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预测良性前列腺增生风险:基于三个队列的模型开发和外部验证

Hao Zi^1,2, Yong-Bo Wang¹, Qiao Huang¹

¹Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China.

Global health research and policy

|December 17, 2025

概括

一个新的模型使用五个简单因素预测良性前列腺增生 (BPH) 风险:年龄,高血压,血糖,尿酸和肌. 这个工具有助于识别需要BPH预防策略的男性.

科学领域:

泌尿器科泌尿器科泌尿器科泌尿器科
预测建模预测建模
公共卫生公共卫生

背景情况:

良性前列腺增生症 (BPH) 是老年男性常见的疾病.
准确的BPH风险预测对于及时干预至关重要.
目前用于BPH风险评估的方法可能缺乏简单性或准确性.

研究的目的:

开发和验证一个强大的预测模型,用于识别患BPH高风险的个体.
在BPH风险评估中利用现有医疗特征.
创建BPH风险分层的临床适用工具.

主要方法:

利用英国生物库数据 (n=210,408) 进行模型开发,以及查尔斯 (n=5394) 和申 (n=294) 进行外部验证.
采用六种机器学习方法,包括LightGBM,来构建预测模型.
使用DeLong测试对曲线下面积 (AUC) 的比较和Cox回归对预测因子的显著性进行模型性能评估.

主要成果:

轻GBM模型证明了17个预测器的优越区分能力 (AUC=0.688±0.004).
年龄被确定为最重要的预测因素 (HR=1.091).
开发并验证了一种简化的5个预测模型 (年龄,高血压时间,血糖,尿酸,血清肌素),并创建了一个用户友好的网络工具.

结论:

使用五个可访问的预测因子的简化模型为BPH事件提供了可接受的预测能力.
这种模型可以有效地识别一般人群中BPH高风险的个体.
开发的工具提高了BPH风险评估和管理的临床实用性.

关键词:

良性前列腺增生症良性前列腺增生症事件发生率事件发生率机器学习是机器学习.预测模型的预测模型.

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