西安交通大学学报（医学版）2025，Vol.46Issue(3)：410-418,9.DOI:10.7652/jdyxb202503005
STEMI患者PCI术后急性肾损伤的机器学习预测模型的构建与验证
Construction and validation of machine learning predictive models for acute kidney injury after PCI in STEMI patients
摘要
Abstract
Objective To construct and validate machine learning-based models to predict the risk of acute kidney injury(AKI)following percutaneous coronary intervention(PCI)in patients with acute ST-segment elevation myocardial infarction(STEMI).Methods A total of 2 315 STEMI patients who underwent PCI between January 2020 and June 2023 were included;306(13.2％)of them developed AKI.Baseline variables were screened using LASSO regression,with the optimal λ value selected via 10-fold cross-validation to identify AKI-associated features.Subsequently,eight distinct machine learning models were constructed and evaluated for their predictive performance.SHAP value analysis was employed to assess the impact of key variables on model predictions.Results LASSO regression identified seven variables significantly associated with AKI,including age,multivessel disease,preoperative creatinine,heart failure,white blood cell count,hemoglobin,and albumin levels.Among all the models,the light gradient boosting machine(LGBM)and extreme gradient boosting(XGB)demonstrated the best predictive performance,with training set AUCs being 0.899(95％CI:0.877-0.921)and 0.893(95％CI:0.868-0.918),and validation set AUCs being 0.809(95％CI:0.763-0.856)and 0.871(95％CI:0.833-0.909),respectively.SHAP analysis revealed that albumin,age,preoperative creatinine,and white blood cell count were the primary contributors to AKI risk.Conclusion This study successfully developed and validated machine learning-based predictive models capable of effectively identifying the risk of AKI following PCI in STEMI patients,thus providing valuable support for clinical decision-making.关键词
急性ST段抬高型心肌梗死(STEMI)/经皮冠状动脉介入治疗(PCI)/急性肾损伤(AKI)/机器学习Key words
acute ST-elevation myocardial infarction(STEMI)/percutaneous coronary intervention(PCI)/acute kidney injury(AKI)/machine learning分类
临床医学引用本文复制引用
吕华胜,拉再依·巴合提,袁腾,贾红飞,沈好亮,古丽加依娜·扎安,冀伟,陈铀..STEMI患者PCI术后急性肾损伤的机器学习预测模型的构建与验证[J].西安交通大学学报（医学版）,2025,46(3):410-418,9.基金项目
新疆维吾尔自治区自然科学基金资助项目(No.2022D01E71)Supported by Natural Science Foundation of Xinjiang Uygur Autonomous Region(No.2022D01E71) （No.2022D01E71）
