Establishment of a risk prediction model for peripherally inserted central catheter-related bloodstream infections based on a systematic review and meta-analysis of 20 cohorts.

Background: Peripherally inserted central catheters (PICCs) are commonly used for extended intravenous therapy but are associated with a significant risk of bloodstream infections (BSIs), which increase morbidity and healthcare costs.

Aim: The aim of this study was to identify patients at high risk of developing PICC-related bloodstream infections (PICC-RBSIs) to establish new and more specific targets for precise prevention and intervention.

Methods: A search was conducted from the earliest available record to May 2024 among the following databases: Embase, MEDLINE, Cochrane Central Register of Controlled Trials, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science, Scopus, and Chinese National Knowledge Infrastructure (CNKI). Hand searching for gray literature and reference lists of included papers was also performed. We assessed the quality of the studies using the Newcastle-Ottawa Scale (NOS) checklist. Two reviewers screened all the retrieved articles, extracted the data, and critically appraised the studies. Data analysis was performed using RevMan statistical software.

Results: A total of 20 cohort studies involving 51,907 individuals were included in the analysis. The statistically significant risk factors identified were hospital length of stay, line type (tunneled), history of PICC placement, multiple lumens, previous infections, chemotherapy, total parenteral nutrition, hematological cancers, delays in catheter care, local signs of infection (e.g., localized rashes), previous BSIs, and diabetes mellitus. Due to high heterogeneity among studies regarding previous BSIs, this factor was excluded from the final predictive model, while all other risk factors were included.

Conclusions: The present meta-analysis identified risk factors for PICC-RBSIs and developed a predictive model based on these findings, incorporating 10 risk factors that integrate both patient-specific and procedural factors.

Linking Evidence To Action: Integrating the risk prediction model for PICC-RBSI into clinical guidelines and training is essential. Healthcare providers should be trained to use this model to identify high-risk patients and implement preventive measures proactively. This integration could enhance personalized care, reduce infection incidence, and improve patient outcomes. Future research should update the model with new risk factors and validate its effectiveness in diverse clinical settings.