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Prediction of central line-associated bloodstream infection: focus on time of insertion

Published online by Cambridge University Press:  10 March 2025

Ari Moskowitz ,
Melissa Fazzari ,
Luke Andrea Open the ORCID record for Luke Andrea [Opens in a new window] ,
Jianwen Wu ,
Arup Gope Open the ORCID record for Arup Gope [Opens in a new window] ,
Thomas Butler Open the ORCID record for Thomas Butler [Opens in a new window] ,
Amira Mohamed Open the ORCID record for Amira Mohamed [Opens in a new window] ,
Christine Shen Open the ORCID record for Christine Shen [Opens in a new window] ,
Fran Ganz-Lord Open the ORCID record for Fran Ganz-Lord [Opens in a new window]  and
Inessa Gendlina Open the ORCID record for Inessa Gendlina [Opens in a new window]
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Ari Moskowitz*
Affiliation:
Division of Critical Care Medicine, Montefiore Medical Center, Bronx, NY, USA The Bronx Center for Critical Care Outcomes and Resuscitation Research, Bronx, NY, USA
Melissa Fazzari
Affiliation:
Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
Luke Andrea
Affiliation:
Division of Critical Care Medicine, Montefiore Medical Center, Bronx, NY, USA The Bronx Center for Critical Care Outcomes and Resuscitation Research, Bronx, NY, USA
Jianwen Wu
Affiliation:
Division of Critical Care Medicine, Montefiore Medical Center, Bronx, NY, USA The Bronx Center for Critical Care Outcomes and Resuscitation Research, Bronx, NY, USA
Arup Gope
Affiliation:
Division of Critical Care Medicine, Montefiore Medical Center, Bronx, NY, USA The Bronx Center for Critical Care Outcomes and Resuscitation Research, Bronx, NY, USA
Thomas Butler
Affiliation:
Division of Critical Care Medicine, Montefiore Medical Center, Bronx, NY, USA The Bronx Center for Critical Care Outcomes and Resuscitation Research, Bronx, NY, USA
Amira Mohamed
Affiliation:
Division of Critical Care Medicine, Montefiore Medical Center, Bronx, NY, USA The Bronx Center for Critical Care Outcomes and Resuscitation Research, Bronx, NY, USA
Christine Shen
Affiliation:
The Bronx Center for Critical Care Outcomes and Resuscitation Research, Bronx, NY, USA Albert Einstein College of Medicine, Bronx, NY, USA
Fran Ganz-Lord
Affiliation:
Division of General Internal Medicine, Montefiore Medical Center, Bronx, NY, USA
Inessa Gendlina
Affiliation:
Division of Infectious Diseases, Montefiore Medical Center, Bronx, NY, USA
Michelle Ng Gong
Affiliation:
Division of Critical Care Medicine, Montefiore Medical Center, Bronx, NY, USA The Bronx Center for Critical Care Outcomes and Resuscitation Research, Bronx, NY, USA
*
Corresponding author: Ari Moskowitz; Email: [email protected]
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Abstract

Objective:

Central line-associated bloodstream infections (CLABSIs) result in morbidity and mortality among hospitalized patients. Hospital interventions to reduce the incidence of CLABSI are often broadly applied to all patients with central venous access. Identifying central lines at high risk for CLABSI at time of insertion will allow for a more focused delivery of preventative interventions.

Design:

This was an observational cohort study conducted at three hospitals including all patients who received central venous access. CLABSIs were identified using an institutional CLABSI database maintained by the hospital epidemiology team. Logistic regression (LASSO) and machine learning (random forest, XGboost) techniques were applied for the prediction of CLABSI occurrence, adjusting for selected patent and insertion-level characteristics.

Results:

A total of 40,008 central venous catheters were included, of which 409 (1.02%) were associated with CLABSI. The random forest and the XGBoost models had the highest discrimination (Area Under the Received Operating Curve [AUC] 0.79) followed by LASSO (0.73). High illness severity, receipt of total parenteral nutrition, receipt of hemodialysis, pre-insertion hospital length-of-stay, and low albumin levels were all predictive of CLABSI occurrence. Precision for all models was poor owing to a high false-positive rate.

Discussion:

CLABSI can be predicted based upon patient and insertion level factors in the electronic health record. In this study, random forest and gradient-boosted models had the highest AUC. Prediction cut-offs for the identification of CLABSI can be adjusted based upon the acceptable rate of false-positives for a given CLABSI preventative intervention.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , First View , pp. 1 - 8
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America

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