Journal
NEPHROLOGY DIALYSIS TRANSPLANTATION
Volume 38, Issue 4, Pages 834-844Publisher
OXFORD UNIV PRESS
DOI: 10.1093/ndt/gfac003
Keywords
AKI; biomarkers; outcome; machine learning; pragmatic trial
Categories
Ask authors/readers for more resources
Acute kidney injury is a growing epidemic that is associated with increased risk of death, chronic kidney disease, and cardiovascular events. Clinical trials in this field are challenging due to the heterogeneity of the disease. Identifying subphenotypes of acute kidney injury can help elucidate its diverse etiologies and enhance prevention and treatment strategies.
Acute kidney injury (AKI) is a growing epidemic and is independently associated with increased risk of death, chronic kidney disease (CKD) and cardiovascular events. Randomized-controlled trials (RCTs) in this domain are notoriously challenging and many clinical studies in AKI have yielded inconclusive findings. Underlying this conundrum is the inherent heterogeneity of AKI in its etiology, presentation and course. AKI is best understood as a syndrome and identification of AKI subphenotypes is needed to elucidate the disease's myriad etiologies and to tailor effective prevention and treatment strategies. Conventional RCTs are logistically cumbersome and often feature highly selected patient populations that limit external generalizability and thus alternative trial designs should be considered when appropriate. In this narrative review of recent developments in AKI trials based on the Kidney Disease Clinical Trialists (KDCT) 2020 meeting, we discuss barriers to and strategies for improved design and implementation of clinical trials for AKI patients, including predictive and prognostic enrichment techniques, the use of pragmatic trials and adaptive trials.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available