.23,0.12) 7.27; (7.27,0.13) 7.22; (7.22,0.13) 0.02 Bicarbonate (mmol/L), mean ± SD 18.7 ± 5.3 18.2 ± 6.2 18.9 ± 5.1 0.511 RRT clinical data and parameters Anticoagulation only with 4% sodium citrate (Number (%)) 38 (24.7) 7 (19.4) 31 (26.3) Anticoagulation only with low molecular heparin (Number (%)) 56 (36.4) 7 (19.4) 49 (41.5) Anticoagulation only with conventional heparin (Number (%)) 10 (6.5) 3 (8.3) 7 (5.9) Whole duration of RRT (hours); mean; (median, IQR) 42.9; (33,45) 55.3; (33,44) 39.3; (33,45) 0.087 Number of RRT sessions; mean; (median, IQR) 3.3; ( 2 , 3 ) 4.3; ( 3 , 6 ) 3; ( 2 , 3 ) 0.022 Cumulative ultrafitration (ml); mean; (median, IQR) 6202; (3575,7200) 7420;(4590,7688) 5839; (3420,7180) 0.363 CRRT / IHD as first RRT modality 125 / 29 100 / 18 25 / 11 Abbreviations: BUN, blood urea nitrogen; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; RRT, renal replacement therapy; CRRT, continuous RRT; IHD, intermittent hemodialysis; Patient outcome During the observation period of 60 days after admission to the ICU, 118 (76.6%) patients died. Most patients died during treatment in the ICU (108 patients, 70.1%). The average duration of treatment of patients in the ICU was 15.7 ± 12.7 days (1–72 days). Patients who died were older (p < 0.001), more of them had previous known heart failure (p = 0.01) and had a higher severity of illness on ICU admission as shown by the CCI (p < 0.001), while the SOFA score and APACHE II showed no statistically significant difference between survivors and non-survivors (Table 1 ). Using ROC curves comparing all three predictors, we found that only the CCI had predictive value (area under the curve 0.768, p < 0.0001) (Fig. 2 ). It is interesting to note that after 30 days of observation, only 4 patients were dialysis dependent. To investigate the association between the risk factors and mortality 60 days after ICU admission, age, serum lactate and CCI were significantly associated with a higher risk of 60-day mortality using a univariate Cox regression model (Table 3 ). Table 3 Association between risk factors and 60-day mortality in ICU patients with AKI and RRT using univariate and multivariate Cox-regression analysis Risk factors 60-day mortality Univariate Multivariate Hazard ratio (95% CI) p -value Hazard ratio (95% CI) p -value Age 1.036 (1.020–1.053) < 0.001 1.023 (1-1.047) 0.048 Gender 1.093 (0.736–1.624) 0.658 - - Diabetes 0.797 (0.555–1.146) 0.220 - - Serum lactate (mmol/L) 1.075 (1.015–1.140) 0.014 1.065 (1.005–1.127) 0.033 Hypertension 0.841 (0.566–1.251) 0.393 - - CKD 0.639 (0.414–0.985) 0.042 0.719 (0.434–1.193) 0.202 CRP (mg/L) 0.999 (0.997-1.000) 0.096 - - eGFR (mL/min/1.73m 2 ) 0.998 (0.992–1.005) 0.596 - - CCI 1.214 (1.121–1.315) < 0.001 1.089 (0.959–1.287) 0.236 SOFA 0.995 (0.934–1.059) 0.875 - - APACHE II 1.014 (0.994–1.033) 0.165 - - Abbreviations: APACHE II, Acute Physiologic Assessment and Chronic Health Evaluation II; CCI, Charlton morbidity index; CKD, chronic kidney disease; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; SOFA Sepsis-related Organ Failure Assessment; In a multivariable Cox regression model that included age, previous CKD, serum lactate, and CCI at admission, only age (HR 1.023; 95% CI 1–1.047; p = 0.048) and serum lactate (HR 1.065; 95% CI 1.005–1.127; p = 0.033) were found to be independent predictors of death (Table 3 ). We also performed a separate analysis for the type of RRT using univariate and multivariate Cox regression analysis (Tables 4 and 5 ). Unfortunately, we did not find the same results as for the entire cohort, regardless of whether IHD or CRRT. Table 4 Association between risk factors and 60-day mortality in ICU patients with AKI and CRRT (N = 125) using univariate and multivariate Cox-regression analysis Risk factors 60-day mortality Univariate Multivariate Hazard ratio (95% CI) p -value Hazard ratio (95% CI) p -value Age 1.028 (1.011–1.044) < 0.001 1.022 (0.996–1.047) 0.096 Gender 1.147 (0.747–1.760) 0.531 - - Hypertension 0.808 (0.530–1.233) 0.324 - - Diabetes 0.845 (0.569–1.254) 0.403 - - CKD 0.568 (0.351–0.919) 0.021 0.619 (0.353–1.088) 0.096 Serum lactate (mmol/L) 1.057 (0.992–1.127) 0.089 1.050 (0.986–1.118) 0.125 CRP (mg/L) 0.998 (0.997-1.000) 0.063 - - eGFR (mL/min/1.73m 2 ) 0.999 (0.993–1.006) 0.876 - - CCI 1.171(1.073–1.279) < 0.001 1.042 (0.898–1.208) 0.590 SOFA 0.991 (0.924–1.063) 0.800 - - APACHE II 1.012 (0.990–1.034) 0.295 - - Abbreviations: APACHE II, Acute Physiologic Assessment and Chronic Health Evaluation II; CCI, Charlton morbidity index; CKD, chronic kidney disease; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; SOFA Sepsis-related Organ Failure Assessment; Table 5 Association between risk factors and 60-day mortality in ICU patients with AKI and IHD (N = 29) using univariate and multivariate Cox-regression analysis Risk factors 60-day mortality Univariate Multivariate Hazard ratio (95% CI) p -value Hazard ratio (95% CI) p -value Age 1.142 (1.058–1.234) < 0.001 1.094 (1.001–1.195) 0.047 Gender 0.902 (0.321–2.535) 0.846 - - Hypertension 0.729 (0.211–2.523) 0.618 - - Diabetes 0.535 (0.207–1.386) 0.198 - - CKD 0.788 (0.280–2.218) 0.652 1.746 (0.450–6.773) 0.450 Serum lactate (mmol/L) 1.158 (0.989–1.357) 0.069 1.003 (0.854–1.178) 0.973 CRP (mg/L) 0.999 (0.994–1.003) 0.559 - - eGFR (mL/min/1.73m 2 ) 0.990 (0.974–1.007) 0.259 - - CCI 1.671(1.304–2.140) < 0.001 1.594 (1.141–2.228) 0.006 SOFA 1.002 (0.866–1.159) 0.977 - - APACHE II 1.040 (0.988–1.093) 0.132 - - Abbreviations: APACHE II, Acute Physiologic Assessment and Chronic Health Evaluation II; CCI, Charlton morbidity index; CKD, chronic kidney disease; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; SOFA Sepsis-related Organ Failure Assessment; Kaplan-Meier analysis showed that patients with a serum lactate above 4 mmol/L on admission to the ICU had a worse outcome at 60 days (log rank (Mantel Cox) = 4.587; p = 0.032) (Fig. 3 ). DISCUSSION Based on our research findings, we found that patients admitted to the ICU and requiring RRT due to AKI have a very high mortality rate. Two-thirds of the patients died during the observation period of 60 days after admission to the ICU. Serum lactate determined on admission to the ICU has been shown to be an important prognostic factor for survival, and patients with serum lactate levels below 4 mmol/L have better survival. Similar to the study by De Corte et al. ( 4 ), in our study, lactate on admission did not differ between those who died and those who survived (Table 1 ). However, it is necessary to emphasise an important difference between these two studies; as in our study we analysed mortality 60 days after patient admission to the ICU, but in the study by De Corte et al. mortality was only analysed within 24 hours of the start of RRT. The high mortality in our patients may also be the result of a higher severity of illness and organ dysfunction, which we can confirm with the highest CCI in the group of deceased patients. The study by Kim et al. also found a significant association between hyperlactatemia and mortality, but their observation time was only until discharge with a median of 10 days ( 17 ). Lactate has a molecular weight of 90 Da, similar to that of urea (60 Da), and can therefore be easily removed by RRT. However, data on the clearance of lactate through the hemodialysis membrane are sparse and contradictory. It is suggested that the beneficial effect of RRT on hyperlactatemia lies in the improvement of acid-base and metabolic status, leading to improved lactate metabolism, rather than in the direct removal of lactate by ultrafiltration and dialysis ( 17 , 18 ). Unfortunately, in our study we did not measure the effects of RRT on lactate levels, which is also one of the weaknesses of a retrospective study. As we know, with RRT we can only remove lactate, but we do not influence the cause or a disease that causes high lactate production. Bellomo assumes that lactate clearance during dialysis reaches 20% of endogenous clearance ( 6 ). However, Levraut et al. conducted a study of 10 patients with AKI in the ICU in which lactate was measured in serum and ultrafiltrate samples of patients during CRRT and compared with endogenous clearance ( 19 ). They found that lactate clearance through the hemodialysis filter was < 3% of total lactate clearance ( 19 ). Acute LA in critically ill patients is associated with cellular dysfunction and increased mortality ( 20 ). Elimination or control of the precipitating conditions remains the only effective therapy. The serum creatinine before the start of RRT was statistically significantly higher in the patients who survived than in those who died. This indirectly indicates that patients who survived had more muscle mass. Even on admission to the ICU, patients who survived beyond 60 days had a higher serum creatinine than those who died, but this difference was not statistically significant. Also in the study by Gleeson et al. among 157 patients with AKI who required RRT, the authors found that a higher serum creatinine level at the start of RRT was associated with better ICU survival [odds ratio (OR) 0.33, 95% CI 0.17–0.62; p = 0.001] ( 21 ). The choice of anticoagulation during RRT was made by consensus between the treating intensive care physicians and the nephrologist. The main factor for the choice was the bleeding tendency and patients at risk of bleeding were anticoagulated with sodium citrate regardless of lactate levels on admission. It is known that metabolic impairment of citrate metabolism can lead to accumulation of citrate, exacerbating lactic acidosis and the anion gap ( 22 ). In a retrospective study of 60 severely polytraumatised patients by Mariano et al., early CRRT with citrate anticoagulation showed better safety and haemodynamic stability in the presence of low blood flow and circulatory citraemia, suggesting that citrate should be the anticoagulant of first choice in this patient group ( 22 ). Survival analysis for the citrate and heparin groups showed a mortality rate of 43.5 and 57.1%, respectively, and the 90-day Kaplan-Meier curve showed a better survival trend for citrate (p = 0.0957)( 22 ). In our study, the Kaplan-Meier analysis did not show a better survival rate depending on the type of anticoagulation (sodium citrate vs. unfractionated heparin; p = 0.739; data not shown in Results). An important objective of our retrospective study was to evaluate the outcomes of dialysis treatment in critically ill patients within the ICU. These patients are typically in severe condition upon admission, rendering their prognosis uncertain . Nevertheless, such a high mortality rate is too high even for those who treat these patients, so it would be necessary to investigate how we can improve the survival rate of patients in this area. We started dialysis treatment on average 114 hours after admission to the ICU. The earliest we started dialysing a patient was one hour after admission, and the latest was after almost nearly 23 days (547 hours after ICU admission) (data not shown in the results section). At the same time, the question arises of whether the patients' outcome would have been different if they had started dialysis earlier. The optimal time to start RRT remains unclear and still needs to be determinated. It is also controversial whether LA itself is a suitable indication for RRT. The ethics of a prospective, randomised study on this topic are questionable, so it is difficult to find a clear answer. The weakness of our study is the small number of patients included in the study and the fact that it is only a single-centre study. Another shortcoming of our study is the lack of and analysis of data on serial measurements of serum lactate before and after the start of RRT until discharge or death or until the end of the observation period of 60 days. In summary, we can conclude that serum lactate on admission to the ICU is an important indicator of individual patient survival and that serum lactate levels above 4 mmol/L are associated with higher mortality. It may be useful to reduce serum lactate below 4 mmol/L as soon as possible after admission to the ICU, which is often very difficult due to the nature of the disease. The question also arises as to whether it is feasible to start treatment with RRT with the sole aim of lowering the serum lactate level. In any case, future studies analysing the importance of lowering serum lactate for survival are also warranted. Abbreviations AKI: acute kidney injury APACHE II: Acute Physiologic Assessment and Chronic Health Evaluation II CCI: Charlson Comorbidity Index CI: confidence interval CKD: chronic kidney disease CRRT: continuous renal replacement therapy DM: diabetes mellitus HR: hazard ratio ICU: intensive care unit IHD: intermittent hemodialysis LA: lactic acidosis ROC: receiver operating characteristic RRT: renal replacement therapy SOFA: Sepsis-related Organ Failure Assessment Declarations Ethics approval and consent to participate: The study protocol was reviewed and approved by the Ethics Committees of the University Medical Centre Maribor under the number UKC-MB-KME-65/22 and was conducted in accordance with the Declaration of Helsinki. At admission to the hospital, patients or their relatives signed an informed consent form for all treatment, including RRT. Given the nature of the research, additional patient consent was not expected. Consent for publication: Not appicable. Availability of data and materials: The datasets used and/or analysed in the current study are available upon reasonable request to the corresponding author. Competing Interests Statement: The authors declare that they have no competing interests. Founding sources: This research was no supported by any founding. The results presented in this paper have not been published previously in whole or part, except in an abstract format. Author contributions: Study conception and design: RE Acqusition of data: BK and TK Analysis and interpretation the data: RE, SB and RH Drafting of manuscript: RE All authors read and approved the final manuscript. Acknowledgements: Not applicable. References Uchino S, Kellum JA, Bellomo R, Doig GS, Morimatsu H, Morgera S, et al. Acute renal failure in critically ill patients: a multinational, multicenter study. JAMA. 2005; 294(7):813-8. Metnitz PG, Krenn CG, Steltzer H, Lang T, Ploder J, Lenz K, et al. Effect of acute renal failure requiring renal replacement therapy on outcome in critically ill patients. Crit Care Med. 2002; 30(9):2051-8. Chertow GM, Soroko SH, Paganini EP, Cho KC, Himmelfarb J, Ikizler TA, et al. Mortality after acute renal failure: models for prognostic stratification and risk adjustment. Kidney Int. 2006;70(6):1120-6. De Corte W, Vuylsteke S, De Waele JJ, Dhondt AW, Decruyenaere J, Vanholder R, et al. Severe lactic acidosis in critically ill patients with acute kidney injury treated with renal replacement therapy. Journal of Critical Care. 2014;29(4):650-5. Van De Ginste L, Vanommeslaeghe F, Hoste EAJ, Kruse JM, Van Biesen W, Verbeke F. Patients with Severe Lactic Acidosis in the Intensive Care Unit: A Retrospective Study of Contributing Factors and Impact of Renal Replacement Therapy. Blood purification. 2022; 51(7):577-83. Bellomo R. Bench-to-bedside review: lactate and the kidney. Critical Care. 2002;6(4): 322-6. Foucher CD, Tubben RE. StatPearls [Internet]. StatPearls Publishing ; Treasure Island (FL): January 21, 2024. Emmett M, Szerlip H. Causes of lactic acidosis. [Accessed July 24, 2024]; UpToDate.com website. https://www.uptodate.com/contents/causes-of-lactic-acidosis?search=lactate&source=search_result&selectedTitle=1%7E150&usage_type=default&display_rank=1#H11 Husain FA, Martin MJ, Mullenix PS, Steele SR, Elliott DC. Serum lactate and base deficit as predictors of mortality and morbidity. Am J Surg. 2003; 185(5): 485-91. Shapiro NI, Howell MD, Talmor D, Nathanson LA, Lisbon A, Wolfe RE, et al. Serum lactate as a predictor of mortality in emergency department patients with infection. Ann Emerg Med. 2005;45(5):524-8. Nguyen HB, Rivers EP, Knoblich BP, Jacobsen G, Muzzin A, Ressler JA, et al. Early lactate clearance is associated with improved outcome in severe sepsis and septic shock. Crit Care Med. 2004;32(8):1637-42. Trzeciak S, Dellinger RP, Chansky ME, Arnold RC, Schorr C, Milcarek B, et al. Serum lactate as a predictor of mortality in patients with infection. Intensive Care Med. 2007;33(6):970-7. KDIGO Clinical Practice Guideline for Acute Kidney Injury. Kidney Int. 2012; Suppl; 2 (1):6. Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: A severity of disease classification system. Crit Care Med. 1985;13(10): 818-29. Vincent JL, de Mendonça A, Cantraine F, Moreno R, Takala J, Suter PM, et al. Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: re- sults of a Multicenter, Prospective Study. Crit Care Med. 1998;26(11):1793-800. Charlson ME, Carrozzino D, Guidi J, Patierno C. Charlson Comorbidity Index: A Critical Review of Clinimetric Properties. Psychother Psychosom. 2022;91(1):8-35. Kim SG, Lee J, Yun D, Kang MW, Kim YC, Kim DK, et al. Hyperlactatemia is a predictor of mortality in patients undergoing continuous renal replacement therapy for acute kidney injury. BMC Nephrol. 2023;24(1): 11. Levy B. Lactate and shock state: the metabolic view. Curr Opin Crit Care. 2006;12(4):315-21. Levraut J, Ciebiera J-P, Jambou P, Ichai C, Labib Y, Grimaud D. Effect of continuous veno-venous hemofiltration with dialysis on lactate clearance in critically ill patients. Crit Care Med. 1997;25:58-62. Kraut JA, Madias NE. Lactic acidosis: Current treatments and future directions. Am J Kidney Dis. 2016;68(3):473-82. Gleeson PJ, Crippa IA, Sannier A, Koopmansch C, Bienfait L, Allard J, et al. Critically ill patients with acute kidney injury: clinical determinants and post-mortem histology. Clin Kidney J. 2023;16(10):1664-73. Mariano F, Mella A, Randone P, Agostini F, Bergamo D, Berardino M, et al. Safety and Metabolic Tolerance of Citrate Anticoagulation in Critically Ill Polytrauma Patients with Acute Kidney Injury Requiring an Early Continuous Kidney Replacement Therapy. Biomedicines. 2023;11(9): 2570. 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-5806235","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":437054318,"identity":"a23deb05-a3ec-413b-a7ba-ef7148b77483","order_by":0,"name":"Robert Ekart","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYFACHgglASI+MCRARQwIaUkAa2FsnEGylmYeuBY8wLy99+Djyh8McpLtZ58/tm1Lk2cQO3uA8UcBbi0yZ84lG55JYDCW5kk3bM5tyzFskM5LYObB4zAJiRwzyYYEhsR5DGmMQC0VCfa3cwyY8fkFqMX8J1BL/Tz+Z4zNlkAtDNI5Bow/CNjCCNSSIC0BtIWxLQeshQGvw3jOJUs2pEkYzpzxjHFmz7k0sF8O49XC3nvwY4ONjbzE+TSGDz/KkuUZpHMPPvzxB7cWmE5U7gGCGkbBKBgFo2AU4AUAuN9FbYd9MRIAAAAASUVORK5CYII=","orcid":"","institution":"University Medical Centre Maribor","correspondingAuthor":true,"prefix":"","firstName":"Robert","middleName":"","lastName":"Ekart","suffix":""},{"id":437054319,"identity":"43d42b95-819c-4db4-b905-e71895dc9745","order_by":1,"name":"Barbara Kobal","email":"","orcid":"","institution":"University of Maribor","correspondingAuthor":false,"prefix":"","firstName":"Barbara","middleName":"","lastName":"Kobal","suffix":""},{"id":437054320,"identity":"6ff1c00c-bd16-4291-a183-6278af1d975e","order_by":2,"name":"Tea Korošec","email":"","orcid":"","institution":"University of Maribor","correspondingAuthor":false,"prefix":"","firstName":"Tea","middleName":"","lastName":"Korošec","suffix":""},{"id":437054321,"identity":"cbf57f57-5604-45b4-8743-3af9320d9f08","order_by":3,"name":"Eva Jakopin","email":"","orcid":"","institution":"University Medical Centre Maribor","correspondingAuthor":false,"prefix":"","firstName":"Eva","middleName":"","lastName":"Jakopin","suffix":""},{"id":437054324,"identity":"95c06814-5844-413b-a9f2-1a96a2d1a30a","order_by":4,"name":"Franc Svenšek","email":"","orcid":"","institution":"University Medical Centre Maribor","correspondingAuthor":false,"prefix":"","firstName":"Franc","middleName":"","lastName":"Svenšek","suffix":""},{"id":437054325,"identity":"72825fc4-9cfe-43cd-8337-c2da9c6f134d","order_by":5,"name":"Nejc Piko","email":"","orcid":"","institution":"University Medical Centre Maribor","correspondingAuthor":false,"prefix":"","firstName":"Nejc","middleName":"","lastName":"Piko","suffix":""},{"id":437054327,"identity":"1d90ae46-bc6b-42bb-95a4-35f7015e3514","order_by":6,"name":"Sebastjan Bevc","email":"","orcid":"","institution":"University Medical Centre Maribor","correspondingAuthor":false,"prefix":"","firstName":"Sebastjan","middleName":"","lastName":"Bevc","suffix":""},{"id":437054332,"identity":"0e3e1569-bfab-42e2-99ad-f73d12059608","order_by":7,"name":"Radovan Hojs","email":"","orcid":"","institution":"University Medical Centre Maribor","correspondingAuthor":false,"prefix":"","firstName":"Radovan","middleName":"","lastName":"Hojs","suffix":""}],"badges":[],"createdAt":"2025-01-10 20:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5806235/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5806235/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12882-025-04149-5","type":"published","date":"2025-04-30T15:57:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79805278,"identity":"7de2d0ee-9d92-45a6-89d3-41ebd008b357","added_by":"auto","created_at":"2025-04-03 05:11:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":70884,"visible":true,"origin":"","legend":"\u003cp\u003ePatient flow chart.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5806235/v1/a8b8370836d300e4cf10395a.png"},{"id":79806111,"identity":"627599a8-0524-49cd-af6b-924ae3b864ec","added_by":"auto","created_at":"2025-04-03 05:28:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":506851,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves for prediction of 60 days mortality after ICU admission\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5806235/v1/ce53893a983d827bd8e1d68f.png"},{"id":79805281,"identity":"e7192167-a43e-4e81-80fc-29a5b0ffeb97","added_by":"auto","created_at":"2025-04-03 05:11:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":172229,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves according to the serum lactate level at ICU admission\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5806235/v1/86e49ef7929a574591247e49.png"},{"id":81987448,"identity":"fc106c2a-38ce-4f7f-ba57-0f96454061ba","added_by":"auto","created_at":"2025-05-05 16:02:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1764116,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5806235/v1/0e904938-e7cf-4635-b1d6-8c4f1d717d54.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hyperlactatemia in critically ill patients with acute kidney injury treated with renal replacement therapy in the intensive care unit","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003ePatients with acute kidney injury (AKI) who are treated in the intensive care unit (ICU) with renal replacement therapy (RRT) are among the sickest patients in the ICU. The prevalence of patients with AKI requiring RRT in the ICU is between 5% and 6% (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Despite technical improvements in dialysis in recent decades, mortality in critically ill patients with AKI remains high, exceeding 40\u0026ndash;60% (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). A variety of factors have been associated with increased mortality, including male gender, race, older age, oliguria, sepsis, respiratory or liver failure, cerebrovascular events and, most importantly, overall disease severity (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSerum lactate is widely recognised as an important biomarker for assessing the hemodynamic status of critically ill patients. It reflects the balance between lactate production and excretion (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Lactate metabolism via the liver and kidney has a remarkable physiological reserve under resting conditions. Renal excretion normally accounts for less than 2% under resting conditions, but increases in hyperlactatemia and especially in acidemia (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Hyperlactatemia occurs when lactate production exceeds lactate excretion in the body, resulting in a serum lactate concentration above 2 mmol/L (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, hyperlactatemia can progress to lactic acidosis (LA), which is characterised by elevated lactate levels (\u0026gt;\u0026thinsp;4 mmol/L) and subsequent acidemia (pH\u0026thinsp;\u0026lt;\u0026thinsp;7.35) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Severe LA worsens the function of various organs and has a significant impact on the outcome. Although lactate is a non-toxic molecule, the increase in concentration indicates important alterations in homeostasis and is therefore associated with increased mortality (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Some studies have suggested that serum lactate is a useful biomarker for risk stratification, particularly in septic patients (10\u0026ndash;12). Studies on the use of serum lactate to predict outcomes in patients with AKI receiving RRT in the ICU are lacking.\u003c/p\u003e \u003cp\u003eThe aim of our study was to investigate the impact of serum lactate at ICU admission on mortality in critically ill patients with AKI treated with RRT.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003e We conducted a retrospective clinical study over a 4-year period (January 2017 to December 2020) in the 12-bed medical intensive care unit at the tertiary University Medical Centre Maribor, Slovenia. We wanted to exclude the influence of the Covid pandemic and for this reason we did not include patients treated in the ICU in the years 2021\u0026ndash;2023.\u003c/p\u003e \u003cp\u003eInclusion criteria were: i) age\u0026thinsp;\u0026gt;\u0026thinsp;18 years, ii) presence of AKI, stage 3 according to KDIGO criteria (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e13\u003c/span\u003e), iii) treatment with RRT, and iv) laboratory data of serum lactate at admission.\u003c/p\u003e \u003cp\u003eExclusion criteria were: i) patients with chronic kidney disease (CKD) receiving chronic RRT, ii) previous known malignancy.\u003c/p\u003e \u003cp\u003eAll patients were treated with either continuous RRT (CRRT) or intermittent hemodialysis (IHD). We did not treat any patient with peritoneal dialysis in the acute stage. The indications for RRT as well as the modality, anticoagulation and all dialysis parameters, including the selected ultrafiltration, were determined by consensus between the treating intensive care physicians and the nephrologist. It should be emphasised that each patient received an appropriate form of treatment depending on the indication and clinical condition, which complied with all relevant recommendations and guidelines.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003ePatient and clinical data, data on comorbidity and diagnostic categories were collected from the electronic hospital database \u0026ldquo;Medis\u0026rdquo;, the medical files in the intensive care unit and in the dialysis department in paper form as well as laboratory data from the laboratory database.\u003c/p\u003e \u003cp\u003eDisease severity was assessed at the time of ICU admission using the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) score (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e14\u003c/span\u003e), organ dysfunction parameters (Sepsis-related Organ Failure Assessment - SOFA score)(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e15\u003c/span\u003e) and comorbidity using the Charlson Comorbidity Index (CCI)(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll patients were followed up during their ICU stay and the primary outcome considered was ICU all-cause mortality. Mortality status was determined from the medical records. Survival time was counted from ICU admission to death or 60 days after ICU admission.\u003c/p\u003e \u003cp\u003e The study protocol was reviewed and approved by the Ethics Committees of the University Medical Centre Maribor under the number UKC-MB-KME-65/22 and was conducted in accordance with the Declaration of Helsinki. At admission to the hospital, patients or their relatives signed an informed consent form for all treatment, including RRT. Given the nature of the research, additional patient consent was not expected.\u003c/p\u003e \u003cp\u003eSerum lactate\u003c/p\u003e \u003cp\u003eWe analysed only data on serum lactate at ICU admission. Hyperlactatemia was defined as a serum lactate concentration above 4 mmol/L on admission to the ICU. Due to the retrospective nature of the study, it was not possible to obtain data on serial measurements of serum lactate during ICU treatment and serum lactate values immediately before the start of RRT.\u003c/p\u003e\n\u003ch3\u003eRRT procedure\u003c/h3\u003e\n\u003cp\u003eRRT was performed in the ICU according to the indications and consensus between the attending intensivists and nephrologists. All CRRT sessions were performed using the Monitor Multifiltrate (Fresenius Medical Care, Bad Homburg, Germany) equipped with a highly permeable polysulfone hemofilter (AV1000 or filter of the same series, Fresenius Medical Care). All IHD sessions were performed with the Fresenius 5008 monitor and a highly permeable polysulfone or polyamide hemofilter. Commercially available bicarbonate-containing bags for exchange/infusion fluids (Fresenius Medical Care) were used for all CRRT sessions.\u003c/p\u003e \u003cp\u003eDialysis sessions were performed as continuous, prolonged (\u0026gt;\u0026thinsp;6 h) or standard (\u0026lt;\u0026thinsp;6 h), hemodiafiltration or hemodialysis. The duration of sessions, prescribed dialysis dose and net fluid removal were based on patients' clinical needs and best practice recommendations. Patients received unfractionated heparin or sodium citrate for anticoagulation in the extracorporeal circuit.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics for continuous variables were calculated using mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (normally distributed) or median (interquartile range, IQR) (non-normally distributed). Normality in continuous variables was tested with Kolmogorov-Smirnov and Shapiro-Wilk tests. For categorical variables, the frequency and proportion (n, %) were used. The Student's T-test for independent samples was used to compare two groups. The chi-square test was used to analyse categorical variables. The time-dependent receiver operating characteristic (ROC) curve was used to analyse the diagnostic accuracy of a single continuous variable measured at baseline in relation to the occurrence of an outcome. Risk factors for 60-day mortality were analysed using univariate and multivariate Cox regression models and presented as hazard ratio (HR) and the associated 95% confidence interval (CI). The estimated survival probability of the patients was analysed using the log-rank test and presented using the Kaplan-Meier curve. Statistical significance was considered as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and all reported probability tests were two-sided. Statistical analysis was conducted using IBM SPSS software, version 29.0.0.0 (IBM, Armonk, NY, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eIn the period between 1 January 2017 and 31 December 2020, a total of 2939 patients were admitted to our medical intensive care unit. Of these, 503 patients had a diagnosis of AKI. After excluding patients undergoing chronic dialysis, patients with known malignancy or insufficient data for analysis, a total of 154 critically ill patients with stage 3 AKI who received RRT were included in the final study cohort. The flowchart of study participants is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eBaseline demographic, clinical and laboratory data of the entire study population and the two groups after 60-day survival from ICU admission are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. One hundred and seven patients (69.5%) were men and 47 (30.5%) women with a mean age of 62.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9 years. The most common indications for admission to ICU were: acute respiratory failure (56 (36.4%) patients), cardiopulmonary resuscitation (25 (16.2%) patients), shock (17 (11%9 patients), acute coronary syndrome (11 (7.1%) patients) and sepsis (7 (4.5%) patients). Regarding the main comorbidities, sixty-six (42.9%) of the participants had diabetes mellitus (DM), 105 (68.2%) had hypertension, 33 (21.4%) had chronic kidney disease (CKD), 44 (28.6%) had heart failure and 36 patients (23.4%) had a history of coronary artery disease. Sixty-six (41.6%) patients were current or former smokers. One hundred and twenty-five (81.2%) patients received CRRT and 29 (18.8%) received IHD as their first RRT modality. Fourteen (9.1%) patients had been prescribed metformin prior to ICU admission. In our cohort, six (3.9%) patients had liver cirrhosis and none of these patients were prescribed metformin.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the study population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal cohort N\u0026thinsp;=\u0026thinsp;154\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurvivors\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;36\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-survivors\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;118\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years); mean; (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.8; (64,19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.4; (54,19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.7; (68,17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107 (69.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83 (70.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.683\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), mean; (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.1; (29.4,7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.9; (29.4,9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.1; (29.3,7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderlying diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105 (68.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (61.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83 (70.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart failure, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary artery disesase, N(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver cirrhosis, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory tests at ICU admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine (\u0026micro;mol/L); mean; (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e290; (190,292)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e344; (239,474)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e274; (177,254)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN (mmol/L);mean; (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20; (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21; (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20; (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (mL/min/1.73m2); mean; (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36; (28,48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34; (21,51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37; (32,47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.627\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/L); mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119\u0026thinsp;\u0026plusmn;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117\u0026thinsp;\u0026plusmn;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119\u0026thinsp;\u0026plusmn;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L); mean; (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121; (91,147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147; (115,284)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114; (87,132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcalcitonin (\u0026micro;g/L); mean; (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.7; (1.7,10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.7; (2,27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.3; (1.6,6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBicarbonate (mmol/L); mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate (mmol/L); mean; (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8; (2.5,3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2; (2.2,3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4; (2.6,3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium (mmol/L); mean; (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138; (139,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137; (136,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139; (139,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium (mmol/L); mean; (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.7; (4.4,1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.6; (4.4,1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.8; (4.4,1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium (mmol/L); mean; (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.91; (1.93,0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.85; (1.87,0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.92; (1.94,0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum albumin (g/L); mean; (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26; (26,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26; (27,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26; (26,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease severity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE II score; mean; (median, IQR)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27; (29,14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25; (28,20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28; (29,13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA score; mean; (median, IQR) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11; (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11; (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11; (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI; mean; (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.7; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.2; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilation, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136 (88.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106 (89.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*N\u0026thinsp;=\u0026thinsp;144\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e**N\u0026thinsp;=\u0026thinsp;149\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: APACHE II, Acute Physiologic Assessment and Chronic Health Evaluation II; BMI, body mass index; BUN, blood urea nitrogen; CCI, Charlton morbidity index; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; SOFA Sepsis-related Organ Failure Assessment;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOf the patients studied, 36.4% (n\u0026thinsp;=\u0026thinsp;56) had hyperlactatemia prior to ICU admission. Compared to those without hyperlactatemia, these patients had lower pH (7.16 vs 7.29; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher serum potassium (5.05 vs 4.54 mmol/L; p\u0026thinsp;=\u0026thinsp;0.012), and a shorter time from ICU admission to RRT initiation (88 vs 128 hours; p\u0026thinsp;=\u0026thinsp;0.046). Age, CCI, eGFR, CRP, serum albumin, and serum creatinine showed no significant differences between groups.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinical profile and characteristics of the RRT sessions\u003c/h2\u003e \u003cp\u003eMost patients (81.2%) were treated with CRRT, the main reason being hemodynamic (in)stability. During the ICU stay, one hundred and forty-five (94.2%) were treated with noradrenaline, seventeen (11%) with adrenaline, forty-five (29.2%) with dobutamine and five (3.2%) with dopamine. Unfortunately, we found no data on treatment with vasopressor drugs for three patients. At the start of RRT, all patients were either uremic with metabolic acidosis (mean creatinine and pH were 477 \u0026micro;mol/L and 7.23, respectively; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) or hypervolemic. The time interval between ICU admission and the start of RRT was 114 hours, with a mean duration of RRT of 43 hours. Chronological time from ICU admission to starting RRT was not significantly associated with 60 days mortality in univariable Cox regression analysis (HR 0.99, 95% CI 0.997\u0026ndash;1.00, p\u0026thinsp;=\u0026thinsp;0.126). Unfractionated heparin (56 patients), sodium citrate (38 patients) and heparin (10 patients) were the most commonly used anticoagulants, with the remaining patients receiving a combination of heparin, unfractionated heparin and citrate during the RRT sessions (some details in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Lactate levels on ICU admission were higher in patients anticoagulated with sodium citrate than with unfractionated heparin during RRT (4.65\u0026plusmn;3.27 vs 3.14\u0026plusmn;2.5 mmol/L; p\u0026thinsp;=\u0026thinsp;0.013).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBiochemical data and dialytic parameters for RRT patients in ICU\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal cohort N\u0026thinsp;=\u0026thinsp;154\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSurvivors\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;36\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-survivors\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;118\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAt RRT start\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN (mmol/L), mean; (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40; (36,28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39; (35,28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40; (36,29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine (\u0026micro;mol/L); mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e477\u0026thinsp;\u0026plusmn;\u0026thinsp;233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e547\u0026thinsp;\u0026plusmn;\u0026thinsp;261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e455\u0026thinsp;\u0026plusmn;\u0026thinsp;220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (mL/min/1.73m2); mean; (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15; (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16; (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14; (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium (mmol/L); mean; (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.98; (4.82,1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.01; (5.06,1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.97; (4.78,1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L); mean; (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167; (123,200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177; (159,200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e165; (123,199)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH; mean; (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.23; (7.23,0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.27; (7.27,0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.22; (7.22,0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBicarbonate (mmol/L), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRRT clinical data and parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnticoagulation only with 4% sodium citrate (Number (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnticoagulation only with low molecular heparin (Number (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnticoagulation only with conventional heparin (Number (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole duration of RRT (hours); mean; (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.9; (33,45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.3; (33,44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.3; (33,45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of RRT sessions; mean; (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.3; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.3; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative ultrafitration (ml); mean; (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6202; (3575,7200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7420;(4590,7688)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5839; (3420,7180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRRT / IHD as first RRT modality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125 / 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 / 18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 / 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: BUN, blood urea nitrogen; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; RRT, renal replacement therapy; CRRT, continuous RRT; IHD, intermittent hemodialysis;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePatient outcome\u003c/h3\u003e\n\u003cp\u003eDuring the observation period of 60 days after admission to the ICU, 118 (76.6%) patients died. Most patients died during treatment in the ICU (108 patients, 70.1%). The average duration of treatment of patients in the ICU was 15.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7 days (1\u0026ndash;72 days). Patients who died were older (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), more of them had previous known heart failure (p\u0026thinsp;=\u0026thinsp;0.01) and had a higher severity of illness on ICU admission as shown by the CCI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the SOFA score and APACHE II showed no statistically significant difference between survivors and non-survivors (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Using ROC curves comparing all three predictors, we found that only the CCI had predictive value (area under the curve 0.768, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is interesting to note that after 30 days of observation, only 4 patients were dialysis dependent. To investigate the association between the risk factors and mortality 60 days after ICU admission, age, serum lactate and CCI were significantly associated with a higher risk of 60-day mortality using a univariate Cox regression model (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between risk factors and 60-day mortality in ICU patients with AKI and RRT using univariate and multivariate Cox-regression analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60-day mortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUnivariate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMultivariate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.036 (1.020\u0026ndash;1.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.023 (1-1.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\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 \u003cp\u003e1.093 (0.736\u0026ndash;1.624)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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\u003e0.797 (0.555\u0026ndash;1.146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum lactate (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.075 (1.015\u0026ndash;1.140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.065 (1.005\u0026ndash;1.127)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\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\u003e0.841 (0.566\u0026ndash;1.251)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.639 (0.414\u0026ndash;0.985)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.719 (0.434\u0026ndash;1.193)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.999 (0.997-1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.998 (0.992\u0026ndash;1.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.214 (1.121\u0026ndash;1.315)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.089 (0.959\u0026ndash;1.287)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.995 (0.934\u0026ndash;1.059)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.014 (0.994\u0026ndash;1.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: APACHE II, Acute Physiologic Assessment and Chronic Health Evaluation II; CCI, Charlton morbidity index; CKD, chronic kidney disease; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; SOFA Sepsis-related Organ Failure Assessment;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn a multivariable Cox regression model that included age, previous CKD, serum lactate, and CCI at admission, only age (HR 1.023; 95% CI 1\u0026ndash;1.047; p\u0026thinsp;=\u0026thinsp;0.048) and serum lactate (HR 1.065; 95% CI 1.005\u0026ndash;1.127; p\u0026thinsp;=\u0026thinsp;0.033) were found to be independent predictors of death (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). We also performed a separate analysis for the type of RRT using univariate and multivariate Cox regression analysis (Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Unfortunately, we did not find the same results as for the entire cohort, regardless of whether IHD or CRRT.\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\u003eAssociation between risk factors and 60-day mortality in ICU patients with AKI and CRRT (N\u0026thinsp;=\u0026thinsp;125) using univariate and multivariate Cox-regression analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60-day mortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUnivariate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMultivariate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.028 (1.011\u0026ndash;1.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.022 (0.996\u0026ndash;1.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.096\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 \u003cp\u003e1.147 (0.747\u0026ndash;1.760)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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\u003e0.808 (0.530\u0026ndash;1.233)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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\u003e0.845 (0.569\u0026ndash;1.254)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.568 (0.351\u0026ndash;0.919)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.619 (0.353\u0026ndash;1.088)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum lactate (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.057 (0.992\u0026ndash;1.127)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.050 (0.986\u0026ndash;1.118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.998 (0.997-1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.999 (0.993\u0026ndash;1.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.171(1.073\u0026ndash;1.279)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.042 (0.898\u0026ndash;1.208)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.590\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.991 (0.924\u0026ndash;1.063)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.012 (0.990\u0026ndash;1.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: APACHE II, Acute Physiologic Assessment and Chronic Health Evaluation II; CCI, Charlton morbidity index; CKD, chronic kidney disease; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; SOFA Sepsis-related Organ Failure Assessment;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between risk factors and 60-day mortality in ICU patients with AKI and IHD (N\u0026thinsp;=\u0026thinsp;29) using univariate and multivariate Cox-regression analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60-day mortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUnivariate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMultivariate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.142 (1.058\u0026ndash;1.234)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.094 (1.001\u0026ndash;1.195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\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 \u003cp\u003e0.902 (0.321\u0026ndash;2.535)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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\u003e0.729 (0.211\u0026ndash;2.523)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\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\u003e0.535 (0.207\u0026ndash;1.386)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.788 (0.280\u0026ndash;2.218)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.746 (0.450\u0026ndash;6.773)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum lactate (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.158 (0.989\u0026ndash;1.357)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.003 (0.854\u0026ndash;1.178)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.999 (0.994\u0026ndash;1.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.990 (0.974\u0026ndash;1.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.671(1.304\u0026ndash;2.140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.594 (1.141\u0026ndash;2.228)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.002 (0.866\u0026ndash;1.159)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.040 (0.988\u0026ndash;1.093)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: APACHE II, Acute Physiologic Assessment and Chronic Health Evaluation II; CCI, Charlton morbidity index; CKD, chronic kidney disease; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; SOFA Sepsis-related Organ Failure Assessment;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eKaplan-Meier analysis showed that patients with a serum lactate above 4 mmol/L on admission to the ICU had a worse outcome at 60 days (log rank (Mantel Cox)\u0026thinsp;=\u0026thinsp;4.587; p\u0026thinsp;=\u0026thinsp;0.032) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eBased on our research findings, we found that patients admitted to the ICU and requiring RRT due to AKI have a very high mortality rate. Two-thirds of the patients died during the observation period of 60 days after admission to the ICU. Serum lactate determined on admission to the ICU has been shown to be an important prognostic factor for survival, and patients with serum lactate levels below 4 mmol/L have better survival.\u003c/p\u003e \u003cp\u003eSimilar to the study by De Corte et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), in our study, lactate on admission did not differ between those who died and those who survived (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, it is necessary to emphasise an important difference between these two studies; as in our study we analysed mortality 60 days after patient admission to the ICU, but in the study by De Corte et al. mortality was only analysed within 24 hours of the start of RRT. The high mortality in our patients may also be the result of a higher severity of illness and organ dysfunction, which we can confirm with the highest CCI in the group of deceased patients. The study by Kim et al. also found a significant association between hyperlactatemia and mortality, but their observation time was only until discharge with a median of 10 days (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLactate has a molecular weight of 90 Da, similar to that of urea (60 Da), and can therefore be easily removed by RRT. However, data on the clearance of lactate through the hemodialysis membrane are sparse and contradictory. It is suggested that the beneficial effect of RRT on hyperlactatemia lies in the improvement of acid-base and metabolic status, leading to improved lactate metabolism, rather than in the direct removal of lactate by ultrafiltration and dialysis (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Unfortunately, in our study we did not measure the effects of RRT on lactate levels, which is also one of the weaknesses of a retrospective study. As we know, with RRT we can only remove lactate, but we do not influence the cause or a disease that causes high lactate production. Bellomo assumes that lactate clearance during dialysis reaches 20% of endogenous clearance (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). However, Levraut et al. conducted a study of 10 patients with AKI in the ICU in which lactate was measured in serum and ultrafiltrate samples of patients during CRRT and compared with endogenous clearance (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e19\u003c/span\u003e). They found that lactate clearance through the hemodialysis filter was \u0026lt;\u0026thinsp;3% of total lactate clearance (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Acute LA in critically ill patients is associated with cellular dysfunction and increased mortality (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Elimination or control of the precipitating conditions remains the only effective therapy.\u003c/p\u003e \u003cp\u003eThe serum creatinine before the start of RRT was statistically significantly higher in the patients who survived than in those who died. This indirectly indicates that patients who survived had more muscle mass. Even on admission to the ICU, patients who survived beyond 60 days had a higher serum creatinine than those who died, but this difference was not statistically significant. Also in the study by Gleeson et al. among 157 patients with AKI who required RRT, the authors found that a higher serum creatinine level at the start of RRT was associated with better ICU survival [odds ratio (OR) 0.33, 95% CI 0.17\u0026ndash;0.62; p\u0026thinsp;=\u0026thinsp;0.001] (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe choice of anticoagulation during RRT was made by consensus between the treating intensive care physicians and the nephrologist. The main factor for the choice was the bleeding tendency and patients at risk of bleeding were anticoagulated with sodium citrate regardless of lactate levels on admission. It is known that metabolic impairment of citrate metabolism can lead to accumulation of citrate, exacerbating lactic acidosis and the anion gap (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e22\u003c/span\u003e). In a retrospective study of 60 severely polytraumatised patients by Mariano et al., early CRRT with citrate anticoagulation showed better safety and haemodynamic stability in the presence of low blood flow and circulatory citraemia, suggesting that citrate should be the anticoagulant of first choice in this patient group (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Survival analysis for the citrate and heparin groups showed a mortality rate of 43.5 and 57.1%, respectively, and the 90-day Kaplan-Meier curve showed a better survival trend for citrate (p\u0026thinsp;=\u0026thinsp;0.0957)(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e22\u003c/span\u003e). In our study, the Kaplan-Meier analysis did not show a better survival rate depending on the type of anticoagulation (sodium citrate vs. unfractionated heparin; p\u0026thinsp;=\u0026thinsp;0.739; data not shown in Results).\u003c/p\u003e \u003cp\u003eAn important objective of our retrospective study was to evaluate the outcomes of dialysis treatment in critically ill patients within the ICU. These patients are typically in severe condition upon admission, rendering their prognosis uncertain \u003cspan fontcategory=\"NonProportional\" class=\"\" name=\"Emphasis\"\u003e.\u003c/span\u003e Nevertheless, such a high mortality rate is too high even for those who treat these patients, so it would be necessary to investigate how we can improve the survival rate of patients in this area. We started dialysis treatment on average 114 hours after admission to the ICU. The earliest we started dialysing a patient was one hour after admission, and the latest was after almost nearly 23 days (547 hours after ICU admission) (data not shown in the results section). At the same time, the question arises of whether the patients' outcome would have been different if they had started dialysis earlier. The optimal time to start RRT remains unclear and still needs to be determinated. It is also controversial whether LA itself is a suitable indication for RRT. The ethics of a prospective, randomised study on this topic are questionable, so it is difficult to find a clear answer.\u003c/p\u003e \u003cp\u003eThe weakness of our study is the small number of patients included in the study and the fact that it is only a single-centre study. Another shortcoming of our study is the lack of and analysis of data on serial measurements of serum lactate before and after the start of RRT until discharge or death or until the end of the observation period of 60 days.\u003c/p\u003e \u003cp\u003eIn summary, we can conclude that serum lactate on admission to the ICU is an important indicator of individual patient survival and that serum lactate levels above 4 mmol/L are associated with higher mortality. It may be useful to reduce serum lactate below 4 mmol/L as soon as possible after admission to the ICU, which is often very difficult due to the nature of the disease. The question also arises as to whether it is feasible to start treatment with RRT with the sole aim of lowering the serum lactate level. In any case, future studies analysing the importance of lowering serum lactate for survival are also warranted.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAKI: acute kidney injury\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAPACHE II: Acute Physiologic Assessment and Chronic Health Evaluation II\u003c/p\u003e\n\u003cp\u003eCCI: Charlson Comorbidity Index\u003c/p\u003e\n\u003cp\u003eCI: confidence interval\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCKD: chronic kidney disease\u003c/p\u003e\n\u003cp\u003eCRRT: continuous renal replacement therapy\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDM: diabetes mellitus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHR: hazard ratio\u003c/p\u003e\n\u003cp\u003eICU: intensive care unit\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIHD: intermittent hemodialysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLA: lactic acidosis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eROC: receiver operating characteristic\u003c/p\u003e\n\u003cp\u003eRRT: renal replacement therapy\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSOFA: Sepsis-related Organ Failure Assessment\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003ch4\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by the Ethics Committees of the University Medical Centre Maribor under the number UKC-MB-KME-65/22 and was conducted in accordance with the Declaration of Helsinki. At admission to the hospital, patients or their relatives signed an informed consent form for all treatment, including RRT. Given the nature of the research, additional patient consent was not expected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot appicable.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThe datasets used and/or analysed in the current study are available upon reasonable request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFounding sources:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was no supported by any founding.\u003c/p\u003e\n\u003cp\u003eThe results presented in this paper have not been published previously in whole or part, except in an abstract format.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy conception and design: RE\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcqusition of data: BK and TK\u003c/p\u003e\n\u003cp\u003eAnalysis and interpretation the data: RE, SB and RH\u003c/p\u003e\n\u003cp\u003eDrafting of manuscript: RE\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eUchino S, Kellum JA, Bellomo R, Doig GS, Morimatsu H, Morgera S, et al. Acute renal failure in critically ill patients: a multinational, multicenter study. JAMA. 2005; 294(7):813-8.\u003c/li\u003e\n\u003cli\u003eMetnitz PG, Krenn CG, Steltzer H, Lang T, Ploder J, Lenz K, et al. Effect of acute renal failure requiring renal replacement therapy on outcome in critically ill patients. Crit Care Med. 2002; 30(9):2051-8.\u003c/li\u003e\n\u003cli\u003eChertow GM, Soroko SH, Paganini EP, Cho KC, Himmelfarb J, Ikizler TA, et al. Mortality after acute renal failure: models for prognostic stratification and risk adjustment. Kidney Int. 2006;70(6):1120-6.\u003c/li\u003e\n\u003cli\u003eDe Corte W, Vuylsteke S, De Waele JJ, Dhondt AW, Decruyenaere J, Vanholder R, et al. Severe lactic acidosis in critically ill patients with acute kidney injury treated with renal replacement therapy. Journal of Critical Care. 2014;29(4):650-5.\u003c/li\u003e\n\u003cli\u003eVan De Ginste L, Vanommeslaeghe F, Hoste EAJ, Kruse JM, Van Biesen W, Verbeke F. Patients with Severe Lactic Acidosis in the Intensive Care Unit: A Retrospective Study of Contributing Factors and Impact of Renal Replacement Therapy. Blood purification. 2022; 51(7):577-83.\u003c/li\u003e\n\u003cli\u003eBellomo R. Bench-to-bedside review: lactate and the kidney. Critical Care. 2002;6(4): 322-6.\u003c/li\u003e\n\u003cli\u003eFoucher CD, Tubben RE. StatPearls [Internet]. \u003cem\u003eStatPearls Publishing\u003c/em\u003e; Treasure Island (FL): January 21, 2024.\u003c/li\u003e\n\u003cli\u003eEmmett M, Szerlip H. Causes of lactic acidosis. [Accessed July 24, 2024]; UpToDate.com website. https://www.uptodate.com/contents/causes-of-lactic-acidosis?search=lactate\u0026amp;source=search_result\u0026amp;selectedTitle=1%7E150\u0026amp;usage_type=default\u0026amp;display_rank=1#H11\u003c/li\u003e\n\u003cli\u003eHusain FA, Martin MJ, Mullenix PS, Steele SR, Elliott DC. Serum lactate and base deficit as predictors of mortality and morbidity. Am J Surg. 2003; 185(5): 485-91.\u003c/li\u003e\n\u003cli\u003eShapiro NI, Howell MD, Talmor D, Nathanson LA, Lisbon A, Wolfe RE, et al. Serum lactate as a predictor of mortality in emergency department patients with infection. Ann Emerg Med. 2005;45(5):524-8.\u003c/li\u003e\n\u003cli\u003eNguyen HB, Rivers EP, Knoblich BP, Jacobsen G, Muzzin A, Ressler JA, et al. Early lactate clearance is associated with improved outcome in severe sepsis and septic shock. Crit Care Med. 2004;32(8):1637-42.\u003c/li\u003e\n\u003cli\u003eTrzeciak S, Dellinger RP, Chansky ME, Arnold RC, Schorr C, Milcarek B, et al. Serum lactate as a predictor of mortality in patients with infection. Intensive Care Med. 2007;33(6):970-7.\u003c/li\u003e\n\u003cli\u003eKDIGO Clinical Practice Guideline for Acute Kidney Injury. \u003cem\u003eKidney Int. 2012;\u003c/em\u003e\u003cem\u003e \u003c/em\u003e\u003cem\u003eSuppl;\u003cem\u003e2\u003c/em\u003e(1):6.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eKnaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: A severity of disease classification system. Crit Care Med. 1985;13(10): 818-29.\u003c/li\u003e\n\u003cli\u003eVincent JL, de Mendonça A, Cantraine F, Moreno R, Takala J, Suter PM, et al. Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: re- sults of a Multicenter, Prospective Study. Crit Care Med. 1998;26(11):1793-800.\u003c/li\u003e\n\u003cli\u003eCharlson ME, Carrozzino D, Guidi J, Patierno C. Charlson Comorbidity Index: A Critical Review of Clinimetric Properties. Psychother Psychosom. 2022;91(1):8-35.\u003c/li\u003e\n\u003cli\u003eKim SG, Lee J, Yun D, Kang MW, Kim YC, Kim DK, et al. Hyperlactatemia is a predictor of mortality in patients undergoing continuous renal replacement therapy for acute kidney injury. BMC Nephrol. 2023;24(1): 11.\u003c/li\u003e\n\u003cli\u003eLevy B. Lactate and shock state: the metabolic view. Curr Opin Crit Care. 2006;12(4):315-21.\u003c/li\u003e\n\u003cli\u003eLevraut J, Ciebiera J-P, Jambou P, Ichai C, Labib Y, Grimaud D. Effect of continuous veno-venous hemofiltration with dialysis on lactate clearance in critically ill patients. Crit Care Med. 1997;25:58-62.\u003c/li\u003e\n\u003cli\u003eKraut JA, Madias NE. Lactic acidosis: Current treatments and future directions. Am J Kidney Dis. 2016;68(3):473-82.\u003c/li\u003e\n\u003cli\u003eGleeson PJ, Crippa IA, Sannier A, Koopmansch C, Bienfait L, Allard J, et al. Critically ill patients with acute kidney injury: clinical determinants and post-mortem histology. Clin Kidney J. 2023;16(10):1664-73.\u003c/li\u003e\n\u003cli\u003eMariano F, Mella A, Randone P, Agostini F, Bergamo D, Berardino M, et al. Safety and Metabolic Tolerance of Citrate Anticoagulation in Critically Ill Polytrauma Patients with Acute Kidney Injury Requiring an Early Continuous Kidney Replacement Therapy. Biomedicines. 2023;11(9): 2570.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"acute kidney injury, lactate, acidosis, survival","lastPublishedDoi":"10.21203/rs.3.rs-5806235/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5806235/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHyperlactatemia is common in intensive care unit (ICU) patients. The aim of our retrospective observational study was to analyse the impact of serum lactate on admission on mortality in patients with acute kidney injury (AKI) treated with renal replacement therapy (RRT).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the study period of 4 years, 2939 patients were admitted to the ICU, 503 patients were diagnosed with AKI and 209 of them required RRT. After excluding patients on chronic dialysis and with known malignant disease, we retrospectively analysed 154 patients. Hyperlactatemia was defined as a serum lactate concentration above 4 mmol/L on admission to the ICU.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean age of patients was 62.8 years, and 69.5% were men. The mean Charlson Comorbidity Index (CCI) on admission to the ICU was 3.7 and fifty-six (36.4%) patients had acute hyperlactatemia. All included patients had AKI stage 3 and were treated with RRT, 125 (81.2%) with continuous RRT and 29 (18.8%) with intermittent hemodialysis. The mean length of stay in the ICU was 15.7 ± 13 days and 118 (76.6%) patients died during the 60-day observation period. A Kaplan-Meier survival analysis showed that the survival rate was statistically significantly lower in the group of patients with hyperlactatemia (log-rank; p = 0.032). The univariate Cox regression analysis showed that serum lactate on admission to the ICU significantly predict 60-day survival (HR 1.075; 95%CI 1.015–1.140; p = 0.014). In the multivariate Cox regression analysis, which included age, gender, diabetes, hypertension, chronic kidney disease, estimated glomerular filtration rate, serum lactate, CCI and C-reactive protein, only age (HR 1.031; 95%CI 1.007–1.056; p = 0.011) and serum lactate (HR 1.067; 95%CI 1.004–1.134; p = 0.035) were independent predictors of mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study underscores the independent association between hyperlactatemia of more than 4 mmol/L on admission to the ICU and increased 60-day mortality in patients with AKI treated with RRT. These findings, which have significant implications for the management and prognosis of critically ill patients with AKI, provide a new understanding of the role of serum lactate in patient outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eName of the registry: ClinicalTrials.gov\u003c/li\u003e\n \u003cli\u003eTrial registration number: NCT06565403\u003c/li\u003e\n \u003cli\u003eDate of registration, followed by the words 'Retrospectively registered'\u003cu\u003e:\u003c/u\u003e August, 19,2024\u003c/li\u003e\n \u003cli\u003eURL of trial registry record: https://clinicaltrials.gov/study/NCT06565403\u003c/li\u003e\n\u003c/ul\u003e","manuscriptTitle":"Hyperlactatemia in critically ill patients with acute kidney injury treated with renal replacement therapy in the intensive care unit","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-03 05:11:53","doi":"10.21203/rs.3.rs-5806235/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-07T14:22:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-06T08:11:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"171418589669243721736981950480470566867","date":"2025-04-06T08:03:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-05T14:27:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18775668427498518090287573977875883138","date":"2025-04-05T03:33:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"258478248200429120124224634652723195318","date":"2025-04-03T20:34:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"190966687111722419094101770806428277381","date":"2025-04-03T07:16:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-02T20:15:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"225911646567269593634685123631176625276","date":"2025-04-02T19:47:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-02T07:32:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260180619149948981938874070251809034334","date":"2025-04-02T01:44:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254412000498088133102718068993465591023","date":"2025-04-01T19:28:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-01T18:31:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-01T17:39:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2025-03-30T18:48:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"838764fa-16bb-4fa4-90a6-0862d3e81d7f","owner":[],"postedDate":"April 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-05T15:58:39+00:00","versionOfRecord":{"articleIdentity":"rs-5806235","link":"https://doi.org/10.1186/s12882-025-04149-5","journal":{"identity":"bmc-nephrology","isVorOnly":false,"title":"BMC Nephrology"},"publishedOn":"2025-04-30 15:57:02","publishedOnDateReadable":"April 30th, 2025"},"versionCreatedAt":"2025-04-03 05:11:53","video":"","vorDoi":"10.1186/s12882-025-04149-5","vorDoiUrl":"https://doi.org/10.1186/s12882-025-04149-5","workflowStages":[]},"version":"v1","identity":"rs-5806235","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5806235","identity":"rs-5806235","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}