Lorne W Walker1,2, Andrew J Nowalk3, Shyam Visweswaran4,5. 1. Division of Pediatric Infectious Diseases, Oregon Health and Sciences University, Portland, Oregon, USA. 2. Department of Medical Informatics and Medical Epidemiology, Oregon Health and Sciences University, Portland, Oregon, USA. 3. Division of Infectious Diseases, Children's Hospital of Pittsburgh, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA. 4. Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. 5. Intelligent Systems Program, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
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 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.
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.
Authors: Leonard A Mermel; Michael Allon; Emilio Bouza; Donald E Craven; Patricia Flynn; Naomi P O'Grady; Issam I Raad; Bart J A Rijnders; Robert J Sherertz; David K Warren Journal: Clin Infect Dis Date: 2009-07-01 Impact factor: 9.079
Authors: David M Vock; Julian Wolfson; Sunayan Bandyopadhyay; Gediminas Adomavicius; Paul E Johnson; Gabriela Vazquez-Benitez; Patrick J O'Connor Journal: J Biomed Inform Date: 2016-03-16 Impact factor: 6.317