AUTHOR=Zhu Wei , Li Wenqiang , Tian Zhongbin , Zhang Mingqi , Zhang Yisen , Wang Kun , Zhang Ying , Yang Xinjian , Liu Jian 

TITLE=Nomogram for Stability Stratification of Small Intracranial Aneurysm Based on Clinical and Morphological Risk Factors

JOURNAL=Frontiers in Neurology

VOLUME=Volume 11 - 2020

YEAR=2021

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2020.598740

DOI=10.3389/fneur.2020.598740

ISSN=1664-2295

ABSTRACT=Background and Purpose: Stability stratification of intracranial aneurysms (IAs) is crucial for individualized clinical management, especially for small IAs. We aim to develop and validate a nomogram based on clinical and morphological risk factors for individualized instability stratification of small IAs.
Methods: 658 patients with unstable (n = 293) and stable (n = 416) IAs less than 7 mm  were randomly divided into derivation and validation cohorts. Twelve clinical risk factors and eighteen aneurysm morphological risk factors were extracted. Combined with important risk factors, a clinical-morphological predictive nomogram was developed. The nomogram performance was evaluated in the derivation and the validation cohorts in terms of discrimination, calibration, and clinical usefulness.
Results: Five independent instability-related risk factors were included in the nomogram: location, irregularity, side/bifurcation type, flow angle, and height-to-width ratio. In the derivation cohort, the area under the curve (95% CI) of the nomogram was 0.803 (95% CI, 0.764 – 0.842), and good agreement between predicted instability risk and actual instability status could be detected in the calibration plot. The nomogram also exhibited good discriminations and calibration in the validation cohort: the area under the curve (95% CI) was 0.744 (95% CI, 0.677 – 0.812). Small IAs with scores less than 90 were considered to have low risk of instability, and those with scores of 90 or greater were considered to have high risk of instability. 
Conclusions: The nomogram based on clinical and morphological risk factors can be used as a convenient tool to facilitate individualized decision-making in the management of small IAs.