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Published on: September 16, 2022
Development and internal validation of a nomogram for predicting referral or hospitalization risk in children with
Xiaoman Cai1, Jiabin Yang1, Shiyou Luo1
1Longdong Community Health Service Center, Longgang Central Hospital, Shenzhen, China.
Insights
A new nomogram accurately predicts hospitalization risk in children with community-acquired influenza using clinical indicators. This tool aids primary care in making evidence-based decisions for patient triage.
Area of Science:
- Pediatrics
- Infectious Diseases
- Health Informatics
Background:
- Community-acquired influenza poses a significant health risk to children.
- Accurate prediction of severe outcomes like referral or hospitalization is crucial for timely intervention.
- Current decision-making tools in primary care may lack precision for influenza risk stratification.
Purpose of the Study:
- To develop and internally validate a nomogram for predicting referral or hospitalization risk in pediatric patients with community-acquired influenza.
- To provide primary healthcare institutions with an evidence-based tool for clinical decision-making.
- To enhance patient triage and resource allocation in primary care settings.
Main Methods:
- Prospective data collection from 388 children (6 months to 6 years) diagnosed with influenza.
- Identification of independent risk factors using univariate and multivariable logistic regression.
- Nomogram construction and validation using ROC curves, calibration plots, decision curve analysis (DCA), and Bootstrap internal validation.
Main Results:
- Lack of influenza vaccination, antibiotic use, sore throat, myalgia, gastrointestinal symptoms, elevated C-reactive protein, and increased medical visits were identified as independent risk factors.
- The nomogram achieved an AUC of 0.95, with accuracy, sensitivity, and specificity of 0.88, 0.92, and 0.88, respectively.
- The model demonstrated excellent fit, clinical utility, and robust stability through validation and sensitivity analyses.
Conclusions:
- The developed nomogram effectively predicts referral or hospitalization risk in children with community-acquired influenza.
- The nomogram utilizes accessible clinical parameters, offering superior predictive performance.
- This tool provides an intuitive and practical solution for precise patient triage in primary care, potentially reducing healthcare resource wastage.
Background:
To develop and internally validate a nomogram for predicting referral or hospitalization risk in children with community-acquired influenza based on clinical indicators, thereby providing primary healthcare institutions with evidence-based decision-making tools.
Methods:
Clinical data were prospectively collected from children aged 6 months to 6 years diagnosed with influenza at Longdong Community Health Service Center, Longgang Central Hospital, Shenzhen, between May 2024 and October 2025. Independent risk factors were identified through univariate and multivariable logistic regression analyses to construct nomograms. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA), and Bootstrap internal validation. To address potential bias from clinical decision-related variables, a sensitivity analysis was further performed to verify the model's robustness.
Results:
A total of 388 children were finally enrolled in this study, among whom 49 (12.6%) required referral or hospitalization. Multivariable analysis revealed that lack of influenza vaccination, antibiotic usage, sore throat, myalgia, gastrointestinal symptoms, elevated C-reactive protein levels, and increased frequency of medical visits were independent risk factors (P < 0.05). The nomogram constructed based on these seven factors demonstrated an area under the curve (AUC) of 0.95 (95% CI: 0.92-0.98), with accuracy of 0.88, sensitivity of 0.92, and specificity of 0.88. Calibration curves indicated excellent model fit (Hosmer-Lemeshow test P = 0.862), while DCA demonstrated significant clinical net benefit. Bootstrap validation confirmed robust model stability, and sensitivity analysis excluding bias-prone variables further validated the reliability of the model's core conclusions.
Conclusion:
This nomogram, utilizing readily accessible clinical parameters, exhibited superior predictive performance for referral or hospitalization risk assessment in children with community-acquired influenza. It provided an intuitive and practical tool for precise patient triage in primary care settings, potentially reducing healthcare resource wastage.
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