Abstract

87 Background: EPIC-CP (Expanded Prostate Cancer Index Composite for Clinical Practice) is a one page, 16-item questionnaire designed and validated to measure patient-reported health related quality of life in prostate cancer (PC) patients at the point of care in the clinical setting. We previously developed and externally validated models predictive of intact sexual function (i.e. achieving an erection firm enough for intercourse) at two years following external beam radiation (EBRT) or brachytherapy (BT) using EPIC-26, the parent tool from which EPIC-CP was derived. We aimed to enable the use of these models in clinical practice by recalibrating them for use with EPIC-CP. Methods: Using a previously described multicenter longitudinal cohort (PROST-QA), we identified 217 men treated with EBRT and 230 with BT with complete sexual domain and model covariate information. We used the established covariates predictive of functional erections in the EPIC-26-based models (baseline sexual score, neoadjuvant hormonal therapy, and baseline PSA for EBRT, and baseline sexual score, age, race, and BMI for BT) to recalibrate the multivariable logistic regression models for use with EPIC-CP. We examined Pearson residuals to determine goodness of fit and compared the individual predictions based on the revised models with those generated by the EPIC-26-based models. Results: The recalibrated EPIC-CP-based models demonstrated excellent discrimination (AUC 0.81 for EBRT, AUC 0.87 for BT). Odds ratio estimates for the EPIC-CP models changed by no more than 0.2 from their EPIC-26 counterparts, and remained statistically significant. EPIC-CP and EPIC-26-based predictions had good concordance: the mean ± SD difference in predicted probability between EPIC-26 and EPIC-CP models was 0.0 ± 0.08 in each treatment group. Predicted probabilities were within 15.4% and 15.8% for 95% of the subjects treated with EBRT and BT, respectively. Conclusions: EPIC-CP-based nomograms predicting erectile function two years after EBRT or BT are in good agreement with established EPIC-26-based tools and offer an easily applied and accurate prediction regarding a common and impactful side effect of PC treatment.

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