You're viewing our updated article page. If you need more time to adjust, you can return to the old layout.

ORIGINAL RESEARCH article

Front. Neurol.

Sec. Neuro-Oncology and Neurosurgical Oncology

Risk Factors for Pituitary Apoplexy: A Meta-Analysis and Development of a Clinical Prediction Nomogram

  • Second Affiliated Hospital, Chongqing Medical University, Chongqing, China

The final, formatted version of the article will be published soon.

Abstract

Purpose: This study aimed to identify significant risk factors for pituitary apoplexy in patients with pituitary adenomas through a meta-analysis and to develop an individualized nomogram for clinical decision-making. Methods: A two-part investigation was conducted. First, a meta-analysis of published studies identified risk factors for pituitary apoplexy and calculated pooled odds ratios (ORs) and 95% confidence intervals (CIs). Second, a retrospective cohort of 234 patients was used to construct and validate a nomogram based on multivariate logistic regression. Results: The meta-analysis included six studies, revealing that non-functioning pituitary adenomas (OR = 1.93, 95% CI: 1.38–2.70), male sex (OR = 2.57, 95% CI: 1.85–3.58), and hypertension (OR = 2.53, 95% CI: 1.54–4.15) were significantly associated with pituitary apoplexy. The nomogram demonstrated excellent predictive performance with AUCs of 0.86 in the training set and 0.83 in the validation set. Calibration curves showed good agreement between predicted and observed probabilities. The Hosmer–Lemeshow test yielded P values of 1 and 0.272 in the training and validation cohorts, respectively. Decision curve analysis demonstrated significant net clinical benefit in both cohorts. Conclusion: This study identified key predictors of pituitary apoplexy and developed a nomogram that may help stratify risk and guide preventive and therapeutic strategies.

Summary

Keywords

Meta-analysis, nomogram, pituitary adenoma, Pituitary Apoplexy, risk prediction

Received

21 December 2025

Accepted

16 February 2026

Copyright

© 2026 Chen, Huang, Tang, Chen and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jin Chen; Guanjian Zhao

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Outline

Share article

Article metrics