A machine learning model for predicting blood concentration of quetiapine in patients with schizophrenia and depression based on real-world data

AimsThis study aimed to establish a prediction model of quetiapine concentration in patients with schizophrenia and depression, based on real-world data via machine learning techniques to assist clinical regimen decisions. MethodsA total of 650 cases of quetiapine therapeutic drug monitoring (TDM) data from 483 patients at the First Hospital of Hebei Medical University from 1 November 2019 to 31 August 2022 were included in the study. Univariate analysis and sequential forward selection (SFS) were implemented to screen the important variables influencing quetiapine TDM. After 10-fold cross validation, the algorithm with the optimal model performance was selected for predicting quetiapine TDM among nine models. SHapley Additive exPlanation was applied for model interpretation. ResultsFour variables (daily dose of quetiapine, type of mental illness, sex and CYP2D6 competitive substrates) were selected through univariate analysis (P < .05) and SFS to establish the models. The CatBoost algorithm with the best predictive ability (mean [SD] R-2 = 0.63 +/- 0.02, RMSE = 137.39 +/- 10.56, MAE = 103.24 +/- 7.23) was chosen for predicting quetiapine TDM among nine models. The mean (SD) accuracy of the predicted TDM within +/- 30% of the actual TDM was 49.46 +/- 3.00%, and that of the recommended therapeutic range (200-750 ng mL(-1)) was 73.54 +/- 8.3%. Compared with the PBPK model in a previous study, the CatBoost model shows slightly higher accuracy within +/- 100% of the actual value. ConclusionsThis work is the first real-world study to predict the blood concentration of quetiapine in patients with schizophrenia and depression using artificial intelligent techniques, which is of significance and value for clinical medication guidance.