基于 GRACE 评分和两种特征选择方法选出的预测因子,预测急性 ST 段抬高型心肌梗死患者接受初级 PCI 治疗后的院内死亡风险。

IF 2.8 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS Frontiers in Cardiovascular Medicine Pub Date : 2024-10-22 eCollection Date: 2024-01-01 DOI:10.3389/fcvm.2024.1419551
Nan Tang, Shuang Liu, Kangming Li, Qiang Zhou, Yanan Dai, Huamei Sun, Qingdui Zhang, Ji Hao, Chunmei Qi
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引用次数: 0

摘要

简介:准确预测经皮冠状动脉介入治疗(PCI)后的院内死亡率对临床决策至关重要。机器学习(ML)和数据挖掘方法有望提高医疗预后的准确性:我们分析了符拉迪沃斯托克滨海边疆区第一临床医院区域血管中心在 2015 年至 2021 年间收集的 4677 名患者的数据集。我们利用极梯度提升、直方图梯度提升、光梯度提升和随机梯度提升技术对急性ST段抬高型心肌梗死患者进行初级PCI术后死亡率风险预测。模型选择采用蒙特卡罗交叉验证法。通过递归特征消除(RFE)和夏普利相加解释(SHAP)加强了特征选择。我们使用增强灰狼优化器(AGWO)、秃鹰搜索优化器(BES)、金豺优化器(GJO)和美洲狮优化器(PO)进一步开发了混合模型,将这些方法选择的特征与传统的 GRACE 分数进行了整合:结果:混合模型显示出更高的预测准确性。在情景(1)中,利用 GRACE 评分特征,经过 BES 优化的轻梯度提升机(LGBM)和极梯度提升(XGB)模型的召回值分别达到了 0.944 和 0.954。在情景(2)和情景(3)中,采用 SHAP 和 RFE 选择的特征,LGB 模型的 Recall 值分别为 0.963 和 0.977,而 XGB 模型的 Recall 值分别为 0.978 和 0.99:研究表明,ML 模型,尤其是经过 BES 优化的 XGB 模型,在预测院内死亡率方面优于传统的 GRACE 评分。混合模型提高了准确性,在PCI术后患者的风险评估方面迈出了重要一步,为现有临床工具提供了潜在的替代方案。这些发现强调了 ML 在心血管医学中优化患者护理和预后的潜力。
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Prediction of in-hospital mortality risk for patients with acute ST-elevation myocardial infarction after primary PCI based on predictors selected by GRACE score and two feature selection methods.

Introduction: Accurate in-hospital mortality prediction following percutaneous coronary intervention (PCI) is crucial for clinical decision-making. Machine Learning (ML) and Data Mining methods have shown promise in improving medical prognosis accuracy.

Methods: We analyzed a dataset of 4,677 patients from the Regional Vascular Center of Primorsky Regional Clinical Hospital No. 1 in Vladivostok, collected between 2015 and 2021. We utilized Extreme Gradient Boosting, Histogram Gradient Boosting, Light Gradient Boosting, and Stochastic Gradient Boosting for mortality risk prediction after primary PCI in patients with acute ST-elevation myocardial infarction. Model selection was performed using Monte Carlo Cross-validation. Feature selection was enhanced through Recursive Feature Elimination (RFE) and Shapley Additive Explanations (SHAP). We further developed hybrid models using Augmented Grey Wolf Optimizer (AGWO), Bald Eagle Search Optimization (BES), Golden Jackal Optimizer (GJO), and Puma Optimizer (PO), integrating features selected by these methods with the traditional GRACE score.

Results: The hybrid models demonstrated superior prediction accuracy. In scenario (1), utilizing GRACE scale features, the Light Gradient Boosting Machine (LGBM) and Extreme Gradient Boosting (XGB) models optimized with BES achieved Recall values of 0.944 and 0.954, respectively. In scenarios (2) and (3), employing SHAP and RFE-selected features, the LGB models attained Recall values of 0.963 and 0.977, while the XGB models achieved 0.978 and 0.99.

Discussion: The study indicates that ML models, particularly the XGB optimized with BES, can outperform the conventional GRACE score in predicting in-hospital mortality. The hybrid models' enhanced accuracy presents a significant step forward in risk assessment for patients post-PCI, offering a potential alternative to existing clinical tools. These findings underscore the potential of ML in optimizing patient care and outcomes in cardiovascular medicine.

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来源期刊
Frontiers in Cardiovascular Medicine
Frontiers in Cardiovascular Medicine Medicine-Cardiology and Cardiovascular Medicine
CiteScore
3.80
自引率
11.10%
发文量
3529
审稿时长
14 weeks
期刊介绍: Frontiers? Which frontiers? Where exactly are the frontiers of cardiovascular medicine? And who should be defining these frontiers? At Frontiers in Cardiovascular Medicine we believe it is worth being curious to foresee and explore beyond the current frontiers. In other words, we would like, through the articles published by our community journal Frontiers in Cardiovascular Medicine, to anticipate the future of cardiovascular medicine, and thus better prevent cardiovascular disorders and improve therapeutic options and outcomes of our patients.
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