tetano
Editor, Senior Moderator
Clin Ther
. 2024 Dec 16:S0149-2918(24)00364-3.
doi: 10.1016/j.clinthera.2024.11.016. Online ahead of print. Analysis of Clinical Characteristics and Risk Factors for Severe Influenza A and Influenza B in Children
Peng Li[SUP] 1 [/SUP], Chang-Qing Li[SUP] 2 [/SUP], Na Chen[SUP] 3 [/SUP], Yu Jing[SUP] 3 [/SUP], Xue Zhang[SUP] 1 [/SUP], Rui-Yang Sun[SUP] 1 [/SUP], Wan-Yu Jia[SUP] 1 [/SUP], Shu-Qin Fu[SUP] 1 [/SUP], Chun-Lan Song[SUP] 4 [/SUP]
Affiliations
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.
Keywords: Dynamic nomogram; Influencing factors; Influenza; Logistic regression model analysis; Receiver operating characteristic curve.
. 2024 Dec 16:S0149-2918(24)00364-3.
doi: 10.1016/j.clinthera.2024.11.016. Online ahead of print. Analysis of Clinical Characteristics and Risk Factors for Severe Influenza A and Influenza B in Children
Peng Li[SUP] 1 [/SUP], Chang-Qing Li[SUP] 2 [/SUP], Na Chen[SUP] 3 [/SUP], Yu Jing[SUP] 3 [/SUP], Xue Zhang[SUP] 1 [/SUP], Rui-Yang Sun[SUP] 1 [/SUP], Wan-Yu Jia[SUP] 1 [/SUP], Shu-Qin Fu[SUP] 1 [/SUP], Chun-Lan Song[SUP] 4 [/SUP]
Affiliations
- PMID: 39690019
- DOI: 10.1016/j.clinthera.2024.11.016
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.
Keywords: Dynamic nomogram; Influencing factors; Influenza; Logistic regression model analysis; Receiver operating characteristic curve.
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