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 Not yet peer-reviewed, but still very impressive.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

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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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From collateral damage to collateral sensitivity

“Every disadvantage has an advantage” is one of the many brilliant quotes from the late Dutch philosopher Johan Cruijff. This now also seems to hold for antibiotic resistance. The conventional belief is that resistance development is unidirectional: pathogens cumulatively acquire resistance traits, until being a multidrug resistant superbug. This now seems not always true; resistance development to antibiotic A, may – at the same time – increase susceptibility to antibiotic B, a phenomenon called “collateral sensitivity” that may help us in treating chronic infections. Continue reading

No more antibiotics for animals

That’s what the WHO stated this week, and it was based on a study, in Lancet Planetary Health. In most news items that I saw animal antibiotic use was directly linked to human infections caused by antibiotic resistant bacteria. A journalist even asked if eating meat was safe. Although most of us (including me) support reduction of unnecessary antibiotic use, it’s worth reading this excellent meta-analysis, initiated by WHO. Did this study answer the burning research question “to what extent does animal antibiotic use influence infections in humans?“ Continue reading

Publish or perish

Our careers (at least partly) depend on our publications. The more, the better and to suit our needs we have a journal for any kind of publication. Sometimes, you read something and you may think “Hey, I have seen that before”. If the new study than confirms a previous finding, we apparently have a reproducible fact, which increases the likelihood that it is indeed true. Here is an example. Or not? Continue reading

On the effects of antibiotic stewardship: I met a analysis

Yet another meta-analysis telling us that we are doing something very valuable: antibiotic stewardship (AS). Nobody wants to (or should) question that good AS is important for our patients, just as hand hygiene, being sober when working and following the latest professional developments. How nice would it be if we could reliably quantify the effects of our good practice. One study is no study (say those that usually don’t perform studies), so the meta-analysis was invented. But what is told by a meta-analysis? Continue reading

Sloppy science & good read

I’m packing for vacation. The book that I will NOT pack is: Rigor Mortis, how sloppy science creates worthless cures, crushes hope and wastes billions by Richard Harris. I read it already two times, and anyone interested in science, or trying to deliver a piece of it once in a while, should read it. It makes you realise what we do, what we publish and what we read. And then, it makes you humble (or sad, or furious, or happy). Continue reading