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Stephen R. Benoit, MD, from the Division of Emergency Preparedness and Response at the National Center for Public Health Informatics, and colleagues, sought to determine the feasibility of using electronic laboratory and admission/discharge/transfer data from BioSense, a national automated surveillance system, to apply new modified Clostridium difficile infection (CDI) surveillance definitions and calculate overall and facility-specific rates of disease.
In this retrospective, multicenter cohort study of 34 hospitals sending inpatient, emergency department, and/or outpatient data to BioSense, the researchers used laboratory codes and text-parsing methods to extract C. difficilepositive toxin assay results from laboratory data sent to BioSense from Jan. 1, 2007, through June 30, 2008; these were merged with administrative records to determine whether cases were community associated or healthcare onset, as well as patient-day data for rate calculations. A patient was classified as having hospital-onset CDI if he or she had a C. difficile toxin-positive result on a stool sample collected three or more days after admission and community-onset CDI if the specimen was collected less than three days after admission or the patient was not hospitalized.
Benoit, et al. report that a total of 4,585 patients in 12 states had C. difficile-positive assay results, and that more than half of the cases were community-onset. Of these, 30.8 percent occurred in patients who were recently hospitalized. The overall rate of healthcare-onset CDI was 7.8 cases per 10,000 patient-days, with a range among facilities of 1.5 to 27.8 cases per 10,000 patient-days.
The researchers conclude that electronic laboratory data sent to the BioSense surveillance system were successfully used to produce disease rates of CDI comparable to those of other studies, which shows the feasibility of using electronic laboratory data to track a disease of public health importance. Their research was published in Infection Control & Hospital Epidemiology.Â
Reference: Benoit SR, McDonald LC, English R and Tokars JI. Automated Surveillance of Clostridium difficile Infections Using BioSense. Infect Control Hosp Epidem. Vol. 32, No. 1. January 2011.