Risk factors for drug-resistant epilepsy in adult patients / Factores de riesgo de epilepsia farmacorresistente en pacientes adultos

Introduction Drug-resistant epilepsy occurs in about 30% of epilepsy patients. It has been suggested that etiology or seizure type would increase the risk of pharmacoresistance. This study aims to compare the characteristics of patients with drug-sensitive epilepsy with patients with drug-resistant epilepsy to identify risk factors. Patient and methods A multicentric cohort study was conducted between 2019 and 2022. We included patients >18 years-old with epilepsy but excluded psychogenic non-epileptic seizures and less than 2 years of follow-up. Results We included 128 patients, of whom 46 had drug-resistance epilepsy, and 82 responding to medication. Both groups showed similar characteristics. Febrile seizures (OR: 7.25), focal epilepsy (OR: 2.4), focal seizures with loss of consciousness (OR: 2.36), structural etiology (OR: 2.2) and abnormal MRI (OR: 4.6) were significant risk factors for drug-resistance epilepsy. Conclusion Following other studies, we observed that factors such as epilepsy type, seizure type, structural etiology, abnormal MRI, and febrile seizure increased the risk for drug-resistance epilepsy, in our population (AU)

Introducción La epilepsia farmacorresistente se presenta en aproximadamente 30% de los pacientes que padecen epilepsia. Se ha sugerido que la etiología o el tipo de crisis aumentarían el riesgo de farmacorresistencia. El objetivo de este estudio es comparar las características de los pacientes con epilepsia fármacosensible con las de los pacientes con epilepsia farmacorresistente para identificar los factores de riesgo. Pacientes y métodos Se realizó un estudio de cohorte multicéntrico entre 2019 y 2022. Se incluyeron pacientes >18 años con epilepsia pero se excluyeron las crisis psicógenas no epilépticas y menos de dos años de seguimiento. Resultados Se incluyeron 128 pacientes, de los cuales 46 tenían epilepsia farmacorresistente y 82 respondían a la medicación. Ambos grupos mostraron características similares. Las crisis febriles (OR: 7,25), la epilepsia focal (OR: 2,4), las crisis focales con pérdida de conciencia (OR: 2,36), la etiología estructural (OR: 2,2) y la resonancia magnética anormal (OR: 4,6) fueron factores de riesgo significativos de epilepsia farmacorresistente. Conclusión Siguiendo otros estudios, observamos que factores como el tipo de epilepsia, el tipo de crisis, la etiología estructural, la RM anormal y las crisis febriles aumentaban el riesgo de epilepsia farmacorresistente, en nuestra población (AU)

Risk factors for drug-resistant epilepsy in adult patients.

INTRODUCTION: Drug-resistant epilepsy occurs in about 30% of epilepsy patients. It has been suggested that etiology or seizure type would increase the risk of pharmacoresistance. This study aims to compare the characteristics of patients with drug-sensitive epilepsy with patients with drug-resistant epilepsy to identify risk factors. PATIENT AND METHODS: A multicentric cohort study was conducted between 2019 and 2022. We included patients >18 years-old with epilepsy but excluded psychogenic non-epileptic seizures and less than 2 years of follow-up. RESULTS: We included 128 patients, of whom 46 had drug-resistance epilepsy, and 82 responding to medication. Both groups showed similar characteristics. Febrile seizures (OR: 7.25), focal epilepsy (OR: 2.4), focal seizures with loss of consciousness (OR: 2.36), structural etiology (OR: 2.2) and abnormal MRI (OR: 4.6) were significant risk factors for drug-resistance epilepsy. CONCLUSION: Following other studies, we observed that factors such as epilepsy type, seizure type, structural etiology, abnormal MRI, and febrile seizure increased the risk for drug-resistance epilepsy, in our population.

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

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.