A Machine Learning-Based Clinical Decision Support Tool to Predict Abdominal Aortic Aneurysm Prognosis Using Existing Longitudinal Data

基于机器学习的临床决策支持工具，利用现有纵向数据预测腹主动脉瘤预后

• 批准号: 10331850
• 负责人: David Alan Vorp
• 金额: $ 11.83万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-22 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10331850
• 关键词: Abdominal Aortic Aneurysm; Address; Adopted; Adverse event; Aneurysm; Annual Reports; Aorta; Biomechanics; Caliber; Cause of Death; Cessation of life; Classification; Clinical; Clinical Data; Clinical Trials; Computer Models; Computer software; Data; Data Reporting; Data Set; Databases; Diagnosis; Dilatation - action; Dimensions; Disease; Event; Evolution; Failure; Finite Element Analysis; Funding; Geometry; Guidelines; Health; Image; Individual; Intervention; Left; Light; Link; Machine Learning; Methodology; Methods; Modeling; Morphology; Patient-Focused Outcomes; Patients; Pharmacological Treatment; Principal Component Analysis; Prognosis; Relative Risks; Research; Risk; Risk Assessment; Rupture; Ruptured Abdominal Aortic Aneurysm; Severities; Specificity; Statistical Methods; Techniques; Testing; Thinness; Time; Training; United States; United States National Institutes of Health; Validation; Work; X-Ray Computed Tomography; base; biomechanical test; clinical decision support; clinical prognosis; cohort; design; experience; feature selection; follow-up; imaging study; improved; indexing; innovation; machine learning algorithm; machine learning classification; mortality; novel; patient prognosis; predictive modeling; rate of change; repaired; screening; serial imaging; support tools; theories; tool

SUMMARY: A Machine Learning-Based Clinical Decision Support Tool to Predict AAA Prognosis Abdominal aortic aneurysm (AAA) is a localized dilatation of the aorta. If left untreated AAA may go on to rupture, an occurrence which has a 90% mortality rate and is the 13th leading cause of death in the United States, with more than 15,000 annual deaths reported annually. After AAA is diagnosed, a clinician must determine its severity; i.e., the relative risk of rupture compared to the risk of intervention. Current clinical guidelines for this determination is based on the one-size-fits-all “maximum diameter criterion”, which states that when a AAA reaches 5.5 cm in diameter, the risk of rupture necessitates repair of the aneurysm. However, smaller sized AAAs (< 5.5 cm) have been seen to rupture at rates of up to 23.4%, demonstrating that this diameter-based criterion is unsuitable for AAA management. A recently completed NIH-funded clinical trial, 1U01-AG037120: “Non-Invasive Treatment of AAA Clinical Trial” (N-TA3CT) was designed to demonstrate the efficacy of pharmacologic treatment of small AAA. During this trial, a highly unique and valuable dataset was collected longitudinally every 6 months for a 3-year period for patients presenting with small AAA. This proposal is designed to test the hypothesis that, at the time of discovery of small AAA, clinical prognosis – i.e., predicting if and when clinical intervention will be required based on rupture risk metrics – can be facilitated using machine learning-based algorithms using real-time biomechanical, morphological, and clinical data. To address this hypothesis, we will pursue two specific aims. Aim 1 will be to quantify the “evolution” of individual small AAA from the N-TA3CT trial. The biomechanical and morphological status of all patient AAAs at each timepoint will be determined from data collected during the trial using finite element analysis and morphometric analysis, respectively, and these will be tabulated along with clinical indices for each AAA at each timepoint. Aim 2 will be to develop and validate machine learning and regression techniques to forecast the clinical prognosis of small AAA. The data from Aim 1 as well as follow-up reporting data from the N- TA3CT trial will be used to train machine learning classification models to determine whether aneurysm prognosis can be accurately predicted. Validation will be performed on a subset of data to assess the accuracy, sensitivity, precision and specificity of the proposed prediction model. The unique dataset from the N-TA3CT trial, paired with the extensive experience of and methods developed by our lab, will allow us, for the first time, to carefully examine and quantify the natural evolution of small AAA and to subsequently develop a predictive model to improve patient prognosis.

摘要：基于机器学习的临床决策支持工具来预测 AAA 预后 腹主动脉瘤（AAA）是主动脉的局部扩张。 If left untreated AAA may 继续破裂，这种情况的死亡率为 90%，是导致破裂的第 13 位。 美国每年报告的死亡人数超过 15,000 人。 After AAA is 确诊后，临床医生必须确定其严重程度；即破裂的相对风险与风险相比 of intervention.目前这一决定的临床指南是基于一刀切的标准 “最大直径标准”，规定当 AAA 直径达到 5.5 厘米时，发生 rupture necessitates repair of the aneurysm. However, smaller sized AAAs (< 5.5 cm) have been 破裂率高达 23.4%，表明这种基于直径的标准是不合适的 for AAA management.最近完成的一项由 NIH 资助的临床试验 1U01-AG037120：“非侵入性 AAA 治疗临床试验（N-TA3CT）旨在证明治疗的有效性 pharmacologic treatment of small AAA.在此试验期间，获得了一个非常独特且有价值的数据集 对于患有小 AAA 的患者，每 6 个月纵向收集一次，为期 3 年。 该提案旨在检验以下假设：在发现小型 AAA 时， 临床预后——即根据破裂情况预测是否以及何时需要临床干预 风险指标——可以使用基于机器学习的实时算法来促进 biomechanical, morphological, and clinical data. To address this hypothesis, we will pursue two 具体目标。 目标 1 是量化 N-TA3CT 试验中个体小 AAA 的“进化”。这 每个时间点所有患者 AAA 的生物力学和形态学状态将根据 试验期间分别使用有限元分析和形态分析收集的数据， 这些将与每个 AAA 在每个时间点的临床指数一起制成表格。 目标 2 是开发和验证机器学习和回归技术来预测 小AAA的临床预后。来自目标 1 的数据以及来自 N- 的后续报告数据 TA3CT试验将用于训练机器学习分类模型以确定是否 可以准确预测动脉瘤的预后。将对数据子集进行验证 评估所提出的预测模型的准确性、敏感性、精密度和特异性。 N-TA3CT 试验的独特数据集，搭配丰富的经验和方法 由我们实验室开发的，将使我们第一次能够仔细检查和量化自然 小 AAA 的演变，并随后开发预测模型以改善患者预后。