Evidence map›Paper›PMID 41055752›Full record

ArticleNeurosurgical review2025

Development and validation of a nomogram model for predicting infarction after superficial temporal artery‒middle cerebral artery bypass in patients with intracranial atherosclerotic stenosis.

Tao Sun, Lixin Huang, Yibo Zhao, Jun Sun, Zhimin Wu, Baoyu Zhang, Cong Ling, Chuan Chen, Hui Wang

Abstract readValidation Study
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Article in Neurosurgical review, 2025. 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 · Who and what money

Authors and funding

9 authors.

Tao Sun *Department of Neurosurgery, Third Affiliated Hospital of Sun Yat-Sen University, No. 600th Tianhe Road, Guangzhou, 510630, Guangdong, China.
Lixin Huang *Department of Neurosurgery, Third Affiliated Hospital of Sun Yat-Sen University, No. 600th Tianhe Road, Guangzhou, 510630, Guangdong, China.
Yibo Zhao *Department of Neurosurgery, Third Affiliated Hospital of Sun Yat-Sen University, No. 600th Tianhe Road, Guangzhou, 510630, Guangdong, China.
Jun SunDepartment of Neurosurgery, Third Affiliated Hospital of Sun Yat-Sen University, No. 600th Tianhe Road, Guangzhou, 510630, Guangdong, China.
Zhimin WuDepartment of Neurosurgery, Third Affiliated Hospital of Sun Yat-Sen University, No. 600th Tianhe Road, Guangzhou, 510630, Guangdong, China.
Baoyu ZhangDepartment of Neurosurgery, Third Affiliated Hospital of Sun Yat-Sen University, No. 600th Tianhe Road, Guangzhou, 510630, Guangdong, China.
Cong LingDepartment of Neurosurgery, Third Affiliated Hospital of Sun Yat-Sen University, No. 600th Tianhe Road, Guangzhou, 510630, Guangdong, China.
Chuan ChenDepartment of Neurosurgery, Third Affiliated Hospital of Sun Yat-Sen University, No. 600th Tianhe Road, Guangzhou, 510630, Guangdong, China.
Hui WangDepartment of Neurosurgery, Third Affiliated Hospital of Sun Yat-Sen University, No. 600th Tianhe Road, Guangzhou, 510630, Guangdong, China. wangh22@mail.sysu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aimed to develop and validate a nomogram model for predicting cerebral infarction risk after superficial temporal artery-middle cerebral artery (STA-MCA) bypass in patients with intracranial atherosclerotic stenosis (ICAS).

methodsPatients with ICAS who received STA-MCA bypass were enrolled in this study. The independent risk factors for post bypass infarction were identified using univariate and multivariate logistic regression analyses. A nomogram model was developed and subsequently evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

resultsEventually, 316 patients with ICAS were included in the study. Diabetes, smoking, and high triglyceride and total cholesterol levels were identified as the independent risk factors, and a nomogram model was developed. The model achieved areas under the curve (AUCs) of 0.88 (95% confidence interval [CI] = 0.79-0.97) in the training cohort and 0.84 (95% CI = 0.72-0.97) in the validation cohort. Moreover, the calibration curves matched well, and the DCA indicated favorable clinical utility of the model.

conclusionsWe develop a nomogram model for infarction after STA-MCA bypass in patients with ICAS, which could assess the risk of infarction quickly. The model could significantly guide clinical decisions and reduce the incidence of infarction. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Cerebral InfarctionCerebral RevascularizationIntracranial ArteriosclerosisMiddle Cerebral ArteryNomogramsPostoperative ComplicationsTemporal ArteriesAdultAgedConstriction, PathologicFemaleHumansMaleMiddle AgedRisk FactorsCerebral infarctionIntracranial atherosclerotic stenosisNomogram modelRisk factorSuperficial temporal artery‒middle cerebral artery bypass

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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 OpenQuestion graph.