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

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.

KEYWORDS:

• quetiapine
• machine learning
• plasma concentration prediction
• prediction model
• CatBoost

Declaration of interest

J Zhang, Z Yu, X Hao and F Gao are employees of Beijing Medicinovo Technology Co. Ltd. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Ethics statement

The study was approved by the Hospital Ethics Committee of the First Hospital of Hebei Medical University (number: 20220403) on 13 March 2022. The study was performed in accordance with the Declaration of Helsinki. Written informed consent was obtained from the patients at the time of treatment.

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by L Yang, J Zhang, J Yu, and X Hao. The first draft of the manuscript was written by L Yang and all authors commented on previous versions of the manuscript. C Zhou provided valuable suggestions for this study. Z Yu and F Gao designed the research and revised the paper. All authors read and approved the final manuscript.

Additional information

Funding

This paper was funded by the Key research and development project of Hebei Provincial Department of Science and Technology, Grant/Award Number: 22377782D; Medical science research project of Hebei Provincial Health Commission, Grant/Award Numbers: 20221434, 20221440.