A gradient boosting machine learning algorithm predicted early postoperative complications after intracranial tumor surgery better than conventional statistical methods (AUC 0.73 vs 0.64).
Cohort (n=668)
No
Does a machine learning algorithm improve the prediction of early postoperative complications in patients undergoing elective intracranial tumor surgery compared to conventional statistical methods?
A gradient boosting machine learning algorithm outperformed conventional statistical methods in predicting early postoperative complications within 24 hours of elective intracranial tumor surgery.
Effect estimate: AUC 0.73
INTRODUCTION: Reliable 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. OBJECTIVE: To train such a model and to assess its predictive ability. METHODS: This 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. RESULTS: EPC 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)). CONCLUSION: Using 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.
Niftrik et al. (Wed,) conducted a cohort in intracranial tumor (n=668). Gradient boosting machine learning algorithms vs. Conventional statistical methods was evaluated on Early postoperative complications (EPC) within 24 h (AUC 0.73). A gradient boosting machine learning algorithm predicted early postoperative complications after intracranial tumor surgery better than conventional statistical methods (AUC 0.73 vs 0.64).