Department of Cardiology, The Third Affiliated Hospital of Zunyi Medical University (The First People's Hospital of Zunyi), Zunyi, China.
Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
Clin Cardiol. 2024 Aug;47(8):e24332. doi: 10.1002/clc.24332.
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.
ML models based on optical coherence tomography (OCT) imaging, laboratory tests, and clinical characteristics can predict the occurrence of SM.
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.
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.
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.
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方面显示出了潜力。
基于光学相干断层扫描（OCT）成像、实验室检查和临床特征的 ML 模型可以预测 SM 的发生。
我们研究了来自中国遵义医科大学附属医院的 337 名 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。我们开发了基于钙化特征的五个 ML 模型：XGBoost、LR、RF、SVM 和 NB。使用 ROC 曲线评估性能。Lasso 回归从 46 个临床和 21 个 OCT 成像特征中选择特征，然后由五个 ML 算法对其进行优化。
在基于钙化特征的预测模型中，XGBoost 模型和 SVM 模型的 AUC 值较高。Lasso 回归从临床和影像学数据中识别出五个关键特征。将选定特征纳入模型进行优化后，所有算法模型的 AUC 值均有显著提高。XGBoost 模型显示出最高的校准准确性。SHAP 值显示，影响 XGBoost 模型的前五个排名特征是钙化长度、年龄、冠状动脉夹层、脂质角和肌钙蛋白。
使用斑块成像特征和临床特征开发的 ML 模型可以预测 SM 的发生。基于临床和影像学特征的 ML 模型表现更好。
