Authors say decision support tool could help reduce antibiotic resistance by cutting use of broad-spectrum antibiotics<\/p>\n<\/h3>\n
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\n\tTUESDAY, Nov. 10, 2020 (HealthDay News) — A new decision support tool could optimize antibiotic prescribing for uncomplicated urinary tract infections (UTI) in the outpatient setting, according to a study published in the Nov. 4 issue of Science Translational Medicine<\/em>.\n<\/p>\n \n\tSanjat Kanjilal, M.D., from Harvard Medical School in Boston, and colleagues developed machine learning models to predict antibiotic susceptibility using electronic health record data and built a decision algorithm for recommending the narrowest possible antibiotic to which a specimen is susceptible for the treatment of uncomplicated UTI.\n<\/p>\n \n\tThe researchers found that when applied to a test cohort of 3,629 patients presenting between 2014 and 2016, the algorithm achieved a 67 percent reduction in the use of second-line antibiotics compared with clinicians. The model simultaneously reduced inappropriate antibiotic therapy by 18 percent compared with clinicians. In instances where clinicians chose a second-line drug but the algorithm chose a first-line drug, 92 percent (1,066 of 1,157) of specimens ended up being susceptible to the first-line drug. The algorithm chose an appropriate first-line drug 47 percent of the time (183 of 392) when clinicians chose an inappropriate first-line drug.\n<\/p>\n \n\t“Integrating these models into outpatient care could play an important role in reducing the use of broad-spectrum antibiotics,” Kanjilal said in a statement. “Our future work will focus on integrating these clinical decision support tools into provider workflows and evaluating the clinical outcomes using randomized controlled trials.”\n<\/p>\n \n\t One author disclosed financial ties to the technology industry.\n<\/p>\n \n\tAbstract\/Full Text (subscription or payment may be required)<\/a>\n<\/p>\n <\/i><\/p>\n