Verification of a Simplified Aneurysm Dimensionless Flow Parameter to Predict Intracranial Aneurysm Rupture Status

Overview

Journal Br J Radiol

Publisher Oxford University Press

Specialty Radiology

Date 2024 May 25

PMID 38796680

Authors

Runlin Yang

Yifan Ren

Hong Kuan Kok

Paul D Smith

Parham Mohsenzadeh Kebria

Abbas Khosravi

Julian Maingard

Melissa Yeo

Jonathan Hall

Michelle Foo

Kevin Zhou

Ashu Jhamb

Jeremy Russell

Mark Brooks

Hamed Asadi

Affiliations

Soon will be listed here.

Abstract

Objectives: Aneurysm number (An) is a novel prediction tool utilizing parameters of pulsatility index (PI) and aneurysm geometry. An has been shown to have the potential to differentiate intracranial aneurysm (IA) rupture status. The objective of this study is to investigate the feasibility and accuracy of An for IA rupture status prediction using Australian based clinical data.

Methods: A retrospective study was conducted across three tertiary referral hospitals between November 2017 and November 2020 and all saccular IAs with known rupture status were included. Two sets of An values were calculated based on two sets of PI values previously reported in the literature.

Results: Five hundred and four IA cases were included in this study. The results demonstrated no significant difference between ruptured and unruptured status when using An ≥1 as the discriminator. Further analysis showed no strong correlation between An and IA subtypes. The area under the curve (AUC) indicated poor performance in predicting rupture status (AUC1 = 0.55 and AUC2 = 0.56).

Conclusions: This study does not support An ≥1 as a reliable parameter to predict the rupture status of IAs based on a retrospective cohort. Although the concept of An is supported by hemodynamic aneurysm theory, further research is needed before it can be applied in the clinical setting.

Advances In Knowledge: This study demonstrates that the novel prediction tool, An, proposed in 2020 is not reliable and that further research of this hemodynamic model is needed before it can be incorporated into the prediction of IA rupture status.

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