ORIGINAL RESEARCH article
Front. Med.
Sec. Intensive Care Medicine and Anesthesiology
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1536705
Construction and verification of a risk factor prediction model for neonatal severe pneumonia
Provisionally accepted
Weihua Gong1
Kaijie Gao1
Jiajia Ni2
Ying Shi3
Shan Zhiming1Hongqi Sun1Shanshan Wang1Jiangtao Xu1
Junmei Yang1*- 1Children’s Hospital Affiliated of Zhengzhou University, Zhengzhou, China
- 2Guangzhou National Laboratory, Guangzhou, Guangdong, China
- 3Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
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Objective To construct and validate a risk factor prediction model for neonatal severe pneumonia. Methods 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 2 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). Results 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 to 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 to 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. Conclusion 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.
Keywords: neonatal, Severe pneumonia, predictive model, nomogram, risk factor
Received: 26 Jan 2025; Accepted: 23 May 2025.
Copyright: © 2025 Gong, Gao, Ni, Shi, Zhiming, Sun, Wang, Xu and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Junmei Yang, Children’s Hospital Affiliated of Zhengzhou University, Zhengzhou, China
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