Cargando…

Quantifying Benefit-Risk Preferences for Heart Failure Devices: A Stated-Preference Study

BACKGROUND: Regulatory and clinical decisions involving health technologies require judgements about relative importance of their expected benefits and risks. We sought to quantify heart-failure patients’ acceptance of therapeutic risks in exchange for improved effectiveness with implantable devices...

Descripción completa

Detalles Bibliográficos
Autores principales: Reed, Shelby D., Yang, Jui-Chen, Rickert, Timothy, Johnson, F. Reed, Gonzalez, Juan Marcos, Mentz, Robert J., Krucoff, Mitchell W., Vemulapalli, Sreekanth, Adamson, Philip B., Gebben, David J., Rincon-Gonzalez, Liliana, Saha, Anindita, Schaber, Daniel, Stein, Kenneth M., Tarver, Michelle E., Bruhn-Ding, Dean
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8763248/
https://www.ncbi.nlm.nih.gov/pubmed/34937393
http://dx.doi.org/10.1161/CIRCHEARTFAILURE.121.008797
Descripción
Sumario:BACKGROUND: Regulatory and clinical decisions involving health technologies require judgements about relative importance of their expected benefits and risks. We sought to quantify heart-failure patients’ acceptance of therapeutic risks in exchange for improved effectiveness with implantable devices. METHODS: Individuals with heart failure recruited from a national web panel or academic medical center completed a web-based discrete-choice experiment survey in which they were randomized to one of 40 blocks of 8 experimentally controlled choice questions comprised of 2 device scenarios and a no-device scenario. Device scenarios offered an additional year of physical functioning equivalent to New York Heart Association class III or a year with improved (ie, class II) symptoms, or both, with 30-day mortality risks ranging from 0% to 15%, in-hospital complication risks ranging from 0% to 40%, and a remote adjustment device feature. Logit-based regression models fit participants’ choices as a function of health outcomes, risks and remote adjustment. RESULTS: Latent-class analysis of 613 participants (mean age, 65; 49% female) revealed that two-thirds were best represented by a pro-device, more risk-tolerant class, accepting up to 9% (95% CI, 7%–11%) absolute risk of device-associated mortality for a one-year gain in improved functioning (New York Heart Association class II). Approximately 20% were best represented by a less risk-tolerant class, accepting a maximum device-associated mortality risk of 3% (95% CI, 1%–4%) for the same benefit. The remaining class had strong antidevice preferences, thus maximum-acceptable risk was not calculated. CONCLUSIONS: Quantitative evidence on benefit-risk tradeoffs for implantable heart-failure device profiles may facilitate incorporating patients’ views during product development, regulatory decision-making, and clinical practice.