| Literature DB >> 35589870 |
Langchao Yan1, Wengui Tao1, Qian Zhan1, Zheng Huang1, Fenghua Chen1, Shifu Li2.
Abstract
Seizures are the second most common manifestations of brain arteriovenous malformations (bAVMs). This study was conducted to investigate the clinical and angioarchitectural features of bAVMs with seizures and provide guidelines for the clinical management of these patients. We collected clinical and radiological data on patients with bAVMs diagnosed by digital subtraction angiography between January 2013 and December 2020 and dichotomized the patients into the seizures and non-seizures groups. We identified differences in demographic and angiographic features. Logistic regression and random forest (RF) models were developed and compared. The diagnostic capacity was assessed using receiver operating characteristic (ROC) curves. A nomogram was constructed, and the clinical impact was determined by decision curve analysis. A total of 414 patients with bAVMs were included in the analysis, of which 78 (18.8%) had bAVM-related seizures. In the multivariable logistic regression model, the location and side of bAVMs were independently associated with seizures. In RF models, the maximal diameter of veins and the cross-sectional area of feeding arteries and draining veins were the most important features. ROC curves showed that the RF model was not better than MLR in predicting seizures. Decision curve analysis revealed that the use of a constructed nomogram to stratify the seizure patients was beneficial at all threshold probabilities in our study. The side and location of bAVMs are specific angioarchitectural features independently associated with the occurrences of seizures with bAVMs.Entities:
Keywords: Arteriovenous malformation; Logistic regression; Machine learning; Retrospective study; Seizure
Mesh:
Year: 2022 PMID: 35589870 DOI: 10.1007/s10143-022-01814-3
Source DB: PubMed Journal: Neurosurg Rev ISSN: 0344-5607 Impact factor: 2.800