Acute Kidney Injury Classification Research Articles

A Mild Increase in Serum Creatinine after Surgery Is Associated with Increased Mortality.

Background: Acute kidney injury (AKI), a prevalent postoperative complication, predominantly manifests as stage 1, characterized by a mild elevation in serum creatinine (SCr). There is yet to be a consensus regarding the association between stage 1 AKI and adverse outcomes in surgical patients. Methods: This retrospective study enrolled adult patients who underwent at least one surgery during hospitalization from the MIMIC IV database. AKI was diagnosed when the KDIGO creatinine criteria were satisfied within 7 days after surgery. Stage 1a AKI was defined as an absolute increase in SCr of 26.5 μmol/L, and stage 1b was defined as a 50% relative increase. Stage 1 AKI was also divided into transient and persistent substages based on whether the AKI recovered within 48 h after onset. The association between stage 1 AKI and its substages and in-hospital mortality was evaluated. Results: Among 49,928 patients enrolled, 9755 (19.5%) developed AKI within 7 days after surgery, of which 7659 (78.5%) presented with stage 1 AKI. The median follow-up was 369 (367, 372) days. Stage 1 AKI was significantly associated with in-hospital mortality after adjustment (aHR, 2.73; 95% CI, 2.29, 3.26). Subgroup analyses showed that the risk of stage 1 AKI on in-hospital mortality was attenuated by age ≥ 65 years (p for interaction = 0.017) or a baseline eGFR < 60 mL/min per 1.73 m2 (p for interaction = 0.001). Compared with non-AKI, patients with stage 1b (aHR, 3.06; 95% CI, 2.56, 3.66) and persistent stage 1 (aHR, 2.03; 95% CI, 1.61, 2.57) AKI had an increased risk of in-hospital mortality; however, this risk was not significant in those with stage 1a (aHR, 1.01; 95% CI, 0.68, 1.51) and transient stage 1 (aHR, 1.11; 95% CI, 0.79, 1.57) AKI. Conclusions: Stage 1 AKI exhibits an independent correlation with a heightened mortality risk among surgical patients. Consequently, a tailored adaptation of the KDIGO AKI classification may be necessitated for the surgical population, particularly those presenting with decreased baseline kidney function.

Redefining urine output thresholds for acute kidney injury criteria in critically Ill patients: a derivation and validation study

IntroductionThe current definition of acute kidney injury (AKI) includes increased serum creatinine (sCr) concentration and decreased urinary output (UO). Recent studies suggest that the standard UO threshold of 0.5 ml/kg/h may be suboptimal. This study aimed to develop and validate a novel UO-based AKI classification system that improves mortality prediction and patient stratification.MethodsData were obtained from the MIMIC-IV and eICU databases. The development process included (1) evaluating UO as a continuous variable over 3-, 6-, 12-, and 24-h periods; (2) identifying 3 optimal UO cutoff points for each time window (stages 1, 2, and 3); (3) comparing sensitivity and specificity to develop a unified staging system; (4) assessing average versus persistent reduced UO hourly; (5) comparing the new UO-AKI system to the KDIGO UO-AKI system; (6) integrating sCr criteria with both systems and comparing them; and (7) validating the new classification with an independent cohort. In all these steps, the outcome was hospital mortality. Another analyzed outcome was 90-day mortality. The analyses included ROC curve analysis, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and logistic and Cox regression analyses.ResultsFrom the MIMIC-IV database, 35,845 patients were included in the development cohort. After comparing the sensitivity and specificity of 12 different lowest UO thresholds across four time frames, 3 cutoff points were selected to compose the proposed UO-AKI classification: stage 1 (0.2–0.3 mL/kg/h), stage 2 (0.1–0.2 mL/kg/h), and stage 3 (< 0.1 mL/kg/h) over 6 h. The proposed classification had better discrimination when the average was used than when the persistent method was used. The adjusted odds ratio demonstrated a significant stepwise increase in hospital mortality with advancing UO-AKI stage. The proposed classification combined or not with the sCr criterion outperformed the KDIGO criteria in terms of predictive accuracy—AUC-ROC 0.75 (0.74–0.76) vs. 0.69 (0.68–0.70); NRI: 25.4% (95% CI: 23.3–27.6); and IDI: 4.0% (95% CI: 3.6–4.5). External validation with the eICU database confirmed the superior performance of the new classification system.ConclusionThe proposed UO-AKI classification enhances mortality prediction and patient stratification in critically ill patients, offering a more accurate and practical approach than the current KDIGO criteria.

Biomarkers in pursuit of precision medicine for acute kidney injury: hard to get rid of customs.

Traditional acute kidney injury (AKI) classifications, which are centered around semi-anatomical lines, can no longer capture the complexity of AKI. By employing strategies to identify predictive and prognostic enrichment targets, experts could gain a deeper comprehension of AKI's pathophysiology, allowing for the development of treatment-specific targets and enhancing individualized care. Subphenotyping, which is enriched with AKI biomarkers, holds insights into distinct risk profiles and tailored treatment strategies that redefine AKI and contribute to improved clinical management. The utilization of biomarkers such as N-acetyl-β-D-glucosaminidase, tissue inhibitor of metalloprotease-2·insulin-like growth factor-binding protein 7, kidney injury molecule-1, and liver fatty acid-binding protein garnered significant attention as a means to predict subclinical AKI. Novel biomarkers offer promise in predicting persistent AKI, with urinary motif chemokine ligand 14 displaying significant sensitivity and specificity. Furthermore, they serve as predictive markers for weaning patients from acute dialysis and offer valuable insights into distinct AKI subgroups. The proposed management of AKI, which is encapsulated in a structured flowchart, bridges the gap between research and clinical practice. It streamlines the utilization of biomarkers and subphenotyping, promising a future in which AKI is swiftly identified and managed with unprecedented precision. Incorporating kidney biomarkers into strategies for early AKI detection and the initiation of AKI care bundles has proven to be more effective than using care bundles without these novel biomarkers. This comprehensive approach represents a significant stride toward precision medicine, enabling the identification of high-risk subphenotypes in patients with AKI.

Reframing acute kidney injury as a pathophysiological continuum of disrupted renal excretory function.

Surrogate measures of glomerular filtration rate (GFR) continue to serve as pivotal determinants of the incidence, severity, and management of acute kidney injury (AKI), as well as the primary reference point underpinning knowledge of its pathophysiology. However, several clinically important deficits in aspects of renal excretory function during AKI other than GFR decline, including acid-base regulation, electrolyte and water balance, and urinary concentrating capacity, can evade detection when diagnostic criteria are built around purely GFR-based assessments. The use of putative markers of tubular injury to detect "sub-clinical" AKI has been proposed to expand the definition and diagnostic criteria for AKI, but their diagnostic performance is curtailed by ambiguity with respect to their biological meaning and context specificity. Efforts to devise new holistic assessments of overall renal functional compromise in AKI would foster the capacity to better personalize patient care by replacing biomarker threshold-based diagnostic criteria with a shift to assessment of compromise along a pathophysiological continuum. The term AKI refers to a syndrome of sudden renal deterioration, the severity of which is classified by precise diagnostic criteria that have unquestionable utility in patient management as well as blatant limitations. Particularly, the absence of an explicit pathophysiological definition of AKI curtails further scientific development and clinical handling, entrapping the field within its present narrow GFR-based view. A refreshed approach based on a more holistic consideration of renal functional impairment in AKI as the basis for a new diagnostic concept that reaches beyond the boundaries imposed by the current GFR threshold-based classification of AKI, capturing broader aspects of pathogenesis, could enhance AKI prevention strategies and improve AKI patient outcome and prognosis.

#2317 Did acute kidney injury confer greater mortality in COVID-19 hospitalized patients? A Portuguese hospital experience in the third COVID wave

Abstract Background and Aims The outbreak of coronavirus disease 2019 (COVID-19) pandemic, which originated in December 2019, had a huge impact on healthcare worldwide. Numerous studies have reported alterations in kidney function among hospitalized patients with COVID-19, such as hematuria, proteinuria, and acute kidney injury (AKI). Histopathology exams in autopsies have identified various potential causes for the presence of AKI in these patients, with reports linking AKI to unfavorable outcomes across multiple countries. However, additional data concerning hospitalized COVID patients in Portugal are essential to further understand this aspect. Method Retrospective cohort study enrolling adult patients, who attended the Emergency Department and recorded positive polymerase chain reaction (PCR) results for SARS-CoV-2 from January 1 to January 31, 2021. AKI was diagnosed using the Kidney Disease Improving Global Outcome (KDIGO) classification based on serum creatinine (sCr) criteria and using a background creatinine available in the patient record. We performed an analysis of incidence, lethality, and possible risk factors of AKI in COVID-19 patients. Results The study encompassed a cohort of 239 patients, of whom 62 (25.94%) received a diagnosis of Acute Kidney Injury (AKI) based on KDIGO criteria. Within this subgroup, the average age was 77.65 years (±12.07), with a total of 29 male patients (46.78%). Notably, 30 deaths (48.39%) occurred during the hospital stay in this group. Regarding the AKI classification, 39 patients (62.9%) were classified as KDIGO-AKI stage 1, 14 patients (22.58%) as stage 2, and 9 patients (14.52%) as stage 3. Regarding the timing of the diagnosis of AKI, 54 (87%) occurred in the moment of admission, 7 patients (11.3%) were diagnosed within the first 48 hours and a solitary case (1.6%) was diagnosed after 7 days of hospitalization. In the non-AKI group, the mean age was 69.86 years (±15.95), and approximately 55% were male. Within this group, 49 deaths (27.7%) were recorded during hospitalization. Significantly higher mortality rates were observed in the AKI group, demonstrating a statistically significant difference (p &amp;lt; 0.01) and a relative risk of 1.75 or mortality during hospitalization for AKI patients. Furthermore, the duration of hospitalization was notably prolonged in the AKI group (p &amp;lt; 0.01), with a disparity of approximately three days. Individuals who developed AKI exhibited lower scores on the Barthel-dependence scale, indicative of higher dependence (p &amp;lt; 0.01), and were significantly older (p &amp;lt; 0.01) comparing with the ones that did not develop acute kidney injury. Additionally, it was remarkable that the leukocyte count at admission was more elevated in the AKI group (p = 0.03) compared to non-AKI patients with a difference of approximately 1.62×109/L. Conclusion Respiratory infections, including COVID-19, frequently lead to emergency department admissions and hospitalizations. This study validates an elevated prevalence of acute kidney injury (AKI) both at admission and during the hospital stay in such cases. The presence of AKI is linked to adverse outcomes, including mortality and extended hospital stays, underscoring the critical importance of promptly diagnosing AKI. Larger-scale studies are warranted in this field to enhance our understanding of this entity and therefore improve the outcomes related to this prevalent condition.

#2727 Acute kidney in patients with ANCA associated vasculitis

Abstract Background and Aims Renal involvement in antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis is considered an emergency situation. Early diagnosis and treatment significantly improve prognosis. The aim of this study was to investigate the characteristics of acute kidney injury (AKI) in ANCA-associated vasculitis and assess its developmental aspects. Method Our study, conducted in the Internal Medicine Department A of Charles Nicole Hospital over a four-year period (January 2018-August 2022), was retrospective and descriptive. The diagnosis of ANCA vasculitis was made based on the ACR 1990 and/or EULAR 2012 criteria. The severity of AKI was categorized according to the KDIGO AKI classification. Results Seventeen patients were included, with a mean age of 51 ± 17.42 years and a sex ratio of 0.42. The primary clinical presentation of AKI was rapidly progressive glomerulonephritis (n=11), followed by acute nephritic syndrome (n=4). Pneumo-renal syndrome was observed in 4 patients. Proteinuria and hematuria were present in all 17 patients, with 4 being oligoanuric on dipstick. The mean creatinine level was 584 μmol/L, ranging from 125 to 1151 μmol/L. Thirteen patients had KDIGO stage 3 AKI on admission. The predominant histological lesion was extracapillary crescentic glomerulonephritis, based on the Berden classification (n=6). Hemodialysis was initiated immediately in 12 cases, and plasma exchange was performed in 10 cases. The average follow-up time was 24 months, ranging from 3 months to 4 years. Evolution was marked by partial remission in 7 cases, with a mean creatinine of 173 μmol/L. Progression to end-stage renal disease (ESRD) occurred in 10 cases. Relapses, presenting as pneumo-renal syndrome, were observed in 4 patients. Seven patients succumbed, with vasculitis being the cause of death in 3 cases. In univariate analysis, age &amp;gt;70 years was associated with progression to ESRD (p=0.03). No factor studied emerged as a prognostic factor for overall survival. Conclusion The presence and severity of AKI in ANCA-associated vasculitis are linked to a poorer prognosis, affecting both patient and renal survival.

Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis

BackgroundCurrent classification for acute kidney injury (AKI) in critically ill patients with sepsis relies only on its severity-measured by maximum creatinine which overlooks inherent complexities and longitudinal evaluation of this heterogenous syndrome. The role of classification of AKI based on early creatinine trajectories is unclear.MethodsThis retrospective study identified patients with Sepsis-3 who developed AKI within 48-h of intensive care unit admission using Medical Information Mart for Intensive Care-IV database. We used latent class mixed modelling to identify early creatinine trajectory-based classes of AKI in critically ill patients with sepsis. Our primary outcome was development of acute kidney disease (AKD). Secondary outcomes were composite of AKD or all-cause in-hospital mortality by day 7, and AKD or all-cause in-hospital mortality by hospital discharge. We used multivariable regression to assess impact of creatinine trajectory-based classification on outcomes, and eICU database for external validation.ResultsAmong 4197 patients with AKI in critically ill patients with sepsis, we identified eight creatinine trajectory-based classes with distinct characteristics. Compared to the class with transient AKI, the class that showed severe AKI with mild improvement but persistence had highest adjusted risks for developing AKD (OR 5.16; 95% CI 2.87–9.24) and composite 7-day outcome (HR 4.51; 95% CI 2.69–7.56). The class that demonstrated late mild AKI with persistence and worsening had highest risks for developing composite hospital discharge outcome (HR 2.04; 95% CI 1.41–2.94). These associations were similar on external validation.ConclusionsThese 8 classes of AKI in critically ill patients with sepsis, stratified by early creatinine trajectories, were good predictors for key outcomes in patients with AKI in critically ill patients with sepsis independent of their AKI staging.

Acute kidney injury in acute liver failure: A narrative review.

Acute kidney injury (AKI) is a frequent complication of acute liver failure (ALF) and it worsens the already worse prognoses of ALF. ALF is an uncommon disease, with varying etiologies and varying definitions in different parts of the world. There is limited literature on the impact of AKI on the outcome of ALF with or without transplantation. The multifaceted etiology of AKI in ALF encompasses factors such as hemodynamic instability, systemic inflammation, sepsis and direct nephrotoxicity. Indications of renal replacement therapy (RRT) for AKI in ALF patients extend beyond the conventional criteria for dialysis and continuous renal replacement therapy (CRRT) may have a role in transplant-free survival or bridge to liver transplantation (LT). LT is a life-saving option for ALF, so despite somewhat lower survival rates of LT in ALF patients with AKI, LT is not usually deferred. In this review, we will discuss the guidelines' recommended definition and classification of AKI in ALF, the impact of AKI in ALF, the pathophysiology of AKI and the role of CRRT and LT in ALF patients with AKI.

Classification and Regression Tree Predictive Model for Acute Kidney Injury in Traumatic Brain Injury Patients.

Acute kidney injury (AKI) is prevalent in hospitalized patients with traumatic brain injury (TBI), and increases the risk of poor outcomes. We designed this study to develop a visual and convenient decision-tree-based model for predicting AKI in TBI patients. A total of 376 patients admitted to the emergency department of the West China Hospital for TBI between January 2015 and June 2019 were included. Demographic information, vital signs on admission, laboratory test results, radiological signs, surgical options, and medications were recorded as variables. AKI was confirmed since the second day after admission, based on the Kidney Disease Improving Global Outcomes criteria. We constructed two predictive models for AKI using least absolute shrinkage and selection operator (LASSO) regression and classification and regression tree (CART), respectively. Receiver operating characteristic (ROC) curves of these two predictive models were drawn, and the area under the ROC curve (AUC) was calculated to compare their predictive accuracy. The incidence of AKI on the second day after admission was 10.4% among patients with TBI. Lasso regression identified five potent predictive factors for AKI: glucose, serum creatinine, cystatin C, serum uric acid, and fresh frozen plasma transfusions. The CART analysis showed that glucose, serum uric acid, and cystatin C ranked among the top three in terms of the feature importance of the decision tree model. The AUC value of the decision-tree predictive model was 0.892, which was higher than the 0.854 of the LASSO regression model, although the difference was not statistically significant. The decision tree model is valuable for predicting AKI among patients with TBI. This tree-based flowchart is convenient for physicians to identify patients with TBI who are at high risk of AKI and prompts them to develop suitable therapeutic strategies.

Cardiac Surgery Associated Acute Kidney Injury - Incidence, Risk Factors, Outcomes, and Risk Score Validation from a Single Centre in Bahrain

Background: Cardiac surgery-associated acute kidney injury (CSA-AKI) is a common complication in patients undergoing cardiac surgery. Early prediction of acute kidney injury (AKI) is crucial to estimate and communicate the prognosis and outcome of surgery. This research is particularly significant as it addresses the need for validation of risk scores in non-Caucasian populations with a higher incidence of risk factors, potentially leading to improved patient care and outcomes. Objective: This prospective study aimed to investigate the association between socio-demographic and clinical variables in patients undergoing cardiac surgery in a single center in Bahrain and their correlation with a predictive risk score. Methods: This comprehensive study included 100 patients who underwent cardiac surgery. The distribution of study subjects was meticulously assessed based on the kidney disease: Improving Global Outcomes (KDIGO) classification for AKI. The association of AKI with socio-demographic and clinical variables was rigorously analyzed using chi-square tests and logistic regression analysis and compared to the predicted incidence from the Acute Kidney Injury after Cardiac Surgery (AKICS) score, ensuring the reliability and accuracy of our findings. Results: Among the study subjects, 65% were not affected by acute kidney injury (AKI). In contrast, 19% were classified as having Stage 1 AKI, 8% as Stage 2, and 8% as Stage 3. Additionally, 2% of the patients required dialysis. The findings of our study underscore the significant impact of AKI on hospital stay and the need for dialysis. Female gender, previous myocardial infarction, UF heparin use, and elevated preoperative creatinine levels were significantly associated with AKI. The AKICS score was found to predict AKI in our patients, with a sensitivity of 91% and a specificity of 46.9%. Conclusion: Female gender, previous myocardial infarction, UF heparin use, and elevated preoperative creatinine levels were found to be independent predictors of AKI. We found that patients with AKI had a significantly longer hospital stay duration. The AKICS score has been validated in our local cohort of Middle Eastern patients with high sensitivity. Keywords: Acute kidney injury; Cardiac surgery; Risk factors; Socio-demographic factors; Clinical variables

Review of the limitations of current biomarkers in acute kidney injury clinical practices.

Acute kidney injury is a prevalent disease in hospitalized patients and is continuously increasing worldwide. Various efforts have been made to define and classify acute kidney injury to understand the progression of this disease. Furthermore, deviations from structure and kidney function and the current diagnostic guidelines are not adequately placed due to baseline serum creatinine values, which are rarely known and estimated based on glomerular function rate, resulting in misclassification of acute kidney injury staging. Hence, the current guidelines are still developing to improve and understand the clinical implications of risk factors and earlier predictive biomarkers of acute kidney injury. Yet, studies have indicated disadvantages and limitations with the current acute kidney injury biomarkers, including lack of sensitivity and specificity. Therefore, the present narrative review brings together the most current evidenced-based practice and literature associated with the limitations of the gold standard for acute kidney injury diagnoses, the need for novel acute kidney injury biomarkers, and the process for biomarkers to be qualified for diagnostic use under the following sections and themes. The introduction section situates the anatomy and normal and abnormal kidney functions related to acute kidney injury disorders. Guidelines in providing acute kidney injury definitions and classification are then considered, followed by a discussion of the disadvantages of standard markers used to diagnose acute kidney injury. Characteristics of an ideal acute kidney injury biomarker are discussed concerning sensitivity, specificity, and anatomic location of injury. A particular focus on the role and function of emerging biomarkers is discussed in relation to their applications and significance to the prognosis and severity of acute kidney injury. Findings show emerging markers are early indicators of acute kidney injury prediction in different clinical settings. Finally, the process required for a biomarker to be applied for diagnostic use is explained.

Neutrophil Gelatinase-Associated Lipocalin Cutoff Value Selection and Acute Kidney Injury Classification System Determine Phenotype Allocation and Associated Outcomes.

We explored the extent to which neutrophil gelatinase-associated lipocalin (NGAL) cutoff value selection and the acute kidney injury (AKI) classification system determine clinical AKI-phenotype allocation and associated outcomes. Cutoff values from ROC curves of data from two independent prospective cardiac surgery study cohorts (Magdeburg and Berlin, Germany) were used to predict Kidney Disease: Improving Global Outcome (KDIGO)- or Risk, Injury, Failure, Loss of kidney function, End-stage (RIFLE)-defined AKI. Statistical methodologies (maximum Youden index, lowest distance to [0, 1] in ROC space, sensitivity≍specificity) and cutoff values from two NGAL meta-analyses were evaluated. Associated risks of adverse outcomes (acute dialysis initiation and in-hospital mortality) were compared. NGAL cutoff concentrations calculated from ROC curves to predict AKI varied according to the statistical methodology and AKI classification system (10.6-159.1 and 16.85-149.3 ng/mL in the Magdeburg and Berlin cohorts, respectively). Proportions of attributed subclinical AKI ranged 2%-33.0% and 10.1%-33.1% in the Magdeburg and Berlin cohorts, respectively. The difference in calculated risk for adverse outcomes (fraction of odds ratios for AKI-phenotype group differences) varied considerably when changing the cutoff concentration within the RIFLE or KDIGO classification (up to 18.33- and 16.11-times risk difference, respectively) and was even greater when comparing cutoff methodologies between RIFLE and KDIGO classifications (up to 25.7-times risk difference). NGAL positivity adds prognostic information regardless of RIFLE or KDIGO classification or cutoff selection methodology. The risk of adverse events depends on the methodology of cutoff selection and AKI classification system.

#6535 SEXUAL DIMORPHISMS OF ACUTE KIDNEY INJURY: DIVERGENT PERFORMANCES OF BIOMARKERS OR DIFFERENT MOLECULAR MECHANISMS?

Abstract Background and Aims Before implementing individualized strategies to prevent or treat acute kidney injury (AKI), identifying clusters of patients with common (or divergent) pathophysiological mechanisms, diagnosis criteria or outcome is of upmost importance. Method We compared the characteristics of 1170 male and female patients referred for cardiac surgery with cardiac bypass (CBP) using multivariate logistic regression and propension score-based analysis. Performances of the candidate urinary biomarkers neutrophil gelatinase-associated lipocalin (uNGAL), [IGFBP7].[TIMP-2] product (Nephrocheck), and a recently developed AKI signature of 204 urinary peptides (PeptAKI) were compared, and sex-dependent individual urinary peptide changes were studied. Results In these patients referred for CBP-surgery, incidence and severity (K/DIGO classification) of AKI were similar in men and women (about 25%), even after adjustment of the usual risk factors of AKI including baseline estimated glomerular filtration rate, age, diabetes mellitus, length of CBP and red blood cell transfusion. Performances of uNGAL, Nephrocheck and PeptAKI strongly diverged between males and females. In the overall cohort, as well as in sub-groups of males and females, the multi-markers PeptAKI signature outperformed uNGAL and NephroCheck. Reanalysis of peptides included in PeptAKI at the single peptide level suggested divergences of AKI mechanisms between sexes. In women, the peptide score-derived risk of AKI strongly relied on peptides indicating kidney inflammation (including SERPINs, SAA1, osteopontin, uromodulin fragments) and hemolysis (HBA1, HBB), whereas a peptide derived from a stress-responsive protective gene in kidney tubular epithelial cells (VGF nerve growth factor) was dramatically less abundant in women developing AKI. Conclusion In patients referred for CBP-surgery, significant clinical and biological differences between men and women, as well as sexual dimorphism of AKI biomarkers performances, were identified. Urinary peptide signatures may help to personalize prevention of AKI progression by giving both quantitative and sex-related qualitative information on molecular mechanisms underlying AKI.

Identification of AKI signatures and classification patterns in ccRCC based on machine learning.

Acute kidney injury can be mitigated if detected early. There are limited biomarkers for predicting acute kidney injury (AKI). In this study, we used public databases with machine learning algorithms to identify novel biomarkers to predict AKI. In addition, the interaction between AKI and clear cell renal cell carcinoma (ccRCC) remain elusive. Four public AKI datasets (GSE126805, GSE139061, GSE30718, and GSE90861) treated as discovery datasets and one (GSE43974) treated as a validation dataset were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between AKI and normal kidney tissues were identified using the R package limma. Four machine learning algorithms were used to identify the novel AKI biomarkers. The correlations between the seven biomarkers and immune cells or their components were calculated using the R package ggcor. Furthermore, two distinct ccRCC subtypes with different prognoses and immune characteristics were identified and verified using seven novel biomarkers. Seven robust AKI signatures were identified using the four machine learning methods. The immune infiltration analysis revealed that the numbers of activated CD4 T cells, CD56dim natural killer cells, eosinophils, mast cells, memory B cells, natural killer T cells, neutrophils, T follicular helper cells, and type 1 T helper cells were significantly higher in the AKI cluster. The nomogram for prediction of AKI risk demonstrated satisfactory discrimination with an Area Under the Curve (AUC) of 0.919 in the training set and 0.945 in the testing set. In addition, the calibration plot demonstrated few errors between the predicted and actual values. In a separate analysis, the immune components and cellular differences between the two ccRCC subtypes based on their AKI signatures were compared. Patients in the CS1 had better overall survival, progression-free survival, drug sensitivity, and survival probability. Our study identified seven distinct AKI-related biomarkers based on four machine learning methods and proposed a nomogram for stratified AKI risk prediction. We also confirmed that AKI signatures were valuable for predicting ccRCC prognosis. The current work not only sheds light on the early prediction of AKI, but also provides new insights into the correlation between AKI and ccRCC.

Survival outcomes in older patients with different stages of acute kidney injury defined by the addition of urine output criteria.

Survival outcomes in older patients with different stages of acute kidney injury defined by the addition of urine output criteria.

Increase in Registered Acute Kidney Injuries in German Hospitals.

Currently, the development of the incidence of acute kidney injury (AKI) and the influence of age and gender on the condition in Germany is unclear. Data were extracted from the national database of Federal Health Reporting. It was then normalized for demographic changes. Poisson regression was performed on 933,684 cases to quantify the correlation between age, years, and AKI incidence. Analysis of variance was performed on the same collection to evaluate gender disparities in different age groups. In absolute numbers, registered AKI increased almost sevenfold from 11,964 to 77,719 between 2000 and 2019. After adjusting for demographic changes, the most AKI - 6300.5 per million person-years - occur in the elderly (>79 years old). Males have a higher risk for the development of an AKI. The male and female AKI incidence ratio varies significantly between different age groups, and it is the lowest in people <20 and >79 years old. The registered incidence of AKI has risen substantially in the first 20 years of the millennium. The increase can partly be attributed to an increased diagnostic sensitivity provided by changes in the classification of AKI. It could also be shown that men suffer from AKI more often than women, particularly in the younger age groups.

Comparative Analysis of Intensive Care Prognosis Scoring Systems and Acute Kidney Injury Scores (AKIN and pRIFLE) in Critically Ill Children

A total of 716 children, whose serum creatinine levels were within the normal limits at the time of admission to the PICU between January 2018 and December 2020, were included. Along with the demographic and clinical variables, AKIN and pRIFLE classifications were recorded at the most advanced stage of AKI. Along with the PIM-2, PRISM III, and PELOD-2 scores, the highest value of the pSOFA score was recorded. According to the pRIFLE and AKIN classifications, 62 (8.7%) patients developed kidney injury, which had a statistically significant effect on mortality. The occurrence of renal injury was found to be statistically strongly and significantly correlated with high PRISM III, PELOD-2, and pSOFA scores. When the stages of kidney injury according to the AKIN criteria were compared with the PRISM III, PELOD 2, and pSOFA scores, a significant difference was found between the patients who did not develop AKI and those who developed stage 1, stage 2, and stage 3 kidney injury. For the PRISM III, PELOD 2, and pSOFA scores, there were no significant differences between the stages according to the AKIN criteria. A substantial difference was discovered between the patients who did not develop AKI and those who were in the risk, injury, and failure plus loss stages according to the pRIFLE criteria. According to the PIM-2 ratio and pRIFLE criteria, there was a statistically significant difference between patients in the injury and failure plus loss stages and those who did not develop AKI. Our study is the first pediatric study to show a substantial correlation between the variables associated with the PICU scoring modalities in critically ill children with AKI. Identifying the risk factors for the development of AKI and planning antimicrobial regimens for patients with favorable prognoses at the time of PICU admission could lower mortality rates.

Identification of Pre-Renal and Intrinsic Acute Kidney Injury by Anamnestic and Biochemical Criteria: Distinct Association with Urinary Injury Biomarkers.

Acute kidney injury (AKI) is a syndrome of sudden renal excretory dysfunction with severe health consequences. AKI etiology influences prognosis, with pre-renal showing a more favorable evolution than intrinsic AKI. Because the international diagnostic criteria (i.e., based on plasma creatinine) provide no etiological distinction, anamnestic and additional biochemical criteria complement AKI diagnosis. Traditional, etiology-defining biochemical parameters, including the fractional excretion of sodium, the urinary-to-plasma creatinine ratio and the renal failure index are individually limited by confounding factors such as diuretics. To minimize distortion, we generated a composite biochemical criterion based on the congruency of at least two of the three biochemical ratios. Patients showing at least two ratios indicative of intrinsic AKI were classified within this category, and those with at least two pre-renal ratios were considered as pre-renal AKI patients. In this study, we demonstrate that the identification of intrinsic AKI by a collection of urinary injury biomarkers reflective of tubular damage, including NGAL and KIM-1, more closely and robustly coincide with the biochemical than with the anamnestic classification. Because there is no gold standard method for the etiological classification of AKI, the mutual reinforcement provided by the biochemical criterion and urinary biomarkers supports an etiological diagnosis based on objective diagnostic parameters.

Fatores associados a lesão renal aguda em pacientes submetidos à oxigenação por membrana extracorpórea: coorte retrospectivo

To identify factors associated with acute kidney injury in patients undergoing extracorporeal membrane oxygenation. Retrospective cohort study conducted in an adult Intensive Care Unit with patients undergoing extracorporeal membrane oxygenation from 2012 to 2021. The research used the Kidney Disease Improving Global Outcomes as criteria for definition and classification of acute kidney injury. A multiple logistic regression model was developed to analyze the associated factors. The sample was composed of 122 individuals, of these, 98 developed acute kidney injury (80.3%). In multiple regression, the associated factors found were vasopressin use, Nursing Activities Score, and glomerular filtration rate. The use of vasopressin, the Nursing Activities Score, and the glomerular filtration rate were considered as factors related to the development of acute kidney injury in patients undergoing extracorporeal membrane oxygenation.

Acute Kidney Injury Classifications in the Prediction of In-hospital Mortality and Renal Function Non-recovery.

In the last two decades diagnostic criteria for acute kidney injury (AKI) were developed: Risk, Injury, Failure, Loss of Kidney Function, End-Stage Kidney Disease (RIFLE), Acute Kidney Injury Network (AKIN), and Kidney Disease: Improving Global Outcomes (KDIGO) classifications. The study aimed to determine the incidence of AKI based on the RIFLE, AKIN, and KDIGO criteria, as well as analyze their predictive value for mortality and renal function outcome. This was a single-center prospective study of patients diagnosed with AKI. Acute kidney injury was defined and classified according to the RIFLE, AKIN, and KDIGO criteria. The outcomes were renal function outcome and in-hospital mortality. The incidence rates of AKI based on the RIFLE, AKIN, and KDIGO criteria were 13.4%, 14-36%, and 14.64%, respectively. Multiple regression analysis showed that higher stages of AKI according to the KDIGO criteria were independently associated with non-recovery of renal function (p=0.011). However, the predictive ability of RIFLE, AKIN and KDIGO classifications for renal function recovery was poor (Area Under the Receiver Operating Characteristics-AUROC=0.599, AUROC=0.637, AUROC=0.659, respectively). According to the RIFLE and AKIN criteria, in-hospital mortality was statistically significantly higher in stage Failure/3 (p=0.0403 and p=0.0329, respectively) compared to stages Risk/1 and Injury/2. Receiver Operating Characteristics (ROC) analysis showed that all three classifications had poor predictive ability for in-hospital mortality (AUROC=0.675, AUROC=0.66, AUROC=0.681). KDIGO classification is an independent predictor of renal function non-recovery. However, by ROC analysis, all three classifications have poor predictive ability for renal function outcome and mortality.