Clinical prediction model of Mycoplasma pneumoniae pneumonia combined with influenza virus infection in pediatric patients in Gansu, China.

BACKGROUND

The incidence of respiratory infections in children has been increasing in recent years, and co-infections can lead to additional complications. This study aimed to investigate predictors of Mycoplasma pneumoniae pneumonia (MPP) co-infected with influenza virus through a retrospective analysis of clinical data in pediatric patients.

METHODS

We retrospectively reviewed the medical records of 195 children diagnosed with MPP at the Pediatric Internal Medicine Department of Gansu Provincial Hospital between November 2023 and November 2024. Patients were categorized into two groups: single-infection (n = 128, MPP alone) and mixed-infection (n = 67, MPP co-infected with influenza). Predictors of mixed infection were identified using a multivariate logistic regression-based prediction model. The model's discrimination, accuracy, clinical utility, and generalizability were evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).

RESULTS

Multivariate analysis showed that influenza season, fibrinogen (Fib) level, fever duration, and C-reactive protein (CRP) were significantly associated with MPP co-infection (p < 0.05). The prediction model demonstrated good discrimination, with an area under the curve (AUC) of 0.820 (95% CI: 0.760-0.879) for the ROC analysis. DCA confirmed the model's strong clinical utility.

CONCLUSIONS

A prediction model based on influenza season, Fib level, fever duration, and CRP provide accurate identification of children at risk for MPP co-infected with influenza, demonstrating strong discrimination and clinical applicability.

CLINICAL TRIAL: Not applicable.