The Department of Pediatric Ward, Zhoushan Women and Children Hospital, No. 238 Renmin North Road, Dinghai District, Zhoushan, 316000, Zhejiang, China.
Eur J Pediatr. 2023 Nov;182(11):4875-4888. doi: 10.1007/s00431-023-05133-7. Epub 2023 Aug 19.
The purpose of this study is to develop a prediction nomogram of recurrent febrile seizures in pediatric children based on the identified predictors for developing recurrent febrile seizures. This is a retrospective observational study. The medical records of 320 febrile seizure-afflicted children admitted to Zhoushan Women and Children Hospital from March 2019 to January 2023 were retrospectively reviewed. Children were divided into the recurrent febrile seizures group and the non-recurrent febrile seizures group. The predictors of recurrent febrile seizures were identified by univariate and multivariate analyses. A prediction nomogram model was developed via R software. The performance of the nomogram was internally validated to assess the model's discrimination and consistency, and decision curve analysis was employed to assess clinical utility. There were 41 out of 320 cases that had recurrent febrile seizures during the observation period, with a 12.81% prevalence rate of recurrent febrile seizures. The predictors of recurrent febrile seizures were young age at the first febrile seizures, a family history of febrile seizures in a first-degree relative, diurnal variation of initial febrile seizures occurrence, gender, and a low level of C-reactive protein. The area under the receiver operating characteristic curve of the nomogram is 0.795 (95% confidence interval: 0.720-0.871). Calibration plots and the result of the Hosmer-Lemeshow test (P = 0.472) reveal satisfactory consistency. Decision curve analysis showed a significant net benefit of the nomogram.
The prediction nomogram model demonstrates good performance and clinical utility, which would be a convenient tool for the detection of children in pediatrics with high-risk recurrent febrile seizures. It is useful for pediatric medical staff to provide early medical interventions and family counseling.
• A proportion of children experience recurrences of febrile seizures. • Recognition of risk factors for recurrent FS in pediatrics would be useful for the prediction of risk probabilities and help provide tailored counseling and follow-up.
• A nomogram model is developed for risk prediction of recurrent febrile seizures in this study, which would be a convenient risk prediction tool in pediatrics. • The predictor of diurnal variation of recurrent febrile seizures is with new ideas.
本研究旨在建立一种基于复发性热性惊厥(FS)相关预测因子的预测列线图,以预测儿科热性惊厥患儿的复发。这是一项回顾性观察性研究。回顾性分析了 2019 年 3 月至 2023 年 1 月期间在舟山妇女儿童医院就诊的 320 例热性惊厥患儿的病历。患儿分为复发性热性惊厥组和非复发性热性惊厥组。通过单因素和多因素分析确定复发性热性惊厥的预测因子。使用 R 软件建立预测列线图模型。通过内部验证评估列线图的性能,以评估模型的区分度和一致性,并采用决策曲线分析评估临床实用性。在观察期间,320 例中有 41 例发生复发性热性惊厥,复发性热性惊厥的患病率为 12.81%。复发性热性惊厥的预测因子为首次热性惊厥时年龄较小、一级亲属有热性惊厥史、首发热性惊厥的昼夜变化、性别和 C 反应蛋白水平较低。列线图的受试者工作特征曲线下面积为 0.795(95%置信区间:0.720-0.871)。校准图和 Hosmer-Lemeshow 检验结果(P=0.472)显示一致性良好。决策曲线分析显示列线图具有显著的净获益。
预测列线图模型具有良好的性能和临床实用性,是儿科高危复发性热性惊厥患儿检测的便捷工具。它有助于儿科医务人员提供早期医疗干预和家庭咨询。
一部分儿童会出现热性惊厥复发。
识别儿科热性惊厥复发的危险因素有助于预测风险概率,并为提供针对性的咨询和随访提供依据。
本研究建立了复发性热性惊厥风险预测的列线图模型,是儿科风险预测的便捷工具。
热性惊厥昼夜变化的预测因子具有创新性。