Department of Child Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.
Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.
Epilepsia. 2018 Mar;59(3):e28-e33. doi: 10.1111/epi.14020. Epub 2018 Feb 15.
The objective of this study was to create a clinically useful tool for individualized prediction of seizure outcomes following antiepileptic drug withdrawal after pediatric epilepsy surgery. We used data from the European retrospective TimeToStop study, which included 766 children from 15 centers, to perform a proportional hazard regression analysis. The 2 outcome measures were seizure recurrence and seizure freedom in the last year of follow-up. Prognostic factors were identified through systematic review of the literature. The strongest predictors for each outcome were selected through backward selection, after which nomograms were created. The final models included 3 to 5 factors per model. Discrimination in terms of adjusted concordance statistic was 0.68 (95% confidence interval [CI] 0.67-0.69) for predicting seizure recurrence and 0.73 (95% CI 0.72-0.75) for predicting eventual seizure freedom. An online prediction tool is provided on www.epilepsypredictiontools.info/ttswithdrawal. The presented models can improve counseling of patients and parents regarding postoperative antiepileptic drug policies, by estimating individualized risks of seizure recurrence and eventual outcome.
本研究旨在为抗癫痫药物停药后儿童癫痫手术后癫痫发作的个体化预测创建一种临床有用的工具。我们使用了来自欧洲回顾性 TimeToStop 研究的数据，该研究包括来自 15 个中心的 766 名儿童，进行了比例风险回归分析。2 个结局指标是随访最后一年的癫痫复发和癫痫无发作。通过文献系统回顾确定预后因素。通过向后选择选择每个结局的最强预测因素，然后创建列线图。最终模型每个模型包含 3 到 5 个因素。调整后的一致性统计量的判别度为 0.68（95%置信区间 [CI] 0.67-0.69），用于预测癫痫复发，为 0.73（95%CI 0.72-0.75），用于预测最终的癫痫无发作。在线预测工具可在 www.epilepsypredictiontools.info/ttswithdrawal 上获得。所提出的模型可以通过估计个体癫痫复发和最终结局的风险，改善患者和家长对术后抗癫痫药物治疗方案的咨询。
