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Identification of urinary tract infections using electronic health record data

  • Kathryn L. Colborn
    Correspondence
    Address correspondence to Kathryn L. Colborn, PhD, University of Colorado Anschutz Medical Campus, Mail Stop B119, 13001 E 17th Pl, Rm C3011, Aurora, CO 80045.
    Affiliations
    Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO
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  • Michael Bronsert
    Affiliations
    Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, CO

    Department of Surgery, Surgical Outcomes and Applied Research Program, School of Medicine, University of Colorado Anschutz Medical Campus, University of Colorado, Aurora, CO
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  • Karl Hammermeister
    Affiliations
    Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, CO

    Department of Surgery, Surgical Outcomes and Applied Research Program, School of Medicine, University of Colorado Anschutz Medical Campus, University of Colorado, Aurora, CO

    Department of Cardiology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO
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  • William G. Henderson
    Affiliations
    Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO

    Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, CO

    Department of Surgery, Surgical Outcomes and Applied Research Program, School of Medicine, University of Colorado Anschutz Medical Campus, University of Colorado, Aurora, CO
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  • Abhinav B. Singh
    Affiliations
    Department of Surgery, Surgical Outcomes and Applied Research Program, School of Medicine, University of Colorado Anschutz Medical Campus, University of Colorado, Aurora, CO
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  • Robert A. Meguid
    Affiliations
    Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, CO

    Department of Surgery, Surgical Outcomes and Applied Research Program, School of Medicine, University of Colorado Anschutz Medical Campus, University of Colorado, Aurora, CO
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Published:December 04, 2018DOI:https://doi.org/10.1016/j.ajic.2018.10.009

      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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