Predicting acute kidney injury in severe trauma. A biomarker breakthrough?

We read with great interest the recent article by Stewart et al. highlighting that urinary biomarkers of acute kidney injury (AKI) could identify combat injury patients at high risk of dying or requiring renal replacement therapy (RRT) [1].

According to recent in-depth reviews, the application of biomarkers to predict AKI in critically ill patients is cumbersome at best [2, 3]. Biomarkers have poor discriminative power in heterogeneous patient populations, and levels are significantly influenced by systemic inflammation, pre-existing renal failure, and timing of sampling. Within this context, the study by Stewart et al. is both refreshing and challenging. It focuses on severely injured patients, a fairly well-defined population at substantial risk for developing early AKI [4, 5], by using a biomarker panel that consists mainly of upregulated proteins that are most sensitive to detect true histological kidney damage. Whereas the positive predictive value of these biomarkers would argue against urgent evacuation of combat casualties, intensive care unit physicians evidently will adhere an opposite “triage” and invest in a more early and intensive approach of imminent AKI.

The data provided by Stewart et al. also allow a step forward in creating a more performant biomarker-inclusive model (or score?) for predicting the development of AKI in critically ill trauma patients.

A logical next step would be to evaluate the impact of different RRT techniques (e.g., intermittent versus continuous RRT) on morbidity and mortality in this population.

We agree with the analysis by Honore et al. on the future of biomarker research. The current method for diagnosing AKI, via changes in creatinine, is clearly inadequate. One reason for this is the significant lag function from the insulting event. This and the other well-known limitations of creatinine as a biomarker resulted in the search for urinary biomarkers. Although our study did not have sufficient power to create a biomarker-inclusive model to clinically predict AKI and its outcomes, our work adds to the literature that argues for its development [1]. Such a model of AKI would give us a context to develop effective treatments and better allow us to power randomized trials. We do not know how to optimally modulate fluid replacement [6]. We do not know the optimal type or timing of RRT [7]. If we could use an inclusive model to diagnose AKI earlier and correlate changes in urinary biomarkers to specific subsequent outcomes, we might be able to design better intervention studies to answer these important questions.