Stewardship Effective in C. difficile Prevention: A Meta-Analysis

As Dan mentioned last week, when 15% of asymptomatic hospitalized adults carry toxigenic strains of Clostridium difficile, it should alert us to focus on antimicrobial stewardship as a way to prevent CDI. But how effective are stewardship programs and does it matter what type of program you implement in your hospital? If only there was some sort of systematic review or meta-analysis to guide or decision making.

As if on queue, Leah Feazel and Marin Schweizer at University of Iowa published such a review and meta-analysis titled "Effect of antibiotic stewardship programmes on Clostridium difficile incidence" in JAC earlier this spring. Typical of projects completed by Marin and her group, they thoroughly combed the literature for papers. Here they identified 891 articles, reviewed 78 full articles and included 16 studies in their final analysis. Over all, stewardship programs were associated with a 52% reduction in CDI incidence. Importantly, programs appeared effective when implemented in whole hospital or geriatric settings and when utilizing a persuasive approach or a restrictive approach. I've provided the forest plot of studies below. An additional note is that the studies utilized various quasi-experimental study designs and based on the funnel plot, there appeared to be little publication bias.

Key points: (1) Stewardship works for CDI prevention, but it would have been nice if there was at least one funded RCT or cluster-RCT. (2) The meta-analytic approach, that Marin has pushed through her reviews of SSI bundles and hand hygiene interventions, is a fantastic way to guide medical decision making and should be considered for inclusion in future HAI guidelines. The reality is that infection prevention studies overwhelmingly utilize quasi-experimental designs. Why not identify the highest-quality QE studies and rigorously meta-analyze them as done here?

Didn't we say not to use ICD-9 codes to track MRSA?

When you're trying to improve epidemiological methods, I guess you have to be patient.  For example, when Yehuda Carmeli and Anthony Harris told us which control group to use when assessing risk factors for antibacterial resistant organisms, it took years for people to regularly follow their advice. However, I'm still a bit surprised when authors and journals keep publishing studies that track MRSA infections using ICD-9 codes. 

A couple years ago, ICHE published our multi-center validation study showing that ICD-9 codes for MRSA have a very poor positive-predictive value: 31%. To quote our conclusion: "In its current state, the ICD-9-CM code V09 is not an accurate predictor of MRSA infection and should not be used to measure rates of MRSA infection." ICHE also published Marin Schweizer and Mike Rubin's excellent editorial summarizing the issues with ICD-9 code based surveillance for MRSA.

So, knowing what we know about recent MRSA trends and considering my feelings about ICD-9 codes and MRSA, I was a bit surprised when the August ICHE included a study suggesting that MRSA was increasing in academic medical centers between 2003 and 2008 and it used ICD-9 codes! Sure, they attempted to adjust for the billing code's limited sensitivity.  Unfortunately, sensitivity isn't the key issue. If you say something is increasing, it's more important to know if what you're counting as an MRSA infection is actually an incident MRSA infection from the index admission, and not an MRSA infection from a prior admission, or even newly detected MRSA colonization. Thus positive predictive value and specificity are more important measures. 

Just remember this when you read stuff on the interweb that says "contrary to data from the CDC" or posts saying MRSA has doubled in 5 years.  Stick with the CDC. Fortunately, most of the best evidence suggests MRSA is in decline...at least for now.

Important note: Our validation study included three hospitals and compared ICD-9 to actual culture data, the gold standard.

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Ridiculous Example: What if we wanted to estimate the proportion of 30th Olympiad attendees who were born in England by randomly sampling people walking around the Olympic stadium. We could use a somewhat sensitive test, "is the person wearing a Union Jack t-shirt", and catch many people born in England, but this would be useless (ie very low positive predictive value) since anyone can buy a shirt. We could also use a more specific test like checking their passport. Thus, ICD-9 codes are a bit like t-shirts, while microbiological culture results as used in accurate MRSA estimates are more like a passport.