• Christiaan H B van Niftrik, Frank van der Wouden, Victor E Staartjes, Jorn Fierstra, Martin N Stienen, Kevin Akeret, Martina Sebök, Tommaso Fedele, Johannes Sarnthein, Oliver Bozinov, Niklaus Krayenbühl, Luca Regli, and Carlo Serra.
• Department of Neurosurgery, University Hospital Zurich, Zurich, Switzerland.
• Neurosurgery. 2019 Oct 1; 85 (4): E756-E764.

IntroductionReliable preoperative identification of patients at high risk for early postoperative complications occurring within 24 h (EPC) of intracranial tumor surgery can improve patient safety and postoperative management. Statistical analysis using machine learning algorithms may generate models that predict EPC better than conventional statistical methods.ObjectiveTo train such a model and to assess its predictive ability.MethodsThis cohort study included patients from an ongoing prospective patient registry at a single tertiary care center with an intracranial tumor that underwent elective neurosurgery between June 2015 and May 2017. EPC were categorized based on the Clavien-Dindo classification score. Conventional statistical methods and different machine learning algorithms were used to predict EPC using preoperatively available patient, clinical, and surgery-related variables. The performance of each model was derived from examining classification performance metrics on an out-of-sample test dataset.ResultsEPC occurred in 174 (26%) of 668 patients included in the analysis. Gradient boosting machine learning algorithms provided the model best predicting the probability of an EPC. The model scored an accuracy of 0.70 (confidence interval [CI] 0.59-0.79) with an area under the curve (AUC) of 0.73 and a sensitivity and specificity of 0.80 (CI 0.58-0.91) and 0.67 (CI 0.53-0.77) on the test set. The conventional statistical model showed inferior predictive power (test set: accuracy: 0.59 (CI 0.47-0.71); AUC: 0.64; sensitivity: 0.76 (CI 0.64-0.85); specificity: 0.53 (CI 0.41-0.64)).ConclusionUsing gradient boosting machine learning algorithms, it was possible to create a prediction model superior to conventional statistical methods. While conventional statistical methods favor patients' characteristics, we found the pathology and surgery-related (histology, anatomical localization, surgical access) variables to be better predictors of EPC.Copyright © 2019 by the Congress of Neurological Surgeons.

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