ACR indicates albumin to creatinine ratio; AKI, acute kidney injury; ASSESS-AKI,the Assessment, Serial Evaluation, and Subsequent Sequelae in Acute Kidney Injury Study; eGFR, estimated glomerular filtration function; ESRD, end-stage renal disease
eFigure. Assembly of matched cohort adults with and without acute kidney injury
eTable 1. ASSESS-AKI enrollees follow-up through November 30, 2019 (N=1538)
eTable 2. Incremental improvement in model performance with addition of urine
eTable 3. Results of sensitivity analyses
eTable 4. Urine PCR, AKI stage and risk of kidney disease progression among those with AKI (N=769) and among those without AKI (N=769)
eTable 5. Urine PCR, AKI/stage of AKI and risk of kidney disease progression among all ASSESS-AKI adult match cohort study participants (N=1538)
eTable 6. Kidney Failure Risk Equation (KFRE) score, AKI/stages of AKI and risk ESRD
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Hsu C, Chinchilli VM, Coca S, et al. Post–Acute Kidney Injury Proteinuria and Subsequent Kidney Disease Progression: The Assessment, Serial Evaluation, and Subsequent Sequelae in Acute Kidney Injury (ASSESS-AKI) Study. JAMA Intern Med. 2020;180(3):402–410. doi:10.1001/jamainternmed.2019.6390
Among patients who had acute kidney injury (AKI) during hospitalization, is proteinuria quantified after hospital discharge associated with future loss of renal function?
In this matched cohort study of 1538 participants, half of whom had AKI during hospitalization, higher urine albumin-to-creatinine ratio quantified 3 months after discharge from a hospitalization with AKI was associated with increased risk of kidney disease progression and served as a risk discriminator.
More widespread quantification of proteinuria after hospitalized AKI should be considered to better evaluate the risk of future kidney disease progression.
Among patients who had acute kidney injury (AKI) during hospitalization, there is a need to improve risk prediction such that those at highest risk for subsequent loss of kidney function are identified for appropriate follow-up.
To evaluate the association of post-AKI proteinuria with increased risk of future loss of renal function.
Design, Setting, and Participants
The Assessment, Serial Evaluation, and Subsequent Sequelae in Acute Kidney Injury (ASSESS-AKI) Study was a multicenter prospective cohort study including 4 clinical centers in North America included 1538 patients enrolled 3 months after hospital discharge between December 2009 and February 2015.
Urine albumin-to-creatinine ratio (ACR) quantified 3 months after hospital discharge.
Main Outcomes and Measures
Kidney disease progression defined as halving of estimated glomerular filtration rate (eGFR) or end-stage renal disease.
Of the 1538 participants, 769 (50%) had AKI durring hospitalization. The baseline study visit took place at a mean (SD) 91 (23) days after discharge. The mean (SD) age was 65 (13) years; the median eGFR was 68 mL/min/1.73 m2; and the median urine ACR was 15 mg/g. Overall, 547 (37%) study participants were women and 195 (13%) were black. After a median follow-up of 4.7 years, 138 (9%) participants had kidney disease progression. Higher post-AKI urine ACR level was associated with increased risk of kidney disease progression (hazard ratio [HR], 1.53 for each doubling; 95% CI, 1.45-1.62), and urine ACR measurement was a strong discriminator for future kidney disease progression (C statistic, 0.82). The performance of urine ACR was stronger in patients who had had AKI than in those who had not (C statistic, 0.70). A comprehensive model of clinical risk factors (eGFR, blood pressure, and demographics) including ACR provided better discrimination for predicting kidney disease progression after hospital discharge among those who had had AKI (C statistic, 0.85) vs those who had not (C statistic, 0.76). In the entire matched cohort, after taking into account urine ACR, eGFR, demographics, and traditional chronic kidney risk factors determined 3 months after discharge, AKI (HR, 1.46; 95% CI, 0.51-4.13 for AKI vs non-AKI) or severity of AKI (HR, 1.54; 95% CI, 0.50-4.72 for AKI stage 1 vs non-AKI; HR, 0.56; 95% CI, 0.07-4.84 for AKI stage 2 vs non-AKI; HR, 2.24; 95% CI, 0.33-15.29 for AKI stage 3 vs non-AKI) was not independently associated with more rapid kidney disease progression.
Conclusions and Relevance
Proteinuria level is a valuable risk-stratification tool in the post-AKI period. These results suggest there should be more widespread and routine quantification of proteinuria after hospitalized AKI.
An episode of acute kidney injury (AKI) is strongly associated with more rapid subsequent loss of kidney function.1,2 There is a need to improve risk prediction so that those at highest risk for kidney disease progression are identified for appropriate follow-up.3-10
Several recent publications have stressed the prognostic importance of post-AKI level of serum creatinine (SCr) or estimated glomerular filtration function (eGFR).11-13 For example, Stoumpos et al11 reported that even among patients who had severe AKI requiring dialysis, those who had a post-AKI eGFR level greater than 60 mL/min/1.73 m2 had low risk of accelerated loss of kidney function, leading these authors to suggest that special monitoring is not necessary.
Two new studies14,15 report that proteinuria level increases after an episode of AKI, potentially reflecting residual renal parenchymal injury. We hypothesize that the level of proteinuria after AKI is strongly associated with subsequent loss of kidney function,16-18 although this was not seen in a small prospective study.19 We further hypothesize that known risk factors for future loss of kidney function that are readily available—namely proteinuria, eGFR level, and demographics—can very successfully risk stratify patients after AKI as they do in other at-risk populations.16-18 Indeed, it is possible that once post-AKI proteinuria, post-AKI eGFR, and other known chronic kidney disease (CKD) risk factors are taken into account, patients who had AKI during hospitalization have similar renal prognosis—and thus do not need to be triaged differently—compared with hospitalized patients who did not have AKI.
To test these hypotheses, we analyzed data from a multicenter cohort study of patients enrolled 3 months after hospital discharge—half of whom had an episode of AKI while hospitalized.
The Assessment, Serial Evaluation, and Subsequent Sequelae of Acute Kidney Injury (ASSESS-AKI) study was a prospective, matched cohort study of 1538 hospitalized adults who did or did not have an episode of AKI and survived to complete a study visit 3 months after discharge (eFigure in the Supplement).20 Patients enrolled between December 2009 and February 2015 from 4 North American clinical centers involving various hospital settings (general medical and surgical wards, intensive care units [ICU], and postcardiac surgery). The study was approved by institutional review boards of the participating institutions, and written informed consent was obtained from participants.
For the 769 hospitalized adults (age ≥18 years) who experienced an episode of AKI, AKI was defined based on a relative increase of at least 50% or 0.3 mg/dL or more in inpatient SCr concentration above the nearest outpatient, non–emergency department SCr concentration obtained 7 to 365 days prior to index hospitalization.20 We concurrently enrolled a matched sample of 769 adults without AKI at the index hospitalization. Patients were initially matched on clinical center and preadmission chronic kidney disease (CKD) status (eGFR<60 mL/min/1.73 m2), with additional matching on an integrated priority score based on age, history of cardiovascular disease, diabetes mellitus status, category of preindex hospitalization eGFR,20,21 and treatment in the ICU. Exclusion criteria included lack of outpatient, non–emergency department SCr reading 7 to 365 days prior to index hospitalization, previously receiving chronic renal replacement therapy or having an eGFR of less than 15 mL/min/1.73 m2 prior to hospitalization, prior organ or hematopoietic cell transplant, acute glomerulonephritis or clinically significant urinary tract obstruction, hepatorenal syndrome, metastatic or actively treated cancer, multiple myeloma, New York Heart Association class IV heart failure, an index hospitalization lasting 90 days or longer, remaining on dialysis 3 months after index hospitalization discharge, active pregnancy, or predicted survival of 12 months or less.20
These 1538 ASSESS-AKI study participants all had an outpatient research study visit 3 months after index hospitalization discharge, during which clinical data and biosamples were systematically collected.20 This visit was considered the ASSESS-AKI baseline study visit. A follow-up ASSESS-AKI in-person study visit was conducted annually thereafter, with interim telephone contacts at approximately 6-month intervals.20 Medical history, study events, and use of medications were updated at each in-person or telephone contact, and eGFR requantified at each in-person visit.
All participants had random urine albumin-to-creatinine ratio (ACR) quantified at the ASSESS-AKI baseline study visit. Participant SCr concentration was measured concurrently to calculate CKD-EPI equation eGFR.21 Serum creatinine concentration (and urine creatinine concentration) was measured using the Roche enzymatic method (Roche Diagnostics) on a Roche ModP Chemistry Analyzer before January 2014 and Cobas 6000 Chemistry Analyzer afterwards. The method was calibrated, and checked semiannually, using a National Institute of Standards and Technology (NIST) standard traceable to reference material SRM 909b (Isotope Dilution Mass Spectroscopy [IDMS]). Urine albumin concentration was quantified using a nephelometric method on the Siemens ProSpec analyzer (Siemens GMBH).
Other exposures of interest included presence or absence of AKI and stage of AKI. Participants’ AKI was staged based on SCr concentration changes with 2.0 to 2.9 times change indicating stage 2 AKI and 3.0 or more times change (or initiation of acute renal replacement therapy) indicating stage 3 AKI.22
Our primary outcome was kidney disease progression defined as halving of eGFR or end-stage renal disease (ESRD, defined as receipt of chronic dialysis or kidney transplant).20,23-29 Halving of eGFR was calculated relative to the eGFR measured at the ASSESS-AKI baseline study visit. Follow-up occurred through November 30, 2018 (eTable 1 in the Supplement).
The ASSESS-AKI DCC started the original analysis in November 2018. The revised analyses was undertaken in September 2019. We conducted time-to-event analysis using Cox proportional hazards models,30 after confirming there was no violation of the proportional hazards assumption. We accounted for the 1-to-1 matching with a random (frailty) effect. Our primary metric to assess ability of the model to risk discriminate was the C statistic.31
We conducted analyses separately in the 769 participants who had AKI during the index hospitalization and the 769 participants who did not and then in all 1538 ASSESS-AKI participants. We built separate unadjusted models with only urine ACR or only AKI (or AKI stages) (or only eGFR) as the predictor as well as adjusted models with urine ACR and AKI (or AKI stages) along with concurrently assessed eGFR, demographics, systolic blood pressure (SBP), body mass index (BMI, calculated as weight in kilograms divided by height in meters squared) and presence or absence of diabetes mellitus.16,17,32-34 We also included an interaction term between AKI (or AKI stages) and urine ACR to examine whether urine ACR was associated with kidney disease progression similarly both among those with and without medical history of AKI (and with different severities of AKI).
We then conducted a series of sensitivity analyses, including examining only the subset of AKI participants who had inpatient SCr concentration at least 50% higher than nearest outpatient, non–emergency department SCr concentration obtained 7 to 365 days prior to index hospitalization; requiring that halving of eGFR be confirmed by 2 consecutive values when defining kidney disease progression; additionally adjusting for smoking status and angiotensin converting enzyme inhibitor (ACE-I) or angiotensin receptor blocker (ARB) use at the baseline ASSESS-AKI study visit; additionally adjusting for change in eGFR from prehospitalization to posthospitalization (in mL/min/1.73 m2 per month); and implementing a competing risks regression based on Fine and Gray's proportional subhazards model. We also repeated our entire analysis replacing urine ACR with urine protein-to-creatinine ratio (PCR). Urine total protein was measured using a turbidimetric method (Roche Diagnostics).
Finally, we conducted a secondary analysis examining as exposure the risk of ESRD over 5 years as predicted from the validated Kidney Failure Risk Equation (KFRE),35 which has been shown to have strong discriminatory power and is well calibrated in general CKD populations.17,33 The KFRE is calculated using only age, sex, eGFR, urine ACR, and region (North America or not). The outcome in this secondary analysis was limited to ESRD.
The baseline ASSESS-AKI study visit took place a mean (SD) 91 (23) days after index hospital discharge. At that visit, median age of the 1538 matched adult study participants was 66 (interquartile range [IQR], 57-74) years; median eGFR was 68 (IQR, 50-89) mL/min/1.73 m2, median urine ACR was 15 (IQR, 7-60) mg/g; 574 (37%) were women, 195 (13%) were black, and 660 (43%) had diabetes mellitus. After a median follow-up of 4.7 years, 138 participants had kidney disease progression and there were 58 cases of ESRD observed (eTable 1 in the Supplement).
By design, during the index hospitalization prior to the baseline ASSESS-AKI study visit, 769 participants experienced AKI and 769 did not. Among the former, 118 (15%) were stage 2 in severity and 98 (13%) were stage 3 (including 26 who required renal replacement therapy but then recovered to stop dialysis). Median duration of AKI was 2 (IQR, 1-5) days. As shown in Table 1, those who had AKI had on average lower eGFR before and after the index hospitalization.
Among the 769 participants who had AKI during hospitalization, 97 had kidney disease progression (2.9 events per person-years). Higher urine ACR levels 3 months after discharge were associated with higher risk of kidney disease progression (Figure) (HR, 1.53 for each doubling; 95% CI, 1.43-1.64). Post-AKI urine ACR was associated with increased risk of kidney disease progression, with a C statistic of 0.82 (Table 2). The performance of postdischarge urine ACR was better in patients who experienced AKI than in patients discharged without AKI (C statistic, 0.70) (Table 2). Post-AKI eGFR was also associated with a higher risk of kidney disease progression (HR, 1.50 for each 10 mL/min/1.73 m2 decrease; 95% CI, 1.36-1.66) but the C statistic for post-AKI urine ACR was higher than that for post-AKI eGFR11-13 (0.82 vs 0.77; P < .001) (Table 2).
A comprehensive model of clinical risk factors (eGFR, blood pressure, and demographics) including ACR provided better discrimination for predicting kidney disease progression after hospital discharge among those who had AKI (C statistic, 0.85) vs those who did not (C statistic, 0.74) (Table 2). Among those who had AKI, adding urine ACR to a model with all the covariates listed in Table 2 (including AKI stages) yielded an increase in C statistics from 0.82 to 0.85 (P < .001) (eTable 2 in the Supplement). Net reclassification improvement (NRI)36 for comparing the model with and without urine ACR was 0.56 (P < .001) (eTable 2 in the Supplement).
In the entire ASSESS-AKI matched cohort, after taking into account urine ACR and eGFR, demographics, and traditional CKD risk factors determined 3 months after hospital discharge, neither the presence or absence of AKI nor the severity of AKI was independently associated with more rapid kidney disease progression (Table 3). However, urine ACR remained an independent risk factor after accounting for the other risk factors. There was no interaction between urine ACR and AKI or AKI severity. In other words, urine ACR was associated with kidney disease progression equally both in those with and without a medical history of AKI (and with different AKI severities). The C statistic of the fully adjusted model factoring in AKI severity (0.79) was not higher than that of the model with urine ACR alone (0.80). Estimated glomerular filtration rate remained an independent risk factor for kidney disease progression in all adjusted models (Table 2 and Table 3).
Similar results were seen in all the sensitivity analyses (eTables 3, 4, and 5 in the Supplement). Urine PCR had a stronger association with kidney disease progression than urine ACR (HR, 1.98 vs 1.53 for each doubling). Results of the KFRE secondary analysis are shown in eTable 6 in the Supplement.
These findings highlight the prognostic importance of post-AKI proteinuria. The results support 3 hypotheses. First, proteinuria after AKI is associated with subsequent loss of kidney function. As assessed by the C statistic, proteinuria is more significantly associated with subsequent loss of kidney function than post-AKI eGFR level, which has received more attention in the literature.11-13 Second, the known risk factors for future loss of kidney function which are readily available—including proteinuria and eGFR—can successfully risk discriminate patients after AKI as they successfully risk discriminate in other settings.16-18 Third, once post-AKI proteinuria, post-AKI eGFR, and other known CKD risk factors are taken into account, patients who experience AKI during hospitalization have similar renal prognoses compared with hospitalized patients who did not experience AKI. This is consistent with the recent analysis by James et al,10 which concluded that discharge SCr was more important than AKI stage. Similarly, in a Canadian study by Sawhney et al,35 adding AKI to a base risk model already incorporating information regarding post-AKI proteinuria, post-AKI eGFR, and demographics did not improve predictions on comparison of receiver operating characteristics or decision curve analysis.
We prospectively collected data according to a structured research protocol. We were able to quantify post-AKI proteinuria rigorously at a uniform point in time relative to the AKI hospitalization. In contrast, retrospective studies that rely on clinical data collected as part of routine clinical care suffer from ascertainment bias and missing data problems. For example, in a Scottish study by Sawhney et al,12 post-AKI proteinuria was investigated using a clinical database but fewer than a quarter of participants had this available. Furthermore, timing of assessment was inconsistent and proteinuria could only be dichotomized as normal or abnormal. In their analysis alluded to above, Stoumpos et al11 did not have data on proteinuria. In the study by James et al,10 albuminuria values were only semiquantified, were ascertained at varying time points (including before or during the AKI hospitalization) and had a high degree of missingness.
Our findings suggest that there should be more emphasis on testing of proteinuria after AKI. Prior studies of assessment of renal health after AKI have focused almost exclusively on SCr measurements.37-40 Our results support the Healthy People 2020 objectives (CKD-3) to “Increase the proportion of hospital patients who incurred AKI who have follow-up renal evaluation in 6 months post discharge,”41 in which renal evaluation is identified by having a microalbuminuria test. In clinical practice, proteinuria is infrequently measured after AKI. For example, in a study42 of patients with hospitalized AKI from 5 Veterans Affairs hospitals from 2002 to 2009, at 3 months after discharge, proteinuria was quantified on only 6% of patients. According to US Renal Data System data among Medicare patients aged 65 years or older who had AKI during hospitalization in 2015, only 17% had urinary albumin level measured.41
Most patients who had AKI of mild to moderate severity will be seen by primary care clinicians, not nephrologists.3,37 Many of these patients may have hitherto unappreciated renal parenchymal disease predisposing them to AKI or have residual structural damage from the AKI episode,14,15 placing them at increased risk for future loss of kidney function (and cardiovascular disease).1,43,44 Our findings demonstrate that proteinuria after AKI carries important prognostic information not conveyed by serum creatinine alone and that clinicians should not necessarily be falsely reassured by the latter. Having a more complete picture of kidney health is necessary for proper clinical decision making, for example weighing the risk-benefit ratios of any future interventions which may have nephrotoxic potential.
Furthermore, as proteinuria itself is an important modifiable risk factor, our data suggest that therapies to reduce proteinuria, including blood pressure control and ACE-I or ARB may reduce adverse outcomes after AKI, although these agents appear to be underutilized.45 Indeed, several new studies46-48 have suggested that benefits of ACE-I/ARB use following AKI may outweigh the risks. These findings are relevant given the high prevalence of comorbidities with class 1 indications for ACE-I/ARB among patients who experience AKI (eg, diabetes, chronic kidney disease, cardiovascular disease, heart failure). Recent analyses suggest that RAAS inhibition after AKI may not increase risk of recurrent AKI,49 which is a concern of many clinicians.
Other strengths of our study include the multicenter, prospective study design, and the multiple sensitivity analyses. We were able to take into account risk factors for proteinuria such as concurrently measured blood pressure level, which could not be done in other studies.12 Because of the matched cohort design, we were able to study prognosis among patients who had AKI in the context of, and in comparison with, patients who did not have AKI during a recent hospitalization to shed insights regarding risk stratification.
We acknowledge a number of limitations. The sample size for this prospective cohort study is not as large as reports based on analyzing data collected as part of routine clinical care.10,12,35 We only included research volunteers, many recruited from academic medical centers and there was no validation cohort. We did not attempt to systematically adjudicate etiology of AKI, capture reasons for hospitalization or measure urine output in our study enrollees. We had a relatively small number of patients with severe AKI, so our results may not be generalizable to those patients. In addition, we do not know how much of the proteinuria observed 3 months after index hospitalization was already present prior to index hospitalization. Because proteinuria is a risk factor for incident AKI50,51 some of the higher proteinuria observed after AKI was likely present before. However, this does not diminish the fact that post-AKI proteinuria is a key risk discriminant of subsequent loss of renal function. This study also does not address elements of care that occurred between discharge from index hospitalization and 3 months after that may favorably impact downstream loss of kidney function, such as prevention of rehospitalizations, recurrent AKI, or adverse drug events.52-54 We did not study urine ACR measured at different time points after AKI. Based on this observational study, we are unable to determine if lowering proteinuria after AKI will retard rates of kidney disease progression in the subsequent months to years.
In conclusion, urine ACR measured after AKI is a strong and potentially modifiable risk factor for more rapid loss of renal function. Urine ACR and eGFR measured 3 months after AKI provide key pieces of information to risk stratify patients after AKI, with excellent discrimination. Although AKI and severity of AKI were associated with kidney disease progression, the strengths of associations were greatly attenuated after taking into account urine ACR, eGFR, demographics, and traditional CKD risk factors determined 3 months after hospital discharge. These results together with recently generated data that proteinuria increases after AKI14,15 point to increased proteinuria (and decreased eGFR) as potentially key steps in the causal pathway linking AKI to CKD. Regardless of the exact pathophysiological connections, proteinuria level is a valuable triage tool post-AKI. Our results suggest there should be more widespread and routine quantification of proteinuria after hospitalized AKI, perhaps similar to how patients with diabetes mellitus undergo screening for proteinuria.55 This would represent a substantial change from current clinical practice.
Corresponding Author: Chi-yuan Hsu, MD, MSc, Division of Nephrology, University of California, San Francisco, 533 Parnassus Ave, U-400, PO Box 0532, San Francisco, CA 94143 (firstname.lastname@example.org).
Accepted for Publication: November 1, 2019.
Published Online: January 27, 2020. doi:10.1001/jamainternmed.2019.6390
Author Contributions: All authors had full access to all of the data in the study and Drs C.-y. Hsu, Chinchilli, and Siew take responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: C.-y. Hsu, Devarajan, Ghahramani, Go, R.K. Hsu, Ikizler, Liu, Parikh, Reeves, Zappitelli, Kimmel, Siew.
Acquisition, analysis, or interpretation of data: C.-y. Hsu, Chinchilli, Coca, Devarajan, Ghahramani, Go, R.K. Hsu, Ikizler, Kaufman, Liu, Parikh, Wurfel, Zappitelli, Kimmel, Siew.
Drafting of the manuscript: C.-y. Hsu, Chinchilli, Go, R.K. Hsu, Wurfel.
Critical revision of the manuscript for important intellectual content: C.-y. Hsu, Chinchilli, Coca, Devarajan, Ghahramani, Go, R.K. Hsu, Ikizler, Kaufman, Liu, Parikh, Reeves, Zappitelli, Kimmel, Siew.
Statistical analysis: C.-y. Hsu, Chinchilli, Go, R.K. Hsu, Siew.
Obtained funding: C.-y. Hsu, Ghahramani, Go, Ikizler, Parikh, Zappitelli.
Administrative, technical, or material support: C.-y. Hsu, Devarajan, Go, R.K. Hsu, Kaufman, Parikh, Reeves, Wurfel, Kimmel, Siew.
Supervision: Go, C-y Hsu, Wurfel, Zappitelli, Kimmel.
Conflict of Interest Disclosures: Dr C.-y. Hsu reported grants from the National Institutes of Health (NIH) during the conduct of the study. Dr Coca reported personal fees and equity and stock options from RenaltyixAI, personal fees from Takeda, CHF Solutions, Janssen, Goldfinch, Relypsa, and pulseData outside the submitted work. Dr Devarajan is a co-inventor on patents submitted for the use of NGAL as a biomarker of kidney injury, and has licensing agreements with BioPorto, Inc. Dr Ghahramani reported grants from NIH during the conduct of the study. Dr Go reported grants from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) during the conduct of the study. Dr Ikizler reported personal fees from Fresenius Kabi, Abbott, International Society of Nephrology, and Reata Pharmaceuticals outside the submitted work. Dr Kaufman reported personal fees from NIDDK during the conduct of the study; grant support from Department of Veterans Affairs, Cooperative Studies Program outside the submitted work. Dr Liu reported grants from NIH: National Heart, Lung and Blood Institute, grants from NIDDK, personal fees from Biomerieux, Durect, Theravance, Quark, Potrero Med, stock holdings from Amgen, travel expenses from National Policy Forum on Critical Care and Acute Renal Failure, travel expenses and speaking fees from Baxter, personal fees from Astra Zeneca, associate editor position at the American Thoracic Society, and personal fees from UpToDate outside the submitted work. Dr Parikh reported membership on the advisory board of RenalytixAI and owns equity in the same. He also serves as consultant for Genfit and TriCeda. Dr Wurfel reported grants from NIDDK during the conduct of the study. Dr Kimmel reported being an Editor with Dr Mark Rosenberg of the textbook Chronic Renal Disease. Dr Siew reported honorarium for an educational talk provided at Da Vita Annual Physician conference February, 2019; consulting for Akebia Therapeutics, Inc; royalties as a contributing author for UptoDate; and being an Associate Editor for the Clinical Journal of the American Society of Nephrology. No other disclosures were reported.
Funding/Support: ASSESS-AKI was supported by cooperative agreements from National Institute of Diabetes and Digestive and Kidney Diseases (U01DK082223, U01DK082185, U01DK082192 and U01DK082183). We also acknowledge funding support from R01DK098233, R01DK101507, R01DK114014, K23DK100468, R03DK111881, and P50DK096418.
ASSESS-AKI Investigators: Study Chair: James Kaufman, MD, New York University School of Medicine; Vernon M. Chinchilli, PhD, Pennsylvania State University; Nasrollah Ghahramani, MD, Pennsylvania State University College of Medicine; W. Brian Reeves, MD, University of Texas; Lan Kong, PhD, Pennsylvania State University; Ming Wang, PhD, Pennsylvania State University; Elana Farace, PhD, Pennsylvania State University (Data Coordinating Center); Alan S Go, MD, University of California, Kaiser Permanente Northern California; Chi-yuan Hsu, MD, University of California, Kaiser Permanente Northern California; Kathleen Liu, MD, University of California; Raymond Hsu, MD, University of California; Thida Tan, MPH, Kaiser Permanente Northern California; Juan D. Ordonez, MD, Kaiser Permanente Northern California; Sijie Zheng, MD, Kaiser Permanente Northern California; T. Alp Ikizler, MD, Vanderbilt University Medical Center; Edward D. Siew, MD, Vanderbilt University Medical Center; Tennessee Valley Health Services; Julia B. Lewis, MD, Vanderbilt University Medical Center; Lorraine Ware, MD, Vanderbilt University Medical Center; Vanderbilt; Chirag Parikh, MD, Johns Hopkins School of Medicine; Steven Coca, DO, Icahn School of Medicine at Mount Sinai; Dennis G. Moledina, PhD, Yale; Amit Garg, MD, Western University, Ontario; Prasad Devarajan, MD, University of Cincinnati; Michael Zappitelli, MD, University of Toronto; Jonathan Himmelfarb, MD, University of Washington; Mark Wurfel, MD, University of Washington; Paul L. Kimmel, MD, National Institute of Diabetes and Digestive and Kidney Diseases; Paul W. Eggers, PhD, National Institute of Diabetes and Digestive and Kidney Diseases; and Marva M. Moxey-Mims, MD, Children's National Medical Center.
Disclaimer: The opinions expressed in this article are the authors’ own and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government.
Additional Contributions: We thank Sophia R.M. Hsu for her technical assistance. She was not compensated.