肺炎支原体合并呼吸道合胞病毒感染患儿重症肺炎风险的炎症标志物预测模型构建OA

Objective:Coinfection with Mycoplasma pneumoniae(MP)and respiratory syncytial virus(RSV)is a common type of mixed respiratory infection in clinical practice.Children with MP-RSV coinfection are at increased risk of progressing to severe pneumonia;however,reliable inflammatory biomarkers for the early identification of children at high risk of severe disease remain lacking.This study aims to construct an inflammatory biomarker-based prediction model for the risk of severe pneumonia in children with MP-RSV coinfection and to provide a quantitative tool for early identification of high-risk patients. Methods:Clinical data from 296 children with MP-RSV coinfection admitted to Children's Hospital Affiliated to Zhengzhou University between March 2022 and February 2025 were retrospectively analyzed.According to whether severe pneumonia developed during hospitalization,the children were divided into a severe pneumonia group(n=61)and a non-severe group(n=235).Admission levels of C-reactive protein(CRP),procalcitonin(PCT),ferritin,white blood cell count(WBC),neutrophil count,lymphocyte count,neutrophil-to-lymphocyte ratio(NLR),platelet-to-lymphocyte ratio(PLR),interleukin-6(IL-6),interleukin-8(IL-8),tumor necrosis factor-α(TNF-α),CD4+T lymphocyte count,lactate dehydrogenase(LDH),and D-dimer(D-D)were compared between the 2 groups.Univariate Logistic regression analysis was used to screen factors associated with progression to severe pneumonia in children with MP-RSV coinfection,and multivariate Logistic regression analysis was performed to identify independent risk factors.Receiver operating characteristic(ROC)curve analysis was used to evaluate the predictive value of the combined factors for severe pneumonia,and a prediction model for the risk of severe pneumonia in children with MP-RSV coinfection was constructed. Results:Significant differences between the severe and non-severe pneumonia groups were observed in CRP,PCT,neutrophil count,lymphocyte count,NLR,IL-8,IL-6,TNF-α,D-D,ferritin,and LDH levels(all P<0.05).Univariate Logistic regression analysis showed that CRP,PCT,neutrophil count,lymphocyte count,NLR,IL-8,IL-6,TNF-α,D-D,ferritin,and LDH were all significantly associated with the risk of severe pneumonia in children with MP-RSV coinfection(all P<0.05).Multivariate Logistic regression analysis revealed that elevated CRP,neutrophil count,IL-6,ferritin,and LDH levels were independent risk factors for severe pneumonia in children with MP-RSV coinfection(all P<0.05).The area under the curve(AUC)for the combined prediction of severe pneumonia using these risk factors was 0.962(0.933 to 0.981)(P<0.05). Conclusion:The inflammatory biomarker-based prediction model developed in this study showed high predictive performance for severe pneumonia in children with MP-RSV coinfection and may provide a quantitative reference for the early identification of children at high risk of severe disease and the timely initiation of targeted interventions.