{"title":"使用癫痫儿童风险预测模型确定丙戊酸钠浓度低于标准水平的机器学习算法的开发和内部验证","authors":"Yinhui Yao, Jingyi Zhao, Ying Wang, Wei Qiu, Yingxue Lin, Yaru Zang","doi":"10.1155/2023/9996553","DOIUrl":null,"url":null,"abstract":"Background. There is a narrow therapeutic window for sodium valproate, and the blood concentration is too low to control epilepsy, while it is easy to poison the body if the concentration is too high. It is therefore necessary to monitor drug concentration reasonably in order to control epilepsy. The purpose of this study was to establish a model for predicting concentrations of sodium valproate below 50 μg/mL in children with epilepsy. Methods. The clinical data and biochemical examination results of children with epilepsy treated in the pediatric outpatient department of our hospital from June 2019 to March 2022 were retrospectively collected and divided into a development group and a validation group according to a patient ratio of 8 to 2. Five machine learning algorithms were used to identify the key variable factors, and a risk prediction model for sodium valproate blood concentrations lower than the standard concentration was established. The area under the curve (AUC), calibration curve, GiViTi calibration band, and clinical influence curve were used to evaluate the diagnostic efficacy and clinical application value of the model. Results. A total of 525 children with epilepsy were enrolled. In the development group, the random forest algorithm performed best in predicting that the blood concentration of sodium valproate was lower than the standard concentration, showing the highest AUC (1.00). Six factors were determined as a nomogram to predict the incidence of low concentrations. In the validation group and the development group, the calibration curve, GiViTi calibration band, and clinical influence curve all performed well in the evaluation of the diagnostic efficacy and clinical application value of the model. Conclusions. This finding highlights the importance of examining biochemical indices in patients when data regarding the blood concentration of sodium valproate are lacking.","PeriodicalId":15381,"journal":{"name":"Journal of Clinical Pharmacy and Therapeutics","volume":"243 1","pages":"0"},"PeriodicalIF":2.1000,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and Internal Validation of Machine Learning Algorithms for Determining Sodium Valproate Concentrations below the Standard Level Using a Risk Prediction Model of Children with Epilepsy\",\"authors\":\"Yinhui Yao, Jingyi Zhao, Ying Wang, Wei Qiu, Yingxue Lin, Yaru Zang\",\"doi\":\"10.1155/2023/9996553\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background. There is a narrow therapeutic window for sodium valproate, and the blood concentration is too low to control epilepsy, while it is easy to poison the body if the concentration is too high. It is therefore necessary to monitor drug concentration reasonably in order to control epilepsy. The purpose of this study was to establish a model for predicting concentrations of sodium valproate below 50 μg/mL in children with epilepsy. Methods. The clinical data and biochemical examination results of children with epilepsy treated in the pediatric outpatient department of our hospital from June 2019 to March 2022 were retrospectively collected and divided into a development group and a validation group according to a patient ratio of 8 to 2. Five machine learning algorithms were used to identify the key variable factors, and a risk prediction model for sodium valproate blood concentrations lower than the standard concentration was established. The area under the curve (AUC), calibration curve, GiViTi calibration band, and clinical influence curve were used to evaluate the diagnostic efficacy and clinical application value of the model. Results. A total of 525 children with epilepsy were enrolled. In the development group, the random forest algorithm performed best in predicting that the blood concentration of sodium valproate was lower than the standard concentration, showing the highest AUC (1.00). Six factors were determined as a nomogram to predict the incidence of low concentrations. In the validation group and the development group, the calibration curve, GiViTi calibration band, and clinical influence curve all performed well in the evaluation of the diagnostic efficacy and clinical application value of the model. Conclusions. This finding highlights the importance of examining biochemical indices in patients when data regarding the blood concentration of sodium valproate are lacking.\",\"PeriodicalId\":15381,\"journal\":{\"name\":\"Journal of Clinical Pharmacy and Therapeutics\",\"volume\":\"243 1\",\"pages\":\"0\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2023-09-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Clinical Pharmacy and Therapeutics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1155/2023/9996553\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Clinical Pharmacy and Therapeutics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2023/9996553","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
Development and Internal Validation of Machine Learning Algorithms for Determining Sodium Valproate Concentrations below the Standard Level Using a Risk Prediction Model of Children with Epilepsy
Background. There is a narrow therapeutic window for sodium valproate, and the blood concentration is too low to control epilepsy, while it is easy to poison the body if the concentration is too high. It is therefore necessary to monitor drug concentration reasonably in order to control epilepsy. The purpose of this study was to establish a model for predicting concentrations of sodium valproate below 50 μg/mL in children with epilepsy. Methods. The clinical data and biochemical examination results of children with epilepsy treated in the pediatric outpatient department of our hospital from June 2019 to March 2022 were retrospectively collected and divided into a development group and a validation group according to a patient ratio of 8 to 2. Five machine learning algorithms were used to identify the key variable factors, and a risk prediction model for sodium valproate blood concentrations lower than the standard concentration was established. The area under the curve (AUC), calibration curve, GiViTi calibration band, and clinical influence curve were used to evaluate the diagnostic efficacy and clinical application value of the model. Results. A total of 525 children with epilepsy were enrolled. In the development group, the random forest algorithm performed best in predicting that the blood concentration of sodium valproate was lower than the standard concentration, showing the highest AUC (1.00). Six factors were determined as a nomogram to predict the incidence of low concentrations. In the validation group and the development group, the calibration curve, GiViTi calibration band, and clinical influence curve all performed well in the evaluation of the diagnostic efficacy and clinical application value of the model. Conclusions. This finding highlights the importance of examining biochemical indices in patients when data regarding the blood concentration of sodium valproate are lacking.
期刊介绍:
The Journal of Clinical Pharmacy and Therapeutics provides a forum for clinicians, pharmacists and pharmacologists to explore and report on issues of common interest. Reports and commentaries on current issues in medical and pharmaceutical practice are encouraged. Papers on evidence-based clinical practice and multidisciplinary collaborative work are particularly welcome. Regular sections in the journal include: editorials, commentaries, reviews (including systematic overviews and meta-analyses), original research and reports, and book reviews. Its scope embraces all aspects of clinical drug development and therapeutics, including:
Rational therapeutics
Evidence-based practice
Safety, cost-effectiveness and clinical efficacy of drugs
Drug interactions
Clinical impact of drug formulations
Pharmacogenetics
Personalised, stratified and translational medicine
Clinical pharmacokinetics.