{"title":"Optimizing vancomycin dosing in pediatrics: a machine learning approach to predict trough concentrations in children under four years of age.","authors":"Minghui Yin, Yuelian Jiang, Yawen Yuan, Chensuizi Li, Qian Gao, Hui Lu, Zhiling Li","doi":"10.1007/s11096-024-01745-7","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Vancomycin trough concentration is closely associated with clinical efficacy and toxicity. Predicting vancomycin trough concentrations in pediatric patients is challenging due to significant inter-individual variability and rapid physiological changes during maturation.</p><p><strong>Aim: </strong>This study aimed to develop a machine learning model to predict vancomycin trough concentrations and determine optimal dosing regimens for pediatric patients < 4 years of age using ML algorithms.</p><p><strong>Method: </strong>A single-center retrospective observational study was conducted from January 2017 to March 2020. Pediatric patients who received intravenous vancomycin and underwent therapeutic drug monitoring were enrolled. Seven ML models [linear regression, gradient boosted decision trees, support vector machine, decision tree, random forest, Bagging, and extreme gradient boosting (XGBoost)] were developed using 31 variables. Performance metrics including R-squared (R<sup>2</sup>), mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) were compared, and important features were ranked.</p><p><strong>Results: </strong>The study included 120 eligible trough concentration measurements from 112 patients. Of these, 84 measurements were used for training and 36 for testing. Among the seven algorithms tested, XGBoost showed the best performance, with a low prediction error and high goodness of fit (MAE = 2.55, RMSE = 4.13, MSE = 17.12, and R<sup>2</sup> = 0.59). Blood urea nitrogen, serum creatinine, and creatinine clearance rate were identified as the most important predictors of vancomycin trough concentration.</p><p><strong>Conclusion: </strong>An XGBoost ML model was developed to predict vancomycin trough concentrations and aid in drug treatment predictions as a decision-support technology.</p>","PeriodicalId":13828,"journal":{"name":"International Journal of Clinical Pharmacy","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Clinical Pharmacy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s11096-024-01745-7","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/11 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Vancomycin trough concentration is closely associated with clinical efficacy and toxicity. Predicting vancomycin trough concentrations in pediatric patients is challenging due to significant inter-individual variability and rapid physiological changes during maturation.
Aim: This study aimed to develop a machine learning model to predict vancomycin trough concentrations and determine optimal dosing regimens for pediatric patients < 4 years of age using ML algorithms.
Method: A single-center retrospective observational study was conducted from January 2017 to March 2020. Pediatric patients who received intravenous vancomycin and underwent therapeutic drug monitoring were enrolled. Seven ML models [linear regression, gradient boosted decision trees, support vector machine, decision tree, random forest, Bagging, and extreme gradient boosting (XGBoost)] were developed using 31 variables. Performance metrics including R-squared (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) were compared, and important features were ranked.
Results: The study included 120 eligible trough concentration measurements from 112 patients. Of these, 84 measurements were used for training and 36 for testing. Among the seven algorithms tested, XGBoost showed the best performance, with a low prediction error and high goodness of fit (MAE = 2.55, RMSE = 4.13, MSE = 17.12, and R2 = 0.59). Blood urea nitrogen, serum creatinine, and creatinine clearance rate were identified as the most important predictors of vancomycin trough concentration.
Conclusion: An XGBoost ML model was developed to predict vancomycin trough concentrations and aid in drug treatment predictions as a decision-support technology.
期刊介绍:
The International Journal of Clinical Pharmacy (IJCP) offers a platform for articles on research in Clinical Pharmacy, Pharmaceutical Care and related practice-oriented subjects in the pharmaceutical sciences.
IJCP is a bi-monthly, international, peer-reviewed journal that publishes original research data, new ideas and discussions on pharmacotherapy and outcome research, clinical pharmacy, pharmacoepidemiology, pharmacoeconomics, the clinical use of medicines, medical devices and laboratory tests, information on medicines and medical devices information, pharmacy services research, medication management, other clinical aspects of pharmacy.
IJCP publishes original Research articles, Review articles , Short research reports, Commentaries, book reviews, and Letters to the Editor.
International Journal of Clinical Pharmacy is affiliated with the European Society of Clinical Pharmacy (ESCP). ESCP promotes practice and research in Clinical Pharmacy, especially in Europe. The general aim of the society is to advance education, practice and research in Clinical Pharmacy .
Until 2010 the journal was called Pharmacy World & Science.