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

IF 2.1 4区 医学 Q3 PHARMACOLOGY & PHARMACY
Yinhui Yao, Jingyi Zhao, Ying Wang, Wei Qiu, Yingxue Lin, Yaru Zang
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引用次数: 0

摘要

背景。丙戊酸钠的治疗窗口期较窄,血药浓度过低无法控制癫痫,而浓度过高又容易对机体造成毒害。因此,合理监测药物浓度是控制癫痫的必要措施。本研究旨在建立癫痫患儿丙戊酸钠浓度低于50 μg/mL的预测模型。方法。回顾性收集2019年6月至2022年3月在我院儿科门诊就诊的癫痫患儿的临床资料及生化检查结果,按8比2的比例分为发展组和验证组。采用5种机器学习算法识别关键变量因素,建立丙戊酸钠血药浓度低于标准浓度的风险预测模型。采用曲线下面积(AUC)、校正曲线、GiViTi校正带、临床影响曲线评价模型的诊断效果及临床应用价值。结果。共有525名癫痫患儿被纳入研究。在发育组中,随机森林算法在预测丙戊酸钠血药浓度低于标准浓度方面表现最好,AUC最高(1.00)。确定6个因素作为预测低浓度发生率的nomogram。验证组和开发组的校正曲线、GiViTi校正带、临床影响曲线在评价模型的诊断疗效和临床应用价值方面均表现较好。结论。这一发现强调了在缺乏丙戊酸钠血药浓度数据时检查患者生化指标的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
CiteScore
4.10
自引率
5.00%
发文量
226
审稿时长
6 months
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.
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