Peng Li, Chang-Qing Li, Na Chen, Yu Jing, Xue Zhang, Rui-Yang Sun, Wan-Yu Jia, Shu-Qin Fu, Chun-Lan Song
{"title":"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。","authors":"Peng Li, Chang-Qing Li, Na Chen, Yu Jing, Xue Zhang, Rui-Yang Sun, Wan-Yu Jia, Shu-Qin Fu, Chun-Lan Song","doi":"10.1016/j.clinthera.2024.11.016","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>The goal of this study was to develop and validate an online dynamic nomogram system for early differential diagnosis of influenza A and B.</p><p><strong>Methods: </strong>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.</p><p><strong>Findings: </strong>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.</p><p><strong>Implications: </strong>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.</p>","PeriodicalId":10699,"journal":{"name":"Clinical therapeutics","volume":" ","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analysis of Clinical Characteristics and Risk Factors for Severe Influenza A and Influenza B in Children.\",\"authors\":\"Peng Li, Chang-Qing Li, Na Chen, Yu Jing, Xue Zhang, Rui-Yang Sun, Wan-Yu Jia, Shu-Qin Fu, Chun-Lan Song\",\"doi\":\"10.1016/j.clinthera.2024.11.016\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>The goal of this study was to develop and validate an online dynamic nomogram system for early differential diagnosis of influenza A and B.</p><p><strong>Methods: </strong>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.</p><p><strong>Findings: </strong>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.</p><p><strong>Implications: </strong>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.</p>\",\"PeriodicalId\":10699,\"journal\":{\"name\":\"Clinical therapeutics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical therapeutics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.clinthera.2024.11.016\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PHARMACOLOGY & PHARMACY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical therapeutics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.clinthera.2024.11.016","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
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.
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.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
