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

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.