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

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.