Literature DB >> 28295429

Urinary [TIMP-2] × [IGFBP7] for risk prediction of acute kidney injury in decompensated heart failure.

Moritz Schanz1, Jing Shi2, Christoph Wasser1, Mark Dominik Alscher1, Martin Kimmel1.   

Abstract

BACKGROUND: In acute decompensated heart failure (ADHF) the risk of acute kidney injury (AKI) is high. Early detection of patients at risk for AKI is important. We tested urinary [TIMP-2] × [IGFBP7], a new US Food and Drug Administration-cleared test to assess AKI risk, in a cohort of hospitalized ADHF patients. HYPOTHESIS: In patients with ADHF, urinary [TIMP-2] × [IGFBP7] is associated with moderate to severe AKI and related to increased mortality.
METHODS: We enrolled 400 patients in the emergency department at Robert-Bosch Hospital, Stuttgart, Germany. We examined the predictive ability of urinary [TIMP-2] × [IGFBP7] (units: [ng/mL]2 /1000) for development of AKI stage 2 or 3 within 24 hours of sample collection in patients with ADHF. Operating characteristics were determined for the validated cutoffs of 0.3 and 2.0.
RESULTS: Forty patients had ADHF upon presentation and sufficient data for AKI staging. 27.5% developed AKI stage 2-3 within 7 days. Urinary [TIMP-2] × [IGFBP7] discriminated for AKI stage 2-3 over the first day with an area under the ROC curve of 0.84 (95% confidence interval: 0.72-0.93) and over 7 days with an AUC of 0.77 (95% confidence interval: 0.65-0.88). For the first day, sensitivity was 86% at the 0.3 cutoff and specificity was 95% at the 2.0 cutoff for prediction of AKI stage 2-3. There was a trend (P = 0.08) for higher mortality in patients with urinary [TIMP-2] × [IGFBP7] >2.0 and AKI 2-3.
CONCLUSIONS: Urinary [TIMP-2] × [IGFBP7] is a promising marker for AKI risk assessment in patients with ADHF.
© 2017 Wiley Periodicals, Inc.

Entities:  

Keywords:  [TIMP-2]×[IGFBP7]; acute decompensated heart failure; acute kidney injury; biomarkers; insulin like-growth factor binding protein-7; tissue inhibitor of metalloproteinases-2

Mesh:

Substances:

Year:  2017        PMID: 28295429      PMCID: PMC6490429          DOI: 10.1002/clc.22683

Source DB:  PubMed          Journal:  Clin Cardiol        ISSN: 0160-9289            Impact factor:   2.882


  28 in total

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7.  Using a machine learning model to predict the development of acute kidney injury in patients with heart failure.

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