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Siew ED, Hellwege JN, Hung AM, Birkelo BC, Vincz AJ, Parr SK, Denton J, Greevy RA, Robinson-Cohen C, Liu H, Susztak K, Matheny ME, Velez Edwards DR. Genome-wide association study of hospitalized patients and acute kidney injury. Kidney Int 2024; 106:291-301. [PMID: 38797326 PMCID: PMC11260539 DOI: 10.1016/j.kint.2024.04.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 03/15/2024] [Accepted: 04/05/2024] [Indexed: 05/29/2024]
Abstract
Acute kidney injury (AKI) is a common and devastating complication of hospitalization. Here, we identified genetic loci associated with AKI in patients hospitalized between 2002-2019 in the Million Veteran Program and data from Vanderbilt University Medical Center's BioVU. AKI was defined as meeting a modified KDIGO Stage 1 or more for two or more consecutive days or kidney replacement therapy. Control individuals were required to have one or more qualifying hospitalizations without AKI and no evidence of AKI during any other observed hospitalizations. Genome-wide association studies (GWAS), stratified by race, adjusting for sex, age, baseline estimated glomerular filtration rate (eGFR), and the top ten principal components of ancestry were conducted. Results were meta-analyzed using fixed effects models. In total, there were 54,488 patients with AKI and 138,051 non-AKI individuals included in the study. Two novel loci reached genome-wide significance in the meta-analysis: rs11642015 near the FTO locus on chromosome 16 (obesity traits) (odds ratio 1.07 (95% confidence interval, 1.05-1.09)) and rs4859682 near the SHROOM3 locus on chromosome 4 (glomerular filtration barrier integrity) (odds ratio 0.95 (95% confidence interval, 0.93-0.96)). These loci colocalized with previous studies of kidney function, and genetic correlation indicated significant shared genetic architecture between AKI and eGFR. Notably, the association at the FTO locus was attenuated after adjustment for BMI and diabetes, suggesting that this association may be partially driven by obesity. Both FTO and the SHROOM3 loci showed nominal evidence of replication from diagnostic-code-based summary statistics from UK Biobank, FinnGen, and Biobank Japan. Thus, our large GWA meta-analysis found two loci significantly associated with AKI suggesting genetics may explain some risk for AKI.
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Affiliation(s)
- Edward D Siew
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Center for Kidney Disease (VCKD) and Integrated Program for AKI Research (VIP-AKI), Nashville, Tennessee, USA.
| | - Jacklyn N Hellwege
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Adriana M Hung
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Center for Kidney Disease (VCKD) and Integrated Program for AKI Research (VIP-AKI), Nashville, Tennessee, USA
| | - Bethany C Birkelo
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Center for Kidney Disease (VCKD) and Integrated Program for AKI Research (VIP-AKI), Nashville, Tennessee, USA
| | - Andrew J Vincz
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Center for Kidney Disease (VCKD) and Integrated Program for AKI Research (VIP-AKI), Nashville, Tennessee, USA
| | - Sharidan K Parr
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Center for Kidney Disease (VCKD) and Integrated Program for AKI Research (VIP-AKI), Nashville, Tennessee, USA
| | - Jason Denton
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA
| | - Robert A Greevy
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Cassianne Robinson-Cohen
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Vanderbilt Center for Kidney Disease (VCKD) and Integrated Program for AKI Research (VIP-AKI), Nashville, Tennessee, USA
| | - Hongbo Liu
- Division of Renal Electrolyte and Hypertension, Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA; Philadelphia Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA
| | - Katalin Susztak
- Division of Renal Electrolyte and Hypertension, Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA; Philadelphia Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA
| | - Michael E Matheny
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Digna R Velez Edwards
- Tennessee Valley Health Systems, Nashville Veterans Affairs, Nashville, Tennessee, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Papathanakos G, Andrianopoulos I, Xenikakis M, Papathanasiou A, Koulenti D, Blot S, Koulouras V. Clinical Sepsis Phenotypes in Critically Ill Patients. Microorganisms 2023; 11:2165. [PMID: 37764009 PMCID: PMC10538192 DOI: 10.3390/microorganisms11092165] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 08/10/2023] [Accepted: 08/24/2023] [Indexed: 09/29/2023] Open
Abstract
Sepsis, defined as the life-threatening dysregulated host response to an infection leading to organ dysfunction, is considered as one of the leading causes of mortality worldwide, especially in intensive care units (ICU). Moreover, sepsis remains an enigmatic clinical syndrome, with complex pathophysiology incompletely understood and a great heterogeneity both in terms of clinical expression, patient response to currently available therapeutic interventions and outcomes. This heterogeneity proves to be a major obstacle in our quest to deliver improved treatment in septic critical care patients; thus, identification of clinical phenotypes is absolutely necessary. Although this might be seen as an extremely difficult task, nowadays, artificial intelligence and machine learning techniques can be recruited to quantify similarities between individuals within sepsis population and differentiate them into distinct phenotypes regarding not only temperature, hemodynamics or type of organ dysfunction, but also fluid status/responsiveness, trajectories in ICU and outcome. Hopefully, we will eventually manage to determine both the subgroup of septic patients that will benefit from a therapeutic intervention and the correct timing of applying the intervention during the disease process.
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Affiliation(s)
- Georgios Papathanakos
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
| | - Ioannis Andrianopoulos
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
| | - Menelaos Xenikakis
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
| | - Athanasios Papathanasiou
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
| | - Despoina Koulenti
- UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane, QL 4029, Australia;
- Second Critical Care Department, Attikon University Hospital, Rimini Street, 12462 Athens, Greece
| | - Stijn Blot
- Department of Internal Medicine & Pediatrics, Ghent University, 9000 Ghent, Belgium;
| | - Vasilios Koulouras
- Department of Intensive Care Medicine, University Hospital of Ioannina, 45500 Ioannina, Greece; (I.A.); (M.X.); (A.P.); (V.K.)
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Vasquez-Rios G, Oh W, Lee S, Bhatraju P, Mansour SG, Moledina DG, Gulamali FF, Siew ED, Garg AX, Sarder P, Chinchilli VM, Kaufman JS, Hsu CY, Liu KD, Kimmel PL, Go AS, Wurfel MM, Himmelfarb J, Parikh CR, Coca SG, Nadkarni GN. Joint Modeling of Clinical and Biomarker Data in Acute Kidney Injury Defines Unique Subphenotypes with Differing Outcomes. Clin J Am Soc Nephrol 2023; 18:716-726. [PMID: 36975209 PMCID: PMC10278836 DOI: 10.2215/cjn.0000000000000156] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 03/13/2023] [Indexed: 03/29/2023]
Abstract
BACKGROUND AKI is a heterogeneous syndrome. Current subphenotyping approaches have only used limited laboratory data to understand a much more complex condition. METHODS We focused on patients with AKI from the Assessment, Serial Evaluation, and Subsequent Sequelae in AKI (ASSESS-AKI). We used hierarchical clustering with Ward linkage on biomarkers of inflammation, injury, and repair/health. We then evaluated clinical differences between subphenotypes and examined their associations with cardiorenal events and death using Cox proportional hazard models. RESULTS We included 748 patients with AKI: 543 (73%) of them had AKI stage 1, 112 (15%) had AKI stage 2, and 93 (12%) had AKI stage 3. The mean age (±SD) was 64 (13) years; 508 (68%) were men; and the median follow-up was 4.7 (Q1: 2.9, Q3: 5.7) years. Patients with AKI subphenotype 1 ( N =181) had the highest kidney injury molecule (KIM-1) and troponin T levels. Subphenotype 2 ( N =250) had the highest levels of uromodulin. AKI subphenotype 3 ( N =159) comprised patients with markedly high pro-brain natriuretic peptide and plasma tumor necrosis factor receptor-1 and -2 and low concentrations of KIM-1 and neutrophil gelatinase-associated lipocalin. Finally, patients with subphenotype 4 ( N =158) predominantly had sepsis-AKI and the highest levels of vascular/kidney inflammation (YKL-40, MCP-1) and injury (neutrophil gelatinase-associated lipocalin, KIM-1). AKI subphenotypes 3 and 4 were independently associated with a higher risk of death compared with subphenotype 2 and had adjusted hazard ratios of 2.9 (95% confidence interval, 1.8 to 4.6) and 1.6 (95% confidence interval, 1.01 to 2.6, P = 0.04), respectively. Subphenotype 3 was also independently associated with a three-fold risk of CKD and cardiovascular events. CONCLUSIONS We discovered four AKI subphenotypes with differing clinical features and biomarker profiles that are associated with longitudinal clinical outcomes.
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Affiliation(s)
- George Vasquez-Rios
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Wonsuk Oh
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Samuel Lee
- Icahn School of Medicine at Mount Sinai, New York, New York
| | - Pavan Bhatraju
- Division of Nephrology, Department of Medicine, Kidney Research Institute, University of Washington, Seattle, Washington
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, Washington
| | - Sherry G. Mansour
- Section of Nephrology, Yale University School of Medicine, New Haven, Connecticut
| | - Dennis G. Moledina
- Section of Nephrology, Yale University School of Medicine, New Haven, Connecticut
| | - Faris F. Gulamali
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Edward D. Siew
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Amit X. Garg
- Division of Nephrology, Department of Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Pinaki Sarder
- Department of Biomedical Engineering, SUNY Buffalo, Buffalo, New York
| | - Vernon M. Chinchilli
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania
| | - James S. Kaufman
- Division of Nephrology, Veterans Affairs New York Harbor Healthcare System and New York University School of Medicine, New York, New York
| | - Chi-yuan Hsu
- Division of Nephrology, Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Kathleen D. Liu
- Division of Nephrology, Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Paul L. Kimmel
- Division of Kidney, Urologic, and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
| | - Alan S. Go
- Kaiser Permanente Northern California, Oakland, California
| | - Mark M. Wurfel
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, Washington
| | - Jonathan Himmelfarb
- Division of Nephrology, Department of Medicine, Kidney Research Institute, University of Washington, Seattle, Washington
| | - Chirag R. Parikh
- Division of Nephrology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Steven G. Coca
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Girish N. Nadkarni
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Data-Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
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Tan Y, Huang J, Zhuang J, Huang H, Jiang S, She M, Tian M, Liu Y, Yu X. Identifying acute kidney injury subphenotypes using an outcome-driven deep-learning approach. J Biomed Inform 2023; 143:104393. [PMID: 37209975 DOI: 10.1016/j.jbi.2023.104393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 05/12/2023] [Accepted: 05/14/2023] [Indexed: 05/22/2023]
Abstract
OBJECTIVE Acute kidney injury (AKI), a common condition on the intensive-care unit (ICU), is characterized by an abrupt decrease in kidney function within a few hours or days, leading to kidney failure or damage. Although AKI is associated with poor outcomes, current guidelines overlook the heterogeneity among patients with this condition. Identification of AKI subphenotypes could enable targeted interventions and a deeper understanding of the injury's pathophysiology. While previous approaches based on unsupervised representation learning have been used to identify AKI subphenotypes, these methods cannot assess time series or disease severity. METHODS In this study, we developed a data- and outcome-driven deep-learning (DL) approach to identify and analyze AKI subphenotypes with prognostic and therapeutic implications. Specifically, we developed a supervised long short-term memory (LSTM) autoencoder (AE) with the aim of extracting representation from time-series EHR data that were intricately correlated with mortality. Then, subphenotypes were identified via application of K-means. RESULTS In two publicly available datasets, three distinct clusters were identified, characterized by mortality rates of 11.3%, 17.3%, and 96.2% in one dataset and 4.6%, 12.1%, and 54.6% in the other. Further analysis demonstrated that AKI subphenotypes identified by our proposed approach were statistically significant on several clinical characteristics and outcomes. CONCLUSION In this study, our proposed approach could successfully cluster the AKI population in ICU settings into 3 distinct subphenotypes. Thus, such approach could potentially improve outcomes of AKI patients in the ICU, with better risk assessment and potentially better personalized treatment.
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Affiliation(s)
- Yongsen Tan
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, 518055, China
| | - Jiahui Huang
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, 518055, China
| | - Jinhu Zhuang
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, 518055, China
| | - Haofan Huang
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, 518055, China
| | - Song Jiang
- Department of Intensive Care Unit, Shenzhen Hospital, Southern Medical University, Shenzhen, China
| | - Miaowen She
- Taihe Hospital, Hubei University of Medicine, Hubei, China
| | - Mu Tian
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, 518055, China
| | - Yong Liu
- Department of Intensive Care Unit, Shenzhen Hospital, Southern Medical University, Shenzhen, China
| | - Xiaxia Yu
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, 518055, China.
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Tallarico RT, Neto AS, Legrand M. Pragmatic platform trials to improve the outcome of patients with acute kidney injury. Curr Opin Crit Care 2022; 28:622-629. [PMID: 36170383 PMCID: PMC9613599 DOI: 10.1097/mcc.0000000000000990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
PURPOSE OF REVIEW There is an important need for improved diagnostic strategies and treatment among patients with acute kidney injury (AKI). Classical randomized clinical trials have generated relevant results in AKI but are associated with shortcomings, such as high costs and sometimes lack of generalizability. In this minireview, we discuss the value and limits of pragmatic trials and platform trials for AKI research. RECENT FINDINGS The implementation of pragmatic and platform trials in critical care settings has generated relevant clinical evidence impacting clinical practice. Pragmatic and platform designs have recently been applied to patients at risk of AKI and represent a crucial opportunity to advance our understanding of optimized treatment and strategies in patients at risk of AKI or presenting with AKI. Trials embedded in electronic health records can facilitate patient enrollment and data collection. Platform trials have allowed for a more efficient study design. Although both pragmatic and platform trials have several advantages, they also come with the challenges and shortcomings discussed in this review. SUMMARY Pragmatic and platform trials can provide clinical answers in 'real-life' settings, facilitate a significant sample size enrollment at a limited cost, and provide results that can have a faster implementation in clinical practice.
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Affiliation(s)
- Roberta T Tallarico
- Division of Critical Care Medicine, Department of Anesthesia & Perioperative Care, University of California, San Francisco (UCSF), San Francisco, California, USA
| | - Ary S Neto
- Australian and New Zealand Intensive Care Research Centre (ANZIC-RC), School of Public Health and Preventive Medicine, Monash University
- Department of Intensive Care, Austin Hospital
- Department of Critical Care, University of Melbourne, Melbourne, Victoria, Australia
- Department of Critical Care Medicine, Hospital Israelita Albert Einstein, São Paulo, Brazil
| | - Matthieu Legrand
- Division of Critical Care Medicine, Department of Anesthesia & Perioperative Care, University of California, San Francisco (UCSF), San Francisco, California, USA
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Legrand M, Bagshaw SM, Koyner JL, Schulman IH, Mathis MR, Bernholz J, Coca S, Gallagher M, Gaudry S, Liu KD, Mehta RL, Pirracchio R, Ryan A, Steubl D, Stockbridge N, Erlandsson F, Turan A, Wilson FP, Zarbock A, Bokoch MP, Casey JD, Rossignol P, Harhay MO. Optimizing the Design and Analysis of Future AKI Trials. J Am Soc Nephrol 2022; 33:1459-1470. [PMID: 35831022 PMCID: PMC9342638 DOI: 10.1681/asn.2021121605] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
AKI is a complex clinical syndrome associated with an increased risk of morbidity and mortality, particularly in critically ill and perioperative patient populations. Most AKI clinical trials have been inconclusive, failing to detect clinically important treatment effects at predetermined statistical thresholds. Heterogeneity in the pathobiology, etiology, presentation, and clinical course of AKI remains a key challenge in successfully testing new approaches for AKI prevention and treatment. This article, derived from the "AKI" session of the "Kidney Disease Clinical Trialists" virtual workshop held in October 2021, reviews barriers to and strategies for improving the design and implementation of clinical trials in patients with, or at risk of, developing AKI. The novel approaches to trial design included in this review span adaptive trial designs that increase the knowledge gained from each trial participant; pragmatic trial designs that allow for the efficient enrollment of sufficiently large numbers of patients to detect small, but clinically significant, treatment effects; and platform trial designs that use one trial infrastructure to answer multiple clinical questions simultaneously. This review also covers novel approaches to clinical trial analysis, such as Bayesian analysis and assessing heterogeneity in the response to therapies among trial participants. We also propose a road map and actionable recommendations to facilitate the adoption of the reviewed approaches. We hope that the resulting road map will help guide future clinical trial planning, maximize learning from AKI trials, and reduce the risk of missing important signals of benefit (or harm) from trial interventions.
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Affiliation(s)
- Matthieu Legrand
- Department of Anesthesia and Perioperative Care, Division of Critical Care Medicine, University of California San Francisco, San Francisco, California
- French Clinical Research Infrastructure Network, Investigation Network Initiative Cardiovascular and Renal Trialists, Nancy, France
| | - Sean M Bagshaw
- Department of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada
| | - Jay L Koyner
- Section of Nephrology, Department of Medicine, University of Chicago, Chicago, Illinois
| | - Ivonne H Schulman
- Division of Kidney, Urologic and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
| | - Michael R Mathis
- Department of Anesthesiology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan
| | | | - Steven Coca
- Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Martin Gallagher
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - Stéphane Gaudry
- French Clinical Research Infrastructure Network, Investigation Network Initiative Cardiovascular and Renal Trialists, Nancy, France
- Département de Réanimation, Medical and surgical intensive care unit, Assistance Publique-Hôpitaux de Paris Hôpital Avicenne, Bobigny, France
- Common and Rare Kidney Diseases, Sorbonne Université, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-S 1155, Paris, France
| | - Kathleen D Liu
- Divisions of Nephrology and Critical Care Medicine, Departments of Medicine and Anesthesia, University of California San Francisco, San Francisco, California
| | - Ravindra L Mehta
- Department of Medicine, University of California San Diego, San Diego, California
| | - Romain Pirracchio
- Department of Anesthesia and Perioperative Medicine, University of California San Francisco, San Francisco, California
| | - Abigail Ryan
- Division of Chronic Care Management, Chronic Care Policy Group, Center for Medicare, Center for Medicare and Medicaid Services, Baltimore, Maryland
| | - Dominik Steubl
- Boehringer Ingelheim International GmbH, Ingelheim, Germany
- Department of Nephrology, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
| | - Norman Stockbridge
- Office of New Drugs, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland
| | | | - Alparslan Turan
- Department of Anesthesiology, Lerner College of Medicine of Case Western University, Cleveland, Ohio
- Department of Outcomes Research, Cleveland Clinic, Cleveland, Ohio
| | - F Perry Wilson
- Section of Nephrology and Clinical and Translational Research Accelerator, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Alexander Zarbock
- Department of Anesthesiology, Intensive Care and Pain Medicine, University Hospital Münster, Münster, Germany
| | - Michael P Bokoch
- Department of Anesthesia and Perioperative Care, Division of Critical Care Medicine, University of California San Francisco, San Francisco, California
| | - Jonathan D Casey
- Division of Allergy, Pulmonary, and Critical Care, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Patrick Rossignol
- French Clinical Research Infrastructure Network, Investigation Network Initiative Cardiovascular and Renal Trialists, Nancy, France
- University of Lorraine, INSERM CIC 1433, Nancy, France
- Nancy CHRU, INSERM U1116, Nancy, French national institute of Health and Medical Research, unit 1116, Nancy, France
| | - Michael O Harhay
- Clinical Trials Methods and Outcomes Laboratory, PAIR (Palliative and Advanced Illness Research) Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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