National Automated Surveillance of Hospital-Acquired Bacteremia in Denmark Using a Computer Algorithm

Abstract

BACKGROUND In 2015, Denmark launched an automated surveillance system for hospital-acquired infections, the Hospital-Acquired Infections Database (HAIBA). OBJECTIVE To describe the algorithm used in HAIBA, to determine its concordance with point prevalence surveys (PPSs), and to present trends for hospital-acquired bacteremia SETTING Private and public hospitals in Denmark METHODS A hospital-acquired bacteremia case was defined as at least 1 positive blood culture with at least 1 pathogen (bacterium or fungus) taken between 48 hours after admission and 48 hours after discharge, using the Danish Microbiology Database and the Danish National Patient Registry. PPSs performed in 2012 and 2013 were used for comparison. RESULTS National trends showed an increase in HA bacteremia cases between 2010 and 2014. Incidence was higher for men than women (9.6 vs 5.4 per 10,000 risk days) and was highest for those aged 61-80 years (9.5 per 10,000 risk days). The median daily prevalence was 3.1% (range, 2.1%-4.7%). Regional incidence varied from 6.1 to 8.1 per 10,000 risk days. The microorganisms identified were typical for HA bacteremia. Comparison of HAIBA with PPS showed a sensitivity of 36% and a specificity of 99%. HAIBA was less sensitive for patients in hematology departments and intensive care units. Excluding these departments improved the sensitivity of HAIBA to 44%. CONCLUSIONS Although the estimated sensitivity of HAIBA compared with PPS is low, a PPS is not a gold standard. Given the many advantages of automated surveillance, HAIBA allows monitoring of HA bacteremia across the healthcare system, supports prioritizing preventive measures, and holds promise for evaluating interventions. Infect Control Hosp Epidemiol 2017;38:559-566.

Electronically assisted surveillance systems of healthcare-associated infections: a systematic review

“…National trends showed an increase in hospital-acquired bacteraemia (HAIBA) cases between 2010 and 2014. Although comparison of their HAIBA algorithm with the results of point prevalence surveys showed a sensitivity of 0.36 they concluded that given the many advantages of automated surveillance, the HAIBA algorithm allows monitoring of HAIBA across the healthcare system, supports prioritising preventive measures, and holds promise for evaluating interventions [80]. Another approach to automated surveillance of BSI is the reuse of the ICD-coding system.…”

Hospital-acquired infections in the adult intensive care unit—Epidemiology, antimicrobial resistance patterns, and risk factors for acquisition and mortality

“…The European Center for Disease Control (ECDC) is implementing such policies with significant success, leveraging advances in digital technology. 5,6 Intensive care units (ICUs) present a specific setting in which HAIs are acquired at a higher rate and exhibit higher mortality. 7 In Serbia, isolated single-center reports from ICUs have been published and looked at the neonatal population, 8 risk factors for acquisition in trauma patients, 9 and antimicrobial resistance patterns of specific isolates.…”

Automated digital reporting of clinical laboratory information to national public health surveillance systems, results of a EU/EEA survey, 2018

“…In the context of laboratory-based surveillance, this includes use of a structured message syntax, such as Health Level 7 International (HL7) and of semantic standards for coding laboratory tests and diagnostic results as well as clinical observations. A concerted adoption of data standards and their gradual implementation by reporting laboratories coupled with central data processing algorithms has been implemented for national laboratory-based electronic surveillance of communicable diseases in both large countries like the US [ 28 , 29 ] and small countries like Denmark [ 3 , 11 , 12 , 30 ]. Universal adoption occurred faster in Denmark than in the US, where considerably more clinical laboratories had to adapt their LIMS to connect to the national reporting system [ 29 , 30 ].…”

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