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Predictors of Acute Renal Injury Study (PARIS) among HIV-positive individuals: design and methods

  • Celia P. Corona-Villalobos1,
  • Michael G. Shlipak2,
  • Adrienne Tin3,
  • Chirag Parikh4,
  • Richard D. Moore3, 5,
  • Eric Vittinghoff2,
  • Jose Manuel Monroy-Trujillo1,
  • Mohamed G. Atta1 and
  • Michelle M. Estrella2Email author
BMC NephrologyBMC series – open, inclusive and trusted201718:289

https://doi.org/10.1186/s12882-017-0696-1

Received: 8 March 2017

Accepted: 22 August 2017

Published: 7 September 2017

Abstract

Background

Acute kidney injury (AKI), which is common among HIV-positive individuals, may contribute to the excess burden of chronic kidney disease (CKD) in this patient population; however, conventional clinical methods to detect AKI do not capture kidney injury sufficiently early to prevent irreversible damage. Further, large observational and interventional studies of AKI generally exclude HIV-positive persons in spite of their disproportionate risk.

Methods

The Predictors of Acute Renal Injury Study (PARIS) is a prospective observational cohort study among HIV-positive individuals established to determine the ability of candidate kidney injury biomarkers to predict future hospitalized clinical AKI, to characterize hospitalized subclinical AKI, and to discern the risk of progressive kidney disease following subclinical and clinical AKI. Among the candidate kidney injury markers, we will select the most promising to translate into a clinically viable, multiplex panel of urinary biomarkers which we will integrate with clinical factors to develop a model prognostic of risks for AKI and subsequent kidney function decline. This study has a targeted enrollment of 2000 participants. The overall follow-up of participants consists of two phases: 1) a 5-year active follow-up phase which involves serial evaluations at enrollment, annual clinic visits, and among participants who are hospitalized during this period, an evaluation at index hospitalization and 3 and 12 months post-hospitalization; and 2) a subsequent passive follow-up phase for the duration that the participant receives medical care at The Johns Hopkins Hospital.

Discussions

This study will serve as an important resource for future studies of AKI by establishing a repository with both ambulatory and inpatient biospecimens, a resource that is currently lacking in existing HIV clinical cohorts and studies of AKI. Upon completion of this study, the resulting prognostic model which will incorporate results from the multiplex HIV-AKI Risk Pane could serve as a pharmacodynamic endpoint for early phase therapeutic candidates for AKI.

Background

In the modern era of effective antiretroviral therapy, HIV-positive individuals remain at increased risk of developing chronic kidney disease (CKD) and progressing to end-stage renal disease (ESRD). This risk remains substantial even among persons who achieve adequate viral suppression and is not fully explained by traditional risk factors for CKD. Acute kidney injury (AKI), which affects approximately 1 in 6 hospitalized HIV-positive patients, may be contributing to this persistent CKD burden [1].

Studies in the general population indicate that clinical repercussions of AKI persist beyond hospitalizations, even as long as 10 years after AKI has occurred [2]. Among HIV-positive patients who survive at least 90 days after their hospitalization, AKI severity has been associated with a 2- to 4-fold increased risk of heart failure, a 1.3- to 2-fold increased risk of cardiovascular disease, a 3.8- to 20-fold increased risk of ESRD and up to a 1.7-fold increased risk of all-cause mortality [1]. Even among patients who return to their baseline serum creatinine, the risk of progression to ESRD and death remain significantly elevated. These findings suggest that HIV-positive individuals may sustain extensive kidney injury that is not fully captured by currently available clinical methods, such as serum creatinine, to detect AKI and measure its severity.

Although the current national guidelines define AKI based on absolute and relative increases in serum creatinine and/ or declines in urine output, these parameters may not be altered until over half of kidney function has been lost. Given these limitations, many individuals may sustain critical kidney damage despite serum creatinine levels and urine output remaining unchanged. This condition has been described as “subclinical AKI” [3] characterized by elevations in urine biomarkers of kidney injury, such as kidney injury marker-1 (KIM-1) and preserved serum creatinine levels. Recently, the Translational Research Investigating Biomarker Endpoints in AKI (TRIBE-AKI) Consortium has shown that among patients with clinical AKI following major cardiac surgery [4], the highest compared to the lowest tertiles of urine neutrophil gelatinase-associated lipocalin (NGAL), IL-18 and KIM-1 were each independently associated with a more than doubling of mortality risk after hospital discharge [5]. Patients with subclinical AKI (defined by urine IL-18 and KIM-1 elevations in the absence of serum creatinine elevations) also had significantly higher 3-year mortality risk after hospitalization. Urine biomarkers, which are sensitive and specific for renal tubulointerstitial injury, inflammation and fibrosis, are growing in number and reshaping the concept of AKI into a continuum of risk [6] which spans from “at-risk” populations, to subclinical AKI to clinically overt AKI. However, the current understanding of hospitalized AKI has primarily relied on serum creatinine levels at hospital admission. Furthermore, the prognostic and predictive performance of novel urine biomarkers of kidney injury has largely been assessed without baseline ambulatory levels due to the lack of stored blood and urine samples prior to AKI events in most if not all studies [710]. Most importantly, despite the burden of AKI in the HIV-positive population and recent advances in kidney injury biomarkers, very few studies of AKI have comprehensively focused on this high-risk patient population. Consequently, there remains a lack of strategies to identify HIV-positive persons at highest risk of AKI and its related adverse outcomes. We hypothesize that higher ambulatory levels of urine biomarkers denote ongoing subclinical kidney injury that portends higher risk of subsequent hospitalization and clinical AKI. We further postulate that higher urine biomarker levels of kidney injury during hospitalization are predictive of longitudinal kidney function decline.

To address these unmet clinical needs and ultimately mitigate the disproportionate risk of AKI and its consequences in the HIV-positive population, we established the Predictors of Acute Renal Injury Study (PARIS) in 2015. Through support from the National Institute of Diabetes, Digestive and Kidney Diseases (R01-DK-103574) and additional resources from the Johns Hopkins Institute of Clinical and Translational Research (ICTR) and Center for AIDS Research (1P30-AI-094189), this study will determine the ability of candidate kidney injury biomarkers to predict hospitalized clinical AKI, to characterize subclinical AKI, and to discern the risk of progressive kidney disease following subclinical and clinical AKI among a prospective cohort of HIV-positive individuals. PARIS specifically aims to:
  • Establish a prospective cohort of HIV-positive individuals treated in a large academic HIV practice, the Johns Hopkins Hospital HIV Clinic, with detailed clinical data and biological samples collected in both the outpatient and inpatient settings

  • Determine the association of ambulatory kidney damage with incident hospitalized clinical AKI and progressive kidney disease after AKI

  • Investigate the prevalence of subclinical and clinical AKI among hospitalized HIV-positive individuals and their associations with progressive kidney disease after hospitalization

  • Develop a prognostic model that integrates a multiplex panel of complementary urine biomarkers and clinical variables that will distinguish risk for incident AKI and subsequent progressive kidney disease and that will be feasible to implement in clinical practice

The culmination of this work will greatly enhance our understanding of subclinical and clinical AKI and their contribution to adverse health outcomes among HIV-positive persons. In addition, it will yield a prognostic model that incorporates a clinically applicable multiplex HIV-AKI Risk Panel which could then be tested as a screening tool in clinical trials of AKI management as well as a pharmacodynamic endpoint for early phase therapeutic candidates for AKI [11].

Methods

Overall study design

PARIS is a collaboration among investigators at the University of California, San Francisco, San Francisco Veterans Affairs Medical Center, Yale University and Johns Hopkins School of Medicine. This prospective observational cohort study enrolls and follows all eligible HIV-positive individuals who are receiving care through the Johns Hopkins Hospital HIV Clinic in Baltimore, Maryland, USA. To augment data collection, PARIS will leverage the established infrastructure of the Johns Hopkins HIV Clinical Cohort (JHHCC), an NIH-sponsored open cohort established in 1990 to provide longitudinal data on HIV-positive patients cared for at the Moore Clinic [12]. The JHHCC enrolls new patients as they initiate longitudinal HIV care at the HIV Clinic, with a consent rate of over 98%. The Institutional Review Boards at the University of California, San Francisco, the San Francisco Veterans Affairs Medical Center, Yale University, and the Johns Hopkins School of Medicine approved the study.

The overall follow-up of participants consists of two phases: 1) a 5-year active follow-up phase; and 2) a subsequent passive follow-up phase for the duration that the participant receives medical care within the Johns Hopkins Health System. The active follow-up phase enables investigators to prospectively collect data and biospecimens from the participants at scheduled study visits and during hospitalized clinical events of interest. This phase is comprised of a baseline visit and 3 years of annual ambulatory follow-up visits in the Johns Hopkins Outpatient Clinical Research Unit among all participants for questionnaires and biospecimen collection, up to 4 years from enrollment for capture of hospitalization and AKI events among all participants, and an additional year of follow-up among those who are hospitalized. The subsequent passive follow-up phase allows for ongoing evaluation of long-term outcomes, such as cardiovascular disease events, ESRD, and mortality. This phase entails ongoing review of the participants’ health records as well as linkage to outcomes registries, such as the U.S. Renal Data System and the National Death Index (Fig. 1).
Fig. 1

Phases of Follow-up in the PARIS Cohort. The active follow-up phase consists of 3 years of annual ambulatory follow-up visits for questionnaires and biospecimen collection and up to 4 years from enrollment for capture of hospitalization and AKI events among all participants, and an additional year of follow-up among those who are hospitalized. The subsequent passive follow-up phase allows for ongoing evaluation of long-term outcomes for the duration that the participant receives medical care within the Johns Hopkins Health System. AKI: Acute Kidney Injury; CRU: Clinical Research Unit, USRDS: United States Renal Data System, DI: Death Index

While biospecimens are collected on all participants, a nested case-cohort design [13] will be employed to evaluate the association between ambulatory levels of urine biomarkers with incident clinical AKI. In the nested case-cohort, annual ambulatory biomarkers will be measured only among incident cases of hospitalized clinical AKI and a random sample of the overall population (Fig. 2). This approach enables estimation of risks without having to measure biomarkers repeatedly on all participants and yields unbiased estimates of the population prevalence of AKI risk factors [13]. It also provides a subcohort representative of the overall population that allows study of other outcomes. To investigate the prevalence of subclinical and clinical AKI among hospitalized HIV-positive individuals and their associations with progressive kidney disease after hospitalization, a traditional cohort design will be used.
Fig. 2

Nested Case-Cohort Design for Annual Ambulatory Biomarker Measurements. In the nested case-cohort design, annual ambulatory biomarkers will be measured only among incident cases of hospitalized clinical AKI and a random subsample of the overall cohort. AKI: Acute Kidney Injury; t: Time

Cohort participants

Source population

The PARIS Study enrolls among HIV-positive patients who receive ambulatory care at the Johns Hopkins HIV Clinic. This clinic represents the largest program for HIV care within the state of Maryland, USA, currently with over 2800 HIV-positive patients and approximately 20,000 patient-visits annually. It provides multidisciplinary care tailored to the needs of patients with HIV. Based on data captured by the JHHCC, approximately 77% and 3% of the patients self-report as African American and Latino, respectively, and 35% are women. The current median age is 48 years (interquartile range [IQR]: 40 to 54 years). Approximately 27% report homosexual activity while 33% report intravenous drug use. Thirty-seven percent are co-infected with the hepatitis C virus. At enrollment, the median estimated glomerular filtration rate (eGFR) is 105.0 mL/min/1.73 m2 (IQR: 87.0–123.0). More than 90% of ambulatory and inpatient care for Moore Clinic patients is delivered within The Johns Hopkins Hospital. In addition, 98.4% of patients who receive care in the Moore Clinic are also enrolled in the JHHCC.

Participant selection

To participate in PARIS, a patient must meet the following inclusion criteria: 1) HIV-positive; 2) age 18 years or older; 3) English-speaking; 4) actively followed clinically at the Johns Hopkins HIV Clinic; and 5) has not experienced AKI within the preceding 6 months. Patients who have experienced clinical AKI within the preceding 6 months; reside in a hospice, skilled nursing facility or prison; have health conditions that interfere with study participation (e.g. significant cognitive impairment or active psychotic illness); plan to move out of state within the next year; are receiving chronic renal replacement therapy, have an eGFR of <15 mL/min/1.73m2; have had a previous solid organ transplant; or self-report current pregnancy are excluded. To ascertain potential participants’ eligibility on these criteria, we conduct patient interviews and review electronic health records. The participant’s most recent value recorded in the electronic health record within the preceding year is regarded as the baseline serum creatinine. The baseline eGFR is calculated using either the abbreviated Modification of Diet in Renal Disease (MDRD) [14] as used within the Johns Hopkins Medicine Pathology Department or the CKD-Epidemiology Collaboration (CKD-EPI) equation [15] as used by Quest Diagnostics (Quest Diagnostics, Inc., Madison, MJ, USA) and LabCorp (LabCorp Diagnostics, Burlington, NC, USA). All clinical serum creatinine results are measured using an assay traceable to an isotope dilution mass spectrometry.

Screening and enrollment of participants

We aim to enroll 2000 HIV-positive individuals over a period of 2.5 years. Participants are recruited using various approaches, including use of study bulletins and brochures, provider referrals, and study informational “hotline”. All HIV-positive individuals receiving ambulatory care at the Moore Clinic are screened for enrollment at the time a patient attends an ambulatory clinic visit and provides permission to be approached by investigators, at the time a patient contacts study personnel directly, or at a separate screening visit at the Johns Hopkins Outpatient Clinical Research Unit.

Retention strategies

Retention of participants is critical to the successful completion of the study. Based on the data from the JHHCC [12], we anticipate that approximately 5% of participants will be lost to follow-up annually. To minimize participant drop-out, we have implemented several retention strategies. First, we have developed a study tracking system that maintains up-to-date contact information for the participant, secondary contact persons, and clinical providers. This tracking system also includes detailed information on scheduled and upcoming visit dates. Second, we ensure that participants are reminded of upcoming scheduled study visits via written letters and telephone calls. Third, participants receive compensation for each of the study visits they complete. In addition, the study links to registries, such as the U.S. Renal Data System and the National Death Index to ascertain outcomes on renal replacement therapy or kidney transplantation and vital status, respectively. For those participants also enrolled in the JHHCC, we will link with the JHHCC database for follow-up on clinical data. As part of our quality assurance and insurance practices, we monitor completion rates of participant contacts and study visits.

Timeline of study visits during active follow-up

Individuals enrolled in the study will participate in a series of study visits for collection of data and biospecimens which are summarized in Table 1. After the baseline study visit, participants will return for annual ambulatory follow-up visits for the first 3 years of the study or until their first hospital admission during follow-up. Those who are hospitalized and who develop hospitalized clinical AKI will be identified in real-time by using computer-based programs linked to the Johns Hopkins electronic health records. Participants who are hospitalized undergo additional inpatient study visits within 24 h, 48 h, and 72 h of hospitalization, at the time of AKI diagnosis (if applicable), and at the time of hospital discharge. Following hospitalization, these individuals participate in two additional ambulatory in-person study visits at 3 and 12 months post-discharge and one telephone contact at 6 months post-discharge.
Table 1

Overall study timeline and corresponding primary exposures and outcomes of interest

JHHCC Johns Hopkins HIV Clinical Cohort, CRU Clinical Research Unit, GFR Glomerular Filtration Rate, AKI Acute Kidney Injury, CKD Chronic Kidney Injury

Data collection procedures

Data are collected through study visits, participants’ electronic health records, JHHCC database, and national registries (Table 2). During ambulatory study visits, detailed data are collected on participants’ contact information, health provider information, socio-demographics, behavioral history, medical and family history and current as well as prior medication use, including antiretroviral therapy and over-the counter medications. In addition, participants undergo standardized resting blood pressure and anthropometric measurements (height and weight), short assessments of health literacy (SAHL-E) [16] and neurocognitive assessments using Part A and B Trail Making Tests [17].
Table 2

Schedule for data collection and participant contact for the PARIS Study

 

Baseline Visit

Year 1 ambulatory follow -up

Year 2 ambulatory follow -up

Year 3 ambulatory follow -up

Hospitalization within 24 h

Hospitalization within 48 h

Hospitalization within 72 h

Hospitalization AKI

Hospitalization at discharge

3 months post discharge

6 months post discharge

12 months post discharge

 

V0

V1

V2

V3

I0

I1

I2

I3

I4

P1

P2

P3

Passive follow-up

Eligibility screen

x

            

Informed consent

x

            

Contact information

x

x

x

x

    

x

x

x

x

 

Provider contact

x

x

x

x

         

Demographic data

x

x

x

x

         

Baseline Medical history

x

            

Ambulatory follow-up medical history

 

x

x

x

         

Post-hospitalization medical history

         

x

 

x

 

Family history

x

            

Baseline social history

x

            

Ambulatory follow-up social history

 

x

x

x

         

Baseline medication use

x

            

Follow-up medication use

 

x

x

x

     

x

x

x

 

Study visit med list

x

x

x

x

x

    

x

 

x

 

Ambulatory physical exam

(BP, weight, height, waist circumference and hip circumference)

x

x

x

x

x

    

x

 

x

 

Admission physical exam

(BP and weight)

    

x

        

Short assessment health literacy

x

            

Trail-making Test Part A

x

x

x

x

     

x

 

x

 

Trail-making Test Part B

x

x

x

x

     

x

 

x

 

Post-hospitalization phone interview

          

x

  

Hospitalization discharge medical records and summary

            

x

ESRD and kidney transplantation

            

x

Clinic visit medical records

            

x

Vital status

            

x

Stored blood

x

x

x

x

x

x

x

x

x

x

 

x

 

PAXgene RNA

x

x

x

x

     

x

 

x

 

Stored urine

x

x

x

x

x

x

x

x

x

x

 

x

 

Comprehensive/ basic metabolic panel

x

x

x

x

x

x

 

x

x

x

 

x

x

HIV disease stage markers

(HIV RNA level and CD4 cell count)

x

x

x

x

x

    

x

 

x

x

CBC

x

x

x

x

x

x

 

x

x

x

 

x

x

Urinalysis

x

x

x

x

x

x

x

x

x

x

 

x

x

Drug screen

    

x

       

x

V0: Baseline visit; V1: Year 1 ambulatory follow-up; V2: Year 2 ambulatory follow-up; V3: Year 3 ambulatory follow-up; I0: Hospitalization within 24 h.; I1: Hospitalization within 48 h.; I2: Hospitalization within 72 h.; I3: Hospitalization at AKI event; I4: Hospitalization at discharge; P1: 3 months post-discharge visit; P2: 6 months post-discharge visit; P3: 12 months post-discharge visit

In order to account for potential confounders, detailed data on ambulatory clinic visits and hospitalizations are also collected through the participants’ electronic health records (Table 3). These data include pre-existing kidney disease, other co-morbid conditions, and hospitalization-related factors. Specifically, the presence of pre-existing chronic kidney disease based on albuminuria, proteinuria and/ or eGFR is ascertained. Moreover, as hypertension and degree of blood pressure control are relevant to AKI risk and eGFR decline [18, 19], we assess for baseline hypertension and longitudinal blood pressures. Comprehensive, accurate ascertainment of medication use is critical for drug-related impact on AKI risk and subsequent kidney function decline. Therefore, we gather detailed data on use of antiretroviral medications, non-steroidal anti-inflammatory drugs (NSAIDs), antibiotics, and renin-angiotensin system (RAS)-antagonists (e.g. ACE-inhibitors). In addition, we obtain hospitalization-related data, including reason for admission, discharge diagnoses, procedures and if applicable, clinical AKI severity.
Table 3

Potential confounders of the association between biomarkers and outcomes

Potential confounders

Outcomes

Incident hospitalized AKI

Subsequent kidney function

Socio demographic

Age

Baseline

Baseline

Race

Baseline

Baseline

Gender

Baseline

Baseline

Injection drug use

Baseline

Time-varying post-discharge

Baseline CKD

Albuminuria

Baseline

Baseline

eGFR

Baseline

Baseline

Chronic Co-morbid Conditions

HCV co-infection

Baseline history

Baseline history

Diabetes Mellitus

Baseline history

Baseline history

Hypertension

Baseline history, ambulatory systolic & diastolic blood pressure

Baseline history, ambulatory systolic & diastolic blood pressure

Cardiovascular disease

Baseline history

Baseline history

HIV Disease Stage

AIDS history

Time-updated absorbent state

Time-updated absorbent state

CD4+ cell count

Time-varying

Time-varying

HIV-1 RNA level

Time-varying

Time-varying

Medication Exposure

Antiretroviral drugs (evaluated as HAART, ART classes and individual drugs)

Cumulative

Cumulative post-discharge

NSAIDs

Cumulative

Cumulative post-discharge

RAS-antagonist

Cumulative

Cumulative post-discharge

Hospitalization-related factors

Antibiotic (by class)

Cumulative

In-patient exposure

IV contrast

In-patient exposure

AKI

AKI stage; peak serum creatinine

Intensive care unit stay

In-patient exposure

Duration of hospitalization

 

Primary discharge diagnosis

Categorized: Cardiovascular, infectious, respiratory, gastrointestinal, cancer, other

Anthropometric measurements

Weight

Baseline

Baseline

Height

Baseline

Baseline

Waist circumference

Baseline

Baseline

Hip circumference

Baseline

Baseline

AKI Acute Kidney Injury, CKD Chronic Kidney Disease, eGFR estimated Glomerular Filtration Rate; HCV Hepatitis C Virus, AIDS acquired Immune Deficiency Syndrome, CD4 Cluster of Differentiation 4; Human Immunodeficiency Virus; HAART Highly Active Antiretroviral Therapy, ART Antiretroviral Therapy; HIV: NSAIDs Nonsteroidal Anti-inflammatory Drugs, RAS Renin-Angiotensin System, IV Intra-Venous

We will enrich the PARIS data collection through annual linkage with the JHHCC database for participants who are also enrolled in the JHHCC. Similar to PARIS, data are collected through patient interviews, electronic patient records, other electronic sources of ambulatory/ inpatient services, and records from other facilities. These data sources are routinely captured per clinical protocol and include hospitalizations within the Johns Hopkins Health System and other facilities in Baltimore, MD, USA and visits to any of the outpatient primary care and specialty services and emergency department. Clinical data collected by the JHHCC include comprehensive data on clinical chemistry laboratory, radiological and pathological results, pharmacy use (including prescriptions and refills), and healthcare utilization. Of note, more than 95% of laboratory data are collected electronically through direct linkage to Johns Hopkins databases and outside laboratories. The JHHCC also links to vital statistics databases and the USRDS.

Biospecimen collection and repository

As part of PARIS procedures, we collect biospecimens during the baseline and annual ambulatory study visits, hospitalizations, and post-hospitalization study visits as summarized in Table 2. Blood specimens are processed for serum, plasma and whole blood. We also utilize specialized collection and storage tubes for preservation of RNA. As handling and storage of urine specimens influence measurement of urine biomarkers [20], urine specimens are immediately refrigerated at 4 °C until laboratory processing within 2 h of collection. Urine specimens are subsequently processed to yield urine pellets, urine supernatant and uncentrifuged urine samples. All biospecimens are aliquoted and stored at −80 °C. For quality assurance and insurance, the date and timing of biospecimen collections and processing are recorded and monitored.

Laboratory measurements

Method of serum creatinine measurement and GFR estimation

Standardized serum creatinine will be measured locally using an enzymatic method traceable to IDMS. This method has less measurement variability compared alkaline picrate assays for creatinine [21]. The serum creatinine-based CKD-EPI equation [15] will be used to estimate GFR.

Methods for urine biomarker measurement

To assess hospitalized AKI accurately, baseline assessment of kidney function and injury are essential. To determine the association of ambulatory kidney damage with incident hospitalized clinical AKI and progressive kidney disease after AKI and to investigate the prevalence of subclinical and clinical AKI among hospitalized HIV-positive individuals and their associations with progressive kidney disease after hospitalization, individual urine biomarkers of kidney injury will be measured using commercially available enzyme-linked immunosorbent assay (ELISA).

To develop a prognostic panel of complementary urine biomarkers that could be feasibly integrated into clinical practice, we will utilize results from single biomarkers to inform selection of an array of urine biomarkers which we will develop into a multiplex HIV-AKI Risk Panel based on the solid array-based method of Meso Scale Discovery array (Meso Scale Diagnostics, LLC., Rockville, MD, USA). Compared to readers for ELISAs, the electrochemiluminescence reader (MESO SECTOR) is several-fold more sensitive and covers a much broader range of biomarker concentration [22, 23]. Moreover, the electrochemiluminescence reader yields results more rapidly compared to those for ELISAs.

Outcomes of interest

The outcomes of interest include hospitalized clinical AKI, hospitalized subclinical AKI, the composite of renal non-recovery and all-cause mortality at 3 months post-discharge, and kidney disease progression after 3-months post-discharge. Hospitalized clinical AKI and its severity will be defined using serum creatinine thresholds endorsed by the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines on AKI [24] (Table 4). Hospitalized subclinical AKI is defined as an increase in urine biomarker level of 25% or greater from the most recent ambulatory level, among those who do not meet the criteria for clinical AKI. We will also evaluate alternative definitions, defining subclinical AKI among those who do not meet criteria for clinical AKI as: 1) a urine biomarker level above the top quartile of those without AKI; or 2) ≥4 biomarkers with levels ≥25% above the most recent ambulatory levels. Because surveillance for subclinical AKI is completely novel in this setting, we will pursue an iterative analytical approach.
Table 4

Acute kidney injury definition and staging based on serum creatinine

Definition

• Increase =0.3 mg/dl within 48 h OR

• =1.5× baseline which is known OR

• Presumed to have occurred within the prior 7 days

Stage

1

• 1.5–1.9× baseline OR =0.3 mg/dl increase

2

• 2–2.9× baseline

3

• Increase to =4 mg/dl OR

• Initiation of renal replacement therapy

For the composite outcome of renal non-recovery at 3 months and all-cause mortality among participants who are hospitalized and develop clinical AKI, renal non-recovery is defined as >25% decrease in eGFR at 3 months post-discharge from the most recent ambulatory level. Deaths are ascertained through abstraction of medical records, including those from outside institutions and death certificates. The vital status of participants who have not presented to the PARIS study visits or HIV Clinic and were last known to be living is checked annually through various vital statistic sources, including the National Death Index, Maryland Vital Records, and Social Security Death Files. Outcomes assessment at 3 months following hospitalization has been recognized as a potentially meaningful time point by the National Institute of Diabetes and Digestive and Kidney Diseases and the Food and Drug Administration for the assessment of urine biomarkers [25].

Among hospitalized participants with eGFRs ≥15 ml/min/1.73 m2 at 3 months post-discharge, the outcome of kidney disease progression will be evaluated. Post-discharge eGFR will be assessed using repeated estimates of GFR at 3 months, 12 months and up to 4 years of active follow-up. For eGFRs beyond 12 months, we will use the eGFR closest to the 3 months post-discharge date (i.e. annualized eGFR). This approach allows unbiased ascertainment of post-discharge eGFR; however, we will also conduct sensitivity analyses in which we use all available eGFRs after 3 months post-discharge. Kidney disease progression will be defined by the development of >30% decline in eGFR or progression to stage 5 CKD (eGFR <15 mL/min/1.73 m2, or ESRD) during longitudinal follow-up [26]. ESRD events will be ascertained by medical record abstraction and annual linkage to the U.S. Renal Data System, and JHHCC database.

Sample size and power estimations

To determine the cohort sample size, we assumed 80% power and a two-tailed alpha of 0.05. Where possible, the standard deviations (SD) for biomarkers were taken from published literature or observations in TRIBE- AKI Consortium [4]. We estimated the effect size for the nested case-cohort analyses by simulations using the Nested Cohort package in R (R Foundation for Statistical Computing). Other calculations were conducted using SAS 9.3 (SAS Institute, Inc.). We estimated the number of events based on observations in the JHHCC during a 2-year period, with 700 hospitalizations and 229 clinical AKI events. Of those with clinical AKI, we estimated that 25% will have renal non-recovery and 6% will die within the first 3 months of discharge. Of persons who develop clinical AKI and survive beyond 3 months post-discharge, 40% will experience kidney disease progression during follow-up. Among those without clinical AKI, 25% will develop kidney disease progression.

For the nested case-cohort approach to evaluate the association of ambulatory urine biomarkers of kidney injury with hospitalized clinical AKI, we have sufficient power to detect a hazard ratio of 1.24 per 1-SD difference in urine biomarker level. For the composite outcome of renal non-recovery at 3 months and all-cause mortality post-discharge, there is sufficient power to detect an odds ratio of 1.65 per 1-SD difference in urine biomarker level. In addition, we have sufficient power to detect a relative hazard of 1.38 per 1-SD difference in urine biomarker level for the outcome of kidney disease progression. When comparing the risk of kidney disease progression among participants who develop subclinical AKI to those who do not develop clinical AKI, there is sufficient power to detect a hazard ratio of 1.26. For the development and validation of prognostic models, effect sizes exceeding the estimated minimum detectable effects are needed for consequential risk stratification [27], and we will be well-powered to detect the required effect sizes. Lastly, the number of events for each outcome relative to the number of candidate predictors is within recommendations for the development of prediction models [28].

Discussion

In this paper, we highlighted the need for studies to address the excess burden of AKI in the HIV-positive population. The current model of AKI is incomplete since it is defined solely based on changes in kidney function estimates as assessed by serum creatinine. Previous and current studies of AKI have also frequently lacked baseline ambulatory measurements of creatinine levels, leading to AKI misclassifications and biased results. Most importantly, the current structure of HIV clinical research studies do not adequately bridge the ambulatory and inpatient setting. In addition, few studies addressing AKI have included sufficient numbers of African Americans despite their heightened risk of AKI and health consequences. Current methods to measure urine biomarkers are not feasible for clinical application.

PARIS addresses these key gaps in knowledge and will advance the progress towards implementation of urine biomarkers of kidney injury in clinical practice. This study will serve as an important resource for future studies of AKI by establishing a repository with both ambulatory and inpatient biospecimens which are currently unavailable in existing HIV-positive cohorts. Upon completion of this study, the resulting predictive model which will incorporate results from the multiplex HIV-AKI Risk Panel could be evaluated as a screening tool for trials comparing management strategies for AKI, and the Panel could serve as a surrogate endpoint for early phase AKI therapeutics.

Our study comes with some limitations. First, how best to define subclinical AKI remains unclear. We will explore alternative definitions of subclinical AKI and will evaluate how these definitions correlate with clinical variables and influence our estimates. This approach will enable us to examine the robustness of our findings and will inform future research in subclinical AKI. Second, this is a single center cohort enriched with African American patients. While we view this as a strength of the study, the composition of our study population may ultimately limit the extrapolation of our findings to the general HIV-positive population. Third, we cannot externally validate our predictive model due to the lack of paired ambulatory and inpatient urine samples required for studying AKI in existing clinical HIV cohorts. To address some of these limitations, we envision a future clinical trial to assess the utility of the HIV-AKI prognostic model compared to standard of care in reducing time to recognition and sequelae of AKI in the HIV-positive population.

In conclusion, this study will serve as an important resource for future studies of AKI by establishing a repository with both ambulatory and inpatient biospecimens that is currently lacking in existing HIV clinical cohorts and studies of AKI. Upon completion of this study, the resulting prognostic model which incorporates results from the multiplex HIV-AKI Risk Panel could be evaluated as a screening tool in trials comparing early management of AKI or intensive management after AKI with current standards of care, and the Panel could serve as a surrogate endpoint for early phase therapeutic candidates for AKI.

Abbreviations

AKI: 

Acute kidney injury

CKD: 

Chronic kidney disease

eGFR: 

estimated glomerular filtration rate

ELISA: 

Enzyme-linked immunosorbent assay

ESRD: 

End-stage renal disease

HIV: 

Human immunodeficiency virus

IL-18: 

Interleukin-18

JHHCC: 

Johns Hopkins HIV Clinic Cohort

KIM-1: 

Kidney injury marker-1

NGAL: 

Neutrophil gelatinase-associated lipocalin

PARIS: 

Predictors of Acute Renal Injury Study

SAHL-E: 

Short assessments of health literacy

SD: 

Standard deviation

TRIBE-AKI: 

Translational Research Investigating Biomarker Endpoints in Acute Kidney Injury

Declarations

Acknowledgments

We thank all the PARIS participants and staff. PARIS is supported by NIH/NIDDK grant R01DK103574. This research is also supported by the infrastructure and resources provided by the Johns Hopkins University Center for AIDS Research (1P30AI094189) supported by the following NIH Co-Funding and Participating Institutes and Centers: NIAID, NCI, NICHD, NHLBI, NIDA (U01 DA036935), NIMH, NIA, FIC, NIGMS, NIDDK, and OAR as well as by the Johns Hopkins Institute for Clinical and Translational Research (ICTR) which is funded in part by Grant Number UL1 TR 001079 from the National Center for Advancing Translational Sciences (NCATS) a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research.

Funding

This work is supported by NIH/NIDDK grant R01DK103574. This research is also facilitated by the infrastructure and resources provided by the Johns Hopkins University Center for AIDS Research (1P30AI094189) supported by the following NIH Co-Funding and Participating Institutes and Centers: NIAID, NCI, NICHD, NHLBI, NIDA (U01 DA036935), NIMH, NIA, FIC, NIGMS, NIDDK, and OAR as well as by the Johns Hopkins Institute for Clinical and Translational Research (ICTR) which is funded in part by Grant Number UL1 TR 001079 from the National Center for Advancing Translational Sciences (NCATS) a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of the Johns Hopkins ICTR, NCATS or NIH. CP is supported by NIH/NHLBI grant R01HL085757, and MGA is supported by NIH/NIDDK grant 5P01DK056492.

Availability of data and materials

The data that support the findings captured by the JHHCC are available on request from the corresponding author MME. Other data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Authors’ contributions

CPCV supervises recruitment, data collection and analysis for the PARIS study and was the primary person responsible for drafting and completing revisions of this article. MGS helped plan and design the study, including protocol, data collection, and analysis and was involved in editing drafts of this manuscript. AT contributed to the plan, design, and statistical approach of the study, and edited drafts of this manuscript. CRP helped plan and design the study, including protocol, data collection, and analysis and was involved in editing drafts of this manuscript. RDM contributed to the study’s design, protocol development and data collection and edited drafts of this manuscript. EV helped develop the statistical approach for the study and edited drafts of this manuscript. JMMT contributes to the execution of the study and edited drafts of this manuscript. MGA contributes to the execution of the study and was involved in editing drafts of this manuscript. MME serves as the principal investigator of this study, and as such led the design and protocol development of the study and supervises the execution of the study. She helped draft and revise this manuscript for submission. All authors read and approved the final manuscript.

Ethics approval and consent to participate

This prospective study is Health Insurance Portability and Accountability Act (HIPAA) compliant and is approved by the Institutional Review Boards at the San Francisco VA Medical Center and University of California, San Francisco, the Johns Hopkins School of Medicine, and Yale University. For all research subjects, written informed consent to participate in the study will be obtained from participants before study interventions.

Consent for publication

Not applicable.

Competing interests

CPCV, MGS, AT, CP, RDM, EV and MME have no relevant conflicts of interest to report. JMMT reports financial activities non-related to this article by QUARK Pharmaceutical. MGA reports financial activities non related to this article by QUARK Pharmaceutical and GILEAD Pharmaceutical.

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Authors’ Affiliations

(1)
Department of Medicine, Johns Hopkins School of Medicine
(2)
San Francisco and San Francisco VA Health Care System, Kidney Health Research Collaborative, University of California
(3)
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health
(4)
Department of Medicine, Division of Nephrology, Yale University School of Medicine
(5)
Department of Medicine, Division of General Internal Medicine, Johns Hopkins School of Medicine

References

  1. Choi AI, Li Y, Parikh C, Volberding PA, Shlipak MG. Long-term clinical consequences of acute kidney injury in the HIV-infected. Kidney Int. 2010;78(5):478–85.View ArticlePubMedPubMed CentralGoogle Scholar
  2. Newsome BB, Warnock DG, McClellan WM, Herzog CA, Kiefe CI, Eggers PW, Allison JJ. Long-term risk of mortality and end-stage renal disease among the elderly after small increases in serum creatinine level during hospitalization for acute myocardial infarction. Arch Intern Med. 2008;168(6):609–16.View ArticlePubMedGoogle Scholar
  3. Haase M, Kellum JA, Ronco C. Subclinical AKI--an emerging syndrome with important consequences. Nat Rev Nephrol. 2012;8(12):735–9.View ArticlePubMedGoogle Scholar
  4. Greenberg JH, Zappitelli M, Devarajan P, Thiessen-Philbrook HR, Krawczeski C, Li S, Garg AX, Coca S, Parikh CR, Consortium T-A. Kidney Outcomes 5 Years After Pediatric Cardiac Surgery: The TRIBE-AKI Study. JAMA Pediatr. 2016;170(11):1071–8.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Coca SG, Garg AX, Thiessen-Philbrook H, Koyner JL, Patel UD, Krumholz HM, Shlipak MG, Parikh CR, Consortium T-A. Urinary biomarkers of AKI and mortality 3 years after cardiac surgery. J Am Soc Nephrol. 2014;25(5):1063–71.View ArticlePubMedGoogle Scholar
  6. van Veldhuisen DJ, Ruilope LM, Maisel AS, Damman K. Biomarkers of renal injury and function: diagnostic, prognostic and therapeutic implications in heart failure. Eur Heart J. 2016;37(33):2577–85.View ArticlePubMedGoogle Scholar
  7. Go AS, Parikh CR, Ikizler TA, Coca S, Siew ED, Chinchilli VM, Hsu CY, Garg AX, Zappitelli M, Liu KD, et al. The assessment, serial evaluation, and subsequent sequelae of acute kidney injury (ASSESS-AKI) study: design and methods. BMC Nephrol. 2010;11:22.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Endre ZH, Pickering JW, Walker RJ, Devarajan P, Edelstein CL, Bonventre JV, Frampton CM, Bennett MR, Ma Q, Sabbisetti VS, et al. Improved performance of urinary biomarkers of acute kidney injury in the critically ill by stratification for injury duration and baseline renal function. Kidney Int. 2011;79(10):1119–30.View ArticlePubMedGoogle Scholar
  9. Gaiao S, Cruz DN. Baseline creatinine to define acute kidney injury: is there any consensus? Nephrol Dial Transplant. 2010;25(12):3812–4.View ArticlePubMedGoogle Scholar
  10. Siew ED, Matheny ME, Ikizler TA, Lewis JB, Miller RA, Waitman LR, Go AS, Parikh CR, Peterson JF. Commonly used surrogates for baseline renal function affect the classification and prognosis of acute kidney injury. Kidney Int. 2010;77(6):536–42.View ArticlePubMedGoogle Scholar
  11. Parikh CR, Moledina DG, Coca SG, Thiessen-Philbrook HR, Garg AX. Application of new acute kidney injury biomarkers in human randomized controlled trials. Kidney Int. 2016;89(6):1372–9.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Moore RD. Understanding the clinical and economic outcomes of HIV therapy: the Johns Hopkins HIV clinical practice cohort. J Acquir Immune Defic Syndr Hum Retrovirol. 1998;17(Suppl 1):S38–41.View ArticlePubMedGoogle Scholar
  13. Prentice RL. A Case-Cohort Design for Epidemiologic Cohort Studies and Disease Prevention Trials. Biometrika. 1986;73(1):1–11.View ArticleGoogle Scholar
  14. Levey AS, Coresh J, Greene T, Stevens LA, Zhang YL, Hendriksen S, Kusek JW, Van Lente F, Collaboration CKDE. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med. 2006;145(4):247–54.View ArticlePubMedGoogle Scholar
  15. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI, Kusek JW, Eggers P, Van Lente F, Greene T, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–12.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Lee SY, Stucky BD, Lee JY, Rozier RG, Bender DE. Short Assessment of Health Literacy-Spanish and English: a comparable test of health literacy for Spanish and English speakers. Health Serv Res. 2010;45(4):1105–20.View ArticlePubMedPubMed CentralGoogle Scholar
  17. REITAN RM. The relation of the trail making test to organic brain damage. J Consult Psychol. 1955;19(5):393–4.View ArticlePubMedGoogle Scholar
  18. Hsu CY, Hsu RK, Yang J, Ordonez JD, Zheng S, Go AS. Elevated BP after AKI. J Am Soc Nephrol. 2016;27(3):914–23.View ArticlePubMedGoogle Scholar
  19. Hsu CY, Liu KD. Cardiovascular events after AKI: a new dimension. J Am Soc Nephrol. 2014;25(3):425–7.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Liu KD, Siew ED, Reeves WB, Himmelfarb J, Go AS, Hsu CY, Bennett MR, Devarajan P, Ikizler TA, Kaufman JS, et al. Storage Time and Urine Biomarker Levels in the ASSESS-AKI Study. PLoS One. 2016;11(10):e0164832.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Panteghini M. Enzymatic assays for creatinine: time for action. Scand J Clin Lab Investig Suppl. 2008;241:84–8.View ArticleGoogle Scholar
  22. Keirstead ND, Wagoner MP, Bentley P, Blais M, Brown C, Cheatham L, Ciaccio P, Dragan Y, Ferguson D, Fikes J, et al. Early prediction of polymyxin-induced nephrotoxicity with next-generation urinary kidney injury biomarkers. Toxicol Sci. 2014;137(2):278–91.View ArticlePubMedGoogle Scholar
  23. Dabitao D, Margolick JB, Lopez J, Bream JH. Multiplex measurement of proinflammatory cytokines in human serum: comparison of the Meso Scale Discovery electrochemiluminescence assay and the Cytometric Bead Array. J Immunol Methods. 2011;372(1–2):71–7.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Khwaja A. KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract. 2012;120(4):c179–84.PubMedGoogle Scholar
  25. Palevsky PM, Molitoris BA, Okusa MD, Levin A, Waikar SS, Wald R, Chertow GM, Murray PT, Parikh CR, Shaw AD, et al. Design of clinical trials in acute kidney injury: report from an NIDDK workshop on trial methodology. Clin J Am Soc Nephrol. 2012;7(5):844–50.View ArticlePubMedGoogle Scholar
  26. Coresh J, Turin TC, Matsushita K, Sang Y, Ballew SH, Appel LJ, Arima H, Chadban SJ, Cirillo M, Djurdjev O, et al. Decline in estimated glomerular filtration rate and subsequent risk of end-stage renal disease and mortality. JAMA. 2014;311(24):2518–31.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Wolbers M, Koller MT, Witteman JC, Steyerberg EW. Prognostic models with competing risks: methods and application to coronary risk prediction. Epidemiology. 2009;20(4):555–61.View ArticlePubMedGoogle Scholar
  28. Harrell FE, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361–87.View ArticlePubMedGoogle Scholar

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© The Author(s). 2017

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