Table 6_Construction and verification of a risk factor prediction model for neonatal severe pneumonia.docx

ObjectiveTo construct and validate a risk factor prediction model for neonatal severe pneumonia.
MethodsThis 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).
ResultsOut 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.
ConclusionThe 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.