Development and validation of a predictive machine learning model for postoperative long-term diabetes insipidus following transsphenoidal surgery for sellar lesions.

OBJECTIVE

Diabetes Insipidus (DI) is a common complication that occurs following transsphenoidal surgery for sellar lesions. DI is usually transient but can be permanent in select patients. Prior studies have described preoperative risk factors for developing postoperative DI. However, no predictive risk score has been created to risk stratify these patients.

METHODS

A single-center retrospective review from 2017 - 2022 was performed, reviewing all patients who underwent transsphenoidal surgery for resection of a sellar lesion. Longterm DI was defined as a patient who met DI criteria for at least six months and required desmopressin therapy. Baseline patient, operative, and radiographic characteristics were obtained. A machine learning method (Risk-SLIM) was utilized to create a risk stratification score to identify patients at high risk for DI.

RESULTS

In total, 252 patients were identified to have sellar lesions treated with transsphenoidal surgery. Of these, 27 (10.7 %) patients developed long-term DI and required desmopressin therapy. The DI after Transsphenoidal Surgery score (DITSS) was created with an area under the curve of 0.81 and a calibration error (CAL) error of 7.3 %. Predicative factors were tumor pathology, Tumor size, patient age, and endoscopic approach. The probability of developing DI requiring long-term desmopressin therapy ranged from < 1 % for a score of 0 and > 95 % for a score of 10

CONCLUSIONS

The DITSS model is a concise and accurate tool to assist in clinical decision-making for risk stratifying which patients undergoing transsphenoidal surgery for sellar lesions may go on to develop DI.