Suppr超能文献

计算机断层扫描衍生的血流储备分数:为冠状动脉疾病诊断制定金标准。

Computed Tomography-Derived Fractional Flow Reserve: Developing A Gold Standard for Coronary Artery Disease Diagnostics.

作者信息

Hu Liangbo, Wang Yue, Rao Jingjing, Tan Lina, He Min, Zeng Xiaocong

机构信息

Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, 530021 Nanning, Guangxi, China.

Guangxi Key Laboratory Base of Precision Medicine in Cardiocerebrovascular Diseases Control and Prevention & Guangxi Clinical, Research Center for Cardio-cerebrovascular Diseases, 530021 Nanning, Guangxi, China.

出版信息

Rev Cardiovasc Med. 2024 Oct 22;25(10):372. doi: 10.31083/j.rcm2510372. eCollection 2024 Oct.

Abstract

In recent years, a new technique called computed tomography-derived fractional flow reserve (CT-FFR) has been developed. CT-FFR overcomes many limitations in the current gold-standard fractional flow reserve (FFR) techniques while maintaining a better concordance with FFR. This technique integrates static coronary CT angiography data with hydrodynamic models, employing algorithms rather than guidewire interventions to compute the FFR. In addition to diagnosing coronary heart disease, CT-FFR has been applied in the preoperative risk assessment of major adverse cardiovascular events (MACEs) in organ transplantation and transcatheter aortic valve replacement (TAVR). Continuous advancements in CT-FFR techniques and algorithms are expanding their applicability to other methodologies. Subsequently, with robust clinical trial validation, CT-FFR can potentially supersede FFR as the primary "gatekeeper" for interventions.

摘要

近年来,一种名为计算机断层扫描衍生的血流储备分数(CT-FFR)的新技术已被开发出来。CT-FFR克服了当前金标准血流储备分数(FFR)技术的许多局限性,同时与FFR保持了更好的一致性。该技术将静态冠状动脉CT血管造影数据与流体动力学模型相结合,采用算法而非导丝干预来计算FFR。除了诊断冠心病外,CT-FFR还已应用于器官移植和经导管主动脉瓣置换术(TAVR)中主要不良心血管事件(MACE)的术前风险评估。CT-FFR技术和算法的不断进步正在将其适用性扩展到其他方法。随后,经过强有力的临床试验验证,CT-FFR有可能取代FFR成为干预的主要“守门人”。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f5f7/11522765/d17ac33fbe81/2153-8174-25-10-372-g1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验