文章：

利用机器学习预测接受阿比特龙或恩杂鲁胺治疗的前列腺癌患者心血管风险

Predicting Cardiovascular Risk in Patients with Prostate Cancer Receiving Abiraterone or Enzalutamide by Using Machine Learning

原文发布日期：22 July 2025

DOI: 10.3390/cancers17152414

类型: Article

开放获取: 是

英文摘要：

Purpose:The identification of cardiovascular risk factors in metastatic prostate cancer (PCa) patients prior to the initiation of androgen receptor pathway inhibitors (ARPIs) is important yet challenging.Methods and Results:A nationwide cohort study was conducted utilizing data from the National Health Insurance Research Database containing the Taiwan Cancer Registry. The study population comprised 4739 PCa patients who received abiraterone or enzalutamide between 1 January 2014, and 28 February 2022. The cohort was divided into a training set (n= 3318) and a validation set (n= 1421). Machine learning techniques with random survival forest (RSF) model incorporating 16 variables was developed to predict major adverse cardiovascular events (MACEs). Over a mean follow-up period of 2.1 years, MACEs occurred in 10.9% and 11.3% of the training and validation cohorts, respectively. The RSF model identified five key predictive indicators: age < 65 or ≥75 years, heart failure, stroke, hypertension, and myocardial infarction. The model exhibited robust performance, achieving an area under the curve (AUC) of 85.1% in the training set and demonstrating strong external validity with an AUC of 85.5% in the validation cohort. A positive correlation was observed between the number of risk factors and the incidence of MACEs.Conclusions:This machine learning approach identified five predictors of MACEs in PCa patients receiving ARPIs. These findings highlight the need for comprehensive cardiovascular risk assessment and vigilant monitoring in this patient population.

摘要翻译： 

目的：在转移性前列腺癌患者开始雄激素受体通路抑制剂治疗前识别心血管风险因素至关重要，但具有挑战性。方法与结果：本研究利用包含台湾癌症登记资料的国家健康保险研究数据库进行全国性队列研究。研究对象为2014年1月1日至2022年2月28日期间接受阿比特龙或恩杂鲁胺治疗的4739例前列腺癌患者。队列分为训练集（3318例）和验证集（1421例）。研究采用包含16个变量的随机生存森林机器学习模型预测主要不良心血管事件。在平均2.1年的随访期内，训练集和验证集分别有10.9%和11.3%的患者发生主要不良心血管事件。随机生存森林模型识别出五个关键预测指标：年龄<65岁或≥75岁、心力衰竭、卒中、高血压和心肌梗死。该模型表现出稳健性能，训练集曲线下面积达85.1%，验证集曲线下面积为85.5%显示出良好的外部效度。风险因素数量与主要不良心血管事件发生率呈正相关。结论：该机器学习方法确定了接受雄激素受体通路抑制剂治疗的前列腺癌患者发生主要不良心血管事件的五个预测因子。这些发现强调了对该患者群体进行全面心血管风险评估和密切监测的必要性。

原文链接：

Predicting Cardiovascular Risk in Patients with Prostate Cancer Receiving Abiraterone or Enzalutamide by Using Machine Learning