Abstract：Objective To analyze the factors affecting recurrent respiratory tract infection (RRTI) in children, to construct a predictive model, and to explore the predictive value of the model. Methods Using retrospective research, we collected the clinical data and evaluation results of Dietary Inflammatory Index (DII) of 589 children with respiratory tract infections who were treated in Suzhou Municipal Hospital from February to December 2021. The cases were randomly allocated to the modeling set (n=412) and the validation set (n=177) in a ratio of 7:3. Single and multiple logistic regression analyses were performed on the modeling set data to screen the factors influencing RRTI in the children, and a column chart prediction model was constructed. The performance and clinical practicality of the model were evaluated using receiver operating characteristic (ROC) curve, calibration curve and decision curve respectively. Validation set data were simultaneously introduced for external validation of the model. Results In the modeling set of 412 children with respiratory tract infections, 64 (15.53%) had RRTI during the one-year follow-up after their first diagnosis. Multivariate logistic regression analysis revealed that age (OR=0.666, 95%CI:0.485-0.915) and parental awareness of RRTI prevention and control (OR=0.689, 95%CI:0.526-0.903) were protective factors for RRTI in the children (P<0.05). Allergic history (OR=2.809, 95%CI:1.459-5.407), high C-reactive protein (OR=2.296, 95%CI:1.275-4.101), high DII (OR=2.125, 95%CI:1.242-3.637) and family smoking (OR=1.467, 95%CI:1.080-1.991) were risk factors for RRTI in the children (P<0.05). A column chart model for prediction of RRTI in the children was constructed based on the above-mentioned factors. The area under the ROC curve of the model was 0.867 (95%CI:0.815-0.935), and the optimal cutoff value (threshold probability) 0.37, with the sensitivity and specificity at this point being 0.860 and 0.824 respectively. The Brier index for calibration curve analysis was 0.097 (P>0.05). Introducing validation sets for external model validation found that the area under the ROC curve of the model was 0.851 (95%CI:0.793-0.924), with the sensitivity and specificity being 0.850 and 0.805 respectively. The Brier index for calibration curve analysis was 0.112 (P>0.05). When the threshold probability value in the decision curve was set to 37.0%, the clinical benefit rates for the modeling and validation sets were 54% and 59% respectively, which indicated that the prediction model had clinical validity. Conclusion The prediction model constructed based on age, allergy history, C-reactive protein, DII, smoking of family members and parents' awareness of RRTI prevention and control has a certain predictive value for RRTI risk in the children.
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2024, Vol. 31 
