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Updated: May 31, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and internal validation of a nomogram for predicting recurrent respiratory tract infections in children
Fei Xia1, Xi Zhou2, Yan Xiong2
1Department of Respiratory Medicine, The Affiliated Wuxi People's Hospital, Wuxi Children's Hospital of Nanjing Medical University, Wuxi, 214023, China; Department of Pediatrics, Yixing Hospital of Traditional Chinese Medicine, Yixing, 214200, China.
Insights
This study developed a nomogram to predict recurrent respiratory tract infection (RRTI) risk in children. The tool identifies key risk factors for early intervention.
Area of Science:
- Pediatric Medicine
- Epidemiology
- Biostatistics
Background:
- Recurrent respiratory tract infections (RRTI) pose a significant health burden in children.
- Accurate risk prediction is crucial for timely intervention and management.
Purpose of the Study:
- To develop and validate a predictive nomogram for identifying children at high risk of RRTI.
- To establish a graphical tool for personalized risk assessment.
Main Methods:
- Retrospective analysis of 150 children with RRTI and 151 controls (aged 0-14 years).
- LASSO regression identified predictors: picky eating, age at first antibiotic use, prior antibiotic use, allergies, and secondhand smoke exposure.
- Nomogram performance evaluated using ROC curves, calibration plots, and DCA.
Main Results:
- The nomogram demonstrated excellent discriminative ability with an AUC of 0.902 in the training cohort and 0.826 in the validation cohort.
- High predictive accuracy was confirmed, with good calibration and significant clinical net benefit.
- Identified key risk factors include dietary habits, antibiotic history, allergic conditions, and environmental exposures.
Conclusions:
- The developed nomogram is an effective tool for predicting RRTI risk in children.
- Provides a personalized, graphical approach for early detection and intervention strategies.
Objective:
This study aimed to develop and internally validate a nomogram in predicting the risk of recurrent respiratory tract infection (RRTI) in children.
Methods:
A retrospective analysis was performed, involving 150 children with RRTI and 151 healthy controls, aged 0-14 years, admitted to or selected from the Pediatric Department of Yixing Hospital of Traditional Chinese Medicine between June 2022 and June 2023. Data were gathered through a comprehensive questionnaire survey on risk factors associated with RRTI. The dataset was randomly divided into a training cohort (n = 211) and a validation cohort (n = 90) in a 7:3 ratio. Significant variables were selected using LASSO regression in the training cohort to construct the nomogram, the performance of which was evaluated through Receiver Operating Characteristic (ROC) curves, calibration plots, and Decision Curve Analysis (DCA).
Results:
The LASSO regression identified five predictors in the training cohort: picky eating, age at first antibiotic use, antibiotic use within the previous year, allergic conditions, secondhand smoke exposure. Based on them, the nomogram exhibited an excellent discriminative ability, with an AUC of 0.902 (95 % CI: 0.860-0.944) and a C-index of 0.902 in the training cohort. The validation cohort showed an AUC of 0.826 (95 % CI: 0.742-0.909) and a C-index of 0.826, confirming a high predictive accuracy. Calibration plots showed close alignment with the ideal reference line, and DCA indicated a significant clinical net benefit.
Conclusion:
Our nomogram can efficiently predict RRTI risk in children, thereby providing a personalized and graphical tool for early identification and intervention.
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