BMC pediatrics · 2025
A simple, rapid, and cost-effective model for predicting critical influenza a infection in children: a multicentre, retrospective cohort study.
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Xiong Suwan, Guo Yun, Jiang Leihua, Wang Ying, Chen Weiming, Bai Zhenjiang, Zhu Pengwei, Qian Jun, Li Ling
Аннотация
BACKGROUND: Owing to the absence of straightforward and scientifically validated screening and evaluation tools for the timely identification, diagnosis, and treatment of critical influenza A infection in children, this study aimed to construct an effective model for the early identification of patients at high risk of progressing to critical influenza A infection.
METHODS: The prediction model was developed using the registration data of children diagnosed with influenza A who were admitted to Wuxi Children's Hospital, the Children's Hospital Affiliated to Soochow University and the Children's Hospital Affiliated to Fudan University. Patients were randomly divided into a training group and a validation group at a 7:3 ratio. A logistic regression model was established based on the least absolute shrinkage and selection operator to construct the nomogram. The performance of the nomogram was evaluated by the area under the characteristic curve (AUC), calibration ability, decision curve analysis (DCA) and clinical impact curve analysis (CICA).
RESULTS: A total of 170 hospitalized children with influenza A infection were identified, including 92 severe patients and 78 critical patients. The model was composed of the following five predictors: loss of appetite, seizure ≥ 2 times, altered neutrophil-to-lymphocyte ratios, haemoglobin levels, and total number of complications. The AUC of the model in the training set was 0.905, and the specificity and sensitivity were 91.1% and 77.8%, respectively. The score has been translated into an online risk calculator that is freely available to the public ( https://iavchildren.shinyapps.io/DynNomapp/ ).
CONCLUSION: This predictive model, which is based on clinical history and commonly used laboratory test values, is valuable for predicting the risk of critical influenza A infection in hospitalized children.
METHODS: The prediction model was developed using the registration data of children diagnosed with influenza A who were admitted to Wuxi Children's Hospital, the Children's Hospital Affiliated to Soochow University and the Children's Hospital Affiliated to Fudan University. Patients were randomly divided into a training group and a validation group at a 7:3 ratio. A logistic regression model was established based on the least absolute shrinkage and selection operator to construct the nomogram. The performance of the nomogram was evaluated by the area under the characteristic curve (AUC), calibration ability, decision curve analysis (DCA) and clinical impact curve analysis (CICA).
RESULTS: A total of 170 hospitalized children with influenza A infection were identified, including 92 severe patients and 78 critical patients. The model was composed of the following five predictors: loss of appetite, seizure ≥ 2 times, altered neutrophil-to-lymphocyte ratios, haemoglobin levels, and total number of complications. The AUC of the model in the training set was 0.905, and the specificity and sensitivity were 91.1% and 77.8%, respectively. The score has been translated into an online risk calculator that is freely available to the public ( https://iavchildren.shinyapps.io/DynNomapp/ ).
CONCLUSION: This predictive model, which is based on clinical history and commonly used laboratory test values, is valuable for predicting the risk of critical influenza A infection in hospitalized children.