Abstract WMP27: PAT Model Accurately Predicts Aneurysm Enlargement in 16 Growing Aneurysm Cases

Abstract

Objective: Imaging technology for unruptured intracranial aneurysms (UIA) has improved detection of such aneurysms. However, there is limited information on UIA change over time, and how to predict the rate of enlargement. The objective of this study was to quantify the accuracy of the Predicted Aneurysm Trajectory (PAT) model recently developed by Chien et al. (J Neurosurgery. 2019; Mar 1:1-11). Methods: Patients diagnosed with UIA were prospectively enrolled at the UCLA Medical Center, and followed through serial imaging. 16 UIA cases exhibiting growth across multiple follow-ups were included in this study. Prior images and medical records were collected. Characteristics relevant to the PAT model (mean ± stdev), including initial UIA size (7.26 ± 6.38), patient age (67.4 ± 9.48 yrs.), sex (4 male), history of smoking (n=5), hypothyroidism (n=4), and follow-up duration (36.5 ± 50.0 mos.) were used to predict UIA size at each follow-up. Predicted and actual UIA sizes at follow-up were compared using symmetric mean absolute percentage error (SMAPE) with percentage error ranging from 0-100%. Results: The 16 UIA cases were split by initial UIA size. For UIA smaller than 7 mm (10 cases, 23 follow-up), SMAPE = 11.13%. For UIA greater than 7 mm (6 cases, 15 follow-up), SMAPE = 8.07%. For all UIA cases (16 cases, 38 follow-up), SMAPE = 9.92%. Conclusions: The PAT model predicts the rate of enlargement for UIA, as opposed to whether or not UIA will grow. With this new sample of data, we found the predicted UIA size at follow-up to be quite accurate, deviating in the range of 10% from the actual, measured size. Patient characteristics such as the demographics and behavior included in the model influence the growth of UIA, which allows prediction of growth to optimize treatment and management in future cases.