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

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 ve...

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Veröffentlicht in:International journal of clinical pharmacy 2024-08, Vol.46 (4), p.899-909
Hauptverfasser: Chang, Luyao, Hao, Xin, Yu, Jing, Zhang, Jinyuan, Liu, Yimeng, Ye, Xuxiao, Yu, Ze, Gao, Fei, Pang, Xiaolu, Zhou, Chunhua
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container_end_page 909
container_issue 4
container_start_page 899
container_title International journal of clinical pharmacy
container_volume 46
creator Chang, Luyao
Hao, Xin
Yu, Jing
Zhang, Jinyuan
Liu, Yimeng
Ye, Xuxiao
Yu, Ze
Gao, Fei
Pang, Xiaolu
Zhou, Chunhua
description 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 ( R 2  = 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.
doi_str_mv 10.1007/s11096-024-01724-y
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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 ( R 2  = 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.</description><identifier>ISSN: 2210-7703</identifier><identifier>ISSN: 2210-7711</identifier><identifier>EISSN: 2210-7711</identifier><identifier>DOI: 10.1007/s11096-024-01724-y</identifier><identifier>PMID: 38753076</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Adenosine deaminase ; Algorithms ; Analysis ; Antidepressants ; Blood levels ; Data mining ; Deep learning ; Evidence-based medicine ; Health aspects ; Hyperlipidemia ; Internal Medicine ; Learning algorithms ; Machine learning ; Medicine ; Medicine &amp; Public Health ; Patients ; Pharmacy ; Prediction models ; Research Article ; Venlafaxine</subject><ispartof>International journal of clinical pharmacy, 2024-08, Vol.46 (4), p.899-909</ispartof><rights>The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2024. 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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 ( R 2  = 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. 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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 ( R 2  = 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.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>38753076</pmid><doi>10.1007/s11096-024-01724-y</doi><tpages>11</tpages></addata></record>
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subjects Adenosine deaminase
Algorithms
Analysis
Antidepressants
Blood levels
Data mining
Deep learning
Evidence-based medicine
Health aspects
Hyperlipidemia
Internal Medicine
Learning algorithms
Machine learning
Medicine
Medicine & Public Health
Patients
Pharmacy
Prediction models
Research Article
Venlafaxine
title Developing a machine learning model for predicting venlafaxine active moiety concentration: a retrospective study using real-world evidence
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