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Developing a machine learning model for predicting venlafaxine active moiety concentration: a retrospective study using real-world evidence

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International Journal of Clinical Pharmacy Aims and scope Submit manuscript

Abstract

Background

Venlafaxine is frequently prescribed for patients with depression. To control the concentration of venlafaxine within the therapeutic window for the best treatment effect, a model to predict venlafaxine concentration is necessary.

Aim

Our objective was to develop a prediction model for venlafaxine concentration using real-world evidence based on machine learning and deep learning techniques.

Method

Patients who underwent venlafaxine treatment between November 2019 and August 2022 were included in the study. Important variables affecting venlafaxine concentration were identified using a combination of univariate analysis, sequential forward selection, and machine learning techniques. Predictive performance of nine machine learning and deep learning algorithms were assessed, and the one with the optimal performance was selected for modeling. The final model was interpreted using SHapley Additive exPlanations.

Results

A total of 330 eligible patients were included. Five influential variables that affect venlafaxine concentration were venlafaxine daily dose, sex, age, hyperlipidemia, and adenosine deaminase. The venlafaxine concentration prediction model was developed using the eXtreme Gradient Boosting algorithm (R2 = 0.65, mean absolute error = 77.92, root mean square error = 93.58). In the testing cohort, the accuracy of the predicted concentration within ± 30% of the actual concentration was 73.49%. In the subgroup analysis, the prediction accuracy was 69.39% within the recommended therapeutic range of venlafaxine concentration within ± 30% of the actual value.

Conclusion

The XGBoost model for predicting blood concentration of venlafaxine using real-world evidence was developed, guiding the adjustment of regimen in clinical practice.

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Funding

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

Author information

Author notes

  1. Luyao Chang and Xin Hao have contributed equally to this work.

  2. Chunhua Zhou and Xiaolu Pang are corresponding authors and contributed equally to this work.

Authors and Affiliations

  1. Department of Clinical Pharmacy, The First Hospital of Hebei Medical University, 89 Donggang Road, Yuhua District, Shijiazhuang, 066003, China

    Luyao Chang, Jing Yu, Yimeng Liu & Chunhua Zhou

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

    Luyao Chang, Jing Yu, Yimeng Liu & Chunhua Zhou

  3. Dalian Medicinovo Technology Co., Ltd., Dalian, China

    Xin Hao

  4. Beijing Medicinovo Technology Co., Ltd., Beijing, China

    Jinyuan Zhang, Ze Yu & Fei Gao

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

    Xuxiao Ye

  6. Hebei Medical University, 361 Zhongshan East Road, Chang’an District, Shijiazhuang, 050011, China

    Xiaolu Pang

Authors
  1. Luyao Chang
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Corresponding author

Correspondence to Chunhua Zhou.

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Conflicts of interest

Xin Hao is employed by Dalian Medicinovo Technology Co. Ltd., China. Jinyuan Zhang, Ze Yu and Fei Gao are employed by Beijing 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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Chang, L., Hao, X., Yu, J. et al. Developing a machine learning model for predicting venlafaxine active moiety concentration: a retrospective study using real-world evidence. Int J Clin Pharm (2024). https://doi.org/10.1007/s11096-024-01724-y

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