Urinary π-glutathione S-transferase Predicts Advanced Acute Kidney Injury Following Cardiovascular Surgery

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Abstract

Urinary biomarkers augment the diagnosis of acute kidney injury (AKI), with AKI after cardiovascular surgeries being a prototype of prognosis scenario. Glutathione S-transferases (GST) were evaluated as biomarkers of AKI. Urine samples were collected in 141 cardiovascular surgical patients and analyzed for urinary alpha-(α-) and pi-(π-) GSTs. The outcomes of advanced AKI (KDIGO stage 2, 3) and all-cause in-patient mortality, as composite outcome, were recorded. Areas under the receiver operator characteristic (ROC) curves and multivariate generalized additive model (GAM) were applied to predict outcomes. Thirty-eight (26.9%) patients had AKI, while 12 (8.5%) were with advanced AKI. Urinary π-GST differentiated patients with/without advanced AKI or composite outcome after surgery (p < 0.05 by generalized estimating equation). Urinary π-GST predicted advanced AKI at 3 hrs post-surgery (p = 0.033) and composite outcome (p = 0.009), while the corresponding ROC curve had AUC of 0.784 and 0.783. Using GAM, the cutoff value of 14.7 μg/L for π-GST showed the best performance to predict composite outcome. The addition of π-GST to the SOFA score improved risk stratification (total net reclassification index = 0.47). Thus, urinary π-GST levels predict advanced AKI or hospital mortality after cardiovascular surgery and improve in SOFA outcome assessment specific to AKI.

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Longitudinal data analysis for discrete and continuous outcomes.

K Liang, S Zeger (1986)

Longitudinal data sets are comprised of repeated observations of an outcome and a set of covariates for each of many subjects. One objective of statistical analysis is to describe the marginal expectation of the outcome variable as a function of the covariates while accounting for the correlation among the repeated observations for a given subject. This paper proposes a unifying approach to such analysis for a variety of discrete and continuous outcomes. A class of generalized estimating equations (GEEs) for the regression parameters is proposed. The equations are extensions of those used in quasi-likelihood (Wedderburn, 1974, Biometrika 61, 439-447) methods. The GEEs have solutions which are consistent and asymptotically Gaussian even when the time dependence is misspecified as we often expect. A consistent variance estimate is presented. We illustrate the use of the GEE approach with longitudinal data from a study of the effect of mothers' stress on children's morbidity.

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Acute kidney injury associates with increased long-term mortality.

Jean-Philippe Lafrance, Donald Miller (2010)

Acute kidney injury (AKI) associates with higher in-hospital mortality, but whether it also associates with increased long-term mortality is unknown, particularly after accounting for residual kidney function after hospital discharge. We retrospectively analyzed data from US veteran patients who survived at least 90 d after discharge from a hospitalization. We identified AKI events not requiring dialysis from laboratory data and classified them according to the ratio of the highest creatinine during the hospitalization to the lowest creatinine measured between 90 d before hospitalization and the date of discharge. We estimated mortality risks using multivariable Cox regression models adjusting for demographics, comorbidities, medication use, primary diagnosis of admission, length of stay, mechanical ventilation, and postdischarge estimated GFR (residual kidney function). Among the 864,933 hospitalized patients in the study cohort, we identified 82,711 hospitalizations of patients with AKI. In the study population of patients who survived at least 90 d after discharge, 17.4% died during follow-up (AKI 29.8%, without AKI 16.1%). The adjusted mortality risk associated with AKI was 1.41 (95% confidence interval [CI] 1.39 to 1.43) and increased with increasing AKI stage: 1.36 (95% CI 1.34 to 1.38), 1.46 (95% CI 1.42 to 1.50), and 1.59 (95% CI 1.54 to 1.65; P < 0.001 for trend). In conclusion, AKI that does not require dialysis associates with increased long-term mortality risk, independent of residual kidney function, for patients who survive 90 d after discharge. Long-term mortality risk is highest among the most severe cases of AKI.

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Biomarkers of AKI: a review of mechanistic relevance and potential therapeutic implications.

Joseph L Alge, John M Arthur (2015)

AKI is a common clinical condition associated with a number of adverse outcomes. More timely diagnosis would allow for earlier intervention and could improve patient outcomes. The goal of early identification of AKI has been the primary impetus for AKI biomarker research, and has led to the discovery of numerous novel biomarkers. However, in addition to facilitating more timely intervention, AKI biomarkers can provide valuable insight into the molecular mechanisms of this complex and heterogeneous disease. Furthermore, AKI biomarkers could also function as molecular phenotyping tools that could be used to direct clinical intervention. This review highlights the major studies that have characterized the diagnostic and prognostic predictive power of these biomarkers. The mechanistic relevance of neutrophil gelatinase-associated lipocalin, kidney injury molecule 1, IL-18, liver-type fatty acid-binding protein, angiotensinogen, tissue inhibitor of metalloproteinase-2, and IGF-binding protein 7 to the pathogenesis and pathobiology of AKI is discussed, putting these biomarkers in the context of the progressive phases of AKI. A biomarker-integrated model of AKI is proposed, which summarizes the current state of knowledge regarding the roles of these biomarkers and the molecular and cellular biology of AKI.

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Author and article information

Journal

Journal ID (nlm-ta): Sci Rep

Journal ID (iso-abbrev): Sci Rep

Title: Scientific Reports

Publisher: Nature Publishing Group

ISSN (Electronic): 2045-2322

Publication date (Electronic): 16 August 2016

Publication date Collection: 2016

Volume: 6

Electronic Location Identifier: 26335

Affiliations

[1 ]Division of Nephrology, Department of Internal Medicine, National Taiwan University Hospital , Taipei, Taiwan

[2 ]NSARF group (National Taiwan University Hospital Study Group of ARF) , Taipei, Taiwan

[3 ]Division of Nephrology, Department of Internal Medicine, Far Eastern Memorial Hospital , New Taipei City, Taiwan

[4 ]Department of Surgery, National Taiwan University Hospital , Taipei, Taiwan

[5 ]Division of Nephrology, Department of Internal Medicine, Taipei Tzu Chi Hospital , New Taipei City, Taiwan

[6 ]Division of Nephrology, Department of Internal Medicine, National Taiwan University Hospital Yun-Lin Branch , Douliou, Taiwan

[7 ]Division of Nephrology, Department of Internal Medicine, Mackay Memorial Hospital , Taipei, Taiwan

[8 ]EKF Diagnostics , Cardiff, Wales, UK

Author notes

[a ] q91421028@ 123456ntu.edu.tw

Article

Publisher Item ID: srep26335

DOI: 10.1038/srep26335

PMC ID: 4985825

PubMed ID: 27527370

SO-VID: 043647b3-ad98-4c02-925f-e852973c4181

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Urinary π-glutathione S-transferase Predicts Advanced Acute Kidney Injury Following Cardiovascular Surgery

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SICOT-J (Orthopedic surgery and traumatology)

Most cited references 31

Longitudinal data analysis for discrete and continuous outcomes.

Acute kidney injury associates with increased long-term mortality.

Biomarkers of AKI: a review of mechanistic relevance and potential therapeutic implications.

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