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.
