Modeling study of the effect of placebo and medical therapy on storage and voiding symptoms, nocturia, and quality of life in men with prostate enlargement at risk for progression

Background: Modeling studies using large datasets from men with lower urinary tract symptoms/benign prostate enlargement (LUTS/BPE) can predict changes in International Prostate Symptom Score (IPSS) and risk of acute urinary retention/surgery under different treatment regimens and according to predictors (baseline characteristics) that commonly define risk of progression. We assessed the impact of treatments on different symptom types (storage, voiding, and nocturia), quality of life (QoL; IPSS Q8), and BPH Impact Index [BII]). Methods: Generalized least squares models were used to predict each outcome. Data from the CombAT study were used to predict outcomes for active treatments (dutasteride, tamsulosin, combination therapy). Predictors included: age; IPSS total, storage, voiding, nocturia and QoL (IPSS Q8) scores; BII; prostate volume; maximum urine flow rate (Qmax), prostate-specific antigen, postvoid residual urine (PVR); alpha-blocker usage within 12 months. Data from phase III dutasteride monotherapy studies were used to predict placebo outcomes. Results were visualized using an interactive web-based tool (www.bphtool.com). Results: Combination therapy provided greater predicted benefit than either monotherapy for all five outcomes for most patient profiles within the CombAT inclusion criteria. PVR and corresponding subscores were significant predictors of change in both storage and voiding subscores. Alpha-blocker use within 12 months, age (storage subscore), and Qmax (voiding subscore) were also significant predictors. PVR, age, Qmax, and nocturia score were significant predictors of change in nocturia. PVR, Qmax, previous alpha-blocker use, total IPSS, and QoL (IPSS Q8) score were significant predictors of change in QoL (IPSS Q8) score. For BII, significant predictors were PVR, age, total IPSS, and BII score. The multivariable effect of covariates and treatments is best visualized through the interactive web-based tool. Conclusions: This predictive modeling study informs our understanding of how risk factors for disease progression interact and affect treatment impact on different symptom types and QoL scores.