| 儿童热性惊厥风险列线图预测模型的构建及验证 |
| 投稿时间：2024-08-13 修订日期：2024-10-14 点此下载全文 |
| 引用本文：王称,赵美韬,张伟.儿童热性惊厥风险列线图预测模型的构建及验证[J].医学研究杂志,2025,54(3):73-79 |
| DOI：
10.11969/j.issn.1673-548X.2025.03.014 |
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| 基金项目:甘肃省自然科学基金资助项目(22JR11RA174) |
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| 中文摘要:目的 探讨儿童热性惊厥(febrile seizures, FS)的危险因素,构建FS可视化列线图预测模型并验证其效能。方法回顾性分析2019年1月～2022年12月甘肃省妇幼保健院(甘肃省中心医院)收治的1320例发热患儿的临床资料,以7∶3的比例随机分为训练集和验证集。比较两组患者的临床特征及实验室检查情况,应用LASSO回归筛选FS发生的危险因素,对预测变量进行多因素Logistic回归分析,构建FS预测模型并评价模型的区分度、校准度和临床适用性。结果 年龄、低体重、峰值体温、发病昼夜变化、上呼吸道感染、血钠、血钙、白细胞计数、降钙素原为儿童FS的危险因素,并以此构建预测模型及绘制列线图。训练集、验证集受试者工作特征(receiver operating characteristic,ROC)曲线下面积分别为0.870(95% CI:0.847～0.893)、0.855(95% CI:0.817～0.894)。校准曲线和Hosmer-Lemeshow拟合优度检验显示模型的预测准确性较高。决策曲线分析(decision curve analysis,DCA)显示训练集阈值概率>5%,验证集的阈值概率>8%时模型具有良好的净收益。结论 基于LASSO-Logistic回归分析法构建的预测儿童FS的列线图风险预测模型,可为FS患儿发生风险的早期评估提供参考依据。 |
| 中文关键词:热性惊厥 儿童 预测模型 列线图 |
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| Construction and Validation of A Nomogram Prediction Model of Febrile Seizure in Children. |
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| Abstract:Objective To investigate the risk factors of febrile seizures (FS) in children, construct a visualization nomogram prediction model of FS and verify its effectiveness. Methods A retrospective analysis was conducted on the clinical data of 1320 children with fever admitted to the Gansu Provincial Maternity and Child-care Hospital (Gansu Provincial Central Hospital) from January 2019 to December 2022. The samples were randomly divided into the training set and the validation set at a ratio of 7∶3. The clinical characteristics and laboratory examination of the two groups were compared. LASSO regression was used to select the risk factors for FS, and multivariate Logistic regression analysis was performed on the predictors, and construction a prediction mode, and the discrimination, calibration and clinical applicability of the model were evaluated. Results The results showed that age, low body weight, peak temperature, diurnal changes in onset, upper respiratory tract infection, serum sodium, serum calcium, white blood cell, procalcitonin were independent risk factors for FS in children. A predictive model was constructed and a nomogram was developed with these factors. The area under the receiver operating characteristic (ROC) curve of the training set and the validation set were 0.870 (95% CI:0.847-0.893) and 0.855 (95% CI:0.817-0.894), respectively. The calibration plots and Hosmer-Lemeshow goodness-of-fit test showed its satisfactory calibration. The decision curve analysis (DCA) curve showed that the model provided a good net benefit with threshold probabilities when training set was >5%, while in the validation set it was > 8%. Conclusion The risk prediction model based on LASSO-Logistic regression analysis can provide reference for early risk assessment of children with FS. |
| keywords:Febrile seizure Children Prediction model Nomograms |
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