Prediction of duloxetine blood concentration in patients with depression based on machine learning
- VernacularTitle:基于机器学习模型预测抑郁症患者度洛西汀的血药浓度
- Author:
Ming QIAO
1
;
Lu JIN
2
;
Yi ZHU
1
;
Junping HU
3
Author Information
1. Dept. of Pharmacy，the First Affiliated Hospital of Xinjiang Medical University，Urumqi 830011，China;Xinjiang Key Laboratory of Clinical Drug Research，Urumqi 830011，China
2. Psychological Medicine Center，the First Affiliated Hospital of Xinjiang Medical University，Urumqi 830011，China
3. College of Pharmacy，Xinjiang Medical University，Urumqi 830017，China
- Publication Type:Journal Article
- Keywords:
depression;
duloxetine;
blood concentration
- From:
China Pharmacy
2025;36(6):752-757
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE To provide medication reference for duloxetine use in clinical settings， particularly for patients with depression in primary medical institutions in Xinjiang that lack therapeutic drug monitoring conditions. METHODS The medical records of 281 depression inpatients taking duloxetine in the First Affiliated Hospital of Xinjiang Medical University from January 2022 to December 2023 were retrospectively collected. They were divided into training set （196 cases） and test set （85 cases） in the ratio of 7∶3. Feature selection was performed by encapsulating random forests （RF） with recursive feature elimination. Four machine learning algorithms， namely support vector machine， RF， extreme gradient boosting （XGBoost） and artificial neural network， were used to construct duloxetine blood concentration prediction model. The prediction performance of the models was evaluated and compared by coefficient of determination （R2）， mean absolute error （MAE） and root mean squared error （RMSE）. The feature of the selected optimal model was explained by Shapley additive explanation method， and the importance ranking of the features and the influence on the prediction results of duloxetine blood concentration were determined. RESULTS A total of 29 characteristic variables were selected， including age， ethnicity， body mass index（BMI）， etc. XGBoost showed the highest R2 （0.808）， and the lowest MAE （7.644） and RMSE （10.808）. The ranking of feature importance for predicting the blood concentration of duloxetine was as follows： BMI＞age＞other 20 feature sets （including liver and kidney function and biochemical indicators）＞daily dosage＞comorbidities＞combination therapy＞ethnicity＞white blood cell count＞hemoglobin＞height. CONCLUSIONS XGBoost model possesses the best prediction performance of duloxetine blood concentration； BMI and age have a greater impact on the prediction of duloxetine blood concentration.
