Unwarping the playing field



Easing comfortably into my role as a “blogger,” I’ve realized how easy it is to adopt a few key platform issues that tend to drive one a bit mad.  This blog isn’t so fond of CAUTI, contact precautions, or devices that blow moist, warm particles over sterile fields (but then again, who is?).  We love diagnostic stewardship, influenza vaccination (um, mostly), and fecal transplantation.

Add advocating for better risk-adjustment of publically-reported HAI performance to my list.  A few months ago, I blogged about this issue and poor reporting validation by CMS — now some excellent papers on improving risk adjustment have emerged, both from many FOB (friends of the blog) with senior authorship by Anthony Harris and his group at Maryland.  One focuses on SSI and one on CLABSI.  Their methodology is very similar and has some key features:

  • They used comorbid conditions that are components of the Charlson and Elixhauser comorbidity indices
  • These conditions were captured by diagnostic discharge coding that are currently routinely collected and submitted to CMS, limiting the data collection burden 
  • They used conditions identified using Delphi consensus methodology from a survey of ID and infection prevention experts, providing some clinical credibility to the process

The authors examined the model performance and assessed changes in hospital rankings when compared to the traditional NHSN models. For SSI, a model containing procedure type, patient age, race, smoking history, diabetes, liver disease, obesity, renal failure and malnutrition showed good discrimination, and 86% of hospitals changed ranks within the cohort when the risk-adjusted model was used — with 4 hospitals changing by >10 ranking spots.  For CLABSIs within the ICU, a model using coagulopathy, paralysis, renal failure, malnutrition and age showed improved predictability when compared to the NHSN ICU model, and 45% of hospitals changed ranking.  The authors note the clear limitations, including some of the challenges with using coded data, but these papers are important advances in the area of publically-reported HAI data.

 You can read some of my more detailed thoughts in the accompanying editorial for the CLABSI paper (shameless plug).  At a time when there are many consequences for a hospital’s performance on these surveillance metrics (e.g. last year my hospital received a draft quality incentive contract from a private insurer that required our large, tertiary care center to have ZERO of the Big 6 reported HAIs, to the tune of several million dollars in incentives), leveling the playing field to adjust for those factors that lead to HAIs that are beyond the control of the hospital is essential.  Thankfully, the CDC and HICPAC have recently chartered a new NHSN work group (disclaimer: Hilary and I serve on this group) and the issue of improved risk adjustment seems to be a major emphasis – fingers crossed that the field will start to level soon.

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