Open Access Research article

Classification of positive blood cultures: computer algorithms versus physicians' assessment - development of tools for surveillance of bloodstream infection prognosis using population-based laboratory databases

Kim O Gradel12*, Jenny Dahl Knudsen3, Magnus Arpi4, Christian Østergaard34, Henrik C Schønheyder5, Mette Søgaard5 and for the Danish Collaborative Bacteraemia Network (DACOBAN)

Author Affiliations

1 Research Unit of Clinical Epidemiology, Institute of Clinical Research, University of Southern Denmark, Odense, Denmark

2 Centre for National Clinical Databases, South, Odense University Hospital, Odense, Denmark

3 Department of Clinical Microbiology, Copenhagen University Hospital Hvidovre Hospital, Hvidovre, Denmark

4 Department of Clinical Microbiology, Copenhagen University Hospital Herlev Hospital, Herlev, Denmark

5 Department of Clinical Microbiology, Aalborg Hospital, Aarhus University Hospital, Aalborg, Denmark

For all author emails, please log on.

BMC Medical Research Methodology 2012, 12:139  doi:10.1186/1471-2288-12-139

Published: 12 September 2012



Information from blood cultures is utilized for infection control, public health surveillance, and clinical outcome research. This information can be enriched by physicians’ assessments of positive blood cultures, which are, however, often available from selected patient groups or pathogens only. The aim of this work was to determine whether patients with positive blood cultures can be classified effectively for outcome research in epidemiological studies by the use of administrative data and computer algorithms, taking physicians’ assessments as reference.


Physicians’ assessments of positive blood cultures were routinely recorded at two Danish hospitals from 2006 through 2008. The physicians’ assessments classified positive blood cultures as: a) contamination or bloodstream infection; b) bloodstream infection as mono- or polymicrobial; c) bloodstream infection as community- or hospital-onset; d) community-onset bloodstream infection as healthcare-associated or not. We applied the computer algorithms to data from laboratory databases and the Danish National Patient Registry to classify the same groups and compared these with the physicians’ assessments as reference episodes. For each classification, we tabulated episodes derived by the physicians’ assessment and the computer algorithm and compared 30-day mortality between concordant and discrepant groups with adjustment for age, gender, and comorbidity.


Physicians derived 9,482 reference episodes from 21,705 positive blood cultures. The agreement between computer algorithms and physicians’ assessments was high for contamination vs. bloodstream infection (8,966/9,482 reference episodes [96.6%], Kappa = 0.83) and mono- vs. polymicrobial bloodstream infection (6,932/7,288 reference episodes [95.2%], Kappa = 0.76), but lower for community- vs. hospital-onset bloodstream infection (6,056/7,288 reference episodes [83.1%], Kappa = 0.57) and healthcare-association (3,032/4,740 reference episodes [64.0%], Kappa = 0.15). The 30-day mortality in the discrepant groups differed from the concordant groups as regards community- vs. hospital-onset, whereas there were no material differences within the other comparison groups.


Using data from health administrative registries, we found high agreement between the computer algorithms and the physicians’ assessments as regards contamination vs. bloodstream infection and monomicrobial vs. polymicrobial bloodstream infection, whereas there was only moderate agreement between the computer algorithms and the physicians’ assessments concerning the place of onset. These results provide new information on the utility of computer algorithms derived from health administrative registries.