Abstract
Background. Electronic surveillance systems (ESSs) that utilize existing information in databases are more efficient than conventional infection surveillance methods. The objective was to assess an ESS for bloodstream infections (BSIs) in the Calgary Zone for its agreement with traditional medical record review. Methods. The ESS was developed by linking related data from regional laboratory and hospital administrative databases and using set definitions for excluding contaminants and duplicate isolates. Infections were classified as hospital-acquired (HA), healthcare-associated community-onset (HCA), or community-acquired (CA). A random sample of patients from the ESS was then compared with independent medical record review. Results. Among the 308 patients selected for comparative review, the ESS identified 318 episodes of BSI of which 130 (40.9%) were CA, 98 (30.8%) were HCA, and 90 (28.3%) were HA. Medical record review identified 313 episodes of which 136 (43.4%) were CA, 97 (30.9%) were HCA, and 80 (25.6%) were HA. Episodes of BSI were concordant in 304 (97%) cases. Overall, there was 85.5% agreement between ESS and medical record review for the classification of where BSIs were acquired (kappa = 0.78, 95% Confidence Interval: 0.75–0.80). Conclusion. This novel ESS identified and classified BSIs with a high degree of accuracy. This system requires additional linkages with other related databases.
Highlights
Bloodstream infections (BSIs) constitute an important health problem and in severe cases have a high fatality rate [1]
This study shows that the Electronic surveillance systems (ESSs) is a valid tool for the accurate identification of incident episodes of bloodstream infections (BSIs)
The majority of discrepancies were due to multiple false positive blood cultures of coagulase-negative staphylococci (CoNS) being classified as true episodes of BSI by the ESS but as contaminants by the medical record review (MRR)
Summary
Bloodstream infections (BSIs) constitute an important health problem and in severe cases have a high fatality rate [1]. Surveillance of BSIs is important to measure and monitor the burden of disease, evaluate risk factors for acquisition, monitor temporal trends in occurrence, and identify emerging and reemerging infections with changing severity. Traditional surveillance methods are dependent on manual collection of clinical data from the medical record, clinical laboratory, and pharmacy by trained infection control professionals (ICPs). This approach is time-consuming and costly and focuses infection control resources on counting rather than preventing infections [3].
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