Improving clinical prediction rules in acute kidney injury with the use of biomarkers of cell cycle arrest: a pilot study

Hodgson, Luke E., Venn, Richard M., Short, Steve, Roderick, Paul J., Hargreaves, Duncan, Selby, Nicholas and Forni, Lui G. (2018) Improving clinical prediction rules in acute kidney injury with the use of biomarkers of cell cycle arrest: a pilot study. Biomarkers, 24 (1), 23-28. (doi:10.1080/1354750X.2018.1493617).

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.

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Contributors

Author: Paul J. Roderick

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