Subgroups and Special Populations in Heart Failure Clinical Trials

In the evolving landscape of heart failure (HF) management, the identification and analysis of subgroups and special populations within clinical trials are crucial for enhancing clinical decision-making, guiding further research, and understanding heterogeneity in study outcomes. This expert consensus document results from the collaborative efforts of the Heart Failure Collaboratory and the Heart Failure Collaboratory Academic Research Consortium, which brought together stakeholders from academia, industry, the U.S. Food and Drug Administration, and patient representatives. The purpose of this assembly was to propose standardized definitions and critical endpoint considerations essential for shaping the design and conduct of clinical trials for drugs and devices in the field of HF. In this context, we propose definitions and endpoints for specific subgroups and special populations in the spectrum of HF. We enhanced the precision, efficacy, and applicability of clinical research and promote more "personalized" approaches to interpretation of clinical trials. Furthermore, we explore the burgeoning field of gene therapy as a promising avenue for addressing the genetic basis of certain cardiomyopathies within these specialized patient groups. We focus especially on methodological considerations for subgroup analyses in large-scale trials, highlighting the importance of proper interpretation of subgroups and best practices for identifying heterogeneity suggestive of differential treatment effects, including when these analyses should be considered hypothesis-generating and requiring subsequent validation. We advocate for a methodical approach to clinical trial design, one that prioritizes the strategic identification of subgroups and employs appropriate statistical methodologies to ensure the reliability and clinical relevance of findings. Through this lens, we envision a pathway toward more personalized and effective treatments for HF, ultimately aiming to improve patient outcomes by leveraging the insights garnered from meticulously designed and comprehensively analyzed clinical trials.