AUTHOR=Zeng Fan-ying , Li Wen-yan , Ye Hong-yun , Xie Chun-lian , Zhong Hai-li 

TITLE=A predictive model for catheter-related bloodstream infection in neonates with peripherally inserted central catheter

JOURNAL=Frontiers in Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1665068

DOI=10.3389/fmed.2025.1665068

ISSN=2296-858X

ABSTRACT=ObjectiveTo explore the influencing factors of catheter-related bloodstream infection (CRBSI) in neonates with peripherally inserted central catheter (PICC) in the neonatal intensive care unit (NICU).MethodsA total of 200 neonates who underwent PICC placement were selected. They were randomly divided into a training set (n = 140) and a validation set (n = 60) at a ratio of 7:3. Clinical data of the neonates were collected, including general information, catheterization-related indicators, laboratory indicators, and other relevant indicators. Univariate analysis and multivariate Logistic regression analysis were used to screen the independent risk factors for CRBSI. The random forest algorithm was used to rank the importance of the risk factors, and the variance inflation factor (VIF) was used for multicollinearity diagnosis. A nomogram prediction model was constructed based on the independent risk factors. The predictive efficacy of the model was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).ResultsIn the training set, 32 cases (22.86%) developed CRBSI, and in the validation set, 14 cases (23.33%) developed CRBSI, with no statistically significant difference (P > 0.05). Multivariate analysis showed that the age at catheterization, number of punctures, white blood cell count, number of days of antimicrobial use, and number of days of parenteral nutrition were independent risk factors for CRBSI (all P < 0.05), and the 5-min Apgar score was an independent protective factor for CRBSI (P < 0.05). The C-indexes of the nomogram model in the training set and the validation set were 0.923 and 0.881, respectively. The ROC curve showed that the area under the curve (AUC) in the training set was 0.921 (95% CI: 0.819–1.000) and in the validation set was 0.880 (95% CI: 0.768–0.992). The sensitivity and specificity in the training set were 0.909 and 0.844, respectively, and in validation set were 0.857 and 0.857, respectively.ConclusionThe nomogram prediction model constructed based on the screened independent risk factors can effectively predict the risk of CRBSI in neonates with PICC in the NICU, providing a basis for the clinical early identification of high-risk neonates and the formulation of preventive measures.