Cardiac Positioning System (CPS) - An automated navigation system to guide catheter ablation therapy

心脏定位系统 (CPS) - 引导导管消融治疗的自动导航系统

• 批准号: MR/R024995/1
• 负责人: Yolanda Hill
• 金额: $ 33.67万
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FR024995%2F1
• 关键词: Cardiac; Positioning; System; CPS; automated

Why is it important to improve treatments used after a heart attack?Each year in the UK, approximately 150,000 people have a heart attack when the blood supply to their heart is compromised. As a result, affected regions of the heart can become diseased and scarred. In a healthy person, electrical waves propagate across the heart in a regulated pattern which triggers contraction to pump blood around the body. The scar tissue that forms as a result of a heart attack can disrupt the propagation of the electrical waves. If significant disruptions occur, blood cannot be pumped out of the body effectively, leading to sudden death. Ablation therapy aims to eliminate areas of diseased tissue that cause disruption to the heart rhythm, by applying radiofrequency using catheters inserted into the heart. The most accurate techniques used to locate the region to ablate require the induction of dangerous heart rhythms, which are only inducible in about 65% of people.Pace mapping is a technique used to locate regions to ablate, which can be performed during normal heart rhythm. ECG data, which records electrical signals from the heart, is collected when the patient has an abnormal heart rhythm. From this template ECG, a clinician can tell the approximate location of the diseased tissue. A catheter is directed to that location, the heart stimulated, and another ECG, called the paced ECG is recorded. If the paced ECG matches the template ECG, it is assumed that the heart was paced in the location that requires ablation.Current ablation techniques are difficult, time consuming, and inaccurate. As a result, the procedure may work in only half of all patients, and result in unnecessary damage to healthy tissue, leading to later impairment of heart function.How will we improve the treatment?The CPS project's overall goal is to increase the success rates of ablation therapy by improving the accuracy and efficiency of locating the optimal region of tissue to eliminate during the pace mapping procedure. Our research will involve using machine learning to find patterns in data which may be able to help locate diseased tissue. Machine learning involves passing some input and corresponding output data to a machine learning algorithm which can then 'learn' how to predict output data.The first aim of the CPS project is to make the initial prediction about the location of diseased tissue more accurate in order to guide the initial placement of the catheter. Patient specific data known to affect ECGs, such as medication, and heart and chest size, as well as ECG data, will be incorporated into a machine learning algorithm. Once the computer has 'learnt' how these data correlate with the location of diseased tissue, it will be able to predict the location of diseased tissue in a new patient. The second aim involves accurately locating the critical region of diseased tissue responsible for disrupting the heart rhythm, in order to pinpoint the optimal target for ablation. A machine learning algorithm will be used to compare template and paced ECGs, and understand how the differences between them, can indicate the specific location of the tissue which is causing the heart rhythm disturbance. Once the algorithm has 'learnt' how to locate the ablation target, we can use it to indicate where the clinician should ablate in new patients.What is the significance of this research?Increasing ablation therapy success rates will mean that patients will be unlikely to suffer from future heart rhythm disorders as a result of their heart attack, increasing the life expectancy of heart attack patients. Excess damage caused to the heart as a result of unnecessary ablation lesions will be limited, decreasing the likelihood of future complications. In addition, dangerous heart rhythms do not need to be induced in the patient, significantly decreasing the risk of death during the treatment.

为什么改进心脏病发作后的治疗方法很重要？在英国，每年大约有15万人在心脏血液供应受到影响时心脏病发作。结果，心脏的受影响区域可能会生病并留下疤痕。在健康人身上，电波以一种有规律的模式在心脏中传播，引发收缩，将血液输送到全身。心脏病发作时形成的疤痕组织会扰乱电波的传播。如果出现严重的血液中断，血液就不能有效地被泵出体外，导致猝死。消融治疗的目的是通过使用插入心脏的导管应用射频来消除导致心律紊乱的病变组织区域。用于定位消融区域的最准确的技术需要诱发危险的心律失常，只有大约65%的人可以诱发。Pace标测是一种用于定位消融区域的技术，可以在正常心律期间执行。记录来自心脏的电信号的心电数据是在患者心率异常时收集的。从这个模板心电中，临床医生可以知道病变组织的大致位置。将导管指向该位置，刺激心脏，然后记录另一种被称为起搏心电的心电图。如果起搏的心电信号与模板心电信号匹配，则认为心脏是在需要消融的位置起搏的。目前的消融技术困难、耗时且不准确。因此，这种手术可能只对一半的患者有效，并对健康组织造成不必要的损害，导致后来的心功能损害。我们将如何改进治疗？CPS项目的总体目标是通过提高在起搏标测过程中定位要消除的最佳组织区域的准确性和效率来提高消融治疗的成功率。我们的研究将涉及使用机器学习来寻找数据中的模式，这些模式可能能够帮助定位病变组织。机器学习涉及将一些输入和相应的输出数据传递给机器学习算法，然后机器学习算法可以‘学习’如何预测输出数据。CPS项目的第一个目标是使对病变组织位置的初始预测更加准确，以便指导导管的初始放置。已知的影响心电图的患者特定数据，如药物、心脏和胸部大小，以及心电图数据，将被合并到机器学习算法中。一旦计算机了解到这些数据如何与病变组织的位置相关联，它就能够预测新患者的病变组织的位置。第二个目标是准确定位导致心率紊乱的病变组织的关键区域，以便准确定位最佳消融目标。将使用机器学习算法来比较模板和起搏的心电图，并了解它们之间的差异如何指示导致心律紊乱的组织的特定位置。一旦算法学会了如何定位消融目标，我们就可以用它来指示临床医生应该在新患者的哪里消融。这项研究的意义是什么？消融治疗成功率的提高将意味着患者未来不太可能因心脏病发作而患上心律紊乱，从而延长心脏病发作患者的预期寿命。由于不必要的消融损伤对心脏造成的过度损害将是有限的，降低了未来并发症的可能性。此外，患者不需要诱发危险的心律失常，大大降低了治疗期间的死亡风险。