Development and Evaluation of the Automated Risk Assessment System for Catheter-Associated Urinary Tract Infection

Catheter-associated urinary tract infection is one of the most common healthcare-acquired infections. It is important to institute preventive measures such as surveillance of the appropriate use of indwelling urinary catheters and timely removal by identifying patients at high risk for catheter-associated urinary tract infection. The purpose of this study was to develop an Automated Risk Assessment System for Catheter-Associated Urinary Tract Infection and evaluate its predictive validity. This study involved secondary data analysis based on a case-control study and used the data extracted from electronic health records. The Automated Risk Assessment System for Catheter-Associated Urinary Tract Infection was developed using a risk-scoring algorithm that was based on a logistic regression model and integrated into the electronic health records. The following eight risk factors for urinary tract infection were included in the logistic regression model: length of stay, admission to the Intensive Care Unit, dependent physical activity, highest neutrophil level (%), lowest blood sodium level of less than 136 mEq/L, lowest blood albumin level of less than 3.5 g/dL, highest blood urea nitrogen level of greater than 20 mg/dL, and indwelling urinary catheter application period (days). The risk groups classified by the Automated Risk Assessment System for Catheter-Associated Urinary Tract Infection were automatically displayed on the patient summary screen of the electronic health record. The predictive validity of the Automated Risk Assessment System for Catheter-Associated Urinary Tract Infection gradually increased up to the fifth and sixth assessment data after patients' admission; then, it leveled. It is possible to allocate nurses' time and effort for catheter-associated urinary tract infection risk assessment to surveillance of the use, removal, and management of indwelling urinary catheters and education and training by using the Automated Risk Assessment System for Catheter-Associated Urinary Tract Infection in clinical settings. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2010-0027077, NRF-2014R1A2A2A01003313). The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. All the authors contributed to the design of the study, collection of data, analysis and interpretation of data, and writing and approval of the manuscript. Corresponding author: Sun-Mi Lee, PhD, MSN, The Catholic University of Korea, College of Nursing, 222, Banpo-daero, Seocho-gu, Seoul, Republic of Korea 06591 (leesunmi@catholic.ac.kr). Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.

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