Investigation of commonly used aortic aneurysm growth rate metrics: Comparing their suitability for clinical and research applications

PLOS ONE RESEARCH ARTICLE Investigation of commonly used aortic aneurysm growth rate metrics: Comparing their suitability for clinical and research applications Kayley Abell-Hart ID1, Janos Hajagos1, Victor Garcia1, James Kaan2, Wei Zhu3, Mary Saltz1, Joel Saltz1*, Apostolos Tassiopoulos2 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States of America, 2 Department of Vascular Surgery, Stony Brook University Hospital, Stony Brook, NY, United States of America, 3 Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States of America * Abstract OPEN ACCESS Citation: Abell-Hart K, Hajagos J, Garcia V, Kaan J, Zhu W, Saltz M, et al. (2023) Investigation of commonly used aortic aneurysm growth rate metrics: Comparing their suitability for clinical and research applications. PLoS ONE 18(8): e0289078. https://doi.org/10.1371/journal.pone.0289078 Editor: Emma Rezel-Potts, King’s College London, UNITED KINGDOM Received: December 16, 2022 Accepted: July 11, 2023 Published: August 11, 2023 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0289078 Copyright: © 2023 Abell-Hart et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. An aneurysm is a pathological widening of a blood vessel. Aneurysms of the aorta are often asymptomatic until they rupture, killing approximately 10,000 Americans per year. Fortunately, rupture can be prevented through early detection and surgical repair. However, surgical risk outweighs rupture risk for small aortic aneurysms, necessitating a policy of surveillance. Understanding the growth rate of aneurysms is essential for determining appropriate surveillance windows. Further, identifying risk factors for fast growth can help identify potential interventions. However, studies in the literature have applied many different methods for defining the growth rate of abdominal aortic aneurysms. It is unclear which of these methods is most accurate and clinically meaningful, and whether these heterogeneous methodologies may have contributed to the varied results reported in the literature. To help future researchers best plan their studies and to help clinicians interpret existing studies, we compared five different models of aneurysmal growth rate. We examined their noise tolerance, temporal bias, predictive accuracy, and statistical power to detect risk factors. We found that hierarchical mixed effects models were more noise tolerant than traditional, unpooled models. We also found that linear models were sensitive to temporal bias, assigning lower growth rates to aneurysms that were detected earlier in their course. Our exponential mixed model was noise-tolerant, resistant to temporal bias, and detected the greatest number of clinical risk factors. We conclude that exponential mixed models may be optimal for large studies. Because our results suggest that choice of method can materially impact a study’s findings, we recommend that future studies clearly state the method used and demonstrate its appropriateness. Data Availability Statement: The data used in this paper is clinical data and cannot be made public due to IRB rules, Stony Brook institutional policies and US HIPAA regulations. Requests for data PLOS ONE | https://doi.org/10.1371/journal.pone.0289078 August 11, 2023 1 / 18 PLOS ONE access should be made to David Cyrille, Stony Brook Chief Research Information Officer - David. . Funding: KAH This work was supported by grant T32GM127253 from the Scholars in BioMedical Sciences Training Program and the Science Training and Research to Inform Decisions fellowship via the National Science Foundation Research Traineeship program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Investigation of commonly used aortic aneurysm growth rate metrics Introduction An aortic aneurysm (AA) is a pathological widening of the body’s largest artery. This condition is common, with abdominal aortic aneurysms (AAAs) present in about 4–7% of men over age 50, and about 1% of such women [1]. AAAs are often asymptomatic until rupture, a catastrophic event that is fatal in at least 75% of cases [1]. In 2018, nearly 10,000 people died from AAs in the United States [2]. Because rupture can be prevented through surgical repair of the aneurysm, screening programs have been deemed cost-effective in high-risk groups [1, 3]. However, surgery carries a risk of complications and mortality. For this reason, patients are not indicated for repair until their risk of rupture exceeds the risks associated with surgery [4]. The main predictor of rupture risk is size; therefore, the threshold for surgical intervention is largely dictated by the maximum diameter of the AA [5]. However, most newly discovered abdominal aortic aneurysms are small, and it is unknown when, if ever, a patient’s aneurysm will cross the threshold for intervention. Therefore, standard of care requires regular imaging appointments to monitor the size of the AAA [3, 6, 7]. Choosing optimal surveillance windows is complicated by the fact that the rate of AAA growth can vary markedly among individuals. If the AAA will grow faster than expected, standardized surveillance windows may permit unchecked growth and elevated risk of rupture and death. If the AAA will grow more slowly than expected or even stabilize, the standard surveillance windows may be unnecessarily burdensome. In order to optimize the surveillance windows, various attempts have been made to predict AAA growth rate. An additional goal of such predictions is the potential identification of intervenable risk factors. For example, the observation that diabetes predicts slower AAA growth has led to a trial using the diabetes drug metformin to slow the growth of AAAs [8]. In order to predict growth rate or determine the risk factors associated with growth rate, it is first necessary to select a meaningful mathematic definition of growth rate itself. In the case of AAAs, this process is non-trivial. Because AAAs are known to grow faster at larger sizes, non-linear models may be needed. However, real-world datasets may contain only a few measurements of the aortic diameter per patient, cover a short time period, and/or be subject to substantial noise due to the imprecision of ultrasound measurements. F (...truncated)