Abstract:
Objectives: The purpose of the current study is to develop an accurate supervised ML model able to predict the risk of intraoperative CSF leakage by comparing different machine learning methods.
Design: Retrospective diagnostic-prognostic analysis.
Setting : Endoscopic transsphenoidal surgery for pituitary adenomas (PAs).
Participants: A cohort of patients consecutively treated via E-TNS for PAs was selected. Clinical, radiological and endocrinological preoperative data were reviewed and elaborated through a feature selecting algorithm. A customized pipeline of several ML models was programmed and trained in parallel for CSF leakage prediction; the best five models were included for further analyses. Selected risk factors were then used for training and hyperparameters optimization.
Main outcome measures : The main outcome metrics were: accuracy, sensitivity, specificity, PPV, NPV and F1-score of each ML model.
Results: Intraoperative CSF leak occurred in 54 (22,6%) of 238 patients. Best risk’s predictors were: non secreting status, older age, x-, y- and z-axes diameters, ICD and R ratio. The random forest (RF) classifier outperformed other models, with an AUC of 0,84, high sensitivity (87%) and specificity (82%). Positive predictive value and negative predictive value were 69% and 93% respectively. F1 score was 0,87.
Conclusion : A supervised machine learning prediction model able to identify patients at higher risk of intraoperative CSF leakage was trained and internally validated. The random forest classifier showed the best performance across all models selected by the authors. RF models might predict surgical outcomes in heterogeneous multimorbid and fragile populations outperforming classical statistical analyses and other machine learning models.
Keywords :
Machine learning;
Supervised machine learning;
Pituitary adenoma;
Intraoperative CSF leakage;
CSF fistula;