背景：心肌梗死经皮冠状动脉介入治疗（PCI）后支架贴壁不良（SM）仍然存在重大的临床挑战。近年来,机器学习(ML)模型在疾病风险分层和预测建模中显示出潜力。
目的：基于光学相干断层扫描（OCT）成像的ML模型，实验室测试,临床特征可以预测SM的发生。
方法：我们研究了遵义医学院附属医院337例患者，中国,2023年5月至10月接受PCI和冠状动脉OCT的患者。我们采用嵌套交叉验证将患者分为训练集和测试集。我们开发了五种ML模型：XGBoost，LR,射频,SVM,和基于钙化特征的NB。使用ROC曲线评估性能。Lasso回归从46个临床特征和21个OCT成像特征中选择特征，用五种ML算法进行了优化。
结果：在基于钙化特征的预测模型中，XGBoost模型和SVM模型表现出更高的AUC值。Lasso回归从临床和成像数据中确定了五个关键特征。将选定的特征合并到模型中进行优化后，所有算法模型的AUC值均有显著改善.XGBoost模型显示出最高的校准精度。SHAP值显示，影响XGBoost模型的前五名特征是钙化长度，年龄,冠状动脉夹层,脂质角,和肌钙蛋白.
结论：使用斑块成像特征和临床特征建立的ML模型可以预测SM的发生。基于临床和影像学特征的ML模型表现出更好的性能。
BACKGROUND: Stent malapposition (SM) following percutaneous coronary intervention (PCI) for myocardial infarction continues to present significant clinical challenges. In recent years, machine learning (ML) models have demonstrated potential in disease risk stratification and predictive modeling.
OBJECTIVE: ML models based on optical coherence tomography (OCT) imaging, laboratory tests, and clinical characteristics can predict the occurrence of SM.
METHODS: We studied 337 patients from the Affiliated Hospital of Zunyi Medical University, China, who had PCI and coronary OCT from May to October 2023. We employed nested cross-validation to partition patients into training and test sets. We developed five ML models: XGBoost, LR, RF, SVM, and NB based on calcification features. Performance was assessed using ROC curves. Lasso regression selected features from 46 clinical and 21 OCT imaging features, which were optimized with the five ML algorithms.
RESULTS: In the prediction model based on calcification features, the XGBoost model and SVM model exhibited higher AUC values. Lasso regression identified five key features from clinical and imaging data. After incorporating selected features into the model for optimization, the AUC values of all algorithmic models showed significant improvements. The XGBoost model demonstrated the highest calibration accuracy. SHAP values revealed that the top five ranked features influencing the XGBoost model were calcification length, age, coronary dissection, lipid angle, and troponin.
CONCLUSIONS: ML models developed using plaque imaging features and clinical characteristics can predict the occurrence of SM. ML models based on clinical and imaging features exhibited better performance.
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