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      Biomarkers for the diagnosis and risk stratification of acute kidney injury: a systematic review.

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          Abstract

          The diagnosis of acute kidney injury (AKI) is usually based on changes in serum creatinine, but such measurements are a poor marker of acute deterioration in kidney function. We performed a systematic review of publications that evaluated the accuracy and reliability of serum and urinary biomarkers in human subjects when used for the diagnosis of established AKI or early AKI, or to risk stratify patients with AKI. Two reviewers independently searched the MEDLINE and EMBASE databases (January 2000-March 2007) for studies pertaining to biomarkers for AKI. Studies were assessed for methodologic quality. In total, 31 studies evaluated 21 unique serum and urine biomarkers. Twenty-five of the 31 studies were scored as having 'good' quality. The results of the studies indicated that serum cystatin C, urine interleukin-18 (IL-18), and urine kidney injury molecule-1 (KIM-1) performed best for the differential diagnosis of established AKI. Serum cystatin C and urine neutrophil gelatinase-associated lipocalin, IL-18, glutathione-S-transferase-pi, and gamma-glutathione-S-transferase performed best for early diagnosis of AKI. Urine N-acetyl-beta-D-glucosaminidase, KIM-1, and IL-18 performed the best for mortality risk prediction after AKI. In conclusion, published data from studies of serum and urinary biomarkers suggest that biomarkers may have great potential to advance the fields of nephrology and critical care. These biomarkers need validation in larger studies, and the generalizability of biomarkers to different types of AKI as well as the incremental prognostic value over traditional clinical variables needs to be determined.

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          Author and article information

          Journal
          Kidney Int
          Kidney international
          Springer Science and Business Media LLC
          1523-1755
          0085-2538
          May 2008
          : 73
          : 9
          Affiliations
          [1 ] Clinical Epidemiology Research Center, Veterans Affairs Medical Center, West Haven, Connecticut 06516, USA.
          Article
          S0085-2538(15)53122-0
          10.1038/sj.ki.5002729
          18094679
          292425df-1198-418d-9707-9dbc382e7ef2
          History

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