Predicting plasma concentration of quetiapine in patients with depression using machine learning techniques based on real-world evidence

Abstract

ABSTRACT Objectives We develop a model for predicting quetiapine levels in patients with depression, using machine learning to support decisions on clinical regimens. Methods Inpatients diagnosed with depression at the First Hospital of Hebei Medical University from 1 November 2019, to 31 August were enrolled. The ratio of training cohort to testing cohort was fixed at 80%:20% for the whole dataset. Univariate analysis was executed on all information to screen the important variables influencing quetiapine TDM. The prediction abilities of nine machine learning and deep learning algorithms were compared. The prediction model was created using an algorithm with better model performance, and the model’s interpretation was done using the SHapley Additive exPlanation. Results There were 333 individuals and 412 cases of quetiapine TDM included in the study. Six significant variables were selected to establish the individualized medication model. A quetiapine concentration prediction model was created through CatBoost. In the testing cohort, the projected TDM’s accuracy was 61.45%. The prediction accuracy of quetiapine concentration within the effective range (200–750 ng/mL) was 75.47%. Conclusions This study predicts the plasma concentration of quetiapine in depression patients by machine learning, which is meaningful for the clinical medication guidance.