Biomarker strategies to predict need for renal replacement therapy in acute kidney injury.
Semin Dial. 2011 Mar-Apr;24(2):124-31
Authors: Cruz DN, de Geus HR, Bagshaw SM
Abstract
The early detection and diagnosis of acute kidney injury (AKI) with the standardization of novel kidney-injury-specific biomarkers is one of the highest research priorities in nephrology. Accordingly, the majority of studies of novel AKI biomarkers have focused on the early diagnosis of AKI using serum creatinine-based definitions as the gold standard. However, another potential application of kidney-injury-specific biomarkers is for guiding decisions on when to initiate renal replacement therapy (RRT). The purpose of this review is to summarize recent findings concerning some of the more promising AKI biomarkers on their capacity, either alone or integrated with traditional surrogate measures of kidney injury, for early prediction of whether patients will develop severe AKI requiring RRT. Some studies that have examined neutrophil gelatinase-associated lipocalin, cystatin-C, N-acetyl-?-d-glucosaminidase, kidney injury molecule-1, and ?(1)-microglobulin, among others, have suggested that these novel biomarkers have the potential to distinguish patients in whom RRT will be needed. This would imply that these biomarkers may be integrated into clinical decision algorithms and could synergistically improve our current ability to predict worsening AKI and need for RRT. However, published studies have many recognized limitations, which preclude our ability to adapt their findings into clinical practice today. While currently available data are not sufficient to conclude that biomarkers should be used routinely for clinical decision making for RRT initiation, additional data may in the future significantly modify the clinical variability for initiation of RRT, and potentially translate into improved outcomes and cost-effectiveness. Finally, we propose a potential approach to future biomarker strategies for RRT initiation, integrating these biomarkers with "traditional" clinical factors.
PMID: 21517976 [PubMed - indexed for MEDLINE]