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The accuracy of ful...
The accuracy of fully automated algorithms for surveillance of healthcare-associated urinary tract infections in hospitalized patients
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- van der Werff, S. D. (author)
- Karolinska Institutet
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- Thiman, Emil (author)
- Karolinska Institutet
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- Tanushi, Hideyuki (author)
- Karolinska Institutet
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- Karlsson Valik, John (author)
- Karolinska Institutet
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- Henriksson, Aron (author)
- Stockholms universitet,Institutionen för data- och systemvetenskap
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- Alam, Mahbub Ul (author)
- Stockholms universitet,Institutionen för data- och systemvetenskap
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- Dalianis, Hercules (author)
- Stockholms universitet,Institutionen för data- och systemvetenskap
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- Ternhag, Anders (author)
- Karolinska Institutet
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- Nauclér, Pontus (author)
- Karolinska Institutet
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(creator_code:org_t)
- Elsevier BV, 2021
- 2021
- English.
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In: Journal of Hospital Infection. - : Elsevier BV. - 0195-6701 .- 1532-2939. ; 110, s. 139-147
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Abstract
Subject headings
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- Background: Surveillance for healthcare-associated infections such as healthcareassociated urinary tract infections (HA-UTI) is important for directing resources and evaluating interventions. However, traditional surveillance methods are resourceintensive and subject to bias.Aim: To develop and validate a fully automated surveillance algorithm for HA-UTI using electronic health record (EHR) data.Methods: 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 1/4 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.Findings: 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).Conclusion: 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.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Infektionsmedicin (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Infectious Medicine (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Mikrobiologi inom det medicinska området (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Microbiology in the medical area (hsv//eng)
Keyword
- Automated surveillance
- Algorithms
- Healthcare-associated infection
- Natural language processing
- Urinary tract infections
Publication and Content Type
- ref (subject category)
- art (subject category)
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