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Predicting benign prostatic hyperplasia risks: model development and external validation based on three cohorts
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DOI：10.1186/s41256-025-00456-4.png)
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Abstract
En 中文
As benign prostatic hyperplasia (BPH) becomes increasingly prevalent, there is a growing need for simple and accurate methods to predict its risk. This study aimed to develop and validate a prediction model to identify males at high risk of developing BPH. The model was developed using data from 210,408 participants in the UK Biobank and externally validated with 5394 participants from the China Health and Retirement Longitudinal Study (CHARLS) and 294 participants from the Fengshen study. Six methods were employed to construct prediction models utilizing readily available medical characteristics at baseline. The DeLong tests were used to assess the differences in the area under the curves (AUCs). Cox regression was adopted to examine the relationships between the predictors and BPH. During a median follow-up period of 13.2 years (interquartile range [IQR] 12.3–14.0), 7.0 years (IQR 6.8–7.0) and 4.0 years (IQR 2.2–5.0), 18,681 males in the UK Biobank, 309 males in the CHARLS, and 27 males in the Fengshen study developed BPH. The model developed using the LightGBM method exhibited the highest discriminative capability among the six methods. Following feature reduction based on importance ranking, a full model with 17 predictors was established for BPH prediction (AUC = 0.688 ± 0.004). Age was the most important feature that contributed to the model, with older males showing a higher hazard ratio (HR) of 1.091 (95% confidence interval [CI] 1.089–1.094) for BPH incidence. Furthermore, a final simplified model was developed using five predictors (age, hypertension time, blood glucose, urate, and serum creatinine) identified in both the CHARLS and Fengshen studies for potential clinical application. It has been transformed into a user-friendly web tool to facilitate clinical utility. The model, incorporating five easily accessible predictors with acceptable predictive abilities for incident BPH, can help identify individuals at high risk of BPH in the general population.
Keywords:
Prediction model
Benign prostatic hyperplasia
Incidence
Machine learning
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