Bayesian adaptive trial design in acute heart failure syndromes: moving beyond the mega trial.

Over the last 2 decades, early treatment for patients presenting with acute heart failure syndromes (AHFS) has changed very little. Despite strikingly different underlying disease pathophysiology, presenting signs and symptoms, and precipitants of AHFS, most patients are treated in a homogeneous manner with intravenous loop diuretics. Inhospital studies of new therapies have produced disappointingly neutral results at best. Patients continue to be enrolled in trials long after initial therapy, at a time when vital signs have improved, symptoms have changed, and initiating pathophysiologic processes, such as myocardial and renal injury, have already begun. The "one-size-fits-all" approach to inhospital AHFS trials have been recognized as one potential contributor to the disappointing trial results seen to date. Studies designed to tailor the therapeutic approach to ascertain which treatment modalities are most effective depending on patient phenotypes have not been previously conducted in AHFS because this objective is not traditional in clinical trial design. Utilizing Bayesian adaptive designs in trials of early AHFS provides an opportunity to personalize therapy within the constraints of clinical research. Bayesian adaptive design is increasingly recognized as an efficient method for obtaining valid clinical trial results. At its core, this approach uses existing information at the time of trial initiation, combined with data accumulating during the trial, to identify treatments most beneficial for specific patient subgroups. Based on accumulating evidence, the study then "adapts" its focus to critical differences between treatments within patient subgroups. Bayesian adaptive design is ideally suited for investigating complex, heterogeneous conditions such as AHFS and affords investigators the ability to study multiple treatment approaches and therapies in multiple patient phenotypes within a single trial, while maintaining a reasonable overall sample size. Identifying specific treatment approaches that safely improve symptoms and facilitate early discharge in patients who traditionally are admitted, often for prolonged periods of time, are necessary if we aim to reverse the disappointing trend in clinical trial results. In this study, AHFS clinical researchers and biostatisticians with expertise and experience in designing "personalized medicine" trials describe the development of a Bayesian adaptive design for an emergency department-based AHFS trial.

Duke Scholars

Author Christopher Lindsell Biostatistics & Bioinformatics, Division of Biostatistics