Development and validation of a nomogram for the early prediction of drug resistance in children with epilepsy

Abstract

Background and purpose: This study aimed to effectively identify children with drug-resistant epilepsy (DRE) in the early stage of epilepsy, and take personalized interventions, to improve patients' prognosis, reduce serious comorbidity, and save social resources. Herein, we developed and validated a nomogram prediction model for children with DRE. Methods: The training set was patients with epilepsy who visited the Children's Hospital of Soochow University (Suzhou Industrial Park, Jiangsu Province, China) between January 2015 and December 2017. The independent risk factors for DRE were screened by univariate and multivariate logistic regression analyses using SPSS21 software. The nomogram was designed according to the regression coefficient. The nomogram was validated in the training and validation sets. Internal validation was conducted using bootstrapping analyses. We also externally validated this instrument in patients with epilepsy from the Children's Hospital of Soochow University (Gusu District, Jiangsu Province, China) and Yancheng Maternal and Child Health Hospital between January 2018 and December 2018. The nomogram's performance was assessed by concordance (C-index), calibration curves, as well as GiViTI calibration belts. Results: Multivariate logistic regression analysis of 679 children with epilepsy from the Children's Hospital of Soochow University (Suzhou Industrial Park, Jiangsu Province, China) showed that onset age<1, status epilepticus (SE), focal seizure, > 20 pre-treatment seizures, clear etiology (caused by genetic, structural, metabolic, or infectious), development and epileptic encephalopathy (DEE), and neurological abnormalities were all independent risk factors for DRE. The AUC of 0.92 for the training set compared to that of 0.91 for the validation set suggested a good discrimination ability of the prediction model. The C-index was 0.92 and 0.91 in the training and validation sets. Additionally, both good calibration curves and GiViTI calibration belts (P-value: 0.849 and 0.291, respectively) demonstrated that the predicted risks had strong consistency with the observed outcomes, suggesting that the prediction model in both groups was perfectly calibrated. Conclusion: A nomogram prediction model for DRE was developed, with good discrimination and calibration in the training set and the validation set. Furthermore, the model demonstrated great accuracy, consistency, and prediction ability. Therefore, the nomogram prediction model can aid in the timely identification of DRE in children.