Citations 1
Heterogeneous Treatment Effects in HFpEF: Distinguishing Drug-Specific Response from Prognostic Phenotypes Across Randomized Trials
Santana, Clodomir, Katayama, Asuka, Ballal, Aditya , Liem, David A., Chen, C , Nuno, Miriam, Ebong, Imo, Zhang, Xiao-Dong, Izu, Leighton, Alagna, Laura , Borlaug, Barry A., Butler, Javed, Chirinos, Julio A. , Chutkow, William A., Desai, Akshay S., Desvigne-Nickens, Patrice, Givertz, Michael M., Khan, Sadiya S., Kitzman, Dalane W., Lewis, Gregory D., Redfield, Margaret M., Shah, Sanjiv J., Sharma, Kavita, Taylor, Jennifer , Tinsley, Emily, Wong, Renee, Shah, Sanjiv J., Sirish, Padmini , Liem, David A. , Bidwell, Julie T. , Nuño, Miriam , Ebong, Imo , Izu, Leighton T. , Alagna, Laura, Pesce, Lorenzo L. , Sisk, Ryan K. , Borlaug, Barry A. , Butler, Javed , Desai, Akshay S , Desvigne‐Nickens, Patrice , Givertz, Michael M. , Khan, Sadiya S. , Kitzman, Dalane W. , Lewis, Gregory D. , Rasmussen‐Torvik, Laura J. , Redfield, Margaret M. , Sharma, Kavita , Taylor, Jennifer L., Shah, Sanjiv J , López, Javier E. , Chiamvimonvat, Nipavan , Cadeiras, Martín
No container title available | July 9, 2026 | Volume: No volume available | Issue: No issue available | Pages: No page available
Abstract 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 anti–volume-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. Graphical Abstract