中国肿瘤外科杂志2024，Vol.16Issue(3)：246-252,7.DOI:10.3969/j.issn.1674-4136.2024.03.007
神经内镜下经蝶垂体腺瘤切除术后迟发性低钠血症的预测模型研究
Predictive model for delayed hyponatremia after endoscopic transsphenoidal surgery of pituitary adenomas
摘要
Abstract
Objective This study is to explore the potential risks of delayed hyponatremia after transsphenoidal surgery for pituitary adenomas.Methods The clinical,laboratory and imaging data of 105 patients undergoing transsphenoidal pituitary adenoma resection in the Department of Skull Base Oncology,Affiliated Hospital of Xuzhou Medical University from January 2018 to December 2022 were investigated retrospectively.Analyses of univariate and multivariate Logistic regression were conducted to ascertain the risk factors associated with delayed hyponatremia and to create predictive nomogram.The area under receiver operating curve(ROC),calibration curve,and decision curve analysis were employed to evaluate.Results Out of 105 patients who had undergone transsphenoidal resection of pituitary adenomas,32 cases developed delayed hyponatremia after operation,with an incidence rate of 30.4％.Three risk factors:preoperative prolactin level,preoperative elevation of diaphragma sellae and hyponatremia 1-2 days after operation were included to construct the nomogram.The AUC for forecasting delayed postoperative hyponatremia(DPH)in training and validation sets was 0.886 and 0.869 respectively.The DCA curve indicated a higher benefit in clinical application.Conclusions Patients with higher preoperative prolactin levels,elevated preoperative diaphragma sellae,and the occurrence of hyponatremia within the first 1-2 days after surgery are more likely to develop DPH.A nomogram prognostic model for predicting DPH was constructed and validated,which can provide more accurate reference basis for treatment decision.关键词
垂体腺瘤/经蝶手术/迟发性低钠血症/磁共振成像/危险因素/列线图Key words
Pituitary adenoma/Transsphenoidal surgery/Delayed hyponatremia/Magnetic resonance imaging/Risk factors/Nomograms引用本文复制引用
裴玉康,董峻东,张桐,陈洪福,杨烈驰,苗发安..神经内镜下经蝶垂体腺瘤切除术后迟发性低钠血症的预测模型研究[J].中国肿瘤外科杂志,2024,16(3):246-252,7.基金项目
国家自然科学基金(82002632) （82002632）
