Biomarkers During Recovery From AKI and Prediction of Long-term Reductions in Estimated GFR.

Acute kidney injury (AKI) CKD progression biomarkers chronic kidney disease (CKD) cystatin C eGFR trajectory estimated glomerular filtration rate (eGFR) renal recovery risk stratification soluble tumor necrosis factor receptor

Journal

American journal of kidney diseases : the official journal of the National Kidney Foundation
ISSN: 1523-6838
Titre abrégé: Am J Kidney Dis
Pays: United States
ID NLM: 8110075

Informations de publication

Date de publication:
05 2022
Historique:
received: 27 05 2021
accepted: 22 08 2021
pubmed: 16 10 2021
medline: 27 4 2022
entrez: 15 10 2021
Statut: ppublish

Résumé

The effects of acute kidney injury (AKI) on long-term kidney function, cardiovascular disease, and mortality are well documented. We aimed to identify biomarkers for the estimation of risk of new or worsening chronic kidney disease (CKD) following AKI. Prospective cohort study. Adults from a single clinical center who experienced AKI between May 2013 and May 2016 and survived until 3 years after the hospitalization during which AKI occurred. Participants included those with and without preexisting CKD. Panel of 11 plasma biomarkers measured 3 months after hospitalization. Kidney disease progression, defined as a≥25% decrease in estimated glomerular filtration rate (eGFR) combined with worsening CKD stage, assessed 3 years after the occurrence of AKI. Associations between biomarkers and kidney disease progression were evaluated in multivariable logistic regression models. Importance of predictor variables was assessed by constructing multiple decision trees, with penalized least absolute shrinkage and selection operator logistic regression for variable selection used to produce multivariable models. A total of 500 patients were studied. Soluble tumor necrosis factor receptor (sTNFR) 1, sTNFR2, cystatin C, neutrophil gelatinase-associated lipocalin, 3-month eGFR, and urinary albumin-creatinine ratio were independently associated with kidney disease progression and were more important than AKI severity or duration. A multivariable model containing sTNFR1, sTNFR2, cystatin C, and eGFR discriminated between those with and without kidney disease progression (area under the curve, 0.79 [95% CI, 0.70-0.83]). Optimizing the cutoff point to maximize utility as a "rule-out" test to identify those at low risk increased the sensitivity of the model to 95% and its negative predictive value to 92%. Lack of external validation cohort. Analyses limited to patients who survived for 3 years after AKI. Mixed population of patients with and without baseline CKD. A panel of plasma biomarkers measured 3 months after discharge from a hospitalization complicated by AKI provides a potential opportunity to identify patients who are at very low risk of incident or worsening CKD. Further study is required to determine its clinical utility through independent prospective validation.

Identifiants

pubmed: 34653541
pii: S0272-6386(21)00891-X
doi: 10.1053/j.ajkd.2021.08.017
pii:
doi:

Substances chimiques

Biomarkers 0
Cystatin C 0
Lipocalin-2 0
Creatinine AYI8EX34EU

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

646-656.e1

Commentaires et corrections

Type : CommentIn

Informations de copyright

Copyright © 2021 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

Auteurs

Michelle Wilson (M)

Clinical and Biomedical Proteomics Group, Leeds Institute of Medical Research, University of Leeds, Leeds.

Rebecca Packington (R)

Department of Renal Medicine, Royal Derby Hospital, Derby.

Helen Sewell (H)

Clinical and Biomedical Proteomics Group, Leeds Institute of Medical Research, University of Leeds, Leeds.

Rebecca Bartle (R)

Clinical and Biomedical Proteomics Group, Leeds Institute of Medical Research, University of Leeds, Leeds.

Eibhlin McCole (E)

Randox Teoranta, Donegal, Ireland.

Mary Jo Kurth (MJ)

Randox Laboratories Ltd, Crumlin, United Kingdom.

Ciaran Richardson (C)

Randox Teoranta, Donegal, Ireland.

Sue Shaw (S)

Department of Renal Medicine, Royal Derby Hospital, Derby.

Aleli Akani (A)

Department of Renal Medicine, Royal Derby Hospital, Derby.

Rosamonde E Banks (RE)

Clinical and Biomedical Proteomics Group, Leeds Institute of Medical Research, University of Leeds, Leeds.

Nicholas M Selby (NM)

Department of Renal Medicine, Royal Derby Hospital, Derby; Centre for Kidney Research and Innovation, University of Nottingham, Nottingham, United Kingdom. Electronic address: nicholas.selby@nottingham.ac.uk.

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Classifications MeSH