Kracík Jan, Kubíček Luboš, Staffa Robert, Polzer Stanislav
Department of Applied Mathematics, VSB-Technical University of Ostrava, Ostrava, Czech Republic.
2nd Department of Surgery, St. Anne's University Hospital Brno and Faculty of Medicine, Masaryk University, Brno, Czech Republic.
PLoS One. 2024 Dec 2;19(12):e0314368. doi: 10.1371/journal.pone.0314368. eCollection 2024.
The stochastic rupture risk assessment of an abdominal aortic aneurysm (AAA) critically depends on sufficient data set size that would allow for the proper distribution estimate. However, in most published cases, the data sets comprise no more than 100 samples, which is deemed insufficient to describe the tails of AAA wall thickness distribution correctly. In this study, we propose a stochastic Bayesian model to merge thickness data from various groups. The thickness data adapted from the literature were supplemented by additional data from 81 patients. The wall thickness was measured at two different contact pressures for 34 cases, which allowed us to estimate the radial stiffness. Herein, the proposed stochastic model is formulated to predict the undeformed wall thickness. Furthermore, the model is able to handle data published solely as summary statistics. After accounting for the different contact pressures, the differences in the medians reported by individual groups decreased by 45%. Combined data can be fitted with a lognormal distribution with parameters μ = 0.85 and σ = 0.32 which can be further used in stochastic analyses.
腹主动脉瘤(AAA)的随机破裂风险评估严重依赖于足够大的数据集,以便进行适当的分布估计。然而,在大多数已发表的病例中,数据集包含的样本不超过100个,这被认为不足以正确描述AAA壁厚度分布的尾部。在本研究中,我们提出了一种随机贝叶斯模型,用于合并来自不同组的厚度数据。从文献中获取的厚度数据由81例患者的额外数据进行补充。对34例病例在两种不同的接触压力下测量了壁厚,这使我们能够估计径向刚度。在此,所提出的随机模型用于预测未变形的壁厚。此外,该模型能够处理仅作为汇总统计数据发表的数据。在考虑了不同的接触压力后,各个组报告的中位数差异降低了45%。合并后的数据可以用参数为μ = 0.85和σ = 0.32的对数正态分布拟合,该分布可进一步用于随机分析。