Abstract
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.
Highlights
Healthcare-associated infections (HCAIs) annually affect millions of patients, are a major burden for the healthcare system, and are associated with prolonged hospital stay, increased morbidity, mortality, and costs [1e3]
A fully automated surveillance algorithm based on Natural language processing (NLP) to find UTI symptoms in free-text had acceptable performance to detect healthcareassociated urinary tract infections (HA-UTI) compared to manual record review
Together with 200 admissions with no UCx performed, this amounted to 1258 potential UTI episodes in the validation dataset used for the calculations of algorithm performance (Table I and Figure 1)
Summary
Healthcare-associated infections (HCAIs) annually affect millions of patients, are a major burden for the healthcare system, and are associated with prolonged hospital stay, increased morbidity, mortality, and costs [1e3]. To allocate necessary resources and evaluate the effect of interventions, continuous surveillance with feedback to healthcare personnel and stakeholders is important [4,5]. Much HCAI surveillance is currently based on time-consuming and resource-intensive manual review of patient records, which is prone to subjective interpretation and surveillance bias [6e8]. With the use of electronic health records (EHRs), there is increasing access to detailed electronic health data. This digitalization allows automated surveillance systems to replace manual approaches and to generate standardized and continuous surveillance data [9]. Surveillance algorithms need to be thoroughly validated before being implemented in a clinical setting
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