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Construction and validation of a predictive risk model for nosocomial infections with MDRO in NICUs: a multicenter
Jinyan Zhou1, Feixiang Luo2, Jianfeng Liang3
1Administration Department of Nosocomial Infection, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.
Objectives:
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.
Methods:
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.
Results:
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+ ), X = -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%.
Conclusion:
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.
Insights
This study developed a predictive risk model to identify neonates at high risk for multi-drug resistant organism (MDRO) infections in neonatal intensive care units (NICUs). The model aids in targeted prevention strategies to reduce nosocomial infections.
Area of Science:
- Neonatal Intensive Care Unit (NICU) Medicine
- Infectious Disease Epidemiology
- Medical Informatics
Background:
- Nosocomial infections caused by multi-drug resistant organisms (MDROs) pose a significant threat to vulnerable neonates in NICUs.
- Effective prediction tools are crucial for timely intervention and prevention of MDRO infections.
Purpose of the Study:
- To construct and validate a predictive risk model (PRM) for MDRO infections in NICUs.
- To provide a reliable tool for clinical staff to identify high-risk neonates.
- To inform targeted prevention and control strategies for MDRO infections in NICUs.
Main Methods:
- A multicenter observational study involving neonates in NICUs from two tertiary children's hospitals.
- Cluster sampling was used to enroll neonates into modeling (2018-2020) and validation (2021-2022) groups.
- Univariate and binary logistic regression analyses were employed to build the PRM, with validation using H-L tests, calibration curves, ROC curves, and decision curve analysis.
Main Results:
- The final PRM identified four independent risk factors: low birth weight, maternal age ≥ 35 years, antibiotic use > 7 days, and MDRO colonization.
- The model demonstrated good fitting degree, calibration, discrimination, and clinical validity upon internal and external validation.
- The predictive accuracy of the constructed PRM was 77.19%.
Conclusions:
- The developed PRM is a scientifically sound and reliable tool for predicting MDRO infections in NICU settings.
- Clinical staff can utilize the PRM for early identification of high-risk neonates.
- Targeted prevention strategies based on identified risk factors can help reduce the incidence of MDRO infections in NICUs.
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