Optimizing the Design and Analysis of Future AKI Trials

Abstract

AKI is a complex clinical syndrome associated with an increased risk of morbidity and mortality, particularly in critically ill and perioperative patient populations. Most AKI clinical trials have been inconclusive, failing to detect clinically important treatment effects at predetermined statistical thresholds. Heterogeneity in the pathobiology, etiology, presentation, and clinical course of AKI remains a key challenge in successfully testing new approaches for AKI prevention and treatment. This article, derived from the "AKI" session of the "Kidney Disease Clinical Trialists" virtual workshop held in October 2021, reviews barriers to and strategies for improving the design and implementation of clinical trials in patients with, or at risk of, developing AKI. The novel approaches to trial design included in this review span adaptive trial designs that increase the knowledge gained from each trial participant; pragmatic trial designs that allow for the efficient enrollment of sufficiently large numbers of patients to detect small, but clinically significant, treatment effects; and platform trial designs that use one trial infrastructure to answer multiple clinical questions simultaneously. This review also covers novel approaches to clinical trial analysis, such as Bayesian analysis and assessing heterogeneity in the response to therapies among trial participants. We also propose a road map and actionable recommendations to facilitate the adoption of the reviewed approaches. We hope that the resulting road map will help guide future clinical trial planning, maximize learning from AKI trials, and reduce the risk of missing important signals of benefit (or harm) from trial interventions.

Keywords: AKI; Bayesian; cluster; heterogeneity; pragmatic; randomized controlled trials.

Figure 1.

Graphical representation of the continuum between explanatory and pragmatic trials. Less control in pragmatic trials often comes with a broader population enrolled and more generalizability of the results. The downside is more heterogeneity in response to therapies in the enrolled population.

Figure 2.

Graphical representation of two cluster-randomized trial designs. Panel (A) represents a steppedwedge cluster-randomized trial in which the intervention is implemented sequentially after a control period in each center. Panel (B) represents a crossover cluster-randomized trial where intervention and control periods alternate in each centerduring the study period.

Figure 3.

Road map for more efficient clinical trials in patients at risk for or with AKI (developed by the KDCT 2021 workshop and working group).

Figure 4.

Graphical representation of heterogeneity of treatment effect in a trial population. The left panel shows how overall treatment responses may vary in the full trial population. The middle panel shows how subgroup analyses in randomized trials can fail to capture heterogeneity in treatment effects in a trial using classic subgroup analysis. The right panel shows how machine learning and other newer statistical methods may be used to estimate the treatment effects at the individual level.