Gaming with non-inferiority in antibiotic stewardship

An early switch from IV to oral treatment is one of the pillars of antibiotic stewardship. Oral antibiotics are mostly cheaper, hospital stay shortens and thus also the risk of healthcare-associated infections. One problem: before we change our current practice, we must demonstrate that the new strategy is safe. The best evidence comes from a non-inferiority trial. Yet, that usually implies enrolment of many patients. The solution to that problem: put on your poker face when drafting your sample size calculation and hope for the best. Our Danish colleagues show how.  Continue reading

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Procalcitonin-guided antibiotics for respiratory tract infections (part 2)

Two weeks ago I posted a blog about an impeccable NEJM study on the effects of procalcitonin (PCT) on antibiotic use in patients with lower respiratory tract infection. I stated that this RCT was one of the first diagnostic studies in this disease area targeting the correct patients and ended by an invitation to identify the fatal flaw. Last week one of the PhD students (Valentijn Schweitzer, absent when the paper was discussed in our journal club) told me that searching a fatal flaw was not needed; as the RCT was unnecessary in the first place. Here is why. Continue reading

The reality of AMR in Greece

This week I attend the general assembly of COMBACTE, this year in Athens. COMBACTE stands for COMBatting AntibiotiC resistance in Europe (www.combacte.com) and is part of the New Drugs for Bad Bugs (ND4BB) program of the Innovative Medicines Initiative. Our local host is professor George Daikos, who opened the meeting with an overview of the epidemiology of antibiotic resistance in his country. Continue reading

Preventing S. aureus SSI: Who does what? (part 2)

A month ago I blogged on the practices of pre-operative (or better peri-operative) treatment of nasal S. aureus carriage to prevent S. aureus surgical site infection (SSI) in orthopaedic or cardiothoracic surgery patients. The issue brought forward was that a “treat-all” (thus “screen none”) strategy is more feasible, more effective and cheaper than the “screen & treat” strategy. The latter strategy, is associated with less mupirocin exposure and thus less selective pressure for mupirocin-resistance genes. There was a poll with 2 questions. What is your current practice for patients undergoing orthopaedic or cardiothoracic surgery and what do you think the strategy should be, with 3 options for each question; “do nothing”, “screen & treat”, or “treat all”. Today the results. Continue reading

How to predict ESBL (part 5)

A brief update on the ESBL predict study, after the last update  from 6 months ago. Tim Deelen from our group is still running the show and we are still seeking hospitals for participation. It’s for free, it’s easy, relevant and fun! We passed the 5,500 episodes and we learn a lot, including how countries deal with the ethical aspects of this study. Continue reading

Where does your ESBL come from?

Last Friday the results of the ESBL Attribution study (ESBLAT) were presented. After considerable media attention for ESBL-producing bacteria on our meat (especially retail chicken meat) and a 84-year old woman being “the first deadly victim of the new chicken-ESBL bacterium” a research consortium was asked to quantify the role of ESBL in animal industry for human health. The “research lab” was the Netherlands: one of the most densely populated countries in the world for both humans and animals, with the highest antibiotic use in the world for animals and the lowest for humans. If anywhere, zoonotic transmission should happen there! Continue reading

The future of infection surveillance is ….. Google

If you feel that your  hospitals’ Electronic Health Record (EHR) can do more for you, read this. Not yet peer-reviewed, but still very impressive. Using all 46 billion (!) data points in the EHR from 216.221 patients in 2 hospitals they predicted (at day 1 of admission) in-hospital mortality, long length of stay and readmission, pretty accurately, and much better than existing prediction models. How? Deep learning techniques. Who are they? The paper has 35 authors, of which 32 work at Google Inc, Mountain View, California. Continue reading