Wang Ying, Wang Wei, Huang Qinghua, Yan Wei, Lan Meijuan
Nursing Department, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China.
Neurosurgery Department, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China.
Front Surg. 2024 Jul 19;11:1409298. doi: 10.3389/fsurg.2024.1409298. eCollection 2024.
This study aimed to develop and validate a dynamic nomogram to assess the risk of nasal bleeding after endoscopic transnasal transsphenoidal pituitary tumor resection.
A retrospective analysis was conducted on patients who underwent endoscopic transnasal transsphenoidal pituitary tumor resection from June 2019 to June 2021. Univariate and multivariate logistic regression analyses were used to screen for independent risk factors for nasal bleeding from the training set. A multivariate logistic regression model was established, a nomogram was plotted, and it was validated in an internal validation set. The performance of the nomogram was evaluated based on the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).
The nomogram indicators included anticoagulant use, sphenoid sinus artery injury, nasal irrigation, platelet count (PLT), and constipation. The predictive model had an area under the ROC curve of 0.932 (95% CI: 0.873-0.990) and 0.969 (95% CI: 0.940-0.997) for the training and validation sets, respectively, indicating good discrimination. The calibration curve showed good consistency between the actual and predicted incidence of nasal bleeding ( > 0.05). DCA indicated that the nomogram had good clinical net benefit in predicting postoperative nasal bleeding in patients.
In summary, this study explored the incidence and influencing factors of nasal bleeding after endoscopic transnasal transsphenoidal pituitary tumor resection and established a predictive model to assist clinical decision-making.
本研究旨在开发并验证一种动态列线图,以评估经鼻内镜经蝶窦垂体瘤切除术后鼻出血的风险。
对2019年6月至2021年6月期间接受经鼻内镜经蝶窦垂体瘤切除术的患者进行回顾性分析。采用单因素和多因素逻辑回归分析从训练集中筛选鼻出血的独立危险因素。建立多因素逻辑回归模型,绘制列线图,并在内部验证集中进行验证。基于受试者操作特征(ROC)曲线、校准曲线和决策曲线分析(DCA)评估列线图的性能。
列线图指标包括抗凝药物使用、蝶窦动脉损伤、鼻腔冲洗、血小板计数(PLT)和便秘。预测模型在训练集和验证集中的ROC曲线下面积分别为0.932(95%CI:0.873-0.990)和0.969(95%CI:0.940-0.997),表明具有良好的区分度。校准曲线显示鼻出血的实际发生率与预测发生率之间具有良好的一致性(>0.05)。DCA表明列线图在预测患者术后鼻出血方面具有良好的临床净效益。
总之,本研究探讨了经鼻内镜经蝶窦垂体瘤切除术后鼻出血的发生率及影响因素,并建立了预测模型以辅助临床决策。