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=11
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: Six hundred fifty-eight patients with unstable (n = 293) and stable (n = 416) IAs <7 mm were randomly divided into derivation and validation cohorts. Twelve clinical risk factors and 18 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 <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.