The accuracy of fully automated algorithms for surveillance of healthcare-associated urinary tract infections in hospitalized patients

Surveillance for healthcare-associated infections such as healthcare-associated urinary tract infections (HA-UTI) is important for directing resources and evaluating interventions. However, traditional surveillance methods are resource-intensive and subject to bias. To develop and validate a fully a...

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Published in:The Journal of hospital infection Vol. 110; pp. 139 - 147
Main Authors: van der Werff, S.D., Thiman, E., Tanushi, H., Valik, J.K., Henriksson, A., Ul Alam, M., Dalianis, H., Ternhag, A., Nauclér, P.
Format: Journal Article
Language:English
Published: England Elsevier Ltd 01.04.2021
Subjects:
Algorithms
Automated surveillance
Healthcare-associated infection
Infectious Disease
Natural language processing
Urinary tract infections
Urinary tract infections
Natural language processing
Algorithms
Healthcare-associated infection
Automated surveillance
ISSN:0195-6701, 1532-2939, 1532-2939
Online Access:Get full text
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Summary:Surveillance for healthcare-associated infections such as healthcare-associated urinary tract infections (HA-UTI) is important for directing resources and evaluating interventions. However, traditional surveillance methods are resource-intensive and subject to bias. To develop and validate a fully automated surveillance algorithm for HA-UTI using electronic health record (EHR) data. Five algorithms were developed using EHR data from 2979 admissions at Karolinska University Hospital from 2010 to 2011: (1) positive urine culture (UCx); (2) positive UCx + UTI codes (International Statistical Classification of Diseases and Related Health Problems, 10th revision); (3) positive UCx + UTI-specific antibiotics; (4) positive UCx + fever and/or UTI symptoms; (5) algorithm 4 with negation for fever without UTI symptoms. Natural language processing (NLP) was used for processing free-text medical notes. The algorithms were validated in 1258 potential UTI episodes from January to March 2012 and results extrapolated to all UTI episodes within this period (N = 16,712). The reference standard for HA-UTIs was manual record review according to the European Centre for Disease Prevention and Control (and US Centers for Disease Control and Prevention) definitions by trained healthcare personnel. Of the 1258 UTI episodes, 163 fulfilled the ECDC HA-UTI definition and the algorithms classified 391, 150, 189, 194, and 153 UTI episodes, respectively, as HA-UTI. Algorithms 1, 2, and 3 had insufficient performances. Algorithm 4 achieved better performance and algorithm 5 performed best for surveillance purposes with sensitivity 0.667 (95% confidence interval: 0.594–0.733), specificity 0.997 (0.996–0.998), positive predictive value 0.719 (0.624–0.807) and negative predictive value 0.997 (0.996–0.997). A fully automated surveillance algorithm based on NLP to find UTI symptoms in free-text had acceptable performance to detect HA-UTI compared to manual record review. Algorithms based on administrative and microbiology data only were not sufficient.
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ISSN:0195-6701
1532-2939
1532-2939
DOI:10.1016/j.jhin.2021.01.023