Data-Driven Approaches to Identify Biomarkers for Guiding Coronary Artery Bifurcation Lesion Interventions from Patient-Specific Hemodynamic Models

从患者特异性血流动力学模型中识别生物标志物的数据驱动方法，用于指导冠状动脉分叉病变干预

• 批准号: 10373696
• 负责人: Amanda E Randles
• 金额: $ 21.92万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10373696
• 关键词: Address; Adverse event; Anatomy; Angiography; Biological Markers; Blood flow; Cardiac; Cardiac Surgery procedures; Cardiology; Cessation of life; Classification; Classification Scheme; Clinical; Collaborations; Complex; Computer Models; Computing Methodologies; Coronary; Coronary Arteriosclerosis; Coronary artery; Data; Descriptor; Diagnostic; Evaluation; Event; Fostering; Geometry; Goals; Health; Heart Diseases; Individual; Intervention; Knowledge; Lead; Lesion; Machine Learning; Measures; Mission; Modeling; Myocardial Infarction; Outcome; Patients; Physiological; Physiology; Play; Procedures; Protocols documentation; Randomized Controlled Trials; Recurrence; Research; Role; Severities; Side; Statistical Data Interpretation; Stents; System; Techniques; Testing; Time; Trees; United States National Institutes of Health; Validation; adverse outcome; base; clinical predictors; clinically relevant; cost; hemodynamics; high risk; improved; improved outcome; in silico; mathematical model; parallel computer; patient variability; percutaneous coronary intervention; response; restenosis; simulation; specific biomarkers; stent thrombosis; tool; treatment planning; virtual; virtual surgery

ABSTRACT Coronary artery disease (CAD) is highly prevalent in the US, causing more than 360,000 deaths in 2017 alone. CAD is caused by plaques (a.k.a. lesions) that build up along the walls of coronary arteries, restricting blood flow. In 20% of cases, these lesions occur at arterial bifurcations. Treatment of coronary bifurcation le- sions remains particularly challenging, as their stenting carries a higher risk for adverse cardiac events such as in-stent restenosis, stent thrombosis, myocardial infarction, or need for recurrent percutaneous coronary inter- vention (PCI). For single vessel lesions (not at bifurcations), the Fractional Flow Reserve Versus Angiography for Multivessel Evaluation (FAME) trial played a critical role in establishing a biomarker (fractional flow reserve, FFR) to guide and improve their treatment. However, there is an urgent need for a classification scheme to assess physiological severity and ischemic burden of lesions at bifurcations, particularly in the side branches after main branch intervention. Until this knowledge gap is corrected, patients with bifurcation lesions will continue to have a significantly higher rate of long-term cardiac complications compared to those with single, main branch lesions. Current PCI protocols based on FFR for treating simpler main branch lesions do not translate into effective protocols for more complicated bifurcation lesions. The difficulty in extracting similar metrics is due to the in- creased complexity of the lesion geometry (typically consisting of two distinct lesions, one in the main branch and one in the side branch) and stronger influence of the underlying patient anatomy. While it is known that treat- ing the main branch lesion can improve the outcome, clear guidance is lacking regarding when to treat the side branch. Our long-term goal is to establish a multi-level classification system based on lesion- and patient-specific features that can be used to guide treatment decisions with better precision, and ultimately to reduce the high rate of adverse complications in patients with bifurcation lesions. Our central hypothesis is that criteria describing bifurcation lesion anatomy can be identified to classify ischemic burden and, in turn, guide stenting decisions. Through the use of a systematic, validated computational model, we can now accurately determine the contri- bution of each anatomic feature to physiologic severity. We now have the computing power, validated tools, and machine learning maturity required to undertake a large-scale, in silico study to isolate not only the influence of individual features, but underlying relationships between sets of features. The major objective of this proposal is to enable personalized guidance of bifurcation stenting procedures by identifying both the lesion-specific features that influence functional severity as well as the patient-specific biomarkers that may exacerbate burden.

摘要