# Risk factors for pituitary apoplexy: a meta-analysis and development of a clinical prediction nomogram

## TL;DR

This study finds risk factors for pituitary apoplexy and creates a tool to help doctors predict and manage the condition.

## Contribution

The study introduces a new clinical prediction nomogram for pituitary apoplexy based on meta-analysis and logistic regression.

## Key findings

- Non-functioning pituitary adenomas, male sex, and hypertension are significant risk factors for pituitary apoplexy.
- The developed nomogram showed strong predictive performance with AUCs of 0.86 and 0.83 in training and validation sets.
- Calibration and decision curve analyses confirmed the nomogram's clinical utility and accuracy.

## Abstract

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.

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.

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.

This study identified key predictors of pituitary apoplexy and developed a nomogram that may help stratify risk and guide preventive and therapeutic strategies.