Predictive model of delayed hyponatremia after endoscopic endonasal transsphenoidal resection of pituitary adenoma
Year: 2025
Type: article
Study design: Observational Study
Abstract: 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.
Source: Frontiers in Human Neuroscience
Authors: Hangyi Tan, Xianlong Miao, Yukang Pei, Faan Miao
Institution: Xuzhou Medical College
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W4417024144