Development and internal validation of a nomogram for predicting recurrent respiratory tract infections in children.

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.