Construction and Validation of a Nomogram Model for Predicting the Risk of Neonatal Sepsis Complicated by Purulent Meningitis.

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.