The Difference Between Preoperative and Postoperative Pituitary Stalk Deviation Angles Can Predict Delayed Hyponatremia After Transsphenoidal Surgery

Urinary oxytocin secretion after pituitary surgery, early arginine vasopressin deficiency and syndrome of inappropriate antidiuresis

PURPOSE

Transient arginine vasopressin deficiency (AVP-D), previously called diabetes insipidus, is a well-known complication of transsphenoidal pituitary surgery (TPS) with no definite predictive biomarker to date making it difficult to anticipate. While oxytocin (OXT) was previously suggested as a possible biomarker to predict syndrome of inappropriate diuresis (SIAD)-related hyponatraemia after TPS, its secretion in patients presenting with AVP-D remains poorly understood. We therefore hypothesized that OXT might present a different secretion in the case of AVP-D which would support its potential as an early biomarker of AVP-D. Moreover, we hypothesized that abnormal secretion of OXT might occur later on, notably with SIAD.

METHODS

We measured the urinary output of OXT in 67 consecutive patients subjected to TPS and compared the values of oxytocin between time-points and OXT ratio between groups. The primary endpoint of our study was to identify a difference in urinary OXT excretion in patients suffering from AVP-D compared to patients remaining normonatraemic. As a secondary endpoint, we compared the evolution of OXT secretion after the diagnosis of AVP-D in both groups, comparing the patients that later developed SIAD with the ones that did not.

RESULTS

Patients developing AVP-D showed a delay in the increase of OXT secretion after TPS as shown by a significantly lower ratio of OXT between the first postoperative day and the day of surgery (0.88 VS 1.68, p = 0.0162, IC:0.2979-0.2642) but a significantly higher ratio of OXT between the fourth and the first postoperative days (1.17 VS 0.53, p = 0.0006, IC:-2.109-0.6092). Moreover, normonatraemic patients that did not show normalization of OXT levels at day 4 after surgery tended to develop SIAD later on.

CONCLUSION

Taken together, these results show for the first time that OXT release might help predict AVP-D after TPS and differentiate it from other pathologies of water-sodium balance.

Predictive modeling of arginine vasopressin deficiency after transsphenoidal pituitary adenoma resection by using multiple machine learning algorithms

This study aimed to predict arginine vasopressin deficiency (AVP-D) following transsphenoidal pituitary adenoma surgery using machine learning algorithms. We reviewed 452 cases from December 2013 to December 2023, analyzing clinical and imaging data. Key predictors of AVP-D included sex, tumor height, preoperative and postoperative changes in sellar diaphragm height and pituitary stalk length, preoperative ACTH levels, changes in ACTH levels, and preoperative cortisol levels. Six machine learning algorithms were tested: logistic regression (LR), support vector classification (SVC), random forest (RF), decision tree (DT), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost). After cross-validation and parameter optimization, the random forest model demonstrated the highest performance, with an accuracy (ACC) of 0.882 and an AUC of 0.96. The decision tree model followed, achieving an accuracy of 0.843 and an AUC of 0.95. Other models showed lower performance: LR had an ACC of 0.522 and an AUC of 0.54; SVC had an ACC of 0.647 and an AUC of 0.67; KNN achieved an ACC of 0.64 and an AUC of 0.70; and XGBoost had an ACC of 0.794 and an AUC of 0.91. The study found that a shorter preoperative pituitary stalk length, significant intraoperative stretching, and lower preoperative ACTH and cortisol levels were associated with a higher likelihood of developing AVP-D post-surgery.

Predictors and dynamic online nomogram for postoperative delayed hyponatremia after endoscopic transsphenoidal surgery for pituitary adenomas: a single-center, retrospective, observational cohort study with external validation

BACKGROUND

Postoperative delayed hyponatremia (PDH) is a major cause of readmission after endoscopic transsphenoidal surgery (eTSS) for pituitary adenomas (PAs). However, the risk factors associated with PDH have not been well established, and the development of a dynamic online nomogram for predicting PDH is yet to be realized. We aimed to investigate the predictive factors for PDH and construct a dynamic online nomogram to aid in its prediction.

METHODS

We analyzed the data of 226 consecutive patients who underwent eTSS for PAs at the Department of Neurosurgery in Jinling Hospital between January 2018 and October 2020. An additional 97 external patients were included for external validation. PDH was defined as a serum sodium level below 137 mmol/L, occurring on the third postoperative day (POD) or later.

RESULTS

Hyponatremia on POD 1-2 (OR = 2.64, P = 0.033), prothrombin time (PT) (OR = 1.78, P = 0.008), and percentage of monocytes (OR = 1.22, P = 0.047) were identified as predictive factors for PDH via multivariable logistic regression analysis. Based on these predictors, a nomogram was constructed with great discrimination in internal validation (adjusted AUC: 0.613-0.688) and external validation (AUC: 0.594-0.617). Furthermore, the nomogram demonstrated good performance in calibration plot, Brier Score, and decision curve analysis. Subgroup analysis revealed robust predictive performance in patients with various clinical subtypes and mild to moderate PDH.

CONCLUSIONS

Preoperative PT and the percentage of monocytes were, for the first time, identified as predictive factors for PDH. The dynamic nomogram proved to be a valuable tool for predicting PDH after eTSS for PAs and demonstrated good generalizability. Patients could benefit from early identification of PDH and optimized treatment decisions.

Machine learning models predict delayed hyponatremia post-transsphenoidal surgery using clinically available features

PURPOSE

Delayed hyponatremia (DHN), a unique complication, is the leading cause of unexpected readmission after pituitary surgery. Therefore, this study aimed to develop tools for predicting postoperative DHN in patients undergoing endoscopic transsphenoidal surgery (eTSS) for pituitary neuroendocrine tumors (PitNETs).

METHODS

This was a single-center, retrospective study involving 193 patients with PitNETs who underwent eTSS. The objective variable was DHN, defined as serum sodium levels < 135 mmol/L at ≥ 1 time between post operative days 3 and 9. We trained four machine learning models to predict this objective variable using the clinical variables available preoperatively and on the first postoperative day. The clinical variables included patient characteristics, pituitary-related hormone levels, blood test results, radiological findings, and postoperative complications.

RESULTS

The random forest (RF) model demonstrated the highest (0.759 ± 0.039) area under the curve of the receiver operating characteristic curve (ROC-AUC), followed by the support vector machine (0.747 ± 0.034), the light gradient boosting machine (LGBM: 0.738 ± 0.026), and the logistic regression (0.710 ± 0.028). The highest accuracy (0.746 ± 0.029) was observed in the LGBM model. The best-performing RF model was based on 24 features, nine of which were clinically available preoperatively.

CONCLUSIONS

The proposed machine learning models with pre- and post-resection features predicted DHN after the resection of PitNETs.

Diagnostic criteria of small sellar lesions with hyperprolactinemia: Prolactinoma or else

OBJECTIVE

To evaluate the combined predictive value of MRI criteria with the prolactin-volume-ratio (PVR) to differentiate prolactinoma from non-prolactinoma, in small sellar lesions with hyperprolactinemia.

METHODS

Retrospective analysis of 55 patients with sellar lesions of ≤15 mm diameter on MRI and hyperprolactinemia of ≤150 ng/mL, surgically treated between 2003 and 2020 at the Medical University of Vienna, with a conclusive histopathological report. Serum prolactin levels, extent of pituitary stalk deviation, size and volume of the lesion were assessed. The PVR was calculated by dividing the preoperative prolactin level by tumor volume.

RESULTS

Our study population consisted of 39 patients (71%) with a prolactin-producing pituitary adenoma (group A), while 16 patients (29%) had another type of sellar lesion (group B). Patients in group A were significantly younger (p=0.012), had significantly higher prolactin levels at diagnosis (p<0.001) as well as smaller tumor volume (p=0.036) and lower degree of pituitary stalk deviation (p=0.009). The median PVR was significantly higher in group A (243 ng/mL per cm³) than in group B (83 ng/mL per cm³; p=0.002). Furthermore, the regression operating characteristics analysis revealed a PVR >100 ng/mL per cm³ to be predictive for distinguishing prolactin-producing lesions from other small sellar lesions.

CONCLUSION

In patients with small sellar lesions, Prolactin-Volume-Ratios >100 represents a possible predictive marker for the diagnosis of prolactin-producing pituitary adenomas.