小儿热性惊厥发展为癫痫的危险因素及预测模型的构建

doi:10.11958/20252008

天津医药 ›› 2026, Vol. 54 ›› Issue (1): 52-57.doi: 10.11958/20252008

小儿热性惊厥发展为癫痫的危险因素及预测模型的构建

程云¹(), 夏明农¹, 张帆¹, 李凤²^,^△()

1 安徽医科大学附属六安医院（六安市人民医院）儿科（邮编237000）
2 安徽医科大学附属六安医院（六安市人民医院）神经内科（邮编237000）

收稿日期:2025-05-19 修回日期:2025-08-12 出版日期:2026-01-15 发布日期:2026-01-19
通讯作者: ^△ E-mail：114634569@qq.com
作者简介:程云（1983），女，副主任医师，主要从事儿科疾病临床诊疗方面研究。E-mail：vania_1736000@163.com
基金资助:
六安市科技计划项目(2022lakj024)

Risk factors for the development of febrile convulsions in children into epilepsy and the construction of the predictive model

CHENG Yun¹(), XIA Mingnong¹, ZHANG Fan¹, LI Feng²^,^△()

1 Department of Pediatrics, Lu'an Hospital Affiliated to Anhui Medical University (Lu'an People's Hospital), Lu'an 237000, China
2 Department of Neurology, Lu'an Hospital Affiliated to Anhui Medical University (Lu'an People's Hospital), Lu'an 237000, China

Received:2025-05-19 Revised:2025-08-12 Published:2026-01-15 Online:2026-01-19
Contact: ^△ E-mail：114634569@qq.com

程云, 夏明农, 张帆, 李凤. 小儿热性惊厥发展为癫痫的危险因素及预测模型的构建[J]. 天津医药, 2026, 54(1): 52-57.

CHENG Yun, XIA Mingnong, ZHANG Fan, LI Feng. Risk factors for the development of febrile convulsions in children into epilepsy and the construction of the predictive model[J]. Tianjin Medical Journal, 2026, 54(1): 52-57.

摘要/Abstract

摘要：

目的基于Logistic回归和决策树模型分析小儿热性惊厥发展为癫痫的危险因素并构建预测模型。方法选取210例首次发生热性惊厥的患儿，并进行半年随访（是否发生癫痫），最终完成随访者196例，36例（18.37%）热性惊厥患儿出现癫痫。根据患儿是否出现癫痫分为癫痫组（36例）和非癫痫组（160例）。所有患儿在热性惊厥发作后72 h内完成首次脑电图检查，并于3个月后复查。收集患儿的临床资料，包括性别、发病年龄、出生体质量、母亲生育时年龄、分娩方式、贫血情况、首次惊厥性质、惊厥发生次数、首次发作状态、首次惊厥距发热时间、惊厥持续时间（取最长值）、癫痫家族病史、脑电图结果。采用多因素Logistic回归分析热性惊厥发展为癫痫的影响因素；运用Modeler软件构建预测热性惊厥患儿发展为癫痫决策树风险预测模型；绘制受试者工作特征（ROC）曲线，比较不同模型的曲线下面积（AUC）。结果与非癫痫组比较，癫痫组首次惊厥为复杂性惊厥、惊厥发生次数≥2次、首次惊厥距发热时间<24 h、惊厥持续时间≥15 min、脑电图异常占比更高（P<0.01）。多因素Logistic回归分析结果显示，复杂性惊厥、惊厥发生次数≥2次、首次惊厥距发热时间<24 h、惊厥持续时间≥15 min、脑电图异常是热性惊厥患儿发展为癫痫的危险因素（P<0.05）。决策树模型筛选出惊厥发生次数、首次惊厥距发热时间、首次惊厥性质、脑电图为热性惊厥患儿发展为癫痫的重要变量，信息增益依次为0.47、0.27、0.14、0.13。多因素Logistic模型的AUC值为0.838（95%CI：0.764~0.911），决策树模型的AUC值为0.849（95%CI：0.780~0.916），两种模型的AUC比较差异无统计学意义。结论基于决策树算法和Logistic回归模型相结合的方法对识别热性惊厥发展为癫痫的危险因素具有一定的预测价值，可为临床提供参考。

关键词: 惊厥, 发热性, 癫痫, 决策树, Logistic模型, 脑电描记术, 危险因素

Abstract:

Objective To analyze the risk factors for the development of epilepsy in children with febrile convulsions based on Logistic regression and decision tree models and to construct a predictive model. Methods A total of 210 children with their first occurrence of febrile convulsion were selected and followed up for half a year (whether epilepsy occurred or not). Eventually, 196 cases completed the follow-up, and 36 cases (18.37%) of children with febrile convulsions developed epilepsy. Children were divided into the epilepsy group (36 cases) and the non-epilepsy group (160 cases) based on whether they developed epilepsy or not. All children underwent the first electroencephalogram (EEG) examination within 72 hours after the febrile convulsion and a follow-up EEG examination three months later. The clinical data of the children were collected, including gender, age of onset, birth weight, maternal age at delivery, mode of delivery, anemia status, nature of the first convulsion, number of convulsions, status at the first convulsion, time from fever to the first convulsion, duration of convulsion (taking the longest value), family history of epilepsy and EEG results. Multivariate Logistic regression was used to analyze influencing factors of febrile convulsions developing into epilepsy. Modeler software was used to construct a decision tree risk prediction model for predicting the development of epilepsy in children with febrile convulsions. The receiver operating characteristic (ROC) curve was drawn, and the area under the curve (AUC) of different models was compared. Results Compared with the non-epilepsy group, there were higher proportion of complex febrile convulsions, the number of seizures was ≥2 times, time from fever to the first convulsion was<24 hours, duration of convulsion was ≥ 15 minutes and abnormal EEG results were higher in the epilepsy group (P<0.01). Multivariate Logistic regression analysis showed that complex febrile convulsions, the frequency of convulsions≥2 times, time from fever to the first convulsion<24 hours, convulsion duration ≥ 15 minutes and abnormal EEG were risk factors for the development of epilepsy in children with febrile convulsions (P<0.05). The decision tree model selected the number of convulsions, time from fever to the first convulsion, nature of the first convulsion and EEG as important variables for the development of epilepsy in children with febrile convulsions were important variables for the development of epilepsy in children with febrile convulsions, and with information gains of 0.47, 0.27, 0.14 and 0.13, respectively. The AUC value of the multivariate Logistic model was 0.838 (95%CI: 0.764-0.911), and the AUC value of the decision tree model was 0.849 (95%CI: 0.780-0.916). There was no significant difference in AUC between the two models. Conclusion The method combining decision tree algorithm and Logistic regression model to identify the risk factors for the development of epilepsy in children with febrile convulsions has certain predictive value and can provide a reference for clinical practice.

Key words: seizures, febrile, epilepsy, decision trees, Logistic models, electroencephalography, risk factors

中图分类号:

R441.3

图/表 5

参考文献 18

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组别			n	男性		发病年龄 <1岁			出生体质量 2 500~4 000 g			母亲生育时年龄<30岁
非癫痫组			160	91（56.88）		86（53.75）			93（58.13）			119（74.38）
癫痫组			36	21（58.33）		19（52.78）			24（66.67）			28（77.78）
χ²				0.026		0.011			0.891			0.181
组别	剖宫产				贫血			首次惊厥性质复杂性		惊厥发生次数≥2次			首次发作状态清醒
非癫痫组	46（28.75）				11（6.88）			56（35.00）		12（7.50）			141（88.13）
癫痫组	12（33.33）				4（11.11）			23（63.89）		11（30.56）			29（80.56）
χ²	0.296				0.746			10.194^＊＊		15.082^＊＊			1.463
组别		首次惊厥距发热时间<24 h					惊厥持续时间≥15 min				癫痫家族病史			脑电图异常
非癫痫组		43（26.88）					44（27.50）				10（6.25）			62（38.75）
癫痫组		21（58.33）					20（55.56）				2（5.56）			26（72.22）
χ²		13.225^＊＊					10.519^＊＊				0.050			13.309^＊＊

组别			n	男性		发病年龄 <1岁			出生体质量 2 500~4 000 g			母亲生育时年龄<30岁
非癫痫组			160	91（56.88）		86（53.75）			93（58.13）			119（74.38）
癫痫组			36	21（58.33）		19（52.78）			24（66.67）			28（77.78）
χ²				0.026		0.011			0.891			0.181
组别	剖宫产				贫血			首次惊厥性质复杂性		惊厥发生次数≥2次			首次发作状态清醒
非癫痫组	46（28.75）				11（6.88）			56（35.00）		12（7.50）			141（88.13）
癫痫组	12（33.33）				4（11.11）			23（63.89）		11（30.56）			29（80.56）
χ²	0.296				0.746			10.194^＊＊		15.082^＊＊			1.463
组别		首次惊厥距发热时间<24 h					惊厥持续时间≥15 min				癫痫家族病史			脑电图异常
非癫痫组		43（26.88）					44（27.50）				10（6.25）			62（38.75）
癫痫组		21（58.33）					20（55.56）				2（5.56）			26（72.22）
χ²		13.225^＊＊					10.519^＊＊				0.050			13.309^＊＊

变量	β	SE	Wald χ²	P	OR 95%CI
首次惊厥性质	1.367	0.453	9.106	0.003	3.924（1.61~59.535）
惊厥发生次数	1.387	0.555	6.250	0.012	4.001（1.349~11.867）
首次惊厥距发热时间	1.092	0.438	6.201	0.013	2.979（1.262~7.035）
惊厥持续时间	0.988	0.444	4.960	0.026	2.687（1.126~6.411）
脑电图	1.556	0.472	10.885	0.001	4.742（1.881~11.955）
常数项	-4.121	0.581	50.318	<0.001	0.016

变量	β	SE	Wald χ²	P	OR 95%CI
首次惊厥性质	1.367	0.453	9.106	0.003	3.924（1.61~59.535）
惊厥发生次数	1.387	0.555	6.250	0.012	4.001（1.349~11.867）
首次惊厥距发热时间	1.092	0.438	6.201	0.013	2.979（1.262~7.035）
惊厥持续时间	0.988	0.444	4.960	0.026	2.687（1.126~6.411）
脑电图	1.556	0.472	10.885	0.001	4.742（1.881~11.955）
常数项	-4.121	0.581	50.318	<0.001	0.016

小儿热性惊厥发展为癫痫的危险因素及预测模型的构建

Risk factors for the development of febrile convulsions in children into epilepsy and the construction of the predictive model

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模型	AUC	95%CI	敏感度/%	特异度/%	约登指数
Logistic模型	0.838	0.764~0.911	65.89	90.00	0.539
决策树模型	0.849	0.780~0.916	79.64	88.75	0.554

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