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：一种基于机器学习的临床决策支持工具，可预测AAA预后 腹主动脉瘤（AAA）是主动脉的局部扩张。如果未治疗的AAA可能 继续破裂，发生的死亡率为90％，是第13个主要原因 在美国死亡，每年有15,000多人死亡。 AAA之后 诊断为临床必须确定其严重程度；即，与风险相比，破裂的相对风险 干预。该决定的当前临床指南基于一定大小 “最大直径标准”，该标准指出，当AAA直径达到5.5厘米时， 破裂的动脉瘤必要修复。但是，较小尺寸的AAA（<5.5厘米） 请参阅以高达23.4％的速度破裂，表明该基于直径的标准不合适 用于AAA管理。 NIH资助的最近完成的临床试验，1U01-AG037120：“无创 AAA临床试验的处理”（N-TA3CT）旨在证明 小型AAA的药理学治疗。在此试验中，一个高度独特且有价值的数据集是 每6个月每6个月纵向收集了3年的患者，患有小型AAA。 该建议旨在检验以下假设，即在发现小AAA时 临床预后 - 即预测基于破裂是否需要临床干预 风险指标 - 可以使用基于机器学习的算法使用实时准备 生物力学，形态和临床数据。为了解决这一假设，我们将追求两个 具体目标。 目标1将是量化N-TA3CT试验中单个小型AAA的“演变”。这 所有时间点的所有患者AAA的生物力学和形态状况将从 在试验期间使用有限元分析和形态计量分析收集的数据， 这些将在每个时间点与每个AAA的临床指数一起表格制表。 AIM 2将是开发和验证机器学习和回归技术以预测 小AAA的临床预后。来自AIM 1的数据以及N-的随访报告数据 TA3CT试验将用于训练机器学习分类模型，以确定是否是否 可以准确预测动脉瘤预后。验证将在数据子集上进行 评估所提出的预测模型的准确性，灵敏度，精度和特异性。 N-TA3CT试验中的独特数据集，并与丰富的方法和方法配对 由我们的实验室开发的，将首次允许我们仔细检查和量化自然 小型AAA的演变，并随后开发一个预测模型以改善患者提示。