# Establishment and validation of a predictive model for severe pneumonia in children

## TL;DR

This study created a model to predict severe pneumonia in children using factors like temperature and breathing rate to help with early treatment.

## Contribution

The novel contribution is the development of a predictive model for severe pneumonia in children based on clinical and laboratory indicators.

## Key findings

- High body temperature and high respiratory rate were identified as independent risk factors for severe pneumonia.
- The model uses clinical parameters to distinguish between ordinary and severe pneumonia cases.
- Early identification through the model can improve treatment outcomes and reduce hospital burden.

## Abstract

This study aimed to develop a model for the early identification of severe pneumonia in children by comparing common laboratory indicators between children with ordinary pneumonia and severe pneumonia.

Children aged 1 month to 14 years, diagnosed with pneumonia and admitted to our hospital between January 2017 and June 2022, were included in the study. Participants were divided into two groups based on the severity of their pneumonia. Data, including demographic information, medical history, clinical symptoms, laboratory indicators, and treatment outcomes, were collected from the hospital's medical records system.

The single-factor analysis revealed significant differences (P < 0.05) between the two groups in various parameters, including age, length of hospital stay, repeated hospitalization within 90 days, invasive ventilation, Intensive Care Unit (ICU) stay time, birth history, temperature, respiratory rate, blood pressure, procalcitonin, and C-reactive protein. Binary logistic regression analysis indicated that high body temperature and high respiratory rate were independent risk factors for severe pneumonia (P < 0.05).

A predictive model for severe pneumonia in children can identify the risk of progression to severe disease, enabling prompt treatment and improving patient prognosis. This reduces the burden on families and social security.