2024
Contact pressure explains half of the abdominal aortic aneurysms wall thickness inter-study variability
KRACIK, Jan; Luboš KUBÍČEK; Robert STAFFA a Stanislav POLZERZákladní údaje
Originální název
Contact pressure explains half of the abdominal aortic aneurysms wall thickness inter-study variability
Autoři
KRACIK, Jan; Luboš KUBÍČEK; Robert STAFFA a Stanislav POLZER
Vydání
Plos one, San Francisco, Public Library of Science, 2024, 1932-6203
Další údaje
Jazyk
angličtina
Typ výsledku
Článek v odborném periodiku
Obor
30212 Surgery
Stát vydavatele
Spojené státy
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Impakt faktor
Impact factor: 2.600
Označené pro přenos do RIV
Ano
Kód RIV
RIV/00216224:14110/24:00138588
Organizační jednotka
Lékařská fakulta
UT WoS
EID Scopus
Klíčová slova anglicky
abdominal aortic aneurysms; contact pressure
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 23. 1. 2025 09:40, Mgr. Tereza Miškechová
Anotace
V originále
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 mu = 0.85 and sigma = 0.32 which can be further used in stochastic analyses.