在ACS患者中,OCT衍生的虚拟分数流量储备与PCI后1年的结果有关
Qianhang Xia1, Shuangya Yang2, Li Pan2
1Department of Cardiology, The Third Affiliated Hospital of Zunyi Medical University (The First Peoples Hospital of Zunyi), Zunyi, China.
概括
光学连贯断层扫描衍生的分流储备 (OFR) 在经皮冠状动脉干预 (PCI) 后的急性冠状动脉综合征 (ACS) 患者中显示出预后价值. 机器学习模型有效地分层了主要不良心血管事件 (MACE) 的风险.
科学领域:
- 心血管医学 心血管医学
- 干预心脏病学 干预心脏病学
- 医疗成像医学成像
背景情况:
- 在急性冠状动脉综合征 (ACS) 患者的皮肤冠状动脉干预 (PCI) 往往无法预防主要不良心血管事件 (MACE).
- 传统血管造影有其局限性,侵入性分量流量储备 (FFR) 需要高血压和专业资源.
- 光学连贯断层扫描 (OCT) 衍生FFR (OFR) 提供了非高血压的功能评估,但其在PCI后的预后影响尚未得到充分证实.
研究的目的:
- 在ACS患者中评估PCI后OFR和1年目标血管相关MACE之间的关联.
- 开发和评估机器学习模型,使用OFR和成像功能进行风险分层.
主要方法:
- 对719名接受了OCT指导的PCI的ACS患者进行了回顾性分析.
- 主要终点:与目标血管相关的MACE (心脏死亡,再血管化,心肌梗塞,胸痛再住院).
- 通过LASSO和Boruta进行特征选择;使用ROC曲线和GINI指数构建和评估的分类模型 (XGBoost,随机森林,SVM,后勤回归,LGBoost).
主要成果:
- 在PCI后的ACS患者中,OFR对1年的MACE具有预后意义.
- 结合OFR和成像数据的机器学习模型在风险分层方面表现强.
- 功能重要性分析确定了MACE的关键预测因素.
结论:
- 后PCIOFR是ACS患者长期结果的有价值的预测指标.
- 机器学习方法增强MACE的风险分层,帮助临床决策.
- 在干预性心脏病学中,OFR是功能评估和风险预测的有希望的工具.
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