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A simple, rapid, and cost-effective model for predicting critical influenza a infection in children: a multicentre,
Suwan Xiong1, Yun Guo2, Leihua Jiang3
1Department of Respiratory Medicine, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi Children's Hospital, Wuxi, 214000, China.
Insights
A new predictive model helps identify children with influenza A at high risk of critical illness. This tool uses clinical history and lab results for early detection and intervention, improving patient outcomes.
Area of Science:
- Pediatric Infectious Diseases
- Clinical Prediction Modeling
- Biostatistics
Background:
- Lack of validated tools for early identification of critical influenza A in children.
- Need for timely diagnosis and treatment to prevent severe outcomes.
- Importance of risk stratification for pediatric influenza patients.
Purpose of the Study:
- To develop and validate a predictive model for early identification of children at high risk of critical influenza A infection.
- To provide a scientifically validated screening tool for clinical use.
- To improve the management of severe pediatric influenza cases.
Main Methods:
- Development of a logistic regression model using the least absolute shrinkage and selection operator (LASSO) on data from 170 hospitalized children.
- Randomized training (70%) and validation (30%) groups.
- Performance evaluation using Area Under the Characteristic Curve (AUC), calibration, Decision Curve Analysis (DCA), and Clinical Impact Curve Analysis (CICA).
Main Results:
- The final model included five predictors: loss of appetite, seizures (≥2), altered neutrophil-to-lymphocyte ratio, hemoglobin levels, and complications.
- The model achieved an AUC of 0.905 in the training set with 91.1% specificity and 77.8% sensitivity.
- An online risk calculator is available for public use.
Conclusions:
- The developed predictive model is valuable for assessing the risk of critical influenza A infection in hospitalized children.
- The model integrates readily available clinical history and laboratory data.
- This tool can aid clinicians in early identification and management of high-risk pediatric patients.
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.
