Zhou Jinyan, Luo Feixiang, Liang Jianfeng, Cheng Xiaoying, Chen Xiaofei, Li Linyu, Chen Shuohui
Administration Department of Nosocomial Infection, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.
Neonatal Intensive Care Unit, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.
Front Med (Lausanne). 2023 Jun 26;10:1193935. doi: 10.3389/fmed.2023.1193935. eCollection 2023.
This study aimed to construct and validate a predictive risk model (PRM) for nosocomial infections with multi-drug resistant organism (MDRO) in neonatal intensive care units (NICUs), in order to provide a scientific and reliable prediction tool, and to provide reference for clinical prevention and control of MDRO infections in NICUs.
This multicenter observational study was conducted at NICUs of two tertiary children's hospitals in Hangzhou, Zhejiang Province. Using cluster sampling, eligible neonates admitted to NICUs of research hospitals from January 2018 to December 2020 (modeling group) or from July 2021 to June 2022 (validation group) were included in this study. Univariate analysis and binary logistic regression analysis were used to construct the PRM. H-L tests, calibration curves, ROC curves and decision curve analysis were used to validate the PRM.
Four hundred and thirty-five and one hundred fourteen neonates were enrolled in the modeling group and validation group, including 89 and 17 neonates infected with MDRO, respectively. Four independent risk factors were obtained and the PRM was constructed, namely: P = 1/ (1+ ), = -4.126 + 1.089× (low birth weight) +1.435× (maternal age ≥ 35 years) +1.498× (use of antibiotics >7 days) + 0.790× (MDRO colonization). A nomogram was drawn to visualize the PRM. Through internal and external validation, the PRM had good fitting degree, calibration, discrimination and certain clinical validity. The prediction accuracy of the PRM was 77.19%.
Prevention and control strategies for each independent risk factor can be developed in NICUs. Moreover, clinical staff can use the PRM to early identification of neonates at high risk, and do targeted prevention to reduce MDRO infections in NICUs.
本研究旨在构建并验证新生儿重症监护病房（NICU）耐多药菌（MDRO）医院感染的预测风险模型（PRM），以提供科学可靠的预测工具，为NICU中MDRO感染的临床预防与控制提供参考。
本多中心观察性研究在浙江省杭州市两家三级儿童医院的NICU进行。采用整群抽样，纳入2018年1月至2020年12月入住研究医院NICU的符合条件的新生儿（建模组）或2021年7月至2022年6月入住的新生儿（验证组）。采用单因素分析和二元逻辑回归分析构建PRM。采用H-L检验、校准曲线、ROC曲线和决策曲线分析对PRM进行验证。
建模组和验证组分别纳入435例和114例新生儿，其中分别有89例和17例感染MDRO。获得4个独立危险因素并构建PRM，即：P = 1 / (1 + )， = -4.126 + 1.089×（低出生体重） + 1.435×（母亲年龄≥35岁） + 1.498×（使用抗生素>7天） + 0.790×（MDRO定植）。绘制列线图以直观显示PRM。通过内部和外部验证，PRM具有良好的拟合度、校准度、区分度和一定的临床有效性。PRM的预测准确率为77.19%。
NICU可针对各独立危险因素制定防控策略。此外，临床工作人员可利用PRM早期识别高危新生儿，并进行针对性预防，以减少NICU中MDRO感染。
