Attention: Restrictions on use of AUA, AUAER, and UCF content in third party applications, including artificial intelligence technologies, such as large language models and generative AI.
You are prohibited from using or uploading content you accessed through this website into external applications, bots, software, or websites, including those using artificial intelligence technologies and infrastructure, including deep learning, machine learning and large language models and generative AI.

UPJ INSIGHT Identification of Preference “Phenotypes” in Men With Prostate Cancer

By: Christopher Saigal, MD, MPH, David Geffen School of Medicine at University of California, Los Angeles; Brett Hollenbeck, PhD, University of California, Los Angeles Anderson School of Management; David Penson, MD, MPH, Vanderbilt University School of Medicine, Nashville, Tennessee; Kristen Williams, MA, David Geffen School of Medicine at University of California, Los Angeles; Lorna Kwan, MPH, David Geffen School of Medicine at University of California, Los Angeles; Josemanuel Saucedo, MPH, David Geffen School of Medicine at University of California, Los Angeles; Jon Bergman, MD, MPH, David Geffen School of Medicine at University of California, Los Angeles | Posted on: 17 Jul 2024

Saigal C, Hollenbeck B, Penson D, et al. Identification of preference “phenotypes” in men with prostate cancer. Urol Pract. 2024;11(4):717-725. doi:10.1097/UPJ.0000000000000580

Study Need And Importance

Incorporating patient preferences is key to high-quality decision-making in men with prostate cancer. Guidelines rely on clinical data to create clinical cohorts that may benefit from specific treatments (eg, using D’Amico risk strata). We hypothesized that cohorts of men with similar preference profiles exist (eg, value continence over survival and sexual health). If confirmed, these cohorts, or “phenotypes,” could be incorporated into guidelines. We aimed to determine if phenotypes could be identified among men with prostate cancer, with each phenotype representing a cohort with a distinct combination of preferences.

What We Found

We measured and quantified patient preferences in men choosing treatment for prostate cancer using a software-based decision aid. We used latent class analysis, which revealed 3 phenotypic classes (Figure). Men in Class 1 had the highest concerns around recovery time and the lowest value on improving lifespan. Men in Class 2 had relatively evenly distributed concerns. Men in Class 3 had the lowest concerns around recovery time and risk of surgical complications.

image

Figure. Estimated attribute means by classes derived from latent class analysis among patients with low-risk prostate cancer (N = 250).

Treatment choice was not associated with preference-based phenotype. Only physician recommendation was associated with choice of active treatment.

Limitations

These phenotypes require validation in larger and more diverse cohorts of men. Men received an educational report prior to counseling, which may have influenced treatment decisions.

Interpretations for
Patient Care

We identified the existence of 3 patient preference-based phenotypes in men with prostate cancer. Each phenotype had a unique combination of trade-offs when considering competing treatment outcomes. It appears that men with prostate cancer can be segmented by preference phenotype, but in our study phenotypes were not associated with treatment. The fact that physician recommendation was the primary driver of treatment, eclipsing clinical factors and preferences, suggests that more work needs to be done to support shared decision making in men with prostate cancer.

advertisement

advertisement