Shi Xudong, Liu Yuyang, Zhang Zehan, Tao Bingyan, Zhang Ding, Jiang Qingyu, Chen Guilin, Ma Hengchao, Feng Yaping, Xie Jiaxin, Zheng Xuan, Zhang Jun
Department of Neurosurgery, The First Medical Centre, Chinese PLA General Hospital, Beijing, China.
Medical School of Chinese PLA, Beijing, China.
CNS Neurosci Ther. 2024 Apr;30(4):e14526. doi: 10.1111/cns.14526. Epub 2023 Nov 21.
The purpose of this study was to identify significant prognostic factors associated with facial paralysis after vestibular schwannoma (VS) surgery and develop a novel nomogram for predicting facial nerve (FN) outcomes.
Retrospective data were retrieved from 355 patients who underwent microsurgery via the retrosigmoid approach for VS between December 2017 and December 2022. Univariate and multivariate logistic regression analysis were used to construct a radiographic features-based nomogram to predict the risk of facial paralysis after surgery.
Following a thorough screening process, a total of 185 participants were included. The univariate and multivariate logistic regression analysis revealed that tumor size (p = 0.005), fundal fluid cap (FFC) sign (p = 0.014), cerebrospinal fluid cleft (CSFC) sign (p < 0.001), and expansion of affected side of internal auditory canal (IAC) (p = 0.033) were independent factors. A nomogram model was constructed based on these indicators. When applied to the validation cohort, the nomogram demonstrated good discrimination and favorable calibration. Then we generated a web-based calculator to facilitate clinical application.
Tumor size, FFC and CSFC sign, and the expansion of the IAC, serve as good predictors of postoperative FN outcomes. Based on these factors, the nomogram model demonstrates good predictive performance.
本研究旨在确定与前庭神经鞘瘤(VS)手术后面瘫相关的显著预后因素,并开发一种用于预测面神经(FN)预后的新型列线图。
回顾性收集2017年12月至2022年12月期间355例行乙状窦后入路VS显微手术患者的数据。采用单因素和多因素逻辑回归分析构建基于影像学特征的列线图,以预测术后面瘫风险。
经过全面筛选,共纳入185名参与者。单因素和多因素逻辑回归分析显示,肿瘤大小(p = 0.005)、基底液帽(FFC)征(p = 0.014)、脑脊液裂隙(CSFC)征(p < 0.001)以及患侧内听道(IAC)扩大(p = 0.033)是独立因素。基于这些指标构建了列线图模型。将其应用于验证队列时,列线图显示出良好的区分度和校准度。然后我们生成了一个基于网络的计算器以方便临床应用。
肿瘤大小、FFC和CSFC征以及IAC扩大是术后FN预后的良好预测指标。基于这些因素,列线图模型显示出良好的预测性能。