The final CLABSI surveillance rules are shown in . Each episode, or BSI event, begins with a blood culture with microbial growth (ie, a positive blood culture). Each episode is defined as all unique positive blood cultures found in a patient identified during a five calendar-day period. The collection day of the positive blood culture isolate is assessed; isolates obtained before the third hospital day are considered community-onset and disregarded. Isolates obtained on or after the third hospital day are considered hospital-onset in origin and are classified as infections or contaminants. The classification of common skin commensals (CSCs)—organisms that commonly grow on skin and can contaminate blood cultures as they are being obtained or processed (ie, coagulase-negative staphylococci, Corynebacterium spp., Micrococcus spp., Bacillus spp., and Propionibacterium spp.)—differs among the five algorithms; for all rules, isolates that are not CSCs are considered true infections.
Following the classification of an isolate as a contaminant or a true infection, all additional blood isolates of the same species are ignored for the subsequent 30 days and are considered duplicates. Isolates obtained within a 5 day period are grouped together into an episode. Episodes are characterized as primary BSIs (ie, an intravascular infection) or secondary (ie, related to infection at another site, such as the respiratory or urinary tract infection). Cultures from body sites other than blood are compared to blood culture isolates (all sites for non-CSCs, only wound sites for CSCs); if any blood isolate in an episode is deemed secondary (ie, due to match with culture results from a non-bloodstream site), then the entire episode is classified as a secondary BSI episode. Two rules are used for the assessment of secondary BSIs: in the first (S1), only non-bloodstream isolates obtained within 3 days before, to 7 days after a bloodstream isolate are considered, while the second (S2) uses all non-bloodstream isolates obtained during the same hospital admission as a bloodstream isolate. The ward location of BSI episodes is assigned based on the patient location at the presumed time of incubation of infection, judged based on the location of the patient 2 days prior to collection date of the first positive blood culture in an episode. Prior admissions are used in the deduplication process, in that the 30 day rule for deduplication can cross admissions, but prior admission information is not used in the assignment of community—or nosocomial—onset for BSIs. Clinical information such as the presence of hypotension or fever is not used in the algorithms. Future iterations of the algorithms, with more robust data requirements, may include these data elements and might augment the performance characteristics of rules particularly in the setting of CSCs.
Primary BSI episodes identified by the electronic rules are further classified as CLABSIs if a central venous catheter was present at the time of the episode. Where these data are available electronically (eg, nursing documentation on a structured form), this determination can be automated. If the presence of a central vascular catheter is not determinable electronically, manual assessments of the presence of central lines must be made by chart review. Similarly, determination of the denominator—central-catheter days for the studied unit—relies on either manual data collection or through electronic documentation.
Distribution and implementation of CLABSI surveillance code
Following central testing of the algorithm and correct classification of CLABSI episodes as compared to human review of sampled episodes, code was distributed to partner institutions with the aim of rule implementation and validation at each site. The code was developed at Hospital A, which used Microsoft SQL Server 2000, with microbiology and bed information tables for patient location. Of the three partner hospitals that adopted the CLABSI algorithms, Hospital B did not have a data warehouse incorporating laboratory and other clinical data; Hospital C used SYBASE for its clinical data warehouse and had stored microbiology, bed information and central-line utilization tables; and Hospital D used an ORACLE database platform for its clinical data warehouse.
Several challenges for code implementation were noted—issues in data structure and format and in compatibility of SQL code (). Because code was redeveloped at each center as needed, custom solutions were developed at each center. Two problems were notable. First, microbiology data exhibited significant semantic differences between centers, and in two centers were not available as discrete data, but rather, in free text format. Second, differences in local SQL languages required redevelopment time at each center. Specific examples of issues unique to each hospital will now be described.
Hospitals, database systems and challenges encountered in automated bloodstream infection surveillance
Because Hospital B did not have an accessible data warehouse at the time of the project, a data extract based on an existing report found in the microbiology system, printed to a text file, was imported into the SQL server database used at Hospital A. The data were unstructured, and converted to a flat table using customized templates developed using Monarch (Datawatch Corporation, Chelmsford, MA, USA). The resulting report contained organism names that were reported in free text fields, while specimen source information was discrete and mapped to standard nomenclature. Organism names were further refined using python scripts and validated to ensure accuracy.
Hospital C, which had an internally developed and validated data warehouse, was able to use the SQL-coded algorithms with minor modification, that is, correction of capitalization and formatting of SQL and adaptation of code to local data structures. This was possible because of the similarities in implementation of SQL between Microsoft SQL Server and SYBASE (both use T/SQL).
At Hospital D, reuse of the SQL code was abandoned due to differences between implementation of SQL in ORACLE (used at Hospital D) and Microsoft SQL Server (used at Hospital A) (ie, PL/SQL vs T-SQL). Therefore, the algorithms and flowchart were used to develop a database agnostic, generic code.30
Microbiology data at Hospital D, like Hospital B, were limited by the use of free text fields in organism name fields, prompting standardization of the organism name list and a review of the business process of microbiology reporting of organism names. Natural language processing methods were used to translate free text organism name reports to a set of discrete organisms.
Development of validation data sets
To verify that code redevelopment at each partner hospital faithfully represented the original code, each hospital tested its algorithms against a specially developed test data set (available at http://bsi.cchil.org
). Derived from cleaned Hospital A data, the data set contained de-identified, linked results from microbiology, pharmacy and bed information databases. Five test tables were developed representing a 2 year period: (1) a microbiology data set for all positive and negative blood cultures; (2) a microbiology data set of positive non-blood site cultures; (3) patient location information, based on admission-discharge-transfer transactions; (4) a pharmacy data set of Vancomycin prescriptions; and (5) a SNOMED mapping table, with organism names mapped to SNOMED codes. In addition, sample outputs, with raw counts and aggregated counts for each of the five rules, were provided. Using the example of the test data set, Hospitals B–D compared their rule-generated BSI counts to aggregated monthly data and patient level counts. When discrepancies were noted, algorithms were reviewed and improved iteratively until observed results matched expected results for the test data set. This process continued at each site until an exact match occurred. The time to achieve an exact match varied between sites, occurring within 3–4 weeks at one site (Hospital C) and requiring several months for the entire process at another (Hospital D).