C. Santana, A. Katayama, A. Ballal, P. Sirish, D. A. Liem, J. T. Bidwell, C.-Y. Chen, M. Nuno, I. A. Ebong, X.-D. Zhang, L. Izu, L. Alagna, L. L. Pesce, R. K. Sisk, B. A. Borlaug, J. Butler, J. A. Chirinos, W. A. Chutkow, A. S. Desai, P. Desvigne-Nickens, M. M. Givertz, S. S. Khan, D. W. Kitzman, G. D. Lewis, L. J. Rasmussen-Torvik, M. M. Redfield, S. H. Shah, K. Sharma, J. L. Taylor, E. Tinsley, R. Wong, S. J. Shah, J. E. Lopez, N. Chiamvimonvat, M. Cadeiras
Background: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome comprising multiple pathophysiological phenotypes. HFpEF trials have largely enrolled diverse populations and reported average treatment effects, consistently yielding neutral results that may obscure drug-specific benefits within distinct subgroups. To address this issue, we employ an interaction-based that incorporates treatment-by-variable interactions to uncover drug-specific responses. Methods: We leveraged four HFpEF clinical trials (TOPCAT, RELAX, NEAT-HFpEF, INDIE-HFpEF) and developed a framework comprising two complementary approaches. The first employed a prognostic responder model to evaluate whether conventional responder definitions reflect treatment-specific benefit or instead capture favorable clinical trajectories common to both treatment and placebo groups. The second used an interaction-based individual treatment effect (ITE) modeling to identify baseline variables that modify therapy effect, distinguishing drug-specific response from prognostic phenotypes. Results: Although the prognostic responder model demonstrated good discrimination, further analisys suggested it primarily captured a prognostic signal associated with favorable clinical trajectories common to both treatment and placebo arms. In contrast, the ITE model identified distinct, drug-specific effect modifiers across trials (cardiorenal-inflammatory for spironolactone (TOPCAT), NO-mediated anti-inflammatory for isosorbide mononitrate (NEAT-HFpEF), afterload-reducing for inorganic nitrite (INDIE-HFpEF), and antivolume-overload for sildenafil (RELAX). Each ITE model demonstrated significance only within its own trial suggesting drug-specific signal. Conclusions: The proposed method identifies mechanism-specific effect modifiers, and uncovers clinically meaningful heterogeneity in treatment response, which is not captured by conventional MCID-based approaches. Although exploratory, these findings support phenotype-guided therapy in HFpEF and argue for phenotype-informed trial design to enhance treatment-effect detection and therapy targeting.