Predictors of glucocorticoid treatment intensity after endoscopic transsphenoidal surgery in patients with non-functional pituitary adenomas: a retrospective study - Report - MDSpire
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Factors Influencing Glucocorticoid Treatment Levels Following Endoscopic Transsphenoidal Surgery in Patients with Non-Functional Pituitary Adenomas: A Retrospective Analysis
Factors Influencing Glucocorticoid Treatment Levels Following eTSS
Overview
This study identifies key predictors of glucocorticoid therapy intensity in patients with non-functioning pituitary adenomas (NFPAs) post-endoscopic transsphenoidal surgery (eTSS). Tumor volume, suprasellar extension, pituitary stalk stretch, and preoperative morning cortisol levels were found to significantly influence glucocorticoid requirements.
Background
Non-functioning pituitary adenomas (NFPAs) are prevalent pituitary tumors that can lead to significant endocrine disturbances, particularly adrenal insufficiency, following surgical intervention. Endoscopic transsphenoidal surgery (eTSS) is the preferred treatment, yet many patients require long-term glucocorticoid therapy due to postoperative hypopituitarism.
Data Highlights
This study analyzed data from 116 patients and identified four independent predictors of glucocorticoid therapy intensity.
Key Findings
Tumor volume is a significant predictor of glucocorticoid therapy intensity (p < 0.05).
Suprasellar extension of the tumor correlates with increased glucocorticoid requirements (p < 0.05).
Preoperative morning cortisol levels are an independent predictor of postoperative glucocorticoid therapy intensity (p < 0.05).
The model developed explains 72.1% of the variance in glucocorticoid therapy intensity (adjusted R2 = 0.721).
Bootstrap resampling confirmed the robustness of the predictive model (RMSE: 656.04; 95% CI: 637.50–690.22).
Clinical Implications
The identification of these predictors can aid clinicians in understanding glucocorticoid replacement therapy for NFPA patients post-eTSS.
Conclusion
The study provides a predictive model based on specific clinical and imaging parameters.