Methodological issues in current practice may lead to bias in the development of biomarker combinations for predicting acute kidney injury.

ABSTRACT: Individual biomarkers of renal injury are only modestly predictive of acute kidney injury (AKI). Using multiple biomarkers has the potential to improve predictive capacity. In this systematic review, statistical methods of articles developing biomarker combinations to predict AKI were assessed. We identified and described three potential sources of bias (resubstitution bias, model selection bias, and bias due to center differences) that may compromise the development of biomarker combinations. Fifteen studies reported developing kidney injury biomarker combinations for the prediction of AKI after cardiac surgery (8 articles), in the intensive care unit (4 articles), or other settings (3 articles). All studies were susceptible to at least one source of bias and did not account for or acknow

SUBMITTER: Meisner A 

PROVIDER: S-EPMC4805513 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature