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

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.