儿童严重甲型流感和乙型流感的临床特征和风险因素分析。

IF 3.2 4区 医学 Q2 PHARMACOLOGY & PHARMACY Clinical therapeutics Pub Date : 2024-12-16 DOI:10.1016/j.clinthera.2024.11.016
Peng Li, Chang-Qing Li, Na Chen, Yu Jing, Xue Zhang, Rui-Yang Sun, Wan-Yu Jia, Shu-Qin Fu, Chun-Lan Song
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引用次数: 0

摘要

目的:本研究的目的是开发并验证用于甲型和乙型流感早期鉴别诊断的在线动态图系统。方法:以河南省儿童医院2019年1月至2022年1月收治的甲型和乙型重症流感患者为建模组(n = 161),以2023年1月至9月收治的甲型和乙型流感患者为验证组(n = 52)。采用单因素logistic回归和多因素logistic回归确定建模组儿童甲型和乙型重症流感的危险变量。选取变量构建nomogram,采用C-index、决策曲线分析、校准曲线、受试者工作特征曲线评价模型的差异性、模型的校准性,并结合验证组数据对上述模型进行外部验证。结果:发热3天、呕吐、淋巴细胞计数(LY)和从发病到住院的持续时间是鉴定严重甲型和乙型流感的独立因素。我们创建了一个动态图(https://ertong.shinyapps.io/influenza/),可在线访问。c指数为0.92。在建模组,预测模型的AUC为0.92 (95% CI, 0.87-0.98),校正曲线显示预测概率与实际概率拟合良好,具有较高的可比性,决策曲线分析显示nomogram模型具有显著的临床效益。将该模型应用于外部验证,预测验证组的AUC为0.749 (95% CI为0.61 ~ 0.88),验证结果与实际吻合较好。结论:发热3天、呕吐、淋巴细胞计数、发病至住院时间对甲型和乙型流感的鉴别有影响,该模型的预测值和临床效果令人满意。
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Analysis of Clinical Characteristics and Risk Factors for Severe Influenza A and Influenza B in Children.

Purpose: The goal of this study was to develop and validate an online dynamic nomogram system for early differential diagnosis of influenza A and B.

Methods: Patients with severe influenza A and B admitted to Henan Children's Hospital from January 2019 to January 2022 were used as the modeling group (n = 161), and patients admitted from January to September 2023 were used as the validation group (n = 52). Univariate logistic regression and multivariate logistic regression were used to identify the risk variables of severe influenza A and B in children in the modeling group. The selected variables were used to build the nomogram, and the C-index, decision curve analysis, calibration curves, and receiver operating characteristic curves were used to assess the differentiation, calibration of the models, and external validation of the above models with validation group data.

Findings: Fever for >3 days, vomiting, lymphocyte count (LY), and duration from onset to hospitalization were independent factors for the identification of severe influenza A and B. We created a dynamic nomogram (https://ertong.shinyapps.io/influenza/) that can be accessed online. The C-index was 0.92. In the modeling group, the AUC of the prediction model was 0.92 (95% CI, 0.87-0.98), the calibration curve showed a good fit between the predicted probability and the actual probability, with high comparability, and the decision curve analysis showed that the nomogram model had significant clinical benefits. The application of this model in external verification predicts that the AUC of the verification group is 0.749 (95% CI, 0.61-0.88), and the validation results were in good agreement with reality.

Implications: Fever for >3 days, vomiting, lymphocyte count, and duration from onset to hospitalization have an impact on the differentiation of severe influenza A from severe influenza B. The prediction value and clinical benefit of the nomogram model are satisfactory.

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来源期刊
Clinical therapeutics
Clinical therapeutics 医学-药学
CiteScore
6.00
自引率
3.10%
发文量
154
审稿时长
9 weeks
期刊介绍: Clinical Therapeutics provides peer-reviewed, rapid publication of recent developments in drug and other therapies as well as in diagnostics, pharmacoeconomics, health policy, treatment outcomes, and innovations in drug and biologics research. In addition Clinical Therapeutics features updates on specific topics collated by expert Topic Editors. Clinical Therapeutics is read by a large international audience of scientists and clinicians in a variety of research, academic, and clinical practice settings. Articles are indexed by all major biomedical abstracting databases.
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