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


Journal

European urology focus
ISSN: 2405-4569
Titre abrégé: Eur Urol Focus
Pays: Netherlands
ID NLM: 101665661

Informations de publication

Date de publication:
01 2023
Historique:
received: 29 04 2022
revised: 06 07 2022
accepted: 19 07 2022
pubmed: 20 8 2022
medline: 15 2 2023
entrez: 19 8 2022
Statut: ppublish

Résumé

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). 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. 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. Predictors included age, IPSS, total prostate volume (PV), maximum urinary flow rate (Q 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 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. 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.

Sections du résumé

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
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
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.

Identifiants

pubmed: 35985933
pii: S2405-4569(22)00165-1
doi: 10.1016/j.euf.2022.07.004
pii:
doi:

Substances chimiques

Dutasteride O0J6XJN02I
Tamsulosin G3P28OML5I
Azasteroids 0
Sulfonamides 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

178-187

Informations de copyright

Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.

Auteurs

Stavros Gravas (S)

Department of Urology, Medical School, University of Cyprus, Nicosia, Cyprus.

Juan Manuel Palacios-Moreno (JM)

Global Medical Urology, GlaxoSmithKline, Madrid, Spain. Electronic address: juan-manuel.m.palacios@gsk.com.

Douglas Thompson (D)

Statistics and Data Science Innovation Hub, GlaxoSmithKline, Stevenage, UK.

Federico Concas (F)

Statistics and Data Science Innovation Hub, GlaxoSmithKline, Stevenage, UK.

Piotr J Kamola (PJ)

Genomic Sciences, GlaxoSmithKline, Boston, MA, USA.

Claus G Roehrborn (CG)

Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, USA.

Matthias Oelke (M)

Department of Urology, St. Antonius-Hospital, Gronau, Germany.

Michael W Kattan (MW)

Quantitative Health Sciences Department, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.

Marcio Augusto Averbeck (MA)

Department of Urology, Moinhos de Vento Hospital, Porto Alegre, Brazil.

Michael Manyak (M)

Global Medical Urology, GlaxoSmithKline, Washington DC, USA.

Vanessa Cortés (V)

Global Medical Urology, GlaxoSmithKline, Bogotá, Colombia.

Zrinka Lulic (Z)

Global Medical Classic and Established Products, GlaxoSmithKline, Brentford, UK.

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Classifications MeSH