Prescription Medications and Overall Survival in Metastatic Hormone Sensitive Prostate Cancer.

BACKGROUND/AIM: With new therapies for metastatic prostate cancer, patients are living longer, increasing the need for better understanding of the impact of comorbid disease. Prescription medications may risk-stratify patients independent of established methods, such as the Charlson Comorbidity Index (CCI) and guide treatment selection.

PATIENTS AND METHODS

In a nationwide retrospective study of US Veterans, we used multivariable logistic regression and Cox proportional hazard modeling to evaluate the association between number and class of prescription medications and overall survival (OS) with age, race, body-mass index, prostate specific antigen (PSA), and Charlson comorbidities as covariates in veterans treated for de novo metastatic hormone sensitive prostate cancer (mHSPC) between 2010-2021.

RESULTS

Among 8,434 Veterans, a median of nine medications and five medication classes were filled in the year prior to initial treatment with abiraterone or enzalutamide for mHSPC. Veterans on 1-4 medications had an average survival of 38 months compared to 5-9 medicines (33 months), 10-14 medicines (27 months), and 15+ medicines (22 months) (p<0.001). After adjusting for age, race, body mass index (BMI), PSA, CCI, and year of diagnosis, both the number of medications and medication classes were associated with increased mortality. The adjusted hazard ratio (aHR) [95% confidence interval (CI)] was 1.03 (1.02-1.03) for the number of medications and 1.05 (1.04-1.07) for medication classes. Medications within ATC B (blood/blood forming organs), ATC C (cardiovascular), and ATC N (nervous) were associated with worse OS, with aHRs of 1.14 (1.07, 1.21), 1.14 (1.06, 1.22), and 1.12 (1.06, 1.19), respectively.

CONCLUSION

The number and class of medications were independently associated with overall survival in patients undergoing treatment for mHSPC. With new therapies for advanced prostate cancer, patients are living longer, highlighting the need for a better understanding of the impact of comorbid diseases. Simple methods to assess disease burden and prognosticate survival have the potential to guide treatment decisions.