Vo, Chau D

Abstract INTRODUCTION The development of diabetes insipidus (DI) after pituitary adenoma resection is associated with worse post-surgical morbidity and longer hospital stay. Identification of clinical predictors of development of DI would allow for appropriate stratification of patients to optimize preoperative counseling and postoperative management. METHODS We conducted a retrospective review of 349 patients who underwent pituitary adenoma resection at Johns Hopkins Hospital between 2003 and 2017. We used unadjusted and adjusted logistic regression to identify predictors of developing DI. Factors which were associated with DI development (p< 0.1), were included in our multivariable model. Factors which remained associated with DI development were then used to develop a DI risk score. RESULT: 67 (19.2%) patients developed post-surgical DI. Patients who had symptoms of prolactinoma, surgery through one nare, or were on steroid treatment were more likely to develop DI. Conversely, patients who had unilateral cavernous sinus invasion were less likely to develop DI. We used these predictors to generate the DI risk score. Patients were assigned 1 point for each risk factor (symptoms of prolactinoma, steroids, or surgery through one nare) for developing DI, but 1 point was subtracted if they had a protective factor (unilateral cavernous sinus invasion). Patients with 0 points had a 10.7% probability of developing DI, patients with 1 point had a 18.5% probability of developing DI, patients with 2 points had a 41.2% probability of developing DI, and patients with 3 points had a 44.4% probability of DI. CONCLUSIONS We identified several predictors of DI development after pituitary adenoma resection, and used these to generate a DI risk score. The DI risk score could help stratify patients’ likelihood of postsurgical DI development, although validation through larger prospective multi-center studies are needed.

Abstract INTRODUCTION Repeat surgery after tumor recurrence after pituitary adenoma resection can be challenging due to formation of scar tissue and loss of surgical landmarks, which can lead to increased rates of postoperative complications. Identification of clinical predictors of tumor recurrence would allow for appropriate stratification of patients to optimize preoperative counseling and postoperative management. METHODS We conducted a retrospective review of 349 patients who underwent pituitary adenoma resection at Johns Hopkins Hospital between 2003 and 2017. We used unadjusted and adjusted logistic regression to identify predictors of tumor recurrence. Factors which were associated with tumor recurrence in univariable models (p< 0.1), were included in our multivariable model. Factors that remained significantly associated with tumor recurrence (p< 0.05) in our multivariable model were considered predictors of recurrence. RESULTS 59 (16.9%) patients developed tumor recurrence at the time of our study. Patients who had surgery through one nare, transcranial surgery, visual field deficit, larger tumor volume, bilateral cavernous sinus invasion, carotid artery invasion, and postoperative cerebral spinal fluid (CSF) leak were more likely to have tumor recurrence. Conversely, patients who are younger in age, had sphenoid packing, and gross total resection were less likely to have tumor recurrence. In our multivariable model, only age (adjusted odds ratio[aOR]: 0.96), surgery through one nare (aOR: 2.05), gross total resection(aOR: 0.21), and presence of CSF leak(aOR: 4.43) remained significantly associated with tumor recurrence. CONCLUSIONS We identified several predictors of tumor recurrence after pituitary adenoma resection. Notably, having gross total resection of the primary tumor had the most significant protective effect on tumor recurrence. Given the increased complication associated with repeat surgery, gross total resection should be attempted in all patients, if feasible. Patients with other predictors of tumor recurrence should be monitored more frequently to allow for early detection of recurrence to optimize treatment strategies.