Evidence mapPaperPMID 41676243Full record

ArticleAmerican journal of translational research2026

Construction and validation of a predictive model for intraoperative rupture risk in microscopic surgical clipping of intracranial aneurysms.

Jun Zhang, Yuan Wang, Yanbin Liang, Tao Huang, Jingku Ma

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Article in American journal of translational research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 authors.

Jun ZhangDepartment of Neurosurgery, Hanzhong Central Hospital No. 557, Middle Section of Laodong West Road, Hantai District, Hanzhong 723000, Shaanxi, China.
Yuan WangDepartment of Oncology, 3201 Hospital No. 783 Tianhan Avenue, Hanzhong 723000, Shaanxi, China.
Yanbin LiangDepartment of Neurosurgery, 3201 Hospital No. 783 Tianhan Avenue, Hanzhong 723000, Shaanxi, China.
Tao HuangDepartment of Neurosurgery, 3201 Hospital No. 783 Tianhan Avenue, Hanzhong 723000, Shaanxi, China.
Jingku MaDepartment of Neuro lntensive Care Unit, No. 215 Hospital of Shaanxi Nuclear Industry No. 52 Weiyang West Road, Qindu District, Xianyang 712000, Shaanxi, China.

Funding

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6 · The paper itself

Abstract

objectivesTo identify risk factors for intraoperative rupture during microscopic clipping of intracranial aneurysms (IA) and to develop a predictive nomogram for improved preoperative risk assessment and surgical outcomes.

methodsA retrospective analysis was conducted on 286 IA patients who underwent surgical clipping between January 2018 and January 2023. Patients were classified into rupture (n=56) and non-rupture (n=230) groups based on intraoperative outcomes. Clinical data, including demographics, aneurysm size, morphology, and preoperative functional status, were collected. Independent risk factors were identified using multivariate logistic regression, and a nomogram model was constructed. Model performance was evaluated by ROC curves, calibration plots, and decision curve analysis (DCA). Six-month postoperative outcomes and complication rates were compared between the groups.

resultsUnivariate analysis showed that age ≥60 years, cerebral vasospasm, aneurysm diameter ≥10 mm, irregular morphology, anterior communicating artery location, preoperative Hunt-Hess grade >III, and the use of adjunctive techniques were associated with increased rupture risk. Multivariate regression identified cerebral vasospasm (OR=2.387, P=0.012), aneurysm size ≥10 mm (OR=2.298, P=0.018), anterior communicating artery aneurysm (OR=2.800, P=0.004), Hunt-Hess grade >III (OR=2.625, P=0.006), and adjunctive techniques (OR=2.492, P=0.012) as independent predictors. Interestingly, irregular morphology emerged as a protective factor (OR=0.348, P=0.003). The nomogram achieved an AUC of 0.856 in the training cohort and 0.763 in the validation cohort (P=0.438). Calibration curves demonstrated strong agreement between predicted and observed outcomes, while DCA indicated clinical benefit at threshold probabilities of 0-41%. At six months, patients in the rupture group had significantly worse modified Rankin Scale scores and higher complication rates (P<0.05).

conclusionThe proposed nomogram provides a reliable tool for predicting intraoperative rupture during IA clipping, enabling individualized preoperative risk assessment and optimization of surgical strategies, particularly in high-risk patients.

Indexed as

Intracranial aneurysmintraoperative rupturelogistic regressionmicroscopic surgical clippingnomogramrisk prediction

Identifiers

PMID41676243
PMCPMC12886117

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.