| 摘要: |
| 目的:探讨癫痫患儿停药后复发的危险因素,并基于临床特征及视频脑电图(VEEG)建立风险预测列线图模型。方法:
回顾性分析318 例癫痫患儿的临床资料,按2 ∶ 1 的比例随机分为建模组和验证组。根据建模组停药后复发情况分为复发组和
非复发组,并比较两组患儿临床资料。分析癫痫患儿停药后复发的危险因素,据此建立风险预测列线图模型,并验证其预测效
能。结果:癫痫患儿停药后复发率为29. 25%(93/ 318)。多因素logistic 回归模型分析显示发病年龄小、治疗前发作频率高、停
药时机短、停药时程≤6 个月、停药前VEEG 异常、联合用药均是癫痫患儿停药后复发的危险因素(P<0. 05)。受试者工作特征
(ROC)曲线分析显示,建模组、验证组列线图模型预测的曲线下面积( AUC) 分别为0. 865、0. 857;一致性指数分别为0. 823、
0. 811,校准曲线与理想曲线基本一致(χ2 =2. 545,P =0. 111)。决策曲线分析(DCA)显示,建模组、验证组列线图模型阈值概率
分别为0. 13~1. 00、0. 15~0. 81 时具有正的临床净收益。结论:发病年龄小、治疗前发作频率高、停药时机短、停药时程≤6 个
月、停药前VEEG 异常、联合用药均是癫痫患儿停药后复发的危险因素,据此建立的风险预测列线图模型具有较高的预测效能。 |
| 关键词: 临床特征 视频脑电图 癫痫 复发 列线图 |
| DOI：doi:10.13407/j.cnki.jpp.1672-108X.2025.05.002 |
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| 基金项目: |
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| Prediction Model Establishment and Efficacy Evaluation of Recurrence in Children with Epilepsy AfterDrug Withdrawal Based on Clinical Characteristics and Video Electroencephalography |
| Kuang Meili, Cao Huanzhen, Chen Huanhuan, Li Wenya, Wang Xiaoxing |
| (Henan University of Science and Technology
Affiliated Yellow River Hospital / Huanghe Sanmenxia Hospital, Henan 472000, China) |
| Abstract: |
| Objective: To explore the risk factors for recurrence in children with epilepsy after drug withdrawal, and to establish a risk
prediction nomogram model based on clinical characteristics and video electroencephalogram (VEEG). Methods: Clinical data of 318
children with epilepsy were retrospectively analyzed, and the children were randomly divided into modeling group and validation group in
a 2 ∶ 1 ratio. The model group was divided into recurrence group and non-recurrence group according to the recurrence situation after
drug withdrawal, and the clinical data of two groups were compared. Risk factors for recurrence of epilepsy children after drug
withdrawal was analyzed, a risk prediction nomogram model was established, and the prediction efficiency was verified. Results: The
recurrence rate of epilepsy children after drug withdrawal was 29. 25% (93/ 318). Multivariate logistic regression model analysis showed
that young onset age, high frequency of epilepsy before treatment, short timing of drug withdrawal, drug withdrawal duration ⩽ 6
months, abnormal VEEG before drug withdrawal and combination therapy were all risk factors for recurrence in children with epilepsy
after drug withdrawal (P <0. 05). Receiver operating characteristic (ROC) analysis showed that the area under the curve (AUC)
predicted by the nomogram models for the modeling group and validation group was respectively 0. 865 and 0. 857. The consistency
indices were respectively 0. 823 and 0. 811, and the calibration curve was basically consistent with the ideal curve (χ2 = 2. 545, P =
0. 111). The decision curve analysis (DCA) curve showed that the modeling group and validation group had positive clinical net benefits
when the threshold probabilities of the nomogram models were respectively 0. 13 to 1. 00 and 0. 15 to 0. 81. Conclusion: Young onset
age, high frequency of epilepsy before treatment, short timing of drug withdrawal, drug withdrawal duration ⩽ 6 months, abnormal
VEEG before drug withdrawal, and combination therapy are all risk factors for recurrence in children with epilepsy after drug withdrawal,
and the risk prediction nomogram model established based on this has high predictive value. |
| Key words: clinical characteristics video electroencephalogram epilepsy recurrence nomogram |
