Background: The VES-13 is a well-studied brief frailty screening tool for ≥ 65 older adults (OAs) in the oncology setting. Vulnerable patients (scoring ≥ 3) are at higher risk for adverse outcomes and will benefit from a Comprehensive Geriatric assessment (CGA) and cancer treatment decision optimization. Whether the VES-13 is effective specifically in patients with Genitourinary (GU) malignancies remains to be established. Primary objective: to determine if the VES-13 can predict which OAs with GU cancer (Bladder, Prostate, Kidney) had subsequent treatment modification after CGA. Secondary objective: to investigate if there is any association between VES-13 score with comorbidity and chemotherapy toxicity prediction tool (CARG). Methods: The VES-13 was administered to consecutive patients referred to the geriatric oncology (GO) clinic from GU site at the Princess Margaret Cancer Centre, Canada. All patients underwent CGA. CGA assess 8 domains including cognition, comorbidities, function, falls risk. Among patients referred for pre-treatment assessment, we examined whether the VES-13 predicted changes in the final treatment plan after CGA. Descriptive statistics were used to describe the VES-13 scores and final treatment impact. Results: From July 2015-October 2019, 77 were included in this analysis. The VES-13 ≥ 3 group were 52/77 (67.5%), and significantly associated with higher comorbidities (P = 0.003) and worse CARG scores (P = 0.005). The final treatment plan was modified in 36/77 (47%). In univariate analysis, the odds ratio (OR) for VES-13 ≥3 was 1.92 (95% CI 0.72-5.12) for change in final treatment, which was not statistically significant likely due to modest sample size. Interestingly in the same univariate analysis, there was a strong association between final treatment plan with falls risk (OR 2.63, 95% CI 1.03-6.72), physical performance (OR 2.51, 95% CI 0.98-6.45) and cognition (OR 3.95, 95% CI 1.19-13.19). Conclusions: The VES-13 identified vulnerable GU patients who will benefit from CGA and may predict treatment optimization by identifying patients at higher risk of chemotherapy toxicity and higher comorbidity.