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Nomogram-based geometric and hemodynamic parameters for predicting the growth of small untreated intracranial aneurysms

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Abstract

Previous studies have shown that the growth status of intracranial aneurysms (IAs) predisposes to rupture. This study aimed to construct a nomogram for predicting the growth of small IAs based on geometric and hemodynamic parameters. We retrospectively collected the baseline and follow-up angiographic images (CTA/ MRA) of 96 small untreated saccular IAs, created patient-specific vascular models and performed computational fluid dynamics (CFD) simulations. Geometric and hemodynamic parameters were calculated. A stepwise Cox proportional hazards regression analysis was employed to construct a nomogram. IAs were stratified into low-, intermediate-, and high-risk groups based on the total points from the nomogram. Receiver operating characteristic (ROC) analysis, calibration curves, decision curve analysis (DCA) and Kaplan–Meier curves were evaluated for internal validation. In total, 30 untreated saccular IAs were grown (31.3%; 95%CI 21.8%-40.7%). The PHASES, ELAPSS, and UIATS performed poorly in distinguishing growth status. Hypertension (hazard ratio [HR] 4.26, 95%CI 1.61–11.28; P = 0.004), nonsphericity index (95%CI 4.10–25.26; P = 0.003), max relative residence time (HR 1.01, 95%CI 1.00–1.01; P = 0.032) were independently related to the growth status. A nomogram was constructed with the above predictors and achieved a satisfactory prediction in the validation cohort. The log-rank test showed significant discrimination among the Kaplan–Meier curves of different risk groups in the training and validation cohorts. A nomogram consisting of geometric and hemodynamic parameters presented an accurate prediction for the growth status of small IAs and achieved risk stratification. It showed higher predictive efficacy than the assessment tools.

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Data availability

The data and materials that support the findings of this study are available on request from the corresponding author, Y. H.

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Acknowledgements

We thank Prof. An and team members for technical assistance with the computational fluid dynamics simulations. The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

This project was supported by the Tianjin Science and Technology Program (Grant No. 20JCZDJC00620) and Tianjin Jinnan Science and Technology Program (Grant No. 20230108).

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Authors and Affiliations

  1. Tianjin Key Laboratory of Brain Science and Neuroengineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China

    Yujia Yan, Xingwei An, Yang Di & Tingting Li

  2. Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration, Tianjin, China

    Yujia Yan, Xingwei An, Yang Di & Tingting Li

  3. State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, Tianjin, China

    Yujia Yan, Xingwei An, Yang Di & Tingting Li

  4. Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, China

    Yujia Yan, Hecheng Ren, Bin Luo & Ying Huang

  5. Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China

    Song Jin & Li Liu

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Contributions

YY, XA: Conceptualization, Methodology, Writing—Original Draft. HR, BL, SJ, LL: Formal analysis, Data Curation, Validation. YY, YD, TL: Software, Investigation. XA, YH: Supervision, Writing—Review & Editing, Project administration. All authors approved the final version of the manuscript.

Corresponding author

Correspondence to Ying Huang.

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The authors declare no competing interests.

Ethical approval

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of the Tianjin Huanhu Hospital (No. 2020–34, 8/10/2020). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

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Yujia Yan, Xingwei An have contributed equally to this work and share first authorship.

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Yan, Y., An, X., Ren, H. et al. Nomogram-based geometric and hemodynamic parameters for predicting the growth of small untreated intracranial aneurysms. Neurosurg Rev 47, 169 (2024). https://doi.org/10.1007/s10143-024-02408-x

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