Nomogram for predicting diabetes insipidus following endoscopic transsphenoidal surgery in pituitary adenomas.

Abstract

Postoperative diabetes insipidus (DI) frequently complicates endoscopic transsphenoidal surgery (TSS) in pituitary adenoma (PA) patients, yet reliable predictive methods for DI risk remain lacking. This study aims to identify risk factors associated with DI following endoscopic transsphenoidal resection of PA and to develop a predictive nomogram for assessing postoperative DI risk. This study involved 600 PA patients underwent endoscopic TSS at Shandong Provincial Hospital from 2021 to 2023. Among these patients, 82 developed postoperative DI while 518 did not. The cohort was randomly divided into training (n = 360) and validation (n = 240) groups at 6:4 ratios by R software. Clinical parameters and radiographic features were evaluated using univariable and multivariable logistic regression to construct a predictive nomogram for post-endoscopic TSS DI risk. Model performance was assessed using ROC curves, calibration plots, and decision curve analysis. Subgroup analysis was used to evaluate the model's ability to discriminate between transient and permanent DI. Univariable and multivariable logistic regression analyses on the training group identified several independent risk factors for post-endoscopic TSS DI, including maximum tumor diameter, Knosp grade, Esposito grade, recurrent PA, and pituitary stalk deviation angle. A nomogram was developed based on these factors, demonstrating robust predictive accuracy with ROC areas under curve of 0.840 for the training group and 0.815 for the validation group. Calibration plots indicated excellent agreement between predicted and observed probabilities of postoperative DI. DCA curves highlighted the nomogram's efficacy in guiding clinical decision-making. Subgroup analysis showed that the model was able to discriminate between transient and permanent DI, and the AUC was 0.652 (95% CI 0.525-0.794). This study presents a nomogram designed to predict postoperative DI risk in patients undergoing endoscopic TSS for PA. Internal and external validations underscored the model's high accuracy, calibration, and clinical utility. Simultaneously, the model can also assess the development risk of permanent DI. This predictive tool offers clinicians valuable support in identifying high-risk DI patients, optimizing postoperative care strategies, and tailoring treatment plans to improve patient outcomes.