Automating Real-Time Localization of Target Sites in Catheter Ablation of Ventricular Tachycardia

室性心动过速导管消融中目标部位的自动实时定位

• 批准号: 9590857
• 负责人: Linwei Wang
• 金额: $ 41.98万
• 依托单位: ROCHESTER INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-15 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9590857
• 关键词: Ablation; Arrhythmia; Attention; Cardiac ablation; Cicatrix; Clinical; Clinical Research; Collaborations; Computer Simulation; Computer software; Computers; Data; Data Set; Development; EKG QRS Complex; Electrocardiogram; Encapsulated; Goals; Heart; Heart Diseases; Hybrids; Lead; Learning; Life; Machine Learning; Modeling; Modernization; Morbidity - disease rate; Morphology; Myocardial; Nature; Outcome; Patients; Population Database; Procedures; Process; Recurrence; Research; Scientist; Site; Techniques; Testing; Time; Tissues; Travel; Variant; Ventricular; Ventricular Arrhythmia; Ventricular Tachycardia; Workload; clinical implementation; cost; deep learning; design; distraction; improved; interest; mortality; multidisciplinary; novel; population based; prevent; prospective; simulation; software development; success; sudden cardiac death; usability

Project Summary Ventricular tachycardia (VT) is an important cause of mortality and morbidity in patients with heart diseases. The majority of life-threatening VT episodes are caused by an electrical "short circuit” that travels through narrow strands of surviving tissue inside myocardial scar. Catheter ablation treats scar-related VT by “blocking” the surviving channel that forms the circuit, commonly at the site the circuit exits from the scar. To localize a VT exit, however, remains a significant challenge. A common approach, known as pace-mapping, utilizes the principle that the VT exit serves as the origin of ventricular activation and determines the QRS morphology on 12-lead electrocardiograms (ECGs). It thus involves repetitive electrical simulation at various sites of the heart, until locating the site that reproduces the QRS of the VT on all 12 ECG leads. While the principle behind pace- mapping is time tested, the current practice is of a "trial-and-error" nature and requires rapid qualitative interpretation of the ECG by clinicians, which can be time-consuming and inaccurate. This research proposes to leverage modern machine learning techniques to reform the way the principle behind pace-mapping is used. It aims to learn the relationship between the origin of ventricular activation and ECG morphology, and then use it to directly predict the exit of a VT from its ECG data. To this end, this project will include the following activities: 1) to develop a population-based model to provide pre-procedural initial localizations of VT exits using standard 12-lead ECG; 2) to integrate the population-based model with a patient-specific model in clinically-usable software to provide intra-procedural real-time guidance for localizing the exit site of a clinical VT; and 3) to assess the ability of the proposed software to improve the efficiency and accuracy of pace- mapping in a prospective clinical study. This project will be carried out by a multidisciplinary team of computational and clinical scientists with a fruitful record of collaboration. The software delivered by this project will provide real-time assistance to clinicians for narrowing down a VT exit with a minimum amount of time and localization errors. This will substantially reduce the workload for ablating multiple VTs, potentially allowing clinicians to ablate more or even all VTs seen in a procedure. This may reduce the duration of an ablation procedure while improving its outcome. The development and deployment of the software also adds minimal cost or distractions to routine workflow. With a low barrier to clinical implementation, it will have a real potential to challenge and improve the standard practice of catheter ablation.

项目摘要 心室心动过速（VT）是心脏病患者死亡率和发病率的重要原因。 大多数威胁生命的VT发作是由电动“短路”引起的 心肌疤痕内生存组织的狭窄链。导管消融通过“阻止”与疤痕相关的VT珍宝 形成电路的幸存通道通常在该电路处从疤痕中退出。定位VT 但是，退出仍然是一个重大挑战。一种称为空间图的常见方法，利用了 VT出口作为心室激活的起源并确定QRS形态的原理 12铅心电图（ECG）。因此，它涉及心脏各个部位的重复电气模拟， 直到定位在所有12个ECG引线上重现VT的QRS的位置。而太空背后的原则 - 映射是测试时间的，当前的做法是“试用和错误”的性质，需要快速定性 临床医生对心电图的解释可能是耗时和不准确的。这项研究建议 利用现代机器学习技术来改革速度映射背后的原则。 它旨在了解心室激活与ECG形态的起源之间的关系，然后使用 它可以直接从其ECG数据中预测VT的退出。为此，该项目将包括以下内容 活动：1）开发基于人群的模型，以提供VT出口的术前初始定位 使用标准的12铅ECG； 2）将基于人群的模型与特定于患者的模型集成 临床上可用的软件，可提供临床内部的肠内实时指南 VT; 3）评估提出的软件提高空间效率和准确性的能力 - 在一项前瞻性临床研究中映射。该项目将由一个多学科团队进行 计算和临床科学家的合作记录。该项目提供的软件 将为临床医生提供实时援助，以缩小最短时间的VT出口，然后 本地化错误。这将大大减少烧毁多个VT的工作量，可能允许 临床医生在手术过程中消除更多甚至所有VT。这可能会减少消融的持续时间 在改善结果的同时进行程序。该软件的开发和部署也增加了 常规工作流程的成本或分心。由于临床实施的障碍较低，它将具有真正的潜力 挑战和改善导管消融的标准实践。

项目成果

Learning to Disentangle Inter-Subject Anatomical Variations in Electrocardiographic Data.

• DOI: 10.1109/tbme.2021.3108164
• 发表时间: 2022-03
• 期刊: IEEE transactions on bio-medical engineering
• 作者: Gyawali PK;Murkute JV;Toloubidokhti M;Jiang X;Horacek BM;Sapp JL;Wang L
• 通讯作者: Wang L

Learning Domain Shift in Simulated and Clinical Data: Localizing the Origin of Ventricular Activation From 12-Lead Electrocardiograms.

• DOI: 10.1109/tmi.2018.2880092
• 发表时间: 2019-05
• 期刊: IEEE transactions on medical imaging
• 影响因子: 10.6
• 作者: Alawad M;Wang L
• 通讯作者: Wang L

Mapping Ventricular Tachycardia With Electrocardiographic Imaging.

通过心电图成像绘制室性心动过速图。

• DOI: 10.1161/circep.120.008255
• 发表时间: 2020
• 期刊: Circulation. Arrhythmia and electrophysiology
• 作者: Sapp,JohnL;Zhou,Shijie;Wang,Linwei
• 通讯作者: Wang,Linwei

Understanding the Utility of Endocardial Electrocardiographic Imaging in Epi-Endocardial Mapping of 3D Reentrant Circuits.

了解心内膜心电图成像在 3D 折返回路心外膜标测中的效用。

• DOI: 10.1101/2024.03.13.24304259
• 发表时间: 2024
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Toloubidokhti,Maryam;Gharbia,OmarA;Parkosa,Adityo;Trayanova,Natalia;Nazarian,Saman;Sapp,JohnL;Wang,Linwei
• 通讯作者: Wang,Linwei

A hybrid machine learning approach to localizing the origin of ventricular tachycardia using 12-lead electrocardiograms.

• DOI: 10.1016/j.compbiomed.2020.104013
• 发表时间: 2020-11
• 期刊: Computers in biology and medicine
• 影响因子: 7.7
• 作者: Missel R;Gyawali PK;Murkute JV;Li Z;Zhou S;AbdelWahab A;Davis J;Warren J;Sapp JL;Wang L
• 通讯作者: Wang L