# Construction and verification of a risk factor prediction model for neonatal severe pneumonia

## TL;DR

This study developed a model to predict severe pneumonia in newborns using clinical indicators like respiratory rate and blood markers.

## Contribution

A novel prediction model for neonatal severe pneumonia using LASSO and logistic regression with validated performance metrics.

## Key findings

- Seven independent risk factors were identified, including respiratory rate and blood markers like CRP and NEU.
- The model showed strong predictive accuracy with an AUC of 0.884 in the training set and 0.835 in the testing set.
- Calibration and decision curve analysis confirmed the model's reliability and clinical utility.

## Abstract

To construct and validate a risk factor prediction model for neonatal severe pneumonia.

This study collected data from newborns diagnosed with pneumonia in Children’s Hospital Affiliated to Zhengzhou University. A total of 652 newborns were included. Risk factors were identified using Least Absolute Selection and Shrinkage Operator (LASSO) regression and logistic regression analysis. The nomogram was used to construct a prediction model. The effectiveness of the model was evaluated using calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).

Out of 652 newborns, 186 (29%) were diagnosed with severe pneumonia. The patients were randomly divided into a training set (n = 554) and a testing set (n = 98) in a ratio of 85:15. A total of 30 indicators were analyzed. Respiratory rate (OR = 1.058, 95% CI: 1.035–1.081), weight (OR = 0.483, 95% CI: 0.340–0.686), C-reactive protein (CRP) (OR = 1.142, 95% CI: 1.028–1.268), neutrophil (NEU) (OR = 1.384, 95% CI: 1.232–1.555), hemoglobin (HGB) (OR = 0.989, 95% CI: 0.979–0.999), uric acid (UA) (OR = 1.006, 95% CI: 1.002–1.010), and blood urea nitrogen (BUN) (OR = 1.230, 95% CI: 1.058–1.431) were identified as independent risk factors for neonatal severe pneumonia. The calibration curve showed significant agreement. The area under the ROC curve (AUC) was 0.884 (95% CI: 0.852–0.916) for the training set, and 0.835 (95% CI: 0.747–0.922) for the testing set. DCA demonstrated good predictive properties.

The prediction model based on respiratory rate, weight, CRP, NEU, HGB, UA, and BUN has shown promising predictive value in distinguishing between mild to moderate pneumonia and severe pneumonia in neonates.