Clinical characterization of pediatric supratentorial tumors and prediction of pituitary insufficiency in two tertiary centers in Saudi Arabia.

Abstract

Post-operative pituitary insufficiency (PI) occurs in children with supra-tentorial tumors (STT) because of surgery or the mass effect of the tumor. We assessed the prevalence and clinical characteristics of STTs and predicted postoperative PI in our patients. This retrospective cohort study included children who underwent surgery for STT in two tertiary hospitals in Saudi Arabia (2009-2019). We focused on clinical, radiological, and histopathological features of STTs. We also used a linear regression model to predict post-operative PI. The study included 55 children (1-18 years, mean: 9.5±4.9 years, 32 [54%] females) with an initial presentation of STT that required surgery excluding recurrent episodes. The calculated period prevalence of STT was 18.2%, and the prevalence of postoperative PI was 58.2% (n=32/55). The most common symptoms were headache and visual disturbances, and 20% patients had preoperative symptoms of PI. Baseline preoperative investigations for PI were performed in 60% of patients, and dynamic tests were conducted in only seven patients. A residual cortisol deficiency was presumed in 24 (43.7%) patients and 18 (32.7%) patients who developed central diabetes insipidus (DI) post-operatively. Overall, the brain imaging correlated well with the histopathological diagnosis (kappa=0.48; P<.001). Craniopharyngioma (n=15/55, 27.3%) was the commonest STT. Predictive factors for a postoperative residual PI included age (10.9 ± 4.8 years; p-value=.027), female gender (p-value=.016 [OR=8.31; 95% CI (1.48-46.71)], presentation with headache (P value=.039 [OR=9.27; 95% CI (1.12-76.72)]), and visual disturbances (p-value=.044 [OR=5.07; 95% CI (1.04-24.61)]. STTs commonly occurred in our study population, and females were more prone to develop a residual PI. On-time surveillance of an intact endocrine system during the perioperative period is essential for the prediction and early management of PI.