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Updated: Jan 8, 2026

Endoscopic Endonasal Trans-sphenoidal Approach: Minimally Invasive Surgery for Pituitary Adenomas
Published on: January 17, 2018
Predictive model of delayed hyponatremia after endoscopic endonasal transsphenoidal resection of pituitary adenoma
Hangyi Tan1, Xianlong Miao1, Yukang Pei1
1Department of Neurosurgery, Department of Skull Base Oncology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
Objective:
This study aims to establish the risk factors and predictive model for the occurrence of delayed hyponatremia after endoscopic endonasal transsphenoidal resection of pituitary adenoma.
Methods:
Data from 155 patients who underwent endoscopic endonasal transsphenoidal resection of pituitary adenoma at the affiliated hospital of Xuzhou Medical University from January 2018 to May 2023 were analyzed. These patients were randomly divided into a training group (108 cases, 70%) and a validation group (47 cases, 30%). Univariate and Multivariate Logistic regression analysis were conducted on the training group to identify risk factors for delayed hyponatremia after surgery. A predictive model was established using R software and validated.
Results:
After conducting Univariate and Multivariate Logistic regression analysis, factors influencing the occurrence of delayed hyponatremia after endoscopic endonasal transsphenoidal resection of pituitary adenoma were identified as follows: elevated preoperative prolactin levels, higher preoperative suprasellar cistern height, and hyponatremia in the first 1-2 days after surgery. The area under the ROC curve for forecasting delayed postoperative hyponatremia (DPH) in training and validation sets was 0.943 and 0.959, respectively. The DCA curve indicated a higher benefit in clinical application.
Conclusion:
The risk prediction model for delayed hyponatremia after endoscopic endonasal transsphenoidal resection of pituitary adenoma, developed in this study, demonstrates favorable predictive performance. The nomogram can be utilized for early identification of high-risk individuals for DPH.
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