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Published on: July 13, 2019
Predicting outcomes in central venous catheter salvage in pediatric central line-associated bloodstream infection
Lorne W Walker1,2, Andrew J Nowalk3, Shyam Visweswaran4,5
1Division of Pediatric Infectious Diseases, Oregon Health and Sciences University, Portland, Oregon, USA.
Insights
Machine learning models can predict outcomes for pediatric central venous catheter (CVC) salvage in central line-associated bloodstream infections (CLABSIs). This helps clinicians decide whether to attempt CVC salvage, improving patient care.
Area of Science:
- Pediatric infectious diseases
- Health informatics
- Machine learning in healthcare
Background:
- Central line-associated bloodstream infections (CLABSIs) are a significant healthcare burden in children.
- Salvaging infected central venous catheters (CVCs) is an option, but success is uncertain, often leading to removal and replacement.
- Predicting salvage outcomes is crucial for informed clinical decision-making.
Purpose of the Study:
- To develop and evaluate machine learning models for predicting outcomes of CVC salvage in pediatric CLABSI.
- To aid clinicians in deciding whether to attempt CVC salvage versus immediate removal and replacement.
- To identify key predictors of CVC salvage success and failure over time.
Main Methods:
- Utilized electronic health records from a 14-year period, identifying 969 pediatric CLABSIs.
- Developed four types of machine learning models using 164 potential predictors.
- Predicted two key salvage failure outcomes: infection recurrence and CVC removal, at 10 time points.
Main Results:
- Model performance, measured by AUC, ranged from 0.56 to 0.83.
- The model predicting infection recurrence outperformed the model predicting CVC removal.
- Key predictors of salvage outcomes varied significantly over the 10 time points studied.
Conclusions:
- Machine learning-based prediction models can effectively inform clinical decisions for pediatric CVC salvage.
- The developed models offer valuable insights into predicting outcomes for children with CLABSI.
- Understanding the temporal variability of predictors is essential for accurate outcome prediction.
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 models to predict 2 failed salvage outcomes, infection recurrence and CVC removal, at 10 time points between 7 days and 1 year from infection onset.
Results:
The area under the receiver-operating characteristic curve varied from 0.56 to 0.83, and key predictors varied over time. The infection recurrence model performed better than the CVC removal model did.
Conclusions:
Machine learning-based outcome prediction can inform clinical decision making for children. We developed and evaluated several models to predict clinically relevant outcomes in the context of CVC salvage in pediatric CLABSI and illustrate the variability of predictors over time.
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