Background
Population ascertainment of postoperative urinary tract infections (UTIs) is time-consuming
and expensive, as it often requires manual chart review. Using the American College
of Surgeons National Surgical Quality Improvement Program UTI status of patients who
underwent an operation at the University of Colorado Hospital, we sought to develop
an algorithm for identifying UTIs using data from the electronic health record.
Methods
Data were split into training (operations occurring between 2013-2015) and test (operations
in 2016) sets. A binomial generalized linear model with an elastic-net penalty was
used to fit the model and carry out variables selection. International classification
of disease codes, common procedural terminology codes, antibiotics, catheterization,
and common procedural terminology-specific UTI event rates were included as predictors.
The Youden's J statistic was used to determine the optimal classification threshold.
Results
Of 6,840 patients, 134 (2.0%) had a UTI. The model achieved 92% specificity, 80% sensitivity,
100% negative predictive value, 16% positive predictive value, and an area under the
curve of 0.94 using a decision threshold of 0.03.
Conclusions
A model with 14 predictors from the electronic health record identifies UTIs well,
and it could be used to scale up UTI surveillance or to estimate the impact of large-scale
interventions on UTI rates.
Key Words
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Article info
Publication history
Published online: December 04, 2018
Footnotes
Funding/support: This project was supported by grant number R03HS026019 from the Agency for Healthcare Research and Quality. This study was also supported by a transformational research grant from the University of Colorado School of Medicine's Data-Science-to-Patient-Value initiative.
The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality.
Conflicts of interest: None to report.
Identification
Copyright
© 2018 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.
