| 摘要: |
| 目的:建立个体化预测儿童川崎病(KD)发生风险列线图模型,为临床诊断决策提供参考。方法:本研究共纳入253 例患
儿(101 例KD 发热患儿和152 例非KD 发热患儿),从中随机选取187 例作为建模组,66 例作为验证组,收集临床资料。应用单
因素及多因素Logistic 回归模型,分析发热患儿发生KD 的影响因素。应用R 软件建立预测KD 发生风险的列线图模型,并进行
验证。采用ROC 曲线下面积( ROC-AUC) 来评价模型预测效能。结果:淋巴细胞计数、C 反应蛋白( CRP)、红细胞沉降率
(ESR)、血小板计数(PLT)、大血小板百分比是儿童KD 发生风险的独立影响因素。对列线图模型进行内部验证,校准曲线没有
明显偏离拟合,显示该模型具有良好的区分度与精准度。该模型在建模组与验证组的ROC-AUC 分别为0. 969 和0. 915。结论:
本研究基于儿童KD 发生风险的影响因素,构建了预测儿童KD 发生风险的列线图模型,预测效率高,能为临床早期筛选高风险
人群、尽早制订干预对策提供参考。 |
| 关键词: 川崎病 列线图 临床参数 儿童 |
| DOI：doi:10.13407/j.cnki.jpp.1672-108X.2023.02.008 |
|
| 基金项目:四川省出生缺陷临床医学研究中心2020 年开放课题,编号2019YFS0531。 |
|
| Development and Validation of an Individualized Nomogram to Predict Risk of Kawasaki Disease in Children |
| Wang Dan1, Chen Ai1, 2 |
| ((1. Affiliated Hospital of Southwest Medical University, Sichuan Luzhou 646000, China; 2. Sichuan
Provincial Hospital for Women and Children, Chengdu 610031, China)) |
| Abstract: |
| Objective: To develop a nomogram model to predict the risk of Kawasaki disease (KD) that can inform clinical diagnostic
decision making. Methods: A total of 253 children (101 KD and 152 non-KD febrile illnesses) were included in this study. We
randomly selected 187 children as the modeling cohort and 66 as the validation cohort for clinical data collection. Univariate and
multivariate Logistic regression models were used to analyze the influencing factors of KD in children with fever. A nomogram was
developed by R software and validated to predict the risk of KD in children with fever. The area under the ROC curve (ROC-AUC) was
used to assess the clinical efficacy of the nomogram. Results: Lymphocyte count, C-reactive protein (CRP), erythrocyte sedimentation
rate (ESR), platelet count (PLT), and the percentage of large platelets were the independent factors influencing the risk of KD in
children. The internal verification of the nomogram showed that the calibration curve did not deviate significantly from the fit, indicating
that the model has good discrimination and accuracy. The ROC-AUC of the modeling group and validation group was 0. 969 and 0. 915,
respectively. Conclusion: Based on the risk factors of KD, this study has developed a nomogram model for predicting the risk of KD.
The nomogram model with good predictive efficiency can provide reference for screening high-risk populations in the early stage of
clinical practice, and formulate intervention strategies as soon as possible. |
| Key words: Kawasaki disease nomogram clinical parameter children |
