Integrate prediction of machine learning for single ACoA rupture risk: a multicenter retrospective analysis Article Swipe
Yang Li
,
Linchun Huan
,
Wenpeng Lü
,
Li J
,
Hongping Wang
,
Bangyue Wang
,
Yunfei Song
,
Chao Peng
,
Jiyue Wang
,
Xinyu Yang
,
Jiheng Hao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fneur.2023.1126640
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fneur.2023.1126640
Background Statistically, Anterior communicating aneurysm (ACoA) accounts for 30 to 35% of intracranial aneurysms. ACoA, once ruptured, will have an acute onset and cause severe neurological dysfunction and even death. Therefore, clinical analysis of risk factors related to ACoA and the establishment of prediction model are the benefits to the primary prevention of ACoA. Methods Among 1,436 cases of single ACoA patients, we screened 1,325 valid cases, classified risk factors of 1,124 cases in the ruptured group and 201 cases in the unruptured group, and assessed the risk factors, respectively, and predicted the risk of single ACoA rupture by using the logistic regression and the machine learning. Results In the ruptured group (84.8%) of 1,124 cases and the unruptured group (15.2%) of 201 cases, the multivariable logistic regression (MLR) model shows hemorrhagic stroke history (OR 95%CI, p :0.233 (0.120–0.454),<0.001) and the age stratification of 60–69 years (OR 95%CI, p :0.425 (0.271–0.668),<0.001) has a significant statistic difference. In the RandomForest (RF) model, hemorrhagic stroke history and age are the best predictive factors. Conclusion We combined the analysis of MLR, RF, and PCA models to conclude that hemorrhagic stroke history and gender affect single ACoA rupture. The RF model with web dynamic nomogram, allows for real-time personalized analysis based on different patients’ conditions, which is a tremendous advantage for the primary prevention of single ACoA rupture. Clinical trial registration https://www.chictr.org.cn/showproj.html?proj=178501 .
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fneur.2023.1126640
- https://www.frontiersin.org/articles/10.3389/fneur.2023.1126640/pdf?isPublishedV2=False
- OA Status
- gold
- Cited By
- 3
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387730012
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4387730012Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fneur.2023.1126640Digital Object Identifier
- Title
-
Integrate prediction of machine learning for single ACoA rupture risk: a multicenter retrospective analysisWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-18Full publication date if available
- Authors
-
Yang Li, Linchun Huan, Wenpeng Lü, Li J, Hongping Wang, Bangyue Wang, Yunfei Song, Chao Peng, Jiyue Wang, Xinyu Yang, Jiheng HaoList of authors in order
- Landing page
-
https://doi.org/10.3389/fneur.2023.1126640Publisher landing page
- PDF URL
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https://www.frontiersin.org/articles/10.3389/fneur.2023.1126640/pdf?isPublishedV2=FalseDirect link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/articles/10.3389/fneur.2023.1126640/pdf?isPublishedV2=FalseDirect OA link when available
- Concepts
-
Logistic regression, Medicine, Nomogram, Stroke (engine), Internal medicine, Retrospective cohort study, Mechanical engineering, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
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2025: 1, 2024: 2Per-year citation counts (last 5 years)
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32Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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