Improving clinical prediction rules in acute kidney injury with the use of biomarkers of cell cycle arrest: a pilot study. (2nd January 2019)

• Record Type: Journal Article
• Authors: Hodgson, Luke E.; Venn, Richard M.; Short, Steve; Roderick, Paul J.; Hargreaves, Duncan; Selby, Nicholas; Forni, Lui G.
• Is Part Of: Biomarkers. Volume 24:Number 1(2019)
• Page Start: 23
• Page End: 28
• Publication Date: 2019-01-02
• Subjects: Acute kidney injury (AKI) -- tissue inhibitor of metalloproteinases-2 (TIMP-2) -- insulin-like growth factor binding protein 7 (IGFBP7); Biochemical markers -- Periodicals; 610.28
• DOI: 10.1080/1354750X.2018.1493617
• Languages: English
• ISSNs: 1354-750X

Abstract

Abstract: Introduction: Early recognition of patients developing acute kidney injury (AKI) is of considerable interest, we report the first use of a combination of a clinical prediction rule with a biomarker in emergent adult medical patients to improve AKI recognition. Methods: Single-centre prospective pilot study of medical admissions without AKI identified as high risk by a clinical prediction rule. Urine samples were obtained and tissue inhibitor of metalloproteinases-2 (TIMP-2) and insulin-like growth factor binding protein 7 (IGFBP7) – biomarkers associated with cell cycle arrest, were measured. Outcome: Creatinine-based KDIGO hospital-acquired AKI (HA-AKI). Results: Of 69 patients recruited, HA-AKI developed in 13% ( n = 9), in whom biomarker values were higher (median 0.43 (interquartile range (IQR) 0.21–1.25) vs. 0.07 (0.03–0.16) in cases without (p = 0.008). Peak rise in creatinine was higher in biomarker positive cases (median 30 μmol/L (7–72) vs. 1 μmol/L (0–16), p = 0.002). AUROC was 0.78 (95% CI 0.57–0.98). At the suggested cut-off (0.3) sensitivity for predicting AKI was 78% (95% CI 40–97%), specificity 89% (78–95%), positive predictive value 50% (31–69%) and negative predictive value 96% (89–99%). Discussion: Addition of a urinary biomarker allows exclusion of a significant number of patients identified to be at higher risk of AKI by a clinical prediction rule.