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

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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 Not yet peer-reviewed, but still very impressive.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

All models are wrong…..

Yesterday, our study on antibiotic cycling strategies in ICUs was published. Thanks to Joppe van Duijn, involved in all study phases, we could report that in 8 ICUs in 5 countries with 8,776 patients the unit-wide prevalence of antibiotic resistance was similar when cycling antibiotics every 6 weeks or when cycling antibiotics for every next patient treated (mixing). The study was motivated by prior mathematical models, of which most predicted that cycling would do better. So, now all can raise their voices: (1) “all models are wrong, but some are useful”; (2) “most studies are wrong, but some are useful”; or (3) “if model predictions are not confirmed, where did the study go wrong?”

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Test-negative design: the best study design ever?

To kick off the 2018 Journal Club our PhD students discussed a bewildering new trial design* to determine vaccine effectiveness (VE) published in Lancet ID, from which Meri Varkila reports.  The classical approach to quantify VE was to spend the best 5 years of your life to find 2,000 general practitioners, to invite 600,000 elderly to randomize 85.000 and to find 139 primary endpoints in 57 hospitals while all involved remain blinded. This new approach, called the test-negative design (TND) study would give you that number in a year, by just studying a few hundred patients with community-acquired pneumonia. A true Quality-of-Life enhancer for many…., if reliable. Continue reading

What’s up for 2018?

I hope you enjoyed Christmas time and wish you all the best for this year. From my side, I will continue to reflect what I meet professionally, what surprises me, confirms what I thought to know or what confirms my ignorance. In 2017 I did that 41 times (a surprise to me!) and here are some trending topics that will most likely return in 2018. Continue reading

The prevention paradox: E. coli versus Klebsiella

The prevention paradox, as described in 1981, is the “seemingly contradictory situation where the majority of cases of a disease come from a population at low or moderate risk of that disease, and only a minority of cases come from the high risk population (of the same disease). This is because the number of people at high risk is small”, see. In our world this reflects the question how to prevent transmission of ESBL-producing E. coli (ESBL-EC) or K. pneumoniae (ESBL-KP), or both. A new study may help to decide. Continue reading

What about E. coli ST131?

One of the faces of the global antibiotic resistance crisis is Escherichia coli ST131, frequently portrayed as a pandemic clone, combining hypervirulence, ciprofloxacin resistance and ESBL production. A recent study in Genome Research, a journal you may not read every month, though, sheds a whole new light on this “superbug”. Continue reading