Wang Bin, Li Dong, Geng Yu, Jin Feifei, Wang Yujie, Lv Changhua, Lv Tingting, Xue Yajun, Zhang Ping
School of Clinical Medicine, Tsinghua University, Beijing, China.
Medical Data Science Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
Eur Heart J Digit Health. 2024 Dec 23;6(2):228-239. doi: 10.1093/ehjdh/ztae100. eCollection 2025 Mar.
The aim of this study was to use explainable boosting machine (EBM) to evaluate the predictive value of HDL-2b and HDL-3 levels in comparison with traditional lipid parameters in three-class classification of coronary artery stenosis severity in acute myocardial infarction (AMI) patients.
In this cross-sectional study, 1200 AMI patients were evaluated. HDL subtypes were quantified via microfluidic chip detection, and stenosis severity was assessed via the Gensini scoring system. The Gensini scores were divided into three groups: low group (<36.5), moderate group (36.5-72), and high group (>72). Explainable boosting machine, an interpretable machine learning technique, was employed to assess the predictive value of HDL-2b and HDL-3 compared with traditional lipid markers. Explainable boosting machine was used as the main model in this study, whereas logistic regression, XGBoost, and Random Forest were selected as reference models for predictive performance. Model performance was evaluated using receiver operating characteristic curves. The HDL-3 (%) values were divided into three risk categories: low (>43), moderate (30-43), and high (<30). The incorporation of HDL-2b and HDL-3 levels into lipid profiling significantly increased the group importance scores. The macro-average area under the curve values for the four models were as follows: 0.56 for the logistic model, 0.54 for the EBM model, 0.50 for the Random Forest model, and 0.49 for the XGBoost model.
HDL-3 provides superior predictive value for evaluating coronary artery stenosis severity in AMI patients compared to HDL-2b and other conventional lipid markers.
本研究旨在使用可解释增强机器（EBM）评估高密度脂蛋白2b（HDL-2b）和高密度脂蛋白3（HDL-3）水平相较于传统血脂参数在急性心肌梗死（AMI）患者冠状动脉狭窄严重程度三级分类中的预测价值。
在这项横断面研究中，对1200例AMI患者进行了评估。通过微流控芯片检测对HDL亚型进行定量，并通过Gensini评分系统评估狭窄严重程度。Gensini评分分为三组：低分组（<36.5）、中分组（36.5 - 72）和高分组（>72）。采用可解释增强机器（一种可解释的机器学习技术）评估HDL-2b和HDL-3相较于传统血脂标志物的预测价值。本研究以可解释增强机器作为主要模型，同时选择逻辑回归、XGBoost和随机森林作为预测性能的参考模型。使用受试者工作特征曲线评估模型性能。HDL-3（%）值分为三个风险类别：低风险（>43）、中风险（30 - 43）和高风险（<30）。将HDL-2b和HDL-3水平纳入血脂谱分析显著提高了组重要性得分。四个模型的曲线下面积宏平均值得分如下：逻辑模型为0.56，EBM模型为0.54，随机森林模型为0.50，XGBoost模型为0.49。
与HDL-2b和其他传统血脂标志物相比，HDL-3在评估AMI患者冠状动脉狭窄严重程度方面具有更高的预测价值。
