Identification of Preference "Phenotypes" in Men With Prostate Cancer.

INTRODUCTION: Patient preference assessment is key to high-quality decision-making in men with prostate cancer. 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. We wished to learn if there was an association between phenotype and treatment selection. METHODS: A prospective cohort of men with prostate cancer received a pre-visit decision aid. This software used conjoint analysis to quantify relative patient preferences for treatment-associated survival, quality of life outcomes, and recovery time. We collected patient clinical data, physician recommendation for active treatment or surveillance, and treatments received. Preferences were analyzed using latent class analysis to identify distinct classes of preference phenotypes. We compared patient characteristics and treatment choice across phenotypes, both univariately and in a multivariable logistic regression. RESULTS: In 250 men who used the decision aid as part of routine care, latent class analysis revealed 3 phenotypic classes. 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. On multivariate analysis, treatment choice was not associated with preference-based phenotype. Only physician recommendation was associated with choice of active treatment. CONCLUSIONS: 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. These phenotypes were not associated with treatment. Physician recommendation was the only factor determining treatment choice.

The Future State of Race/Ethnicity in Urology: Urology Workforce Projection From 2021-2061.

OBJECTIVE: To project the proportion of the urology workforce that is from under-represented in medicine (URiM) groups between 2021-2061. METHODS: Demographic data were obtained from AUA Census and ACGME Data Resource Books. The number of graduating urology residents and proportion of URiM graduating residents were characterized with linear models. Stock and Flow models were used to project future population numbers and proportions of URiM practicing urologists, contingent on assumptions regarding trainee demographics, retirement trends, and growth in the field. RESULTS: Currently, there is an increase in the percentage of URiM graduates by 0.145% per year. If historical trends continue, URiM urologists will likely comprise 16.2% of urology residency graduates and 13.3% of the practicing urological workforce in 2061. These percentages would constitute an underrepresentation of URiM urologists relative to the projected 44.2% of the U.S. population who would identify as American Indian/Alaskan Native, Black/African American, Latinx/Hispanic and Native Hawaiian/Pacific Islander by 2060.1 An increase in the percentage of URiM graduates by 0.845% per year would result in 44.2% URiM urology residency graduates and 26.1% URiM practicing urologists by 2061. An interactive app was designed to allow for a range of assumptions to be explored and for future data to be incorporated. CONCLUSION: URiM physician representation within urology over the next 40years will remain disproportionately low compared to that of the projected share of people of color in the general U.S. POPULATION: In order to achieve the AUA's Diversity, Equity and Inclusion goals, a concerted effort to implement interventions to recruit, train, and retain a generation of racially diverse urologists appears necessary.