Using rapid diagnostic tests and algorithms to make suggestions on how to best use antibiotics helped reduce the amount of days people needed to stay on them, a new report finds.
The study was published Monday in Open Forum Infectious Diseases.
A team from Rutgers University and the University of Pittsburgh looked at blood cultures from 182 different people who were put into three groups for separate four-month spans. The first period was before rapid diagnosis tests, or RDTs, were used. The second period was after RDTs were used but before using the algorithm, and the third was from samples after RDTs and algorithms.
The scientists wanted to see whether quickly spotting coagulase-negative Staphylococcus, or CoNS (a common blood contaminant that doesn’t always cause a bloodstream infection) could reduce the rates of antibiotic use. The staph infection often is found on skin.
The researchers noted that 69% of cases in the first period, 19% of cases in the second and 12% of cases in the third period were classified as simple cases.
There was a significant reduction in the number of days of antibiotics people needed in the third group, but not in the second. The median days didn’t vary for people with simple cases between the first two periods, but it was reduced completely for people in the third period.
The percentage of people taking antibiotics for less than 24 hours went up from 33% in the first two periods to 54% in the third period. That means duration on the drugs went down when people got rapid testing and stewardship. And 28% of people didn’t need antibiotics for CoNS in the third group compared to 15% in the first two groups.
“These data have major implications considering that even with robust stewardship efforts and targeted communication, nearly 87% of patients with contaminated blood cultures receive antibiotics for an average of seven days and stay in the hospital for a median duration of seven days,” the authors wrote. “Thus, pairing RDT with early algorithm-based management can significantly reduce healthcare resource utilization.”
