Objectives: Respiratory syncytial virus (RSV) is a leading cause of hospitalization for acute lower respiratory tract infections (ALRIs) in children, with bacterial coinfection complicating diagnosis and often driving antibiotic overuse. This study aimed to develop and validate a clinical prediction model using common laboratory biomarkers to enable early, accurate identification of clinically significant bacterial coinfection in children with RSV infection. Methods: A single-center, retrospective cohort study was conducted at Fujian Children’s Hospital, enrolling 518 hospitalized children with RSV infection, which was confirmed via targeted next-generation sequencing (tNGS). Patients were randomly divided into a training set (n = 363) and a test set (n = 155) at a 7:3 ratio. The primary outcome, bacterial coinfection, was defined by a composite reference standard integrating etiological evidence from tNGS with clinical, inflammatory, and imaging data, and adjudicated by a blinded expert panel. LASSO regression identified independent predictors, followed by multivariable logistic regression modeling. Model performance was assessed via discrimination (AUC), calibration (Hosmer–Lemeshow test), and clinical utility (Decision Curve Analysis) in both sets. Results: Neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), and serum amyloid A (SAA) were selected as predictors. The model achieved an AUC of 0.832 (95% CI: 0.788–0.875) in the training set and 0.811 (95% CI: 0.737–0.885) in the test set, with well-calibrated predictions (p > 0.05). Decision curve analysis demonstrated net clinical benefit across 10–80% threshold probabilities. A nomogram was developed for practical application. Conclusions: This study established a model integrating NLR, CRP, and SAA. It offers a reliable tool for the early detection of bacterial coinfection in RSV-infected children, enabling targeted antibiotic stewardship and improving clinical outcomes.

Objectives: Respiratory Syncytial Virus (RSV) is a leading cause of hospitalization for acute lower respiratory tract infections (ALRIs) in children, with bacterial coinfection complicating diagnosis and often driving antibiotic overuse. This study aimed to develop and validate a clinical prediction model using common laboratory biomarkers to enable early, accurate identification of clinically significant bacterial coinfection in children with RSV infection. Methods: A single-center, retrospective cohort study was conducted at Fujian Children’s Hospital, enrolling 518 hospitalized children with tNGS-confirmed RSV infection. Patients were randomly divided into a training set (n=363) and a test set (n=155) at a 7:3 ratio. The primary outcome, bacterial coinfection, was defined by a composite reference standard integrating etiological evidence from tNGS with clinical, inflammatory, and imaging data, and adjudicated by a blinded expert panel. LASSO regression identified independent predictors, followed by multivariable logistic regression modeling. Model performance was assessed via discrimination (AUC), calibration (Hosmer-Lemeshow test), and clinical utility (Decision Curve Analysis) in both sets. Results: Neutrophil-to-Lymphocyte Ratio (NLR), C-Reactive Protein (CRP), and Serum Amyloid A (SAA) were selected as predictors. The model achieved an AUC of 0.832 (95% CI: 0.788–0.875) in the training set and 0.811 (95% CI: 0.737–0.885) in the test set, with well-calibrated predictions (P > 0.05). Decision Curve Analysis demonstrated net clinical benefit across 10%–80% threshold probabilities. A nomogram was developed for practical application. Conclusions: This study established a model integrating NLR, CRP, and SAA. It offers a reliable tool for the early detection of bacterial coinfection in RSV-infected children, enabling targeted antibiotic stewardship and improving clinical outcomes.

Background: To evaluate the diagnostic value of cytokine levels in paediatric patients with Mycoplasma pneumoniae pneumonia (MPP) complicated by bacterial or viral infections, to enhance early detection and treatment strategies. Methods: A retrospective analysis was conducted on 145 paediatric patients with MPP at Fujian Children’s Hospital between October 2023 and April 2024. The patients were categorised into three groups: MPP only, MPP with bacterial co-infection, and MPP with viral co-infection. Clinical and laboratory data were analysed. Diagnostic accuracy was assessed using receiver operating characteristic (ROC) curves. Machine learning models with Boruta feature selection and 5-fold cross-validation were applied for co-infection classification. Statistical significance was set at P < 0.05. Results: Significant differences were observed between groups. The bacterial co-infection group had higher white blood cell and platelet counts and elevated levels of interferon-alpha, interleukin-1 beta, interleukin-6, and tumour necrosis factor-alpha (all P < 0.05). The viral co-infection group had lower interleukin-12P70 levels, longer fever and cough durations, and higher lactate dehydrogenase levels (all P < 0.05). Correlation analysis showed strong associations between white blood cells, platelets, and proinflammatory cytokines. ROC analysis of single parameters showed modest discrimination: for bacterial co-infections the best single marker was interferon-alpha with an AUC of 0.774 (95% CI 0.649–0.899), whereas for viral co-infections the best was lactate dehydrogenase with an AUC of 0.684 (95% CI 0.563–0.806). In contrast, machine learning models achieved higher performance, reaching an AUC of 0.868 (95% CI 0.772–0.964; SVM) for bacterial co-infections and 0.753 (95% CI 0.636–0.870; LightGBM) for viral co-infections. Conclusion: The combination of clinical data, laboratory parameters and cytokine profiles may improve the early diagnostic accuracy of MPP with bacterial or viral co-infections in some patients, and potentially guide optimised treatment strategies.