OS10.3.A Predicting delirium after craniotomy in neuro-oncology

Abstract

Abstract BACKGROUND Post-operative delirium (POD) is a frequent and severe complication after neurosurgical operations. Good prediction of POD after craniotomy in neuro-oncologic patients is important to install prophylactic measures, increase recognition and apply early treatment. Hence, we compared logistic regression with machine learning to build an accurate predictive model in a large dataset. MATERIAL AND METHODS POD was defined in case of a Delirium Observation Scale (DOS) ≥ 3 or start of antipsychotic treatment for delirium within 10 days after surgery. Adult patients undergoing a craniotomy for a neuro-oncologic disease in the Erasmus Medical Centre in Rotterdam were retrospectively included. The cohort was split into a training (75%), after three-fold cross validation, and test set (25%). Logistic regression and Lasso Elastic-Net Regularized Generalized Linear Models (GLMNet) were trained based on 19 pre- and intra-operative features and risk factors were identified based on the superior model. RESULTS We included 1025 neuro-oncologic craniotomies between June 2017 and September 2020. Overall incidence of POD was 18.6% (95%CI 17.4–19.8). Compared to logistic regression, Lasso GLMNet performed superior (AUC 0.73 vs. 0.76) based on the optimal tuning parameters (α=1, λ=0.014). Several non-modifiable risk factors such as age (OR1.01), prior delirium (OR1.04), memory problems (OR1.12), surgery duration (OR1.01) and modifiable risk factors, such as low potassium (OR0.97) levels and opioid administration (OR1.03), were identified. CONCLUSION POD is a frequent complication after craniotomy in neuro-oncologic patients. Lasso GLMNet was useful in predicting POD in this cohort. Validation in a prospective cohort of this model should be applied to further evaluate its value in diminishing POD.