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Risk Prediction Models for Central Venous Catheter-Related Bloodstream Infections in Continuous Blood Purification:A

Xiaoli Liu1, Zaichun Pu2, Ping Jia3

  • 1Department of Critical Care Medicine, Deyang People's Hospital, Deyang, Sichuan, 618000, China.

The Journal of Hospital Infection
|May 15, 2026
PubMed
Summary

Risk prediction models for central venous catheter-related bloodstream infections (CRBSI) in Continuous Blood Purification (CBP) patients show good performance. Key predictors like diabetes and catheter site can improve early warning systems.

Keywords:
Catheter-Related Bloodstream InfectionCentral Venous CathetersContinuous Blood PurificationMeta-AnalysisRisk Prediction ModelSystematic Review

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Published on: July 13, 2019

Area of Science:

  • Nephrology
  • Infectious Diseases
  • Medical Informatics

Background:

  • Central venous catheter-related bloodstream infections (CRBSI) are a significant complication in Continuous Blood Purification (CBP) patients.
  • Effective risk prediction models are crucial for early detection and prevention of CRBSI.

Purpose of the Study:

  • To systematically review the performance of CRBSI risk prediction models in CBP patients.
  • To identify key predictors of CRBSI in this population.

Main Methods:

  • Systematic literature search of multiple databases up to July 14, 2025.
  • Inclusion of studies developing or validating CRBSI prediction models in hemodialysis patients.
  • Meta-analysis of model performance (AUC) and identified predictors, with risk of bias assessment using PROBAST.

Main Results:

  • Twenty-three studies with 5,998 patients were analyzed, showing CRBSI incidence from 8.5% to 41.15%.
  • Developed models demonstrated good discriminatory ability with a pooled AUC of 0.879 (95% CI: 0.851-0.906).
  • Significant predictors included diabetes history, catheter site, age, albumin, hemoglobin, smoking, antibiotic use, catheter reinsertion, dialysis vintage, catheter care, catheter type, and prior infection.

Conclusions:

  • CRBSI prediction models show good performance and clinical applicability, with identified risk factors valuable for early warning systems.
  • Methodological limitations and lack of external validation necessitate improvements in model development and reporting.
  • Future research should focus on rigorous validation to enhance the robustness of CRBSI prediction tools.