Background: Accurate prediction of cancer outcomes remains a key component of prostate cancer (PC) treatment decision-making. Intermediate-risk PC patients represent a heterogeneous group with highly variable prognoses and it is challenging to provide uniform treatment recommendations for those patients. Previous studies have shown baseline health-related quality of life (HRQOL) to be a robust prognostic indicator for survival outcomes of different cancer entities in the metastatic setting, with scarce evidence on PC. We therefore sought to validate the predictive value of baseline HRQOL for survival outcomes in men with intermediate risk PC undergoing radical prostatectomy (RP). Methods: We identified 4780 patients with intermediate risk PC according to NCCN risk stratification and prospectively assessed baseline HRQOL prior RP. Patients were stratified by global health status (GHS) domain of the QLQ-C30 questionnaire. Oncologic endpoints were metastasis-free survival (MFS) and overall survival (OS). Multivariable Cox regression models were applied to assess the predictive value of baseline GHS on survival outcomes. Harrell's discrimination C-index was applied to calculate the predictive accuracy of the model. Decision curve analysis (DCA) was performed to validate the clinical net benefit associated with adding GHS to our multivariable model (P< .05). Results: Median follow up was 51mo. In multivariable analysis, GHS was confirmed as an independent predictor for increased MFS (HR .98, 95% CI .97-.99; P = .028) and OS (HR .97, 95% CI .95-.99; P = .008), indicating a relative risk reduction of 1.7% for MFS and 2.8% for OS per 1-point increase of baseline GHS. Adding GHS to our model improved predictive accuracy of prognosis for MFS by 6% (c-index .75 vs .72) and for OS by 8% (c-index .78 vs .74). Validation with DCA revealed a net benefit over all thresholds in prediction of MFS and OS when adding GHS to our model. Conclusions: Our findings highlight that baseline HRQOL is a valuable and robust prognostic factor for patients with intermediate-risk PC prior RP. Baseline HRQOL data improve prognostic accuracy of MFS and OS and can therefore support treatment decision-making.