Using Prediction Scores to Guide Empiric Therapy

Julie Ann Justo, PharmD, MS, BCPS-AQ ID, discusses how prediction scores are used to guide empiric therapy.

Julie Ann Justo, PharmD, MS, BCPS-AQ ID, clinical assistant professor, University of South Carolina College of Pharmacy, discusses how prediction scores are used to guide empiric therapy.

Interview transcript (slightly modified for readability):

"Stewardship programs are increasingly using prediction scores as tools to optimize antimicrobial selection. A lot of what we do, particularly to improve the quality of care for patients, is to get the right antibiotic on board as soon as possible. We know there’s lots of literature that would support that the sooner you get a susceptible antibiotic on board, the better the outcomes will be for the patient. Of course, we have national guidelines that will give us some guidance as to what the top tier agents would be for empiric selection. But often times those lists can get kind of long, they can have maybe 9 or 10 options, some of which are moderate in spectrum and some of which are very large. Prediction scores would give you the ability to customize it to the specific patient that you have coming to you. It also creates a language in terms of probabilities that you could discuss, this individual patient that’s coming to me has a 0.1% risk of having Pseudomonas aeruginosa at baseline versus this other patient B, has maybe a 22% risk of Pseudomonas aeruginosa. Using that terminology, we could start to quantify probabilities and then come to an easier way of communicating and negotiating a treatment plan with general providers. So, it’s a great tool for antimicrobial stewards and I find that it helps providers on the frontline, whether they be stewardship or not, to really gain confidence in empirical antimicrobial selection in their patients".