Jingyue Li,1 Chunlan Song,1 Tiewei Li,2 Wanyu Jia,1 Zhuo Qian,1 Yiming Peng,1 Yixin Xu,1 Zhipeng Jin1 

1Children’s Hospital Affiliated to Zhengzhou University, Henan Children’s Hospital, Zhengzhou Children’s Hospital, Zhengzhou, Henan Province, People’s Republic of China; 2Zhengzhou Key Laboratory of Children’s Infection and Immunity, Children’s Hospital Affiliated to Zhengzhou University, Henan Children’s Hospital, Zhengzhou Children’s Hospital, Zhengzhou, Henan Province, People’s Republic of China

Correspondence: Zhipeng Jin, Email 18837614038@163.com

Background: Neonatal purulent meningitis (NPM) is a severe infection with high morbidity and mortality. NPM is a common complication in cases of neonatal sepsis (NS). This study aims to develop and validate a risk prediction model for NS complicated by NPM.
Methods: A retrospective study of 535 neonates diagnosed with sepsis at the Affiliated Children’s Hospital of Zhengzhou University between January 2016 and October 2024 was conducted. The primary outcome was the presence of NPM. Multivariate logistic regression was used to identify predictive factors, and a nomogram model was created using R software.
Results: Multivariate analysis identified fever, seizures, tachycardia, and decreased levels of alkaline phosphatase (ALP) and total bilirubin (TBIL) as independent risk factors for NS complicated by NPM (P < 0.05). The area under the receiver operating characteristic curve (ROC) for the training set was 0.765 (95% CI: 0.711– 0.819), and 0.713 (95% CI: 0.625– 0.800) for the validation set. The Hosmer-Lemeshow test confirmed good model fit (χ² = 8.963, P = 0.345). Calibration and decision curve analysis showed high predictive performance and clinical applicability.
Conclusion: The nomogram developed in this study demonstrates promising predictive ability and clinical value for NS complicated by NPM.

Keywords: neonatal purulent meningitis, NPM, neonatal sepsis, NS, prediction model, nomogram