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.
Keywords: Elastic-net; Electronic health record UTI; National Surgical Quality Improvement Program; Postoperative complication; Supervised learning.
Copyright © 2018. Published by Elsevier Inc.