Predicting outcomes in central venous catheter salvage in pediatric central line-associated bloodstream infection.

ABSTRACT:

Objective

Central line-associated bloodstream infections (CLABSIs) are a common, costly, and hazardous healthcare-associated infection in children. In children in whom continued access is critical, salvage of infected central venous catheters (CVCs) with antimicrobial lock therapy is an alternative to removal and replacement of the CVC. However, the success of CVC salvage is uncertain, and when it fails the catheter has to be removed and replaced. We describe a machine learning approach to predict individual outcomes in CVC salvage that can aid the clinician in the decision to attempt salvage.

Materials and methods

Over a 14-year period, 969 pediatric CLABSIs were identified in electronic health records. We used 164 potential predictors to derive 4 types of machine learning

SUBMITTER: Walker LW 

PROVIDER: S-EPMC7973452 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature