Understanding Treatment Response in Individual Profiles of Men with Prostatic Enlargement at Risk of Progression.

BACKGROUND

It is unclear how cumulative multivariable effects of clinically relevant covariates impact response to pharmacological treatments for lower urinary tract symptoms (LUTS)/benign prostatic enlargement (BPE).

OBJECTIVE

To develop models to predict treatment response in terms of International Prostate Symptom Score (IPSS) and the risk of acute urinary retention (AUR) or BPE-related surgery, based on large data sets and using as predictors baseline characteristics that commonly define the risk of disease progression.

DESIGN, SETTING, AND PARTICIPANTS: A total of 9167 patients with LUTS/BPE at risk of progression in three placebo-controlled dutasteride trials and one comparing dutasteride, tamsulosin, and dutasteride + tamsulosin combination therapy (CT) were included in the analysis to predict response to placebo up to 24 mo and active treatment up to 48 mo.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Predictors included age, IPSS, total prostate volume (PV), maximum urinary flow rate (Q), prostate-specific antigen, postvoid residual urine (PVR), α-blocker usage within 12 mo, and randomised treatment. A generalised least-squares model was developed for longitudinal IPSS and a Cox proportional-hazards model for time to first AUR/surgery.

RESULTS AND LIMITATIONS

The vast majority of patients benefit from dutasteride or CT when compared with tamsulosin alone. The predicted IPSS improvement with dutasteride or CT increased with greater PV and severity of symptoms at baseline. The tamsulosin effect was lower with greater baseline PV and tended to decrease over time. Predicted AUR/surgery risk was greater with tamsulosin versus CT or dutasteride; this risk increased with larger PV, higher PVR, and lower Q (all at baseline). An educational interactive web-based tool facilitates visualisation of the results (www.bphtool.com). Limitations include: the placebo and active-treatment predictions are from different studies, the lack of similar studies for external validation, and the focus on a population at risk of progression from the 4-yr CombAT study.

CONCLUSIONS

Predictive modelling based on large data sets and visualisation of the risk for individual profiles can improve our understanding of how risk factors for disease progression interact and affect response to different treatments, reinforcing the importance of an individualised approach for LUTS/BPE management.

PATIENT SUMMARY

We used data from previous studies to develop statistical models for predicting how men with lower urinary tract symptoms or benign prostate enlargement and at risk of disease complications respond to certain treatments according to their individual characteristics.