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Introduction: Although studies have examined the utility of clinical decision support tools in improving acute kidney injury (AKI) outcomes, no study has evaluated the effect of real-time, personalised AKI recommendations. This study aims to assess the impact of individualised AKI-specific recommendations delivered by trained clinicians and pharmacists immediately after AKI detection in hospitalised patients., Methods and Analysis: KAT-AKI is a multicentre randomised investigator-blinded trial being conducted across eight hospitals at two major US hospital systems planning to enrol 4000 patients over 3 years (between 1 November 2021 and 1 November 2024). A real-time electronic AKI alert system informs a dedicated team composed of a physician and pharmacist who independently review the chart in real time, screen for eligibility and provide combined recommendations across the following domains: diagnostics, volume, potassium, acid-base and medications. Recommendations are delivered to the primary team in the alert arm or logged for future analysis in the usual care arm. The planned primary outcome is a composite of AKI progression, dialysis and mortality within 14 days from randomisation. A key secondary outcome is the percentage of recommendations implemented by the primary team within 24 hours from randomisation. The study has enrolled 500 individuals over 8.5 months. Two-thirds were on a medical floor at the time of the alert and 17.8% were in an intensive care unit. Virtually all participants were recommended for at least one diagnostic intervention. More than half (51.6%) had recommendations to discontinue or dose-adjust a medication. The median time from AKI alert to randomisation was 28 (IQR 15.8-51.5) min., Ethics and Dissemination: The study was approved by the ethics committee of each study site (Yale University and Johns Hopkins institutional review board (IRB) and a central IRB (BRANY, Biomedical Research Alliance of New York). We are committed to open dissemination of the data through clinicaltrials.gov and sharing of data on an open repository as well as publication in a peer-reviewed journal on completion., Trial Registration Number: NCT04040296., Competing Interests: Competing interests: FPW reports research funding unrelated to this project from AstraZeneca, Boehringer-Ingelheim, Vifor Pharma and Whoop, Inc. CP is a member of the advisory board of and owns equity in RenalytixAI. He also serves as a consultant for Genfit and Novartis. All other authors have no disclosures., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Acute kidney injury (AKI) in deceased donors is not associated with graft failure (GF). We hypothesize that hemodynamic AKI (hAKI) comprises the majority of donor AKI and may explain this lack of association., Methods: In this ancillary analysis of the Deceased Donor Study, 428 donors with available charts were selected to identify those with and without AKI. AKI cases were classified as hAKI, intrinsic (iAKI), or mixed (mAKI) based on majority adjudication by three nephrologists. We evaluated the associations between AKI phenotypes and delayed graft function (DGF), 1-year eGFR and GF. We also evaluated differences in urine biomarkers among AKI phenotypes., Results: Of the 291 (68%) donors with AKI, 106 (36%) were adjudicated as hAKI, 84 (29%) as iAKI and 101 (35%) as mAKI. Of the 856 potential kidneys, 669 were transplanted with 32% developing DGF and 5% experiencing GF. Median 1-year eGFR was 53 (IQR: 41-70) ml/min/1.73m2. Compared to non-AKI, donors with iAKI had higher odds DGF [aOR (95%CI); 4.83 (2.29, 10.22)] and had lower 1-year eGFR [adjusted B coefficient (95% CI): -11 (-19, -3) mL/min/1.73 m2]. hAKI and mAKI were not associated with DGF or 1-year eGFR. Rates of GF were not different among AKI phenotypes and non-AKI. Urine biomarkers such as NGAL, LFABP, MCP-1, YKL-40, cystatin-C and albumin were higher in iAKI., Conclusion: iAKI was associated with higher DGF and lower 1-year eGFR but not with GF. Clinically phenotyped donor AKI is biologically different based on biomarkers and may help inform decisions regarding organ utilization., Competing Interests: The authors have declared that no competing interests exist.

Background: The mechanisms underlying long-term sequelae after AKI remain unclear. Vessel instability, an early response to endothelial injury, may reflect a shared mechanism and early trigger for CKD and heart failure., Methods: To investigate whether plasma angiopoietins, markers of vessel homeostasis, are associated with CKD progression and heart failure admissions after hospitalization in patients with and without AKI, we conducted a prospective cohort study to analyze the balance between angiopoietin-1 (Angpt-1), which maintains vessel stability, and angiopoietin-2 (Angpt-2), which increases vessel destabilization. Three months after discharge, we evaluated the associations between angiopoietins and development of the primary outcomes of CKD progression and heart failure and the secondary outcome of all-cause mortality 3 months after discharge or later., Results: Median age for the 1503 participants was 65.8 years; 746 (50%) had AKI. Compared with the lowest quartile, the highest quartile of the Angpt-1:Angpt-2 ratio was associated with 72% lower risk of CKD progression (adjusted hazard ratio [aHR], 0.28; 95% confidence interval [CI], 0.15 to 0.51), 94% lower risk of heart failure (aHR, 0.06; 95% CI, 0.02 to 0.15), and 82% lower risk of mortality (aHR, 0.18; 95% CI, 0.09 to 0.35) for those with AKI. Among those without AKI, the highest quartile of Angpt-1:Angpt-2 ratio was associated with 71% lower risk of heart failure (aHR, 0.29; 95% CI, 0.12 to 0.69) and 68% less mortality (aHR, 0.32; 95% CI, 0.15 to 0.68). There were no associations with CKD progression., Conclusions: A higher Angpt-1:Angpt-2 ratio was strongly associated with less CKD progression, heart failure, and mortality in the setting of AKI., (Copyright © 2022 by the American Society of Nephrology.)

Patients undergoing cardiac surgery are placed under intense physiologic stress. Blood and urine biomarkers measured peri-operatively may help identify patients at higher risk for adverse long-term kidney outcomes.We sought to determine independent associations of various biomarkers with development or progression of chronic kidney disease (CKD) following cardiac surgery. In this sub-study of the prospective cohort -TRIBE-AKI Study, we evaluated 613 adult patients undergoing cardiac surgery in Canada in our primary analysis and tested the association of 40 blood and urinary biomarkers with the primary composite outcome of CKD incidence or progression. In those with baseline estimated glomerular filtration rate (eGFR) over 60 mL/min/1.73m 2 , we defined CKD incidence as a 25% reduction in eGFR and an eGFR under 60. In those with baseline eGFR under 60 mL/min/1.73m 2 , we defined CKD progression as a 50% reduction in eGFR or eGFR under 15. Results were evaluated in a replication cohort of 310 patients from one study site in the United States. Over a median follow-up of 5.6 years, 172 patients developed the primary outcome. Each log increase in basic fibroblast growth factor (adjusted hazard ratio 1.52 [95% confidence interval 1.19, 1.93]), Kidney Injury Molecule-1 (1.51 [0.98, 2.32]), N-terminal pro-B-type natriuretic peptide (1.19 [1.01, 1.41]), and tumor necrosis factor receptor 1 (1.75 [1.18, 2.59]) were associated with outcome after adjustment for demographic factors, serum creatinine, and albuminuria. Similar results were noted in the replication cohort. Although there was no interaction by acute kidney injury in continuous analysis, mortality was higher in the no acute kidney injury group by biomarker tertile. Thus, elevated post-operative levels of blood biomarkers following cardiac surgery were independently associated with the development of CKD. These biomarkers can provide additional value in evaluating CKD incidence and progression after cardiac surgery., (Copyright © 2020 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.)

Introduction: Acute kidney injury (AKI) is common and is associated with negative long-term outcomes. Given the heterogeneity of the syndrome, the ability to predict outcomes of AKI may be beneficial towards effectively using resources and personalising AKI care. This systematic review will identify, describe and assess current models in the literature for the prediction of outcomes in hospitalised patients with AKI., Methods and Analysis: Relevant literature from a comprehensive search across six databases will be imported into Covidence. Abstract screening and full-text review will be conducted independently by two team members, and any conflicts will be resolved by a third member. Studies to be included are cohort studies and randomised controlled trials with at least 100 subjects, adult hospitalised patients, with AKI. Only those studies evaluating multivariable predictive models reporting a statistical measure of accuracy (area under the receiver operating curve or C-statistic) and predicting resolution of AKI, progression of AKI, subsequent dialysis and mortality will be included. Data extraction will be performed independently by two team members, with a third reviewer available to resolve conflicts. Results will be reported using Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Risk of bias will be assessed using Prediction model Risk Of Bias ASsessment Tool., Ethics and Dissemination: We are committed to open dissemination of our results through the registration of our systematic review on PROSPERO and future publication. We hope that our review provides a platform for future work in realm of using artificial intelligence to predict outcomes of common diseases., Prospero Registration Number: CRD42019137274., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background and Aims: Recent evidence suggests that acute kidney injury (AKI) is the main predictor of postparacentesis bleeding in patients with cirrhosis. To assess the factors responsible for bleeding tendency in AKI, we performed a prospective study comparing all three aspects of hemostasis (platelets, coagulation, and fibrinolysis) in patients with decompensated cirrhosis with and without AKI., Approach and Results: Primary hemostasis assessment included platelet aggregation and secretion (platelet function markers) and von Willebrand factor. Secondary hemostasis assessment included pro-coagulant (factor VIII and factor XIII) and anti-coagulant (protein C, protein S, and antithrombin) factors and thrombin generation. Tertiary hemostasis assessment included fibrinolytic factors and plasmin-antiplasmin complex. Eighty patients with decompensated cirrhosis were recruited (40 each with and without AKI). Severity of cirrhosis and platelet count were comparable between groups. Median serum creatinine was 1.8 mg/dL and 0.8 mg/dL in patients with and without AKI, respectively. At baseline, patients with cirrhosis and AKI had lower platelet aggregation and secretion, indicative of impaired platelet function (increased bleeding tendency), without differences in von Willebrand factor. Regarding coagulation factors, factor VIII was higher, whereas protein C, protein S, and antithrombin were all lower, which, together with increased thrombin generation, indicate hypercoagulability. In contrast, factor XIII was lower in AKI (increased bleeding tendency). Finally, while both hypofibrinolytic and hyperfibrinolytic changes were present in AKI, a higher plasmin-antiplasmin complex indicated a hyperfibrinolytic state. After AKI resolution (n = 23 of 40), platelet function and coagulation improved to levels observed in patients with cirrhosis patients without AKI; however, fibrinolysis remained hyperactivated., Conclusions: In patients with decompensated cirrhosis, AKI is associated with both hypocoagulable and hypercoagulable features that can potentially increase the risk of both bleeding and thrombosis., (© 2020 by the American Association for the Study of Liver Diseases.)

As the incidence of acute kidney injury (AKI) increases, prevention strategies are needed across the health care continuum, which begins in the community. Recognizing this knowledge gap, the 22nd Acute Disease Quality Initiative (ADQI) was tasked to discuss the evidence for quality-of-care measurement and care processes to prevent AKI and its consequences in the community. Using a modified Delphi process, an international and interdisciplinary group provided a framework to identify and monitor patients with AKI in the community. The recommendations propose that risk stratification involve both susceptibilities (eg, chronic kidney disease) and exposures (eg, coronary angiography), with the latter triggering a Kidney Health Assessment. This assessment should include blood pressure, serum creatinine, and urine dipstick, followed by a Kidney Health Response to prevent AKI that encompasses cessation of unnecessary medications, minimization of nephrotoxins, patient education, and ongoing monitoring until the exposure resolves. These recommendations give community health care providers and health systems a starting point for quality improvement initiatives to prevent AKI and its consequences in the community., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Importance: Determining the association of contrast volume during percutaneous coronary intervention (PCI) with the risk of acute kidney injury (AKI) is important for optimizing PCI safety., Objective: To quantify how the risk of AKI is associated with contrast volume, accounting for the possibility of nonlinearity and heterogeneity among different baseline risks., Design, Setting, and Participants: This prognostic study used data from the American College of Cardiology National Cardiovascular Data Registry CathPCI Registry for 1694 US hospitals. Derivation analysis included 2 076 694 individuals who underwent PCI from July 1, 2011, to June 30, 2015. Validation analysis included 961 863 individuals who underwent PCI from July 1, 2015, to March 31, 2017. Data analysis took place from July 2018 to May 2019., Exposure: Contrast volume during PCI., Main Outcomes and Measures: Acute kidney injury was defined using 3 thresholds for preprocedure to postprocedure creatinine level increase (ie, ≥0.3 mg/dL, ≥0.5 mg/dL, and ≥1.0 mg/dL). A model quantifying the association of contrast volume with AKI was developed, and the existence of nonlinearity and heterogeneity were examined by likelihood ratio tests. The model was derived in the training set (a random 50% of the derivation cohort), and performance was evaluated in the test set (the remaining 50% of the derivation cohort) and an independent validation set by area under the receiver operating characteristic curve (AUC) and calibration slope of observed vs predicted risks., Results: The 2 076 694 patients in the derivation set had a mean (SD) age of 65.1 (12.1) years, and 662 525 (31.9%) were women; 133 306 (6.4%) had creatinine level increases of at least 0.3 mg/dL, 66 626 (3.2%) had creatinine level increases of at least 0.5 mg/dL, and 28 378 (1.4%) had creatinine level increases of at least 1.0 mg/dL. In the validation set of 961 843 patients (mean [SD] age, 65.7 [12.1] years; 305 577 [31.8%] women), these rates were 62 913 (6.5%), 34 229 (3.6%), and 15 555 (1.6%), respectively. The association of contrast volume and AKI risk was nonlinear (χ226 = 1436.2; P < .001) and varied by preprocedural risk (χ220 = 105.6; P < .001). In the test set, the model yielded an AUC of 0.777 (95% CI, 0.775-0.779) for predicting risk of a creatinine level increase of at least 0.3 mg/dL, 0.839 (95% CI, 0.837-0.841) for predicting risk of a creatinine level increase of at least 0.5 mg/dL, and 0.870 (95% CI, 0.867-0.873) for predicting risk of a creatinine level increase of at least 1.0 mg/dL; it achieved a calibration slope of 0.998 (95% CI, 0.989-1.007), 0.999 (95% CI, 0.989-1.008), and 0.986 (95% CI, 0.973-0.998), respectively, for the AKI severity levels. The model had similar performance in the validation set (creatinine level increase of ≥0.3 mg/dL: AUC, 0.794; 95% CI, 0.792-0.795; calibration slope, 1.039; 95% CI, 1.030-1.047; creatinine level increase of ≥0.5 mg/dL: AUC, 0.845; 95% CI, 0.843-0.848; calibration slope, 1.063; 95% CI, 1.054-1.074; creatinine level increase of ≥1.0 mg/dL: AUC, 0.872; 95% CI, 0.869-0.875; calibration slope, 1.103; 95% CI, 1.089-1.117)., Conclusions and Relevance: The association of contrast volume with AKI risk is complex, varies by baseline risk, and can be predicted by a model. Future research to evaluate the effect of the model on AKI is needed.

Introduction: Acute kidney injury (AKI) is common among hospitalised patients and under-recognised by providers and yet carries a significant risk of morbidity and mortality. Electronic alerts for AKI have become more common despite a lack of strong evidence of their benefits. We designed a multicentre, randomised, controlled trial to evaluate the effectiveness of AKI alerts. Our aim is to highlight several challenges faced in the design of this trial, which uses electronic screening, enrolment, randomisation, intervention and data collection., Methods and Analysis: The design and implementation of an electronic alert system for AKI was a reiterative process involving several challenges and limitations set by the confines of the electronic medical record system. The trial will electronically identify and randomise 6030 adults with AKI at six hospitals over a 1.5-2 year period to usual care versus an electronic alert containing an AKI-specific order set. Our primary outcome will be a composite of AKI progression, inpatient dialysis and inpatient death within 14 days of randomisation. During a 1-month pilot in the medical intensive care unit of Yale New Haven Hospital, we have demonstrated feasibility of automating enrolment and data collection. Feedback from providers exposed to the alerts was used to continually improve alert clarity, user friendliness and alert specificity through refined inclusion and exclusion criteria., Ethics and Dissemination: This study has been approved by the appropriate ethics committees for each of our study sites. Our study qualified for a waiver of informed consent as it presents no more than minimal risk and cannot be feasibly conducted in the absence of a waiver. We are committed to open dissemination of our data through clinicaltrials.gov and submission of results to the NIH data sharing repository. Results of our trial will be submitted for publication in a peer-reviewed journal., Trial Registration Number: NCT02753751; Pre-results., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: The current acute kidney injury (AKI) risk prediction model for patients undergoing percutaneous coronary intervention (PCI) from the American College of Cardiology (ACC) National Cardiovascular Data Registry (NCDR) employed regression techniques. This study aimed to evaluate whether models using machine learning techniques could significantly improve AKI risk prediction after PCI., Methods and Findings: We used the same cohort and candidate variables used to develop the current NCDR CathPCI Registry AKI model, including 947,091 patients who underwent PCI procedures between June 1, 2009, and June 30, 2011. The mean age of these patients was 64.8 years, and 32.8% were women, with a total of 69,826 (7.4%) AKI events. We replicated the current AKI model as the baseline model and compared it with a series of new models. Temporal validation was performed using data from 970,869 patients undergoing PCIs between July 1, 2016, and March 31, 2017, with a mean age of 65.7 years; 31.9% were women, and 72,954 (7.5%) had AKI events. Each model was derived by implementing one of two strategies for preprocessing candidate variables (preselecting and transforming candidate variables or using all candidate variables in their original forms), one of three variable-selection methods (stepwise backward selection, lasso regularization, or permutation-based selection), and one of two methods to model the relationship between variables and outcome (logistic regression or gradient descent boosting). The cohort was divided into different training (70%) and test (30%) sets using 100 different random splits, and the performance of the models was evaluated internally in the test sets. The best model, according to the internal evaluation, was derived by using all available candidate variables in their original form, permutation-based variable selection, and gradient descent boosting. Compared with the baseline model that uses 11 variables, the best model used 13 variables and achieved a significantly better area under the receiver operating characteristic curve (AUC) of 0.752 (95% confidence interval [CI] 0.749-0.754) versus 0.711 (95% CI 0.708-0.714), a significantly better Brier score of 0.0617 (95% CI 0.0615-0.0618) versus 0.0636 (95% CI 0.0634-0.0638), and a better calibration slope of observed versus predicted rate of 1.008 (95% CI 0.988-1.028) versus 1.036 (95% CI 1.015-1.056). The best model also had a significantly wider predictive range (25.3% versus 21.6%, p < 0.001) and was more accurate in stratifying AKI risk for patients. Evaluated on a more contemporary CathPCI cohort (July 1, 2015-March 31, 2017), the best model consistently achieved significantly better performance than the baseline model in AUC (0.785 versus 0.753), Brier score (0.0610 versus 0.0627), calibration slope (1.003 versus 1.062), and predictive range (29.4% versus 26.2%). The current study does not address implementation for risk calculation at the point of care, and potential challenges include the availability and accessibility of the predictors., Conclusions: Machine learning techniques and data-driven approaches resulted in improved prediction of AKI risk after PCI. The results support the potential of these techniques for improving risk prediction models and identification of patients who may benefit from risk-mitigation strategies., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: SSD is supported by the Department of Veterans Affairs. WLS is a consultant for Hugo, a personal health information platform. CIM is a consultant for Cook, Bard, Medtronic, Abbott and Cardinal Health. FPW is supported by the National Science Foundation grant R01DK113191. JSR is the Chief Innovation Officer for the American College of Cardiology. HMK is a recipient of research agreements from Medtronic and from Johnson & Johnson (Janssen), through Yale University, to develop methods of clinical trial data sharing; was the recipient of a grant from the Food and Drug Administration and Medtronic to develop methods for postmarket surveillance of medical devices; works under contract with the Centers for Medicare and Medicaid Services to develop and maintain performance measures; chairs a cardiac scientific advisory board for UnitedHealth; is a member of the Advisory Board for Element Science and the Physician Advisory Board for Aetna; is a participant/participant representative of the IBM Watson Health Life Sciences Board; and is the founder of Hugo, a personal health information platform. JAS is supported by grants from Gilead, Genentech, Lilly, Amorcyte, and EvaHeart and has a patent for the Seattle Angina Questionnaire with royalties paid. He also owns the copyright to the Seattle Angina Questionnaire. He is the PI of an Analytic Center for the American College of Cardiology Foundation and has an equity interest in Health Outcomes Sciences. FAM has a contract (through his primary institution) for his role as Chief Science Officer of the NCDR. BJM is an associate editor for PLOS ONE, which is involved in this special issue. He has a relationship with the American College of Cardiology in selecting and pursuing innovative research based upon their registry data (unrelated to this paper). He has a pending patent application for an EHR-based prediction tool in Yale New Haven Health, as well as two funded studies, one by the DoD-Advanced Research Projects Agency and one with the NSF to support student travel to conferences in the body sensor networks field. American College of Cardiology may incorporate this work, or future iterations, into its registry. No other organisation named above has a competing interest in relation to this work. The other authors report no potential competing interests.

Background: Electronic alerts (e-alerts) for acute kidney injury (AKI) in hospitalized patients are increasingly being implemented; however, their impact on outcomes remains uncertain., Methods: We performed a systematic review. Electronic databases and grey literature were searched for original studies published between 1990 and 2016. Randomized, quasi-randomized, observational and before-and-after studies that included hospitalized patients, implemented e-alerts for AKI and described their impact on one of care processes, patient-centred outcomes or resource utilization measures were included., Results: Our search yielded six studies ( n  = 10 165 patients). E-alerts were generally automated, triggered through electronic health records and not linked to clinical decision support. In pooled analysis, e-alerts did not improve mortality [odds ratio (OR) 1.05; 95% confidence intervals (CI), 0.84-1.31; n  = 3 studies; n  = 3425 patients; I 2  =   0%] or reduce renal replacement therapy (RRT) use (OR 1.20; 95% CI, 0.91-1.57; n  = 2 studies; n  = 3236 patients; I 2  =   0%). Isolated studies reported improvements in selected care processes. Pooled analysis found no significant differences in prescribed fluid therapy., Conclusions: In the available studies, e-alerts for AKI do not improve survival or reduce RRT utilization. The impact of e-alerts on processes of care was variable. Additional research is needed to understand those aspects of e-alerts that are most likely to improve care processes and outcomes., (© The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.)

Introduction: Acute kidney injury (AKI) is a common complication in hospitalised patients. It imposes significant risk for major morbidity and mortality. Moreover, patients suffering an episode of AKI consume considerable health resources. Recently, a number of studies have evaluated the implementation of automated electronic alerts (e-alerts) configured from electronic medical records (EMR) and clinical information systems (CIS) to warn healthcare providers of early or impending AKI in hospitalised patients. The impact of e-alerts on care processes, patient outcomes and health resource use, however, remains uncertain., Methods and Analysis: We will perform a systematic review to describe and appraise e-alerts for AKI, and evaluate their impact on processes of care, clinical outcomes and health services use. In consultation with a research librarian, a search strategy will be developed and electronic databases (ie, MEDLINE, EMBASE, CINAHL, Cochrane Library and Inspec via Engineering Village) searched. Selected grey literature sources will also be searched. Search themes will focus on e-alerts and AKI. Citation screening, selection, quality assessment and data abstraction will be performed in duplicate. The primary analysis will be narrative; however, where feasible, pooled analysis will be performed. Each e-alert will be described according to trigger, type of alert, target recipient and degree of intrusiveness. Pooled effect estimates will be described, where applicable., Ethics and Dissemination: Our systematic review will synthesise the literature on the value of e-alerts to detect AKI, and their impact on processes, patient-centred outcomes and resource use, and also identify key knowledge gaps and barriers to implementation. This is a fundamental step in a broader research programme aimed to understand the ideal structure of e-alerts, target population and methods for implementation, to derive benefit. Research ethics approval is not required for this review., Systematic Review Registration Number: CRD42016033033., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/)

Background: Existing systems for grading severity of acute kidney injury (AKI) rely on a change of serum creatinine concentration over a defined time interval. The rate of change in serum creatinine increases by degree of reduction in glomerular filtration rate, but is mitigated by low creatinine generation rate (CGR). Failure to appreciate variation in CGR may lead to erroneous conclusions regarding severity of AKI and distorted predictions regarding patient outcomes based on AKI severity., Methods: Cohort study of 103 patients who received continuous venovenous hemodialysis (CVVHD) over a 2-year period in a tertiary care hospital setting. Study participants entered the cohort when they were anuric, receiving a stable and uninterrupted dose of CVVHD with serum creatinine in steady state. They were followed until hospital discharge. CGR was measured based on dialyzate effluent volume and effluent creatinine concentration (prospective cohort) and via effluent volume and serum creatinine concentration (retrospective cohort)., Results: CGR (mean 10.5, range 1.7-22.4 mg/kg/day) was substantially lower in this patient population than what would be predicted from existing equations. Correlates of CGR in multivariable analysis included the length of hospitalization prior to measurement and presence of an oncologic diagnosis. Lower CGR was independently associated with in-hospital mortality in unadjusted analysis and after multivariable adjustment for measures of severity of illness., Conclusions: Grading systems for severity of AKI fail to account for variation in CGR, limiting their ability to predict relevant outcomes. Calculation of CGR is superior to other risk metrics in predicting hospital mortality in this population.

Chronic kidney disease (CKD) and acute kidney injury (AKI) are common, heterogeneous, and morbid diseases. Mechanistic characterization of CKD and AKI in patients may facilitate a precision-medicine approach to prevention, diagnosis, and treatment. The Kidney Precision Medicine Project aims to ethically and safely obtain kidney biopsies from participants with CKD or AKI, create a reference kidney atlas, and characterize disease subgroups to stratify patients based on molecular features of disease, clinical characteristics, and associated outcomes. An additional aim is to identify critical cells, pathways, and targets for novel therapies and preventive strategies. This project is a multicenter prospective cohort study of adults with CKD or AKI who undergo a protocol kidney biopsy for research purposes. This investigation focuses on kidney diseases that are most prevalent and therefore substantially burden the public health, including CKD attributed to diabetes or hypertension and AKI attributed to ischemic and toxic injuries. Reference kidney tissues (for example, living-donor kidney biopsies) will also be evaluated. Traditional and digital pathology will be combined with transcriptomic, proteomic, and metabolomic analysis of the kidney tissue as well as deep clinical phenotyping for supervised and unsupervised subgroup analysis and systems biology analysis. Participants will be followed prospectively for 10 years to ascertain clinical outcomes. Cell types, locations, and functions will be characterized in health and disease in an open, searchable, online kidney tissue atlas. All data from the Kidney Precision Medicine Project will be made readily available for broad use by scientists, clinicians, and patients.

AIM:To investigate early postoperative biomarkers for risk discrimination of advanced acute kidney injury (AKI). MATERIALS & METHODS:Postoperative plasma biomarkers including NGAL, h-FABP, CK-MB, hsTNT, NT-proBNP, IL-6, IL-10 and VEGF were analyzed using group-based method among 426 patients with AKI after cardiac surgery. RESULTS:Six patient groups with distinct biomarker patterns were identified. Individual biomarker displayed significant difference across the groups. The groups showed better discrimination for advanced AKI than any single biomarker either with or without adjusting for clinical variables. Average concentration of a single biomarker within each group, mortality and risk of a secondary outcome all demonstrated an approximately U-shaped relationship with proportion of advanced AKI within each group. CONCLUSION:The group-based analysis revealed that the order of the patient groups with an increasing likelihood of advanced AKI had a nonlinear relationship with average concentration of an individual biomarker, mortality and risk of other outcomes.