Development and external validation of clinical prediction models for pituitary surgery

• Published in: Brain & spine 2023, Vol.3, p.102668-102668, Article 102668
• Main Authors: Zanier, Olivier, Zoli, Matteo, Staartjes, Victor E., Alalfi, Mohammed O., Guaraldi, Federica, Asioli, Sofia, Rustici, Arianna, Pasquini, Ernesto, Faustini-Fustini, Marco, Erlic, Zoran, Hugelshofer, Michael, Voglis, Stefanos, Regli, Luca, Mazzatenta, Diego, Serra, Carlo
• Format: Article
• Language: English
• Subjects: Adenoma; Machine learning; Outcome prediction; Pituitary surgery; Transsphenoidal surgery
• ISSN: 2772-5294; 2772-5294
• DOI: 10.1016/j.bas.2023.102668

Gross total resection (GTR), Biochemical Remission (BR) and restitution of a priorly disrupted hypothalamus pituitary axis (new improvement, IMP) are important factors in pituitary adenoma (PA) resection surgery. Prediction of these metrics using simple and preoperatively available data might help improve patient care and contribute to a more personalized medicine. This study aims to develop machine learning models predicting GTR, BR, and IMP in PA resection surgery, using preoperatively available data. With data from patients undergoing endoscopic transsphenoidal surgery for PAs machine learning models for prediction of GTR, BR and IMP were developed and externally validated. Development was carried out on a registry from Bologna, Italy while external validation was conducted using patient data from Zurich, Switzerland. The model development cohort consisted of 1203 patients. GTR was achieved in 207 (17.2%, 945 (78.6%) missing), BR in 173 (14.4%, 992 (82.5%) missing) and IMP in 208 (17.3%, 167 (13.9%) missing) cases. In the external validation cohort 206 patients were included and GTR was achieved in 121 (58.7%, 32 (15.5%) missing), BR in 46 (22.3%, 145 (70.4%) missing) and IMP in 42 (20.4%, 7 (3.4%) missing) cases. The AUC at external validation amounted to 0.72 (95% CI: 0.63–0.80) for GTR, 0.69 (0.52–0.83) for BR, as well as 0.82 (0.76–0.89) for IMP. All models showed adequate generalizability, performing similarly in training and external validation, confirming the possible potentials of machine learning in helping to adapt surgical therapy to the individual patient. •Outcome prediction in pituitary surgery using simple, preoperatively available data.•Rigorous external validation of our machine learning prediction models.•Adequate generalizability using multicenter data.