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Personalized venlafaxine dose prediction using artificial intelligence technology: a retrospective analysis based on real-world data

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Abstract

Background

Venlafaxine dose regimens vary considerably between individuals, requiring personalized dosing.

Aim

This study aimed to identify dose-related influencing factors of venlafaxine through real-world data analysis and to construct a personalized dose model using advanced artificial intelligence techniques.

Method

We conducted a retrospective study on patients with depression treated with venlafaxine. Significant variables were selected through a univariate analysis. Subsequently, the predictive performance of seven models (XGBoost, LightGBM, CatBoost, GBDT, ANN, TabNet, and DT) was compared. The algorithm that demonstrated optimal performance was chosen to establish the dose prediction model. Model validation used confusion matrices and ROC analysis. Additionally, a dose subgroup analysis was conducted.

Results

A total of 298 patients were included. TabNet was selected to establish the venlafaxine dose prediction model, which exhibited the highest performance with an accuracy of 0.80. The analysis identified seven crucial variables correlated with venlafaxine daily dose, including blood venlafaxine concentration, total protein, lymphocytes, age, globulin, cholinesterase, and blood platelet count. The area under the curve (AUC) for predicting venlafaxine doses of 75 mg, 150 mg, and 225 mg were 0.90, 0.85, and 0.90, respectively.

Conclusion

We successfully developed a TabNet model to predict venlafaxine doses using real-world data. This model demonstrated substantial predictive accuracy, offering a personalized dosing regimen for venlafaxine. These findings provide valuable guidance for the clinical use of the drug.

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Acknowledgements

None.

Funding

This work was supported by the Finance Department of Hebei Province in China (Grant Number ZF2023020) and the Medical science research project of Hebei Health Commission (Grant Number 20221440).

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Author notes

  1. Yimeng Liu and Ze Yu have contributed equally to this work.

Authors and Affiliations

  1. Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, Shijiazhuang, 050017, People’s Republic of China

    Yimeng Liu, Jing Yu & Chunhua Zhou

  2. The Technology Innovation Center for Artificial Intelligence in Clinical Pharmacy of Hebei Province, The First Hospital of Hebei Medical University, Shijiazhuang, 050017, People’s Republic of China

    Yimeng Liu, Jing Yu & Chunhua Zhou

  3. Beijing Medicinovo Technology Co., Ltd, Beijing, 100161, People’s Republic of China

    Ze Yu, Jinyuan Zhang & Fei Gao

  4. Department of Urology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, People’s Republic of China

    Xuxiao Ye

  5. Dalian Medicinovo Technology Co., Ltd., Dalian, 116021, People’s Republic of China

    Xin Hao

Author notes

  1. Jing Yu and Chunhua Zhou are corresponding authors and contributed equally to this work.

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    1. Yimeng Liu
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    Correspondence to Jing Yu.

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    Jinyuan Zhang and Fei Gao are employed by Beijing Medicinovo Technology Co. Ltd., China. Xin Hao is employed by Dalian Medicinovo Technology Co. Ltd., China. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationship that could be construed as a potential conflict of interest.

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    Liu, Y., Yu, Z., Ye, X. et al. Personalized venlafaxine dose prediction using artificial intelligence technology: a retrospective analysis based on real-world data. Int J Clin Pharm (2024). https://doi.org/10.1007/s11096-024-01729-7

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