机器学习用于预测移植患者的长期心血管结果
William D Park1, Ava DeLonais-Parker1, Barbara Okeke2
1Saint Louis University School of Medicine, St. Louis, Missouri, USA.
JACC. Advances
|November 19, 2025
概括
机器学习模型在预测移植接受者 (KTRs) 的心血管事件方面表现有前途,优于传统方法. 这一进步为这种脆弱的患者群体提供了更好的风险评估.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 心脏病学 心脏病学
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 心血管疾病 (CVD) 是移植接受者 (KTRs) 的首要死亡原因.
- 现有的风险评估工具通常对KTR不足,原因是独特的健康因素,如末期脏病和免疫抑制.
- 开发准确的CVD风险预测对于KTRs至关重要.
研究的目的:
- 评估机器学习 (ML) 模型在预测主要心血管不良事件 (MACE) 中的有效性,特别是在KTR中.
- 将ML模型的性能与传统的统计方法和现有的风险评分进行比较.
主要方法:
- 利用了518个成年KTR的数据集,以及大量的人口,临床,实验室和成像数据.
- 经过训练和验证的ML模型使用AutoGluon与5倍交叉验证和特征选择技术.
- 使用SHAP解释模型并将最好的ML模型 (CatBoost) 与后勤回归和Soveri风险评分进行比较.
主要成果:
- 在预测MACE时,CatBoost ML模型显示了较高的AUROC (0.72) 与逻辑回归 (0.67) 相比.
- 无论是ML还是物流回归模型,都显著超过了Soveri风险评分.
- 确定的关键预测因素包括冠状动脉疾病,左心房储压力,透析持续时间和左心室末期透气体积指数.
结论:
- 机器学习模型是预测移植患者心血管事件的可行和有效工具.
- 这些发现支持将ML纳入临床实践,以加强KTR中心血管疾病风险分层.
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