Should we track sepsis trends using administrative data?

A: Maybe, but only with extreme caution after adjusting for the influence of policy changes

One of the topics that our group at the University of Iowa is very interested in is the use of administrative databases for tracking the epidemiology of healthcare-associated infections, including those caused by resistant pathogens. Marin Schweitzer reported (open access) in 2011 that the ICD-9 code V09, which was commonly used for tracking MRSA trends, was a poor predictor of actual MRSA infection. More recently, Michi Goto completed a systematic review (free full text) that determined the accuracy of administrative data for surveillance of CAUTI, Clostridium difficile infection (CDI), CLABSI, VAP/VAE, postprocedure pneumonia, MRSA, and surgical site infections (SSIs). He found that administrative data detected CDI and SSI with moderate sensitivity and high specificity. For all other conditions, ICD-9 based algorithms had limited accuracy and utility.

Which brings us to a condition that we haven't much explored - sepsis. Sepsis has been in the news lately, since the Third International Consensus Definitions of Sepsis and Septic Shock (Sepsis-3) were published in February. I've included an image of a monument to a different Third International above for historical purposes. We would expect that these new definition might alter the incidence of sepsis as measured using administrative databases - but only time will tell.

Fortunately, Shruti Gohil and colleagues working with the CDC Prevention Epicenter program just published (free text in CID) an important evaluation of how previous changes in sepsis coding criteria, definitions and reimbursement might have altered sepsis rates. Using retrospective data (2000-2010) from California they determine the association between the release of CMS guidelines of sepsis coding in October 2003 along with the introduction in the MS-DRG reimbursement changes  for sepsis care in October 2007 and sepsis incidence. I've included two figures that showed (A) the change in incidence after each CMS change and (D) mortality per 1000 cases over the same period.

As appears obvious in the top figure, the marked increase in sepsis incidence, 3.6 fold over the study period, is temporally associated changes in CMS coding guidance and reimbursement. And fortunately, mortality appears to be decreasing. The authors suggest that some up-capture of less severely ill septic patients may be driving these findings. They also posit that background increases in sepsis, separate from those associated with CMS changes, could be driven by early recognition programs, such as Surviving Sepsis Campaign (2004), and improved diagnosis. The most important conclusion is this: "it is imperative that reported sepsis rates based on administrative data account for policy-related effects...interpretation of recent epidemiologic trends in sepsis based on administrative data should be approached with caution." 

HAI surveillance definitions update: The good, the bad, and the ugly

Here's an update on NHSN healthcare associated infection (HAI) case definitions.

First, the good:  As of January 1, CDC has modified the definition for catheter associated urinary tract infection (CAUTI). This was sorely needed to improve specificity.

The new CAUTI definition can be found here and a video on the changes can be viewed here. In summary, there are 3 major changes:

• A positive culture requires >100,000 CFUs
• Yeast have been eliminated from the definition
• Urinalysis is no longer part of the definition

The new definition will better align with working clinical definitions used by physicians to diagnose and treat CAUTI.

Now, the bad:  Unfortunately, we still have the problem of CLABSI surveillance only allowing the denominator to include one central line per day even though more than one central line may be present. This punishes academic medical centers where the sickest patients receive care. A recent study in Infection Control and Hospital Epidemiology from the University of Rochester sheds some light on this issue. Investigators there performed a case control study to evaluate the risk of multiple central lines on development of CLABSI. They compared patients with 1 central line to those with more than 1. They found that even when controlling for chemotherapy, hemodialysis, use of TPN, length of stay, age, acute and chronic illness (using APACHE and Charlson indices, respectively), patients with more than 1 central line are 3.4 times more likely to develop CLABSI.

Finally, the ugly:  Although CDC developed a definition for CLAMBI (central line associated mucosal barrier injury bloodstream infection) and hospitals are using it, these infections will still be publicly reported as CLABSIs. The end result is that hospitals with large populations of oncology patients are forced to report falsely elevated CLABSI rates. Since there is agreement that CLAMBI is not preventable and actually not causally associated with central lines, this situation is both ridiculous and harmful.

Over the past several weeks, there have been numerous reports in the media regarding hospitals penalized by CMS in the HAC reduction program. Interestingly, over half of the academic medical centers found themselves in the 25% of hospitals that were financially penalized. It's not surprising given that the cards are clearly stacked against them by the NHSN surveillance methodology. As the stakes get ever higher, the need for more precision in the methodology is imperative.

What we talk about when we talk about MDR-GNR

I’m supposed to give a talk at SHEA 2014 on “Lab Identification and Surveillance for Multidrug-Resistant Organisms” (that title is a real barn-burner, right?). I dashed off material on MRSA and VRE pretty quickly, but got bogged down fast when I hit the category of “multidrug-resistant gram negative rods (MDR-GNR)”. Why? Two major reasons: (1) MDR-GNRs encompass a vast array of different species, each with its own bag of tricks, and (2) most labs are underprepared to accurately detect and characterize the most fearsome of the MDR-GNRs (e.g. carbapenemase-producing Enterobacteriaceae (CRE)). Therefore, it is a major challenge to even define what we are talking about when we talk about MDR-GNRs.

One of the articles in the current special issue of Infection Control and Hospital Epidemiology drives this point home nicely. In a survey of hospitals in the SHEA Research Network, Marci Drees and colleagues tallied 14 unique definitions for MDR-Acinetobacter, 18 for MDR-Pseudomonas, and 22 for MDR-Enterobacteriaceae (that’s a lot of definitions for just 66 responding hospitals!). There was similar variation in what these hospitals did when MDR-GNRs were identified (isolation practices, cohorting, etc.), and in how equipped laboratories were to find the organisms of greatest interest (e.g. CRE). This isn’t an indictment of the hospitals or their labs, it simply reflects the fact that drug-resistance among gram-negative organisms is extraordinarily complex, and the molecular methods needed to rapidly characterize the most troublesome organisms are beyond the reach of most clinical labs.

In the absence of affordable commercial methods for detection of common MDR-GNR resistance mechanisms, we desperately need to develop a network of specialized regional referral labs that can quickly characterize pathogens submitted from clinical laboratories. Whole-genome sequencing could be introduced in such labs as a first step to wider adoption and development of automated data interpretation software. Let’s hope that the $30 million CDC budget allocation for responding to antibiotic resistance will move us in that direction.

Image from Wikipedia Commons