Severe Acute Kidney Injury Patients Research Articles

Prediction of successful weaning from renal replacement therapy in critically ill patients based on machine learning

Background Predicting the successful weaning of acute kidney injury (AKI) patients from renal replacement therapy (RRT) has emerged as a research focus, and we successfully built predictive models for RRT withdrawal in patients with severe AKI by machine learning. Methods This retrospective single-center study utilized data from our general intensive care unit (ICU) Database, focusing on patients diagnosed with severe AKI who underwent RRT. We evaluated RRT weaning success based on patients being free of RRT in the subsequent week and their overall survival. Multiple logistic regression (MLR) and machine learning algorithms were adopted to construct the prediction models. Results A total of 976 patients were included, with 349 patients successfully weaned off RRT. Longer RRT duration (7.0 vs. 9.6 d, p = 0.002, OR = 0.94), higher serum cystatin C levels (1.2 vs. 3.2 mg/L, p < 0.001, OR = 0.46), and the presence of septic shock (28.1% vs. 41.5%, p < 0.001, OR = 0.63) were associated with reduced likelihood of RRT weaning. Conversely, a positive furosemide stress test (FST) (60.2% vs. 40.7%, p < 0.001, OR = 2.75) and higher total urine volume 3 d before RRT withdrawal (755 vs. 125 mL/d, p < 0.001, OR = 2.12) were associated with an increased likelihood of successful weaning from RRT. Next, we demonstrated that machine learning models, especially Random Forest and XGBoost, achieving an AUROC of 0.95. The XGBoost model exhibited superior accuracy, yielding an AUROC of 0.849. Conclusion High-risk factors for unsuccessful RRT weaning in severe AKI patients include prolonged RRT duration. Machine learning prediction models, when compared to models based on multivariate logistic regression using these indicators, offer distinct advantages in predictive accuracy.

Sodium bicarbonate for the treatment of severe metabolic acidosis with moderate or severe acute kidney injury in the critically ill: protocol for a randomised clinical trial (BICARICU-2)

IntroductionWhen both severe metabolic acidemia (pH equal or less than 7.20; PaCO2 equal or less than 45 mm Hg and bicarbonate concentration equal or less than of 20 mmol/L) and...

Short-term prognostic models for severe acute kidney injury patients receiving prolonged intermittent renal replacement therapy based on machine learning

BackgroundAs an effective measurement for severe acute kidney injury (AKI), the prolonged intermittent renal replacement therapy (PIRRT) received attention. Also, machine learning has advanced and been applied to medicine. This study aimed to establish short-term prognosis prediction models for severe AKI patients who received PIRRT by machine learning.MethodsThe hospitalized AKI patients who received PIRRT were assigned to this retrospective case-control study. They were grouped based on survival situation and renal recovery status. To screen the correlation, Pearson’s correlation coefficient, partial ETA square, and chi-square test were applied, eight machine learning models were used for training.ResultsAmong 493 subjects, the mortality rate was 51.93% and the kidney recovery rate was 30.43% at 30 days post-discharge, respectively. The indices related to survival were Sodium, Total protein, Lactate dehydrogenase (LDH), Phosphorus, Thrombin time, Liver cirrhosis, chronic kidney disease stage, number of vital organ injuries, and AKI stage, while Sodium, Total protein, LDH, Phosphorus, Thrombin time, Diabetes, peripherally inserted central catheter and AKI stage were selected to predict the 30-day renal recovery. Naive Bayes has a good performance in the prediction model for survival, Random Forest has a good performance in 30-day renal recovery prediction model, while for 90-day renal recovery prediction model, it’s K-Nearest Neighbor.ConclusionsMachine learning can not only screen out indicators influencing prognosis of AKI patients receiving PIRRT, but also establish prediction models to optimize the risk assessment of these people. Moreover, attention should be paid to serum electrolytes to improve prognosis.

PD19-05 ELEVATED PROTHROMBIN FRAGMENT 1+2 IS A RISK FACTOR OF SEVERE ACUTE KIDNEY INJURY IN PATIENTS WITH UROLOGICAL SEPSIS

You have accessJournal of UrologyCME1 Apr 2023PD19-05 ELEVATED PROTHROMBIN FRAGMENT 1+2 IS A RISK FACTOR OF SEVERE ACUTE KIDNEY INJURY IN PATIENTS WITH UROLOGICAL SEPSIS Naoki Fujita, Masaki Momota, Osamu Soma, Daisuke Noro, Jotaro Mikami, Shingo Hatakeyama, Hiroyuki Ito, Takahiro Yoneyama, Yasuhiro Hashimoto, Kazuaki Yoshikawa, and Chikara Ohyama Naoki FujitaNaoki Fujita More articles by this author , Masaki MomotaMasaki Momota More articles by this author , Osamu SomaOsamu Soma More articles by this author , Daisuke NoroDaisuke Noro More articles by this author , Jotaro MikamiJotaro Mikami More articles by this author , Shingo HatakeyamaShingo Hatakeyama More articles by this author , Hiroyuki ItoHiroyuki Ito More articles by this author , Takahiro YoneyamaTakahiro Yoneyama More articles by this author , Yasuhiro HashimotoYasuhiro Hashimoto More articles by this author , Kazuaki YoshikawaKazuaki Yoshikawa More articles by this author , and Chikara OhyamaChikara Ohyama More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003285.05AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Elevated prothrombin fragment 1+2 (F1+2), produced during conversion of prothrombin to thrombin, indicates excess thrombin production and serves as a molecular marker of activated coagulation. The aim of this study was to investigate the impact of elevated F1+2 on the development of severe acute kidney injury (AKI) in patients with urological sepsis. METHODS: We prospectively collected the data from 143 patients with urological sepsis between March 2017 and March 2019. AKI was defined according to the KDIGO criteria. Patients were divided into two groups: lower F1+2 (plasma F1+2 levels <395 pmol/L) and higher F1+2 groups (plasma F1+2 levels ≥395 pmol/L). Multivariable logistic regression analysis was performed to evaluate the impact of elevated F1+2 on the severe AKI development. We evaluated predictive ability of elevated F1+2 for the severe AKI development using the receiver operating characteristic curve and compared using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). RESULTS: Median age was 78 years old in this cohort. Of 143 patients, 61 were classified as a higher F1+2 group and 36 developed to severe AKI (AKI stage 2-3). Rate of severe AKI development in the higher F1+2 group was significantly higher than that of the lower F1+2 group (Figure 1A; p=0.004). Plasma F1+2 levels in patients with severe AKI was significantly higher than that in patients with stage 0-1 AKI (Figure 1B; p=0.007). In multivariable analysis, plasma F1+2 level ≥395 pmol/L was selected as a significant independent risk factor for severe AKI development (Figure 1C; hazard ratio 3.067, p=0.006). The predictive ability of hydronephrosis plus elevated F1+2 levels for severe AKI development was significantly improved compared with hydronephrosis alone (Figure 1D). CONCLUSIONS: Elevated prothrombin F1+2 is a risk factor of severe AKI in patients with urological sepsis. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e578 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Naoki Fujita More articles by this author Masaki Momota More articles by this author Osamu Soma More articles by this author Daisuke Noro More articles by this author Jotaro Mikami More articles by this author Shingo Hatakeyama More articles by this author Hiroyuki Ito More articles by this author Takahiro Yoneyama More articles by this author Yasuhiro Hashimoto More articles by this author Kazuaki Yoshikawa More articles by this author Chikara Ohyama More articles by this author Expand All Advertisement PDF downloadLoading ...

Analysis of risk factors for severe acute kidney injury in patients with acute myocardial infarction: A retrospective study.

Patients with acute myocardial infarction (AMI) complicated by acute kidney injury (AKI) tend to have a poor prognosis. However, the exact mechanism of the co-occurrence of the two diseases is unknown. Therefore, this study aims to determine the risk factors for severe AKI in patients with AMI. A total of 2022 patients were included in the Medical Information Mart for Intensive Care. Variables were identified via univariate logistic regression, and the variables were corrected via multivariate logistic regression. Restricted cubic splines were used to examine the risks associated with the variables. The Kaplan-Meier method was used to compare the risk of severe AKI among the patients. Patients with severe AKI had a higher in-hospital mortality rate (28.6% vs. 9.0%, P < 0.001) and a longer duration of intensive care (6.5 days vs. 2.9 days, P < 0.001). In patients with AMI, the mean systolic blood pressure (SBP); international normalized ratio (INR); the levels of blood urea nitrogen (BUN), glucose, and calcium; and a history of liver disease were found to be the independent risk factors for developing severe AKI after their admission. Increased levels of BUN and blood glucose and a high INR increased the risk of severe AKI; however, increased levels of calcium decreased the risk; SBP presented a U-shaped curve relationship. Patients with severe AKI have a poor prognosis following an episode of AMI. Furthermore, in patients with AMI, SBP; INR; a history of liver disease; and the levels of BUN, glucose, and calcium are the independent risk factors for developing severe AKI after their admission.

Outcome of Acute Kidney Injury (AKI) Patients in the Intensive Care Unit of Enam Medical College &amp; Hospital During the Period of July 2018 to May 2019

Background: Acute kidney injury (AKI) is a public health issue associated with multiple clinical conditions which may occur due to slight elevation in serum creatinine to anuric renal failure with electrolytes and acid-base imbalance, chronic kidney diseases, end-stage renal diseases, impaired innate immunity associated with higher infection rate, and increased duration of hospital stay with higher cost. Sometimes severe AKI patients may need intensive care support and renal replacement therapy. Severe sepsis is the most common cause of ICU admission. Materials and Methods: This observational study was conducted during the period of July 2018 to May 2019 in the Department of Anesthesiology and Intensive Care Unit of Enam Medical College &amp; Hospital, Savar Dhaka. A total of 87 AKI patients were selected among which 48 were male and 39 were female. Acute kidney injury was selected with an increase in serum creatinine &gt;0.3 mg /dL within 48 hours or ≥1.5 mg /dL from the base line within prior 7 days or urine volume &lt;0.5mL/kg/hour. The last recorded lowest value or value within 24 hrs before admission was considered as baseline S. creatinine. Staging was done according to the KDIGO classification. Chronic Kidney Diseases (CKD) patients and acute on chronic kidney diseases patients aged &lt;18 years were excluded from our study. Results: Highest age group was between 41 to 60 years. According to KDIGO definition 27.58% fell in stage-I, 39.8% in Stage-II and 33.33% in Stage-III. Hypertension (34.48%) and diabetes (24.18%) were found common co-morbid conditions. The most common indication for admission in ICU was septicemia (21.83%). Other causes include pneumonia and other respiratory illness (18.39%), gynae and obstetrical cases (16.09%) AGE (acute gastroenteritis) 13.79%, poisoning 9.19%, polytrauma 8.0%, cardiac diseases 6.89%, cerebrovascular diseases 3.44%, malignancy 2.29%. Total 62.06% (54) patients needed ionotropic support, 55.17% (48) needed artificial ventilation. 26.4% patients needed renal replacement therapy (8.04% HD, 12.64% SLED, 5.74% CRRT). Finally, 66 (75.86%) patients survived among which 59 (67.81%) recovered completely from AKI, 7 (8.04%) turned into CKD and 21 (24.13%) expired. Conclusion: Early detection and extensive ICU management may lead to full recovery of renal function which ultimately reduces adverse outcomes, renal replacement therapy and AKI-related morbidity and mortality. J Enam Med Col 2021; 11(1): 24-33

Prognostic Significance of Baseline Blood Glucose Levels and Glucose Variability in Severe Acute Kidney Injury: A Secondary Analysis from the RENAL Study.

Aim: To study the associations between baseline blood glucose levels (BGL), glycemic variability and clinical outcomes in patients with severe acute kidney injury (AKI) receiving continuous renal replacement therapy (CRRT). Methods: We performed a secondary analysis of the Randomized Evaluation of Normal versus Augmented Level of RRT (RENAL) study. A multivariate Cox regression model was used to assess the association between baseline BGL, glycemic variability and clinical outcomes. The primary outcome was all-cause mortality, and secondary outcomes were duration of hospital and intensive care unit (ICU) stay. Results: Baseline BGL data were available in 1404 out of 1508 patients from the RENAL study. Among them, 627 patients died within 90 days of randomization. Compared to patients in the second quartile (BGL 5.8−7.2 mmol/L), patients in the first quartile (BGL < 5.8 mmol/L) had increased mortality rate (90-day HR 1.48; p = 0.001; 28-day HR 1.47; p = 0.042). However, there were no significant differences in ICU and hospital length of stay (LOS) (p = 0.82 and p = 0.33, respectively). Glycemic variability data were from 1345 out of 1404 patients who had data for BG values within 28 days. Higher coefficient of variation (CV) (HR 1.02; P trend = 0.002) and standard deviation value (SD) (HR 1.29; P trend = 0.027) were associated with higher risk of death at day 90. Conclusions: We identified a low BGL within the normal physiological range at baseline and greater CV and SD values as significant modifiable risk factors for mortality in severe AKI patients in ICU, which may be a target for intervention.

Development and validation of a nomogram for postoperative severe acute kidney injury in acute type A aortic dissection.

Postoperative acute kidney injury (AKI) is a major complication associated with increased morbidity and mortality after surgery for acute type A aortic dissection (AAAD). To the best of our knowledge, risk prediction models for AKI following AAAD surgery have not been reported. The goal of the present study was to develop a prediction model to predict severe AKI after AAAD surgery. A total of 485 patients who underwent AAAD surgery were enrolled and randomly divided into the training cohort (70%) and the validation cohort (30%). Severe AKI was defined as AKI stage III following the Kidney Disease: Improving Global Outcomes criteria. Preoperative variables, intraoperative variables and postoperative data were collected for analysis. Multivariable logistic regression analysis was performed to select predictors and develop a nomogram in the study cohort. The final prediction model was validated using the bootstrapping techniques and in the validation cohort. The incidence of severe AKI was 23.0% (n = 78), and 14.7% (n = 50) of patients needed renal replacement treatment. The hospital mortality rate was 8.3% (n = 28), while for AKI patients, the mortality rate was 13.1%, which increased to 20.5% for severe AKI patients. Univariate and multivariate analyses showed that age, cardiopulmonary bypass time, serum creatinine, and D-dimer were key predictors for severe AKI following AAAD surgery. The logistic regression model incorporated these predictors to develop a nomogram for predicting severe AKI after AAAD surgery. The nomogram showed optimal discrimination ability, with an area under the curve of 0.716 in the training cohort and 0.739 in the validation cohort. Calibration curve analysis demonstrated good correlations in both the training cohort and the validation cohort. We developed a prognostic model including age, cardiopulmonary bypass time, serum creatinine, and D-dimer to predict severe AKI after AAAD surgery. The prognostic model demonstrated an effective predictive capability for severe AKI, which may help improve risk stratification for poor in-hospital outcomes after AAAD surgery.

Association Between Nadir Hematocrit and Severe Acute Kidney Injury After Off-Pump Coronary Artery Bypass Graft Surgery: A Retrospective Cohort Study Based on the MIMIC-IV Database

BACKGROUND We aimed to evaluate the association between postoperative nadir hematocrit (Hct) and severe acute kidney injury (AKI) in patients undergoing off-pump coronary artery bypass graft (OPCABG) surgery. MATERIAL AND METHODS Data of patients who received OPCABG were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. A generalized additive model was applied to explore the relationship between nadir Hct and severe AKI. Patients were divided into 4 groups by quartiles of postoperative nadir Hct, with the lowest group (Hct <25%) as reference. We conducted multivariate logistic regression models to calculate adjusted odds ratios (OR) and 95% CI and evaluate trend among the 4 groups. RESULTS In total, 1783 OPCABG patients were included. A nonlinear association between nadir Hct and severe AKI was identified. After adjusting for potential confounders, nadir Hct was negatively associated with risk of severe AKI when Hct was less than 31%; there was no statistical significance between highest Hct group (Hct ≥31%) and control group (Hct <25%; P>0.05). Tests for trend were significant (P<0.05). Subgroup analyses showed each 1% increase in postoperative nadir Hct was associated with a 23% decrease in risk of severe AKI (OR, 0.77; P=0.002) in lower BMI group (<30 kg/m²). CONCLUSIONS The association between postoperative nadir Hct and severe AKI in patients after OPCABG was nonlinear. Lower nadir Hct may be associated with increased risk of severe AKI when Hct values are less than 31%. However, no statistical significance was found between the highest Hct group and control group.

Circulating and urinary microRNAs profile for predicting renal recovery from severe acute kidney injury

BackgroundThere is little known about the contribution of microRNAs (miRNAs) in the recovery from acute kidney injury (AKI). This study aimed to discover and validate miRNA profiles for predicting renal recovery from severe AKI.Patients and methodsA prospective observational study was conducted between June 2020 and January 2021. Urine and serum samples of participants with AKI stage 3 were collected from two groups: renal recovery and renal non-recovery. Transcriptomic analysis was performed using nCounter miRNA Expression Assay. Expression levels of candidate miRNAs were validated using quantitative real-time polymerase chain reaction (qRT-PCR).ResultsThe discovery phase identified 18 and 11 differentially expressed miRNAs that were statistically significant between the two groups in urine and serum specimens, respectively. Top candidate miRNAs selected included miR-556-3p, miR-1915-3p, miR-4284, miR-32-5p, miR-96-5p, and miR-556-5p in urine, and miR-499b-5p, miR-30a-3p, miR-92b-3p and miR-770-5p in serum. This study enrolled 110 participants in the validation phase. The qRT-PCR analysis indicated that urine miR-556-3p was significantly higher in the renal recovery group than in the renal non-recovery group. Urine miR-556-3p alone predicted renal recovery with an area under the curve (AUC) of 0.64 (95%CI 0.52–0.75, p = 0.03). Combining the clinical model with urine miR-556-3p predicted renal recovery with an AUC of 0.83 (95%CI 0.75–0.92, p < 0.01).ConclusionThis data provides evidence that microtranscriptome profiles of severe AKI patients with renal recovery differed from the non-recovery group. Urine miR-556-3p had the potential to improve the prediction of renal recovery from severe AKI.

The association between acute kidney injury and outcomes in cancer patients receiving immune checkpoint inhibitor therapy: a systematic review and meta-analysis.

Immune checkpoint inhibitors (ICPIs) are a novel therapeutic approach to cancer treatment that have changed the landscape of cancer therapy but also have some considerable drawbacks. Acute kidney injury (AKI) is one of these potential complications that may have effects on patient outcomes. In this review, we assessed the effect of AKI on mortality outcomes in cancer patients receiving this immunotherapy. We performed a systematic review and meta-analysis of prospective, retrospective, randomized and non-randomized studies, which examined the effects of AKI in cancer patients receiving immune checkpoint inhibitors. We searched through PubMed, Medline, Web of Science, Scopus and Cochrane Library databases. Seven studies were included in the final analysis, with a total number of patients of 761. Overall, the risk of death was higher in patients that developed AKI during ICPI treatment [hazard ratio (HR) 1.42, 95% confidence interval (CI) 1.05-1.92, P=0.02; heterogeneity χ2=11.68, I2=66%, P=0.02] compared with patients that did not develop AKI. In addition, there was a trend to a better survival in those with less severe AKI patients compared with those with more severe AKI (HR 1.35, 95% CI 0.99-1.83, P=0.05). Lastly, it was seen that patients with persistent kidney dysfunction (non-recovery) had an increased risk for all-cause mortality (HR 2.93, 95% CI 1.41-6.08, P=0.004; heterogeneity χ2=0.53, I2=0%, P=0.47). Development of AKI in patients with cancer receiving immune checkpoint inhibitors is associated with increased risk of mortality.

Mild and moderate to severe early acute kidney injury following cardiac surgery among patients with heart failure and preserved vs. mid-range vs. reduced ejection fraction: A retrospective cohort study.

Patients with heart failure who undergo cardiac surgery have increased long-term mortality in which acute kidney injury (AKI) plays a role. However, little is known about whether the incidence of AKI differs according to stratified left ventricular ejection fraction (LVEF). To assess the risks of mild AKI and moderate to severe AKI postcardiac surgery among patients with heart failure. Retrospective cohort analysis of patient data. Ejection fractions were categorised as LVEF less than 40%, heart failure with reduced ejection fraction (HFrEF); LVEF 40 to 49%, heart failure with mid-range ejection fraction (HFmrEF); and LVEF at least 50%, heart failure with preserved ejection fraction (HFpEF). Patients who underwent cardiac surgery from 2012 to 2019 in Fuwai Hospital, Beijing, China, were consecutively enrolled. The primary endpoint was postoperative AKI staged either as mild AKI or moderate to severe AKI. The secondary outcome was the peri-operative composite adverse event of dialysis support, tracheotomy, intrasurgical and postsurgical mechanical cardiac support and in-hospital mortality. This study also assessed chronic renal dysfunction at follow-up. Of the 54 696 included patients, 18.9% presented with heart failure. Among these with HFpEF, HFmrEF and HFrEF, the incidence of postoperative mild AKI was 37.0, 33.4 and 37.6%, respectively. Patients with HFpEF and HFmrEF were characterised by numerically greater prevalence of moderate to severe AKI than HFrEF (8.5 vs. 9.1 vs. 5.8%). HFrEF and HFmrEF patients had comparable risks for mild AKI relative to HFpEF patients, odds ratio (OR) 0.885; 95% confidence interval CI 0.763 to 1.027 for HFmrEF vs. HFpEF; OR 1.083; 95% CI 0.933 to 1.256 for HFrEF vs. HFpEF. Patients with HFmrEF were more at risk for moderate to severe AKI than patients with HFpEF (OR, 1.368; 95% CI 1.066 to 1.742), but HFrEF and HFpEF did not differ significantly (OR 1.012; 95% CI 0.752 to 1.346). An increasing number of noncardiac comorbidities led to a higher risk of mild AKI and moderate to severe AKI in patients with heart failure; and its effect on AKI was almost equal among the three heart failure strata. The incidence of postoperative composite adverse outcome increased in a graded manner from HFpEF to HFmrEF to HFrEF. Information on the creatine concentrations at 3 months postoperatively and longer were retained for 5200 out of 10 347 (50.6%) heart failure patients in our charts.The AKI severity and the presence of HFmrEF contributed substantially to the development of renal dysfunction over a median [IQR] follow-up of 10 months [4.0 to 21.0]. Initiative programmes aimed at patients with HFrEF to prevent moderate to severe AKI and chronic kidney dysfunction should also include patients with HFmrEF.

Efficacy and Safety of Chuan Huang Fang Combining Reduced Glutathione in Treating Acute Kidney Injury (Grades 1-2) on Chronic Kidney Disease (Stages 2-4): Study Protocol for a Multicenter Randomized Controlled Clinical Trial.

Background Acute kidney injury (AKI) is a global public health challenge resulting in considerable morbidity and mortality. AKI on chronic kidney disease (CKD) (AKI on CKD, A on C) accounts for about a third of total AKI. For severe AKI grade 3, renal replacement therapy (RRT) should be implemented in time. However, the lack of recognized drug treatment method for AKI grades 1–2 is a crucial problem in clinic. Chuan Huang Fang (CHF) is a Chinese herbal formulation developed for the treatment of A on C from the Shanghai Municipal Hospital of Traditional Chinese Medicine. Our previous studies suggested that CHF might effectively protect renal functions of A on C patients. As a widely used antioxidant in clinic, reduced glutathione (RG) is reported to improve the clinical efficacy of high-flux hemodialysis (HFHD) in severe AKI patients recently. To address the crucial problem mentioned above, thus we design a new clinical protocol of CHF combining RG and try to evaluate the efficacy and safety of this protocol in treating patients diagnosed with CKD stages 2–4 complicated with AKI grades 1–2. Methods This is a multicenter randomized controlled clinical trial. We intend to enroll 162 participants, and these participants will be divided into the RG group, the CHF group, and the RG + CHF group randomly assigning 1 : 1 : 1 principle. The RG group will be general treatments combining RG, the CHF group will be general treatments combining CHF, and the RG + CHF group will be general treatments combining RG and CHF. The duration of treatment will last two weeks. The primary evaluation outcome will be the change in the slope of serum creatinine (Scr) over 2 weeks. Secondary evaluation outcomes include changes in blood urea nitrogen (BUN), estimated glomerular filtration rate (eGFR), urinary AKI biomarkers (neutrophil gelatinase-associated lipocalin (NGAL), interleukin-18 (IL-18), gamma-glutamyl transpeptidase (γ-GT), etc.), traditional Chinese medicine (TCM) symptoms, inflammatory indicators, and oxidative stress indicators. Meanwhile, the vital sign indicators and adverse events (AEs) will be closely observed. These dates will be meticulously recorded and properly handled by investigators throughout the study. Discussion. This study will provide convictive research-derived data to evaluate clinical efficacy and safety of CHF combing RG for CKD stages 2–4 complicated with AKI grades 1–2 and provide an evidence-based recommendation for clinicians. The timely completion of this trial will provide a novel drug treatment method for A on C. This trial is registered with ChiCTR2100043311 and registered on February 9, 2021.

Severe acute kidney injury predicting model based on transcontinental databases: a single-centre prospective study.

ObjectivesThere are many studies of acute kidney injury (AKI) diagnosis models lack of external validation and prospective validation. We constructed the models using three databases to predict severe AKI within 48 hours in intensive care unit (ICU) patients.DesignA retrospective and prospective cohort study.SettingWe studied critically ill patients in our database (SHZJU-ICU) and two other public databases, the Medical Information Mart for Intensive Care (MIMIC) and AmsterdamUMC databases, including basic demographics, vital signs and laboratory results. We predicted the diagnosis of severe AKI in patients in the next 48 hours using machine-learning algorithms with the three databases. Then, we carried out real-time severe AKI prediction in the prospective validation study at our centre for 1 year.ParticipantsAll patients included in three databases with uniform exclusion criteria.Primary and secondary outcome measuresEffect evaluation index of prediction models.ResultsWe included 58 492 patients, and a total of 5257 (9.0%) patients met the definition of severe AKI. In the internal validation of the SHZJU-ICU and MIMIC databases, the best area under the receiver operating characteristic curve (AUROC) of the model was 0.86. The external validation results by AmsterdamUMC database were also satisfactory, with the best AUROC of 0.86. A total of 2532 patients were admitted to the centre for prospective validation; 358 positive results were predicted and 344 patients were diagnosed with severe AKI, with the best sensitivity of 0.72, the specificity of 0.80 and the AUROC of 0.84.ConclusionThe prediction model of severe AKI exhibits promises as a clinical application based on dynamic vital signs and laboratory results of multicentre databases with prospective and external validation.

Urinary immune cell phenotype of severe AKI in critically ill patients.

Cellular mechanisms involved in human renal recovery after an episode of acute kidney injury (AKI) are understudied. We aim to characterize the urinary immune cell phenotype of patients with AKI and evaluate its ability to predict renal recovery. A prospective study of critically ill patients with stage ≥ 2 AKI by KDIGO and sterile leukocyturia at admission was performed. Urine samples were collected fresh at day 0 and 2 and samples were analyzed by flow cytometry for different leukocytes. Patients were categorized in renal recovery or no-recovery groups. 28 patients were included, all with sepsis, 60.7% of which recovered renal function. The main urinary leukocytes present were neutrophils, followed by mononuclear phagocytic cells and B cells. Patients who recovered renal function had more M2 macrophages at day 2 (p = 0.043) and less B cells at admission (p = 0.006). M2 macrophages had an AUC-ROC of 0.796 (0.601-0.990) for recovery prediction and B cells an AUC-ROC of 0.743 (0.560-0.926) for no recovery. B regulatory cells were found in the urine of AKI patients. The urinary immune cell phenotype of severe AKI patients was composed essentially of neutrophils, mononuclear phagocytic cells and B cells. Our data suggest that M2 macrophages may promote and B cells preclude renal recovery. More studies are needed to validate our results and further explore the role of immune cells in renal recovery.

Hepatocyte growth factor and soluble cMet levels in plasma are prognostic biomarkers of mortality in patients with severe acute kidney injury.

BackgroundHepatocyte growth factor (HGF)/cMet pathway is necessary for repair and regeneration following acute kidney injury (AKI). We evaluated the clinical potential of plasma HGF and soluble cMet as prognostic biomarkers for severe AKI requiring continuous renal replacement therapy (CRRT).MethodsOne hundred thirty-six patients with severe AKI who participated in the VENUS (volume management under body composition monitoring in critically ill patients on CRRT) trial between 2017 and 2019 were enrolled in this study. We investigated associations between plasma HGF and cMet concentrations and all-cause mortality.ResultsPlasma HGF and soluble cMet levels were positively correlated. Patients were divided into three groups based on their HGF and soluble cMet concentrations. The day D 0, D2, and D7 highest concentration HGF groups had significantly higher in-hospital mortality after adjusting for sex, body mass index, Acute Physiology and Chronic Health Evaluation II, and age-adjusted Charlson comorbidity index score, especially on D7 (hazard ratio, 4.26; 95% confidence interval, 1.71–10.62; p = 0.002). D7 soluble cMet level was also associated with mortality. Receiver operating characteristic curve analysis indicated that D7 HGF and soluble cMet levels were best at predicting mortality. Addition of plasma HGF and soluble cMet to conventional prognostic indices significantly improved the predictive value for mortality on D7. However, plasma HGF and soluble cMet were not associated with fluid status.ConclusionPlasma HGF and soluble cMet levels were significant predictors of the outcomes of severe AKI patients undergoing CRRT. There was no correlation between plasma HGF and soluble cMet levels and fluid balance.

Mortality predictors in critically ill patients with acute kidney injury requiring continuous renal replacement therapy.

BackgroundBecause of high cost of continuous renal replacement therapy (CRRT) and the high mortality rate among severe acute kidney injury patients, careful identification of patients who will benefit from CRRT is warranted. This study determined factors associated with mortality among critically ill patients requiring CRRT.MethodsThis was a retrospective observational study of 414 patients admitted to the intensive care unit of four hospitals in South Korea who received CRRT from June 2017 to September 2018. Patients were divided according to degree of fluid overload (FO) and disease severity. The Cox proportional hazards model was used to explore the effect of relevant variables on mortality.ResultsIn-hospital mortality rate was 57.2%. Ninety-day mortality rate was 58.5%. Lower creatinine and blood pH were significant predictors of mortality. A one-unit increase in the Sequential Organ Failure Assessment (SOFA) score was associated with increased risk of and 90-day mortality (hazard ratio [HR], 1.07; p < 0.001). The risk of 90-day mortality in FO patients was 57.2% (p < 0.001) higher than in those without FO. High SOFA score was associated with increased risk for 90-day mortality (HR, 1.79; p = 0.03 and HR, 3.05; p = 0.001) in patients without FO and with FO ≤ 10%, respectively. The highest mortality rates were in patients with FO > 10%, independent of disease severity.ConclusionFO increases the risk of mortality independent of other factors, including severity of acute illness. Prevention of FO should be a priority, especially when managing the critically ill.

Timing of continuous renal replacement therapy in severe acute kidney injury patients with fluid overload: A retrospective cohort study

We aimed to evaluate the association of early versus late initiation of Continuous renal replacement therapy (CRRT) with mortality in patients with fluid overload. This was a retrospective cohort study of patients with fluid overload (FO) treated with CRRT due to severe acute kidney injury (AKI) between January 2015 and December 2017 in a mixed medical intensive care unit of a teaching hospital in Beijing, China. Patients were divided into early (≤15 h) and late (>15 h) groups based on the median time from ICU admission to CRRT initiation. The primary outcome was all-cause mortality at day 60. Multivariable Cox model analysis was used for analysis. The study patients were male predominant (84/150) with a mean age of 64.8 ± 16.7 years. The median FO value before CRRT initiation was 10.1% [6.2-16.1%]. The 60-day mortality rates in the early vs the late CRRT groups were 53.9% and 73%, respectively. On multivariable Cox modelling, the late initiation of CRRT was independently associated with an increased risk of death at 60 days (HR 1.75, 95% CI 1.11-2.74, p = 0.015). Early initiation of CRRT was independently associated with survival benefits in severe AKI patients with fluid overload.

Acute Kidney Injury in Pediatric Cardiac Intensive Care Children: Not All Admissions Are Equal: A Retrospective Study

To describe the incidence, associated characteristics, and outcomes of the maximum severity of acute kidney injury (AKI) in a heterogeneous population of critically ill children with cardiac disease. Retrospective cohort study. Pediatric cardiac intensive care unit (PCICU). Patients admitted to the PCICU. None. From January 2018 to July 2020 all patients admitted to a tertiary PCICU were included. Only the first admission was considered. Neonates ≤seven days old were excluded. Of 742 patients, 53 were medical cases, 69 catheterization laboratory cases, and 620 surgical cases (with five subgroups). The median age was 2.47 years (interquartile range [IQR], 0.38-9.85 years), with a median Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery score of 2 (IQR, 1-3). Median PCICU length of stay was three days (IQR, 2-7 days), and 21 (2.8%) patients died. Any incidence of AKI occurred in 70% of patients, 26% of which were classified as mild (stage 1) and 43% as severe (stages 2 and 3). AKI was diagnosed by urine output criteria in 56%, serum creatinine in 28%, and both in 16% of patients. Severe AKI occurred in subgroups as follows: medical (38%), catheterization laboratory (45%), correction (35%), palliation (55%), transplantation (85%), mechanical assistance (70%), and redo surgery (58%). Severe AKI patients were significantly older (p = 0.004), had a higher Pediatric Index of Mortality 3 score (p = 0.0004), had a higher cumulative fluid balance (p < 0.0001), and had a longer cardiopulmonary bypass time (p < 0.0001). Early AKI (≤24 hours from admission) was the most frequent presentation, with a greater proportion of severe cases in the early group compared with the intermediate (>24 and ≤48 hours) and late (>48 hours) (p < 0.0001) groups. Presentation of late severe AKI had a higher mortality (odds ratio, 4.9; 95% confidence interval, 1.8-15; p = 0.001). Severe AKI occurs in 43% of cardiac children and is diagnosed early, most often by urine output criteria. Severe AKI incidence varies significantly within subgroups of cardiac patients. Late AKI is associated with worse outcomes.

POS-027 Critical illness and systemic inflammation are key risk factors of severe AKI in patients with COVID-19

Acute kidney injury (AKI) has emerged as an important complication in COVID-19 patients. However, the relationship of AKI to the overall disease course remains incompletely understood. Here, we provide insights into COVID-19-associated AKI by conducting a detailed longitudinal analysis of clinical parameters and their association with AKI development.