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

## TL;DR

This study develops a model to predict when children with Mycoplasma pneumoniae pneumonia also have influenza, using factors like season and blood markers.

## Contribution

A novel clinical prediction model for MPP co-infected with influenza in children using multivariate logistic regression and validation metrics.

## Key findings

- Influenza season, fibrinogen, fever duration, and CRP are significant predictors of MPP co-infection.
- The model achieved an AUC of 0.820, indicating strong discrimination ability.
- Decision curve analysis confirmed the model's clinical utility for identifying co-infections.

## Abstract

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.

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).

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.

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.

Not applicable.