预测新生儿败血症并发化脓性脑膜炎风险的列线图模型的构建与验证

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

作者信息

Li Jingyue, Song Chunlan, Li Tiewei, Jia Wanyu, Qian Zhuo, Peng Yiming, Xu Yixin, Jin Zhipeng

机构信息

Children's Hospital Affiliated to Zhengzhou University, Henan Children's Hospital, Zhengzhou Children's Hospital, Zhengzhou, Henan Province, People's Republic of China.

Zhengzhou 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.

出版信息

J Inflamm Res. 2025 Jun 4;18:7183-7194. doi: 10.2147/JIR.S522306. eCollection 2025.

DOI:10.2147/JIR.S522306

PMID:40487286

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12145790/

Abstract

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.

摘要

背景

新生儿化脓性脑膜炎（NPM）是一种严重感染，发病率和死亡率高。NPM是新生儿败血症（NS）病例中的常见并发症。本研究旨在建立并验证NS合并NPM的风险预测模型。

方法

对2016年1月至2024年10月在郑州大学附属儿童医院诊断为败血症的535例新生儿进行回顾性研究。主要结局是是否存在NPM。采用多因素逻辑回归确定预测因素，并使用R软件创建列线图模型。

结果

多因素分析确定发热、惊厥、心动过速以及碱性磷酸酶（ALP）和总胆红素（TBIL）水平降低是NS合并NPM的独立危险因素（P<0.05）。训练集的受试者工作特征曲线（ROC）下面积为0.765（95%CI：0.711-0.819），验证集为0.713（95%CI：0.625-0.800）。Hosmer-Lemeshow检验证实模型拟合良好（χ²=8.963，P=0.345）。校准和决策曲线分析显示出较高的预测性能和临床适用性。