Models for predicting the risk of bloodstream infections associated with peripherally inserted central venous catheters: A scoping review

Langping Cao, Yingxiang Tao, Shiqiang Lei, Shihua He, Yuxuan Peng, Sailin Liu, Haoran Chen, Jin Zhou and Yanhui Liu

PLOS ONE, 2025, vol. 20, issue 10, 1-11

Abstract: Background: Peripherally inserted central venous catheters (PICC) associated bloodstream infections is a type of central line-associated bloodstream infection (CLABSI), the purpose of this scoping review was to analyse and summarize the risk prediction models for PICC-CLABSI to provide insights for clinical nursing practice. Methods: A scoping review was conducted from six bibliographic databases including CNKI, Wanfang Database, VIP Chinese Journal Database, PubMed, Embase, and Web of Science, from inception to November 5, 2024. Screening was performed and relevant data was extracted independently by two researchers, the risk of bias was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). Results: Eight studies met the inclusion criteria, which included two score models, six nomograms, and one ELM model. The included studies exhibited a high risk of bias, mainly due to methodological heterogeneity. Four models underwent external validation, two were assessed for goodness of fit. The most frequently identified predictors including catheter indwelling time, maintenance cycle/frequency, multilumen catheters, PICC parenteral nutrition, diabetes, and malignant tumors. Conclusion: The risk prediction models for PICC-CLABSI demonstrated strong predictive performance. Future research should carefully address all elements of PROBAST framework during study design phase. This will facilitate both internal and external validation, as well as differentiation, calibration, and evaluation of clinical practicality. The ultimate objective is to develop a PICC-CLABSI risk prediction model that exhibits low bias risk, robust predictive performance, and high clinical applicability.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0333466

DOI: 10.1371/journal.pone.0333466

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