Machine learning techniques to predict restenosis development in superficial femoral arteries using demographic, clinical and hemodynamic descriptors

使用人口统计、临床和血流动力学描述符预测股浅动脉再狭窄发展的机器学习技术

• 批准号: 2436249
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2436249
• 关键词: Machine; learning; techniques; predict; restenosis

Brief description of the context of the research including potential impactPeripheral arterial disease (PAD) is the third cause of cardiovascular morbidity and is mainly caused by atherosclerosis. If the disease is detected at an early stage, endovascular approaches, such as percutaneous balloon angioplasty (PTA), may prove to be effective as a minimally-invasive treatment for diseased superficial femoral arteries (SFAs). A common negative outcome of this procedure is restenosis, occurring in more than 60% of cases at 1- year follow-up. In addition to clinical risk factors, biomechanical factors play an important role on the lumen remodelling, mainly because of the altered hemodynamics occurring over time after the intervention. There is evidence that the combination of low time-averaged wall shear stress (< 1 Pa) and high oscillatory shear index (> 0.20) favours atherogenesis and restenosis. The distribution of the hemodynamic indexes, along the SFA wall, can be accessed by through patient-specific vessel geometry 3D reconstructions and setting of personalised computational fluid dynamics (CFD) simulations. Once clinical and biomechanical variables having the strongest relationship with restenosis development are identified, the use of artificial intelligence techniques, such as machine learning (ML), may help to classify patients according to their risk of developing restenosis in a prescribed range of time. This would help clinicians to predict whether treated areas are likely to undergo restenosis during a defined time interval, hence tailoring surveillance and prevention programmes for PAD. Aims and ObjectivesThe project aim is to fuse demographic, clinical and hemodynamic descriptors information for a subset of PAD patients to build a ML driven model able to predict the risk of developing restenosis in SFAs, after PTA intervention, in a prescribed time interval. The main steps to accomplish are:i) Perform a critical analysis on the current clinical pathway, in terms of imaging modalities, to identify the information they provide and how they can be feasibly improved to match the needs of the research. Standardised data would make the ML model replicable both in other projects and, potentially, in clinical use.ii) Data collection and preparation, allowing the computation of the hemodynamic indexes, along the SFA wall, by means of personalised CFD analyses and their combination with demographic and clinical information by ML algorithms. A dataset of at least 100 patients will be needed.iii) Development of the ML model best performing for the problem under investigation. Even if supervised ML techniques are generally used to develop models predicting future clinical events, unsupervised ML techniques will not be discarded a priori.iv) Validation and assessment of model performance, by comparing the obtained results with the ones obtained from classical statistical analyses, i.e. standard predictive multivariate logistic regression models, to determine if ML techniques perform better.Novelty of Research MethodologyTo the best of author's knowledge, this is the first study fusing demographic, clinical and hemodynamic information when using ML approaches to predict restenosis risk in the SFA. From literature review, ML techniques generally perform their prediction only using variables obtained in routinely clinical practice and are normally applied to coronary arteries.Alignment to EPSRC's strategies and research areasThe project aligns with EPSRC's strategies since its main aim is to optimise treatment by tailoring it according to patient's needs, to provide clinicians tools to make more efficient surveillance programmes and use available resources at their best.Any companies or collaborators involvedProf. Janice Tsui - Royal Free Hospital, London - Clinical supervisor providing clinical data and putting towards relevant clinical research questions

外周动脉疾病（PAD）是心血管疾病发病的第三大原因，主要由动脉粥样硬化引起。如果在早期发现疾病，血管内方法，如经皮球囊血管成形术（PTA），可能被证明是有效的微创治疗病变股浅动脉（SFA）。这种手术的一个常见的负面结果是再狭窄，在1年随访时超过60%的病例发生再狭窄。除了临床风险因素外，生物力学因素对管腔重塑也起着重要作用，主要是因为介入后随着时间的推移血液动力学发生了变化。有证据表明，低时间平均壁剪切应力（< 1 Pa）和高振荡剪切指数（> 0.20）的组合有利于动脉粥样硬化和再狭窄。血流动力学指标的分布，沿着SFA壁，可以通过患者特定的血管几何形状3D重建和个性化计算流体动力学（CFD）模拟的设置来访问。一旦确定了与再狭窄发展关系最密切的临床和生物力学变量，使用人工智能技术，如机器学习（ML），可能有助于根据患者在规定时间范围内发生再狭窄的风险对患者进行分类。这将有助于临床医生预测治疗区域是否可能在规定的时间间隔内发生再狭窄，从而为PAD制定监测和预防计划。目的和目标该项目旨在融合PAD患者子集的人口统计学、临床和血流动力学描述符信息，以构建ML驱动模型，该模型能够预测PTA干预后在规定时间间隔内发生SFA再狭窄的风险。要完成的主要步骤是：i）在成像模式方面对当前的临床路径进行批判性分析，以确定它们提供的信息以及如何可行地改进它们以满足研究的需求。标准化数据将使ML模型在其他项目中以及潜在的临床使用中可复制。ii）数据收集和准备，允许通过个性化CFD分析及其与ML算法的人口统计学和临床信息的组合来计算沿着SFA壁的血流动力学指标。将需要至少100名患者的数据集。iii）开发最适合所研究问题的ML模型。即使监督ML技术通常用于开发预测未来临床事件的模型，无监督ML技术也不会被先验地丢弃。iv）通过将获得的结果与从经典统计分析获得的结果（即标准预测多变量逻辑回归模型）进行比较，验证和评估模型性能，研究方法的新奇据作者所知，这是第一项研究，在使用ML方法预测SFA中再狭窄风险时，融合了人口统计学、临床和血液动力学信息。从文献回顾来看，ML技术通常仅使用常规临床实践中获得的变量进行预测，并且通常应用于冠状动脉。与EPSRC的策略和研究领域保持一致该项目与EPSRC的策略保持一致，因为其主要目的是根据患者的需求定制治疗方案，为临床医生提供更有效的监测方案和最佳利用现有资源的工具。Janice Tsui -伦敦皇家自由医院-临床主管，提供临床数据并提出相关临床研究问题