机器学习模型用于识别冠心病患者慢性病风险:一项回顾性研究
Ting He1, Jinbo Zhao2, Ling Hou2
1Department of Central Hospital of Tujia and Miao Autonomous Prefecture, Hubei University of Medicine, Shiyan, Hubei, 442000, People's Republic of China.
International journal of general medicine
|December 11, 2025
概括
机器学习有效地识别了冠心病 (CHD) 患者的慢性病 (CKD). 使用常规数据并突出尿酸与白素比率 (UAR) 的XGBoost模型有助于早期检测和干预.
科学领域:
- 心脏病学 心脏病学
- 腎臟病學 (nephrology) 是一種醫學.
- 人工智能在医学中的应用
背景情况:
- 冠心病 (CHD) 经常与慢性病 (CKD) 共同存在.
- 目前缺乏有效的工具来识别未被诊断的CHD患者的CKD.
- 这项研究解决了在这种高风险人群中需要预测模型的需求.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于在患有心脏病的患者中检测未被诊断的CKD.
- 在这个队列中确定CKD的关键临床和实验室预测因素.
- 评估开发的ML模型的临床实用性.
主要方法:
- 对1786名接受冠状动脉干预的冠状动脉疾病患者的回顾性分析.
- 使用拉索和多因素物流回归的特征选择.
- 开发了五种ML模型:物流回归,SVM,随机森林,GBM和XGBoost.
- 模型评估使用AUC,校准和决策曲线.
主要成果:
- 在CHD患者中,CKD的患病率为21.8%.
- 确定的关键预测因素包括男性性别,晚年,高血压,糖尿病,心房动,高根西尼分数,低血红蛋白,低血小板数,高甘油三,高脂蛋白,高血症,高尿酸与白蛋白比率 (UAR),高系统性炎症反应指数 (SIRI),低淋巴细胞与单细胞比率和高ApoB/ApoA1比率.
- 在验证集中,XGBoost模型表现出最佳性能 (AUC=0.909).
- 莎普利补充解释发现UAR,高血压,根西尼得分,年龄和SIRI是最有影响力的特征.
结论:
- XGBoost模型有效地识别了使用常规临床数据的CHD患者的CKD风险.
- 尿酸与白蛋白比率 (UAR) 成为这群人中CKD的新且强有力的预测因素.
- 该模型的临床实用性,通过决策曲线分析得到证实,支持其用于指导监测和早期干预策略.
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