Will Antimicrobial Stewardship be the Next Target for De-implementation?

First, an honest confession, Mike's tweet had nothing to do with antimicrobial stewardship, but rather contact precautions. But his point is just as valid when discussing antimicrobial stewardship and there will come a time when forces will align to question the benefits and costs of stewardship programs since now and in the future they will lack the "necessary" cluster-randomized trial evidence supporting their existence.

There is a longer discussion to be had here sometime in the future, when I'm not writing a Center grant renewal, but the key question is what we consider "high-level" evidence. For most de-implementation supporters and indeed most infection control and stewardship guideline authors, high-level evidence is synonymous with individual or cluster-randomized trials. They simply cannot accept non-randomized, quasi-experimental designs as evidence. It is gotten to the point that the recent CDI Guidelines completely excluded quasi-expermintal designs from their level of evidence figure, despite the fact that one of the original Grade Criteria papers lists QE studies in its table and allows them to be ranked higher than RCTs, if certain criteria are met.

OK.  So why am I rambling on about level of evidence and misapplying a tweet from 2 weeks ago? There was a new systematic review just published in AJIC by Leandro Bertollo and colleagues that asked the question: "Are antimicrobial stewardship programs effective strategies for preventing antibiotic resistance?" To answer this question they reviewed all studies published between from January 2012 to January 2017 and followed the standard PRISMA statement recommendations for reporting their findings.

Results: They identified and extracted data from 26 studies, of which 22 were single-center and four were multicenter studies. Study designs are listed in Table 2, below, with the special note that none of the before/after studies included a contemporaneous, unexposed control group. A major concern that the authors identified was that in 7 of the 26 studies (30%), there was evidence that infection control interventions were implemented at the same time as the stewardship intervention and that the majority (57%) of the stewardship studies that reported positive results were confounded by simultaneous implementation of new infection control practices. High fives for hand hygiene.

Their conclusion: "There is no solid evidence that ASPs are effective in reducing antibiotic resistance in hospital settings. There are still few studies analyzing this matter, most of them with inappropriate study designs. We uphold the need for more studies with appropriate study designs and standardized ASP interventions targeting common microorganism-antibiotic pairs."

The need for more studies. Sounds like the siren call for de-implementation to me. Sure, we can wait around a decade or four for some magical $20 million cluster-randomized study that swabs all patients on admission/discharge, completes a full microbiome analysis and tracks patients for a year post discharge for resistant infections. Or, we can expand our ideas around what "high-level" evidence means and fund well-designed and controlled quasi-experimental studies and also consider strong epidemiological evidence, such as exposure to antibiotics leads to colonization with resistant pathogens. We can be logical. Yeah, not gonna happen. But at least you were warned.

Even NFL Stars LOVE Contact Precautions

What do we love most of all on this blog? Yup - contact precautions. Humor!

Well, I do have respect for the utility of gloves in preventing HAIs and MDRO transmission- but I've never been sure of gowns. It's just that they are pretty annoying to don and doff and little evidence supports any additional benefit above wearing just gloves. I mean, if Rob Gronkowski from the almost champion New England Patriots can't even put on a gown correctly, what chance do we mere mortals have, seriously. Maybe that's the reason why the benefits of gowns and gloves for preventing MRSA and VRE are so hard to estimate?

There are many other reasons why the benefits of contact precautions for endemic MDRO are so hard to quantify, of course. In this week's JAMA, Mike Rubin, Matt Samore and Anthony Harris have written a very nice Viewpoint acknowledging the limitations in the current literature.  In addition, they point out why studying infection prevention interventions is so tricky and suggest a path forward. They should be commended for their thoughtfulness and honesty - something those of us (including me) who support other policies with even weaker evidence bases should remember. If Gronk is having trouble with contact precautions, it's OK if some of the rest of us do too.

Global antibiotic resistance surveillance a.k.a. GLASS: one step closer to saving the world

New surveillance data released a week ago by the World Health Organization (WHO) illuminates a step towards a coordinated and standardized way to perceive the AR problem worldwide. Most of the report focuses on healthcare-related bacteria, but it is not limited to this. Probably a hidden gem includes a standardized characterization of each countries capacity and reporting infrastructure.

In the first report from the WHO's Global Antimicrobial Resistance Surveillance System (GLASS), 22 countries submitted data on 507,746 isolates with antibiotic susceptibility testing results (range 72-167,331 per country). To no surprise, Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, Streptococcus pneumoniae, and Salmonella spp are the most commonly reported resistant bacteria; most of these reports do come from sentinel laboratories reported to their country designee. Although some countries report high values of “percent resistance”, often these reflect a single laboratory.

The direction GLASS is going is forward! Which is great. Its strength right now is to provide a one stop shop and platform to quickly read how a country representative describes their AR surveillance effort.

You can choose a country and view data here:Tableau feature of GLASS

This may be helpful for grant writing, collaborations, and consultations; however, for a variety of reasons, the actual results are a bit data-penic, and somewhat of a convenience sample. This is not to fault the GLASS effort at all - countries clearly with the capacity to have insight, visualization, and concrete values on the magnitude of their resistance problem didn’t submit data to this data call.  Although the reasons may vary and all be very valid, it does make the report less of a comprehensive source of global variations in the magnitude of the problem. The power of standardize reporting of “representative” laboratories can (and I believe will) help local health and government leaders prioritize efforts in country. Hopefully additional countries (including the U.S.) will contribute to future data calls. Full report is here:GLASS 2017

Regardless, WHO now provides all of us with an interactive feature (Tableau) to view country specific infrastructures (I counted 37 countries) and data (from 22 countries, although I have not checked all 22).

I don’t envy WHO staff trying to herd cats (cats as in those of us having [or having had] a say in AR surveillance reporting) to try to get all countries to report out data to them in a standard way, but I do applaud their efforts and hope the data submissions become more comprehensive – and thus the interface more usable.

GLASS, which was launched in 2015 to help achieve the goals of the WHOs Global Action Plan on 
Antimicrobial Resistance (AMR) includes establishing country-level surveillance of antibiotic use and resistance. There is a lot of momentum building globally on advancing in-country surveillance and innovation around tracking and transmission interruption. These include the Global AMR collaboration Hub in Germany (focusing on new drug development), SEDRIC (surveillance and epidemiology of drug resistant infections consortium), a new initiative funded by Wellcome Trust aimed to provide technical expertise and knowledge to address barriers with a focus on bringing new technology to bear on big data. In addition the Gates Foundation has contributed in numerous ways, including Grand Challenge Grants

Communicating Complexity

Healthcare quality metrics are such a struggle.  We all want metrics that best reflect our efforts to keep patients safe at our institutions, while not penalizing institutions who provide care for patients at higher risk for complications.  We also want the data collection burden to be light and the outcome to be simple and easy to understand. When comparisons are going to be made among hospitals of varying sizes, that offer different levels of care, to populations from varying economic and social support systems, we want known risk factors to be taken into consideration.  And not just to avoid financial penalties at our hospitals, but also to provide better information to patients.  While I doubt that many patients actually use the Hospital Compare data to select a facility (most “choices” are driven by insurance coverage, geography and physician referrals), if they did, it would be nice if the metrics actually steered them toward safer healthcare.

And NHSN listened to these concerns, moving to risk adjusted models and the SIR - a summary statistic that accounts for the prevalence of (a few) known risk factors.  But as the stakes get higher, limitations to the current risk adjustment models grow increasingly frustrating. Why can’t we make these models better?

On the other hand, the move to risk adjusted models has increased the complexity of both understanding and communicating our outcomes, internally among  infection prevention program personnel and hospital leadership and externally to the public and consumer organizations.  Recent work by Vineet Chopra and his colleagues at UMichigan have been looking at how well we “experts” even understand these metrics ourselves.  His most recent evaluation was a survey of SHEA research network members, published in ICHE under the title “Do Experts Understand Performance Measures? A Mixed-Methods Study of Infection Preventionists” (though 80% of respondents were physicians).   Respondents were given a table of data about 8 hypothetical hospitals and asked questions about interpreting the presented data and about the impact changes at those facilities might (or might not) have on the data.  Of 67 respondents (only 54 of whom answered every question, so a pretty small sample), performance was mixed.  Particular difficulty was noted on questions that involved risk adjustment, such as the impact of more G tube use at one hospital on the calculated SIR or the impact of implementing antibiotic coated catheters on the projected number of infections.   And this from a group of primarily physician leaders of hospital epidemiology programs, engaged in SHEA, many from academic medical centers.

I brought the survey questions to the monthly meeting of all the infection preventionists from across our healthcare system and I am happy to report we did very well!  We had quibbles with how some of the questions were worded and we benefitted from being able to talk through the questions together as we formulated our answers.

The authors concluded that limitations in understanding the risk adjustment data may make the data ‘less actionable by end users’ and ‘..decision makers’ trying to reduce HAIs.  I’m not sure that is true.  The SIR does at least provide a fairly simple guidepost of “numbers higher than they should be”.  That should be enough to prompt action – but sharing an SIR with leadership and program personnel to develop plans for action requires more in depth understanding than just the SIR itself.  It requires knowledge of what factors are included in the risk adjustment model and what are not, the prevalence of all those factors in your population, and which of those factors are actionable/preventable.  That more in depth understanding is a bigger challenge and is harder to summarize and communicate in a single metric - especially if you don’t fully understand it yourself.

The other issue raised by this complexity, and our own difficulties interpreting and explaining it, is one of trust and transparency with the other ‘end-users’: patients.  While we advocate to improve risk adjustment, to make comparisons among facilities more appropriate, some patients and consumer groups feel that we are purposefully obscuring actual numbers of infections in order to hide poor practices.  The ‘black box’ from which the SIR emerges can erode much needed trust.

Luckily, NHSN heard these concerns as well.  Through HICPAC, two new NHSN working groups have been formed:  data and definitions (including risk adjustment) and communication. And the communication subgroup is co-led by Dr Vineet Chopra! That group will be discussing better ways to communicate the complex inputs and hopefully understandable outputs both verbally and visually.  Good communication provides much needed clarity and builds trust. I look forward to hearing about their work.

PS I especially enjoyed reading the comments in the supplementary material where respondents offered answers to the question “in your opinion, what are the three biggest problems for reliability of quality metric data at your hospital”.  I recommend them to everyone. They call out issues with risk adjustment, data collection, definitions etc.  A couple of favorites include “some preventable infections are more preventable than others”; “we don’t use quality metric data” ; and “gaming the system; gaming the system; gaming the system”. 

Women in Healthcare Epidemiology

Dr. Janet Lane-Claypon

I'm not a big fan of National [fill-in-the-blank] Days—to me they imply we can ignore the topic on the 364 days that we aren’t supposed to celebrate it. So on National Women Physicians Day, we should vow to better recognize the huge contributions women physicians make every day. That way we might not even need a “day” in the future, and can thus focus instead on preparing for National Lima Bean Respect Day (April 20).

In the field of healthcare epidemiology and infection prevention, the list of women leaders is long--and for me to produce one would be very dangerous because I’m sure it’d be incomplete. Instead I’ll point out that the last two SHEA presidents were women, and the 2019 SHEA president will be our esteemed fellow blogger, Hilary Babcock (congrats again, Hilary!).

This also seems like a good day to point interested readers to this piece about Dr. Janet Lane-Claypon, a pioneering physician-epidemiologist who was the first to employ the now-ubiquitous cohort and case-control study designs we use so often in infection prevention. The paper was published in 2004 but I only recent stumbled on it, and found it a fascinating story about a person I clearly should have learned about during my epidemiology coursework (but didn’t!).

I recognize the irony of me posting this from a blog that has a 5:1 male:female ratio. We’ve tried over the years to recruit women to the blog, mostly unsuccessfully. One possible reason (besides the obvious—that we haven’t tried hard enough), is that women physicians put up with substantially more bullshit each day than their male counterparts, and thus have less time for blogging.

To our female readership: if you’re interested in contributing to the blog (either with periodic guest posts, or joining the group), please contact one of us. This isn’t limited to physicians: infection preventionists, non-physician epidemiologists, microbiologists, nurses…pretty much anybody with expertise and strong opinions about infection prevention and healthcare epidemiology!

-->