Li Peng, Li Chang-Qing, Chen Na, Jing Yu, Zhang Xue, Sun Rui-Yang, Jia Wan-Yu, Fu Shu-Qin, Song Chun-Lan
Children's Hospital Affiliated to Zhengzhou University, Henan Children's Hospital, Zhengzhou Children's Hospital, Zhengzhou, China.
Xinzheng Huaxin People's Hospital, Zhengzhou, China.
Clin Ther. 2025 Feb;47(2):123-127. doi: 10.1016/j.clinthera.2024.11.016. Epub 2024 Dec 16.
The goal of this study was to develop and validate an online dynamic nomogram system for early differential diagnosis of influenza A and B.
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.
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.
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.
本研究旨在开发并验证一种用于甲型和乙型流感早期鉴别诊断的在线动态列线图系统。
将2019年1月至2022年1月入住河南儿童医院的甲型和乙型重症流感患者作为建模组(n = 161),将2023年1月至9月入住的患者作为验证组(n = 52)。采用单因素逻辑回归和多因素逻辑回归确定建模组儿童重症甲型和乙型流感的风险变量。将选定的变量用于构建列线图,并使用C指数、决策曲线分析、校准曲线和受试者工作特征曲线来评估模型的鉴别能力、校准情况以及用验证组数据对上述模型进行外部验证。
发热超过3天、呕吐、淋巴细胞计数(LY)以及发病至住院时间是鉴别重症甲型和乙型流感的独立因素。我们创建了一个可在线访问的动态列线图(https://ertong.shinyapps.io/influenza/)。C指数为0.92。在建模组中,预测模型的AUC为0.92(95%CI,0.87 - 0.98),校准曲线显示预测概率与实际概率之间拟合良好,具有较高的可比性,决策曲线分析表明列线图模型具有显著的临床益处。该模型在外部验证中的应用预测验证组的AUC为0.749(95%CI,0.61 - 0.88),验证结果与实际情况吻合良好。
发热超过3天、呕吐、淋巴细胞计数以及发病至住院时间对重症甲型流感与重症乙型流感的鉴别有影响。列线图模型的预测价值和临床益处令人满意。