Progress on HAI progress: CDC portals and data

I spent about 2 hours with the new CDC HAI Progress Report – loosen your belt, it’s a big meal!  We probably had mixed perceptions about the recent alert to the 2016 HA progress report. Loads of information and analysis condensed down to a handful of bullets, all pointing to improvements in patient safety! The (very pleasant) surprise to me was that the format and delivery of the report has advanced to digital!! CDC has added the HAI progress report to the existing (and now updated) HAI AR Patient Safety Atlas Portal If you stop reading now – at least click on that link and explore and I will call this blog a success!

This report is several steps forward.  First, it pushes all of us to go to a place where we, being inquisitive minds, can wander and perhaps connect some dots within and between datasets. With the digitalization and visualization provided in the portal, the novice and experienced can more easily access and utilize these data. One can click through four distinct datasets, which now include state-summary statistics HAI infection rates/SIRs, inpatient stewardship activities, outpatient antibiotic prescribing rates, and inpatient antibiotic resistance metrics.

Now – the email alert. This year, well 2016 data, is the first to use the 2015 “re-baseline” efforts.

Unstated, but implied – the re-baseline effort includes the use of MBI (mucosal barrier injury) LCBSI as an event excluded from reported CLABSI rates, exclusion of yeasts (or low colony counts) from CAUTI rates, exclusion of “infection present on admission” for SSI (along with better patient-level risk adjustment), first use of risk adjusted metrics for VAE, and maybe slightly better models using more contemporary data for MRSA and CDI. 

With that said, 2016 performance suggests nationally patients are safer overall compared to the experience of 2015. Other than VAE which decreased by only 2%, everything else declined about 7-10% (I am rounding) compared to 2015. I understand many of the problems with risk adjustment and reporting bias that make these surveillance events poor performance measures for individual hospitals – but on the national level – I think these data do suggest fewer infections (o.k., perhaps some  widespread under-reporting—mixed reports on validation efforts in place).

Next – the overview of the current HAI Progress Report.   Although at first glance it seems the same as the info posted in the emails – CDC is offering more ways to track progress nationally – the number of states showing improvements (or worsening) compared to 2015, as well as the number states currently performing at levels better (or worse) then their 2016 contemporaries: 12 state perform better on at least 3 infection types compared to other states at the same time (2016). Now positive deviance nerds need to learn from these states to help the other states (or perhaps identify accuracy and validation issues at these states). The contemporary juxtaposition of SIRs is new, perhaps confusing (especially with CDC’s arcane explanation on how to interpret this: “SIRs statistically significantly lower than the 2016 national SIR are considered better than the 2016 national SIR”; curious if it ends up being useful to state programs. This year also is the first with more detail on inpatient rehabilitation facilities and long-term acute care facilities. Fewer data mean fewer statistical significant results, but these data are ripe for academic partners to latch onto as they try to partner with ARHQ, CDC, and state-programs to branch out into stewardship and prevention efforts in these types of facilities.

Finally, the portal – access it here.  Use the table view. No graphics to export for HAIs, only for other datasets. My pet peeve is that CDC still refused to list the no. of SSIs reported next to the number of surgical procedures reported to allow a crude attack rate. We still need to go to the technical tables for these values and calculate ourselves (see below). CDC, please stop making us jump through this hoop to be able to use attack rates for other purposes like planning studies, clinical trials, vaccine research! To all researchers and data nerds - the detailed technical tables should be downloaded examined (here), perhaps parsed out to our students and trainees, and used for different purposes that simply a “reporting requirement”.  I know there are many limitations to the accuracy of any one facilities reports and likely aggregate data up to the state or national level. However, as a long time national surveillance nerd all too familiar with the warts and ugliness of surveillance data, they do inform us, approximate the truth, and can help us ask the right questions and target the right populations. The more eyes using these data the more transparent the process will become, more uses of the data will be identified, patient safety should improve, and CDC will become more accountable to update (c’mon, where’s 2015 and 2016 NHSN AR data?! update the portal please!!),  maintain, and advance the public accessibility of useful data in our field.

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”. 

Are "One-Offs" Becoming Routine

one–off

play

adjective \ˌwən-ˈȯf\

After eight years investigating hospital outbreaks, and about 15 years trying to make the best possible use of surveillance data while at CDC, I still struggle with the tensions inherent in mixing surveillance and performance measurement. The past decade has been a roller-coaster of thrills and perhaps some spills in terms of attention, resources, refinement, and usefulness of HAI surveillance led by CDC; yes, you could probably blame me for several aspects of NHSN reporting you may find unsatisfying (take your pick – perhaps I will expand another day). However, I having recently retired from CDC and am transitioning to Emory Healthcare and Emory University. Although It has been almost eight months. It has been a fascinating transition. The learning curve is steep, and not just for re-entering clinical medicine (that is another story), but also navigating the pathway which integrates the business of healthcare delivery, quality of healthcare delivery, and research opportunities. Slightly easier was learning how to navigate the Emory Parking situation (took 3 months). Much easier was recognizing that the performance quality metrics linked to HAI prevention are getting a lot of attention and a lot of action. It only took a few sessions listening to the quality improvement teams reporting on their target HAIs to understand two things. First, the C suite leaders really care. I had assumed this while at CDC, but it was illuminating to see up close how hard these teams worked to influence HAI prevention. Second, it was becoming somewhat routine to report out on “exceptions to the rules” of HAI reporting. There are many names for those scenarios when an HAI is justifiably reported, but either considered not preventable with evidence based prevention practices or not clinically the infectious event represented by the HAI. While at CDC we routinely heard about these: CLABSIs that “shouldn’t really be counted”, MRSA BSIs that really “weren’t ours”, CAUTIs that really don’t represent an infection. Now these reported HAIs were being called “one-offs.”

The NYT reports the term ”one-off” comes from earlier industrial beginnings with the quantity of items produced in manufacturing process, such as taking one-off, two-off, or twelve-off the line to sample or give-away. However nowadays it can refer to any exception of the rule – such as a recent one-off boxing match that really should not ever have happened. 

In an HAI paradigm where we aim for 0 infections, one-offs may either be unavoidable (not preventable) or wrongly attributed to the device, location, procedure. I have historically known of these in terms of byproduct of using proxy measures. I had previously published an editorial on the value of proxy measures of infection as a tool for quality improvement (Meaningful measure of performance: A foundation built on valid, reproducible findings from surveillance of health care-associated infections).

In that editorial, we outlined necessary steps to reduce the inaccuracies inherent in using such an approach. Now that progress has been made in HAI prevention since 2010/2012, many of these HAI events that conspicuously remain and continue to plague our patients, often don’t fit neatly into the intent of the surveillance definitions. Left with these “one-offs,” it is often difficult to know what to do more to prevent them. Surgical patients with fistulas and central lines that don’t have an infection related to insertion or maintenance processes, neutropenic patients that don’t quite meet the definition of MBI-BSI, I have even heard of tissue transplantation related bacteremia categorized as CLABSI. No doubt, changes have occurred since 2011 to improve CAUTI reporting, and neutropenia-related bacteremia. However, the pace is slow. The one-offs are starting to pile up. Perhaps improved risk adjustment of HAI data will mitigate the influence of the one-offs on healthcare facility performance measures. Until then, kudos to the quality folks and infection control teams making prevention progress. However, I hope we can reward them soon with improved performance measures. Perhaps there are surveillance lessons that can be learned from these one-offs after all. 

If you are interested in sharing one-off stories I have started a registry here - maybe we can fill in some gaps and accelerate the process of changes in surveillance methods.

Give me back my name

“There’s a name for it,
names make all the difference in the world” 

Talking Heads

A little over two years ago, I told the sad story about how a contaminated blood culture became a central-line associated bloodstream infection (CLABSI). In that case, a “coryneform” or “diphtheroid” was more precisely identified by virtue of the adoption of mass spectrometry (MALDI-TOF) for organism identification. 

As more labs adopt technologies that provide this greater precision for species ID, it becomes more important to understand how much of an impact this might have on CLABSI rates (it’s not as if anything is riding on those rates, right?). So I was happy to see this small study in ICHE that begins to examine this issue. While this particular study is too small to help estimate the extent of the problem, it confirms that variation in species ID using different methods can impact a CLABSI designation. I hope this helps spur further evaluation not only of species ID methods, but of the broader issues around how laboratory advances impact healthcare-associated infection (HAI) definitions and rates. 

What are the next steps for evaluating the impact of species identifications on CLABSI rates? First, CDC/NHSN needs to update their master organism lists to keep up with current laboratory capacity to identify organisms, and with changing nomenclature. I know they are doing this, because I’ve been involved in that ongoing effort. Second, we should be able to use NHSN data to drill down on this problem—but can only do so if we know which lab methods are being used in each hospital for organism ID. So NHSN has now added questions to their annual survey to elicit this information. My hope is that we can use that information to compare CLABSIs among those organism groups most likely to affected by more precise species ID methods (e.g. gram positive rods).

As long as HAI definitions hinge on laboratory results, HAI rates will be very sensitive to technological advances (as well as to lab ordering practices). This dynamic receives far too little attention.

We need better HAI metrics

Over the past few months, my Chief Medical Officer and I met with Department Chairs to discuss incentive-based quality and safety metrics. In these meetings we agree upon metrics tied to financial incentives for achieving specified targets. The goal is to achieve a win-win--we improve patient care and the department benefits. We typically include at least one metric that is a healthcare associated infection.

With one of our surgical chairs, we were discussing surgical site infection rates. We all agreed that these are excellent metrics on which to focus. The more contentious issue was the target rate to be achieved. We suggested a 10% reduction from the current rate. His counter-argument was that his department's SSI rates were already very low and further reduction may not be achievable. He suggested that national benchmarks be used. That's a great idea, except that we no longer have national benchmarks. NHSN used to provide mean pooled infection rates with percentile scoring using data that were updated at regular intervals. This is no longer the case. Now NHSN provides SIRs (standardized infection ratios) calculated by comparing the observed number of infections to the expected. SIRs could be very helpful in this case, but in reality they aren't since the expected numbers of infection are derived from data collected from 2006 to 2008. So using the SIR, all I could tell the surgical chair is how his current SSI rates compare to other programs nationally a decade ago. If the expected number of infections is derived from data that are a decade old, it defeats the entire purpose of the SIR. For this purpose, the SIRs that NHSN produces are worthless.

Hospitals have two needs with regards to quality metrics: internal trending (are we getting better or worse over time?), and benchmarking (how do we compare to other hospitals?). The SIRs that are currently being produced can be used for internal trending, but not benchmarking. This leaves hospitals completely in the dark vis-a-vis their comparative performance. CDC is in the process of establishing new baselines for expected infection rates. This will be helpful for a year or so, but then the expected data will become old and benchmarking will again be flawed.

There seems to me to be an easy fix: establish two SIRs. The static SIR can use a fixed data set to derive the expected number of infections. This will allow hospitals to be able to internally trend their performance over time. The dynamic SIR would use data from the previous year, updated annually, to allow for comparative performance. This could be easily accomplished.

While we have seen some improvement in NHSN metrics, the overall trend, in my opinion, is that NHSN is moving towards metrics of lesser value (e.g., lab-based automated metrics), and I get the sense that they're not particularly interested in the viewpoint of hospitals. In the value-based reimbursement era, hospitals need valid comparative performance data more than ever, yet CDC appears out of touch and moving in a completely different direction.

Show me the data!

The following post is from Scott Fridkin, MD, about the new public availability of NHSN antibiotic resistance data:

Finally, some NHSN antibiotic resistance (AR) data for easy access to all! 

Use the HAI Antibiotic Resistance Patient-Safety Atlas to get metrics of AR for the U.S., your region, or your state. It’s currently limited to NHSN defined HAIs, and aggregate measures; but it is dynamic and will grow in size and functionality. Hopefully this will help public health, the public, providers, and researchers to improve patient care. 

Given the public health priority of preventing antibiotic resistance in healthcare, even before the National Action Plan to Combat Antibiotic Resistance was in place, there was a recognition by CDC that it was imperative to make HAI data reported to CDC more accessible to the public, including the public health community, consumers, the press, and industry partners. In addition, academic researchers could benefit from easier access to generate specific hypotheses to test with more definitive research.

Toward this end, CDC finally has expedited the availability of antibiotic resistance data to allow for more time-sensitive evaluations independent of publishing timelines, to allow diverse approaches to ecologic assessments such as geographic comparisons, and to allow evaluation of subsets of data not previously explored in-depth. This month CDC launched the first version of the HAI Antibiotic Resistance Patient Safety Atlas:

As CDC’s National Healthcare Safety Network has migrated from a sentinel surveillance program to a national performance measurement system, the number of facilities reporting has surpassed 4,000 for acute care hospitals, and 15,000 when including dialysis facilities, long term care, inpatient rehabilitation, and long term acute care. The “events” reported into the system have skyrocketed as well. When you consider that the antibiotic resistance data can include up to four pathogens per infection, there is a huge amount of antibiotic resistance data that rarely sees the light of day. In fact, historically all of the antibiotic resistance data reported to NHSN have been released mostly as peer reviewed papers, with very two-dimensional views of the data. This model provided very limited access to the data, diminished relevance when publication lagged several years behind the reporting year, and limited amounts of data presented given the constraints of the paper-based publication model. Although this first version of the Atlas is fairly limited in one’s ability to create customizable queries, it does allow for temporal and geographic evaluation of trends at a superficial level. For now, the identities of facilities are protected and the data are presented at only the national or state level. However, in future iterations more national customized queries will be possible, and perhaps more granular geographic divisions. For now, I urge anyone to access the maps and query functions and let CDC know how to make the site more useful to your professional endeavors.

Exactly how useful these data will be to the public, press, public health, and most importantly patients – is still uncertain. It is a starting point for improved access, transparency, and innovation to advance antibiotic resistance infection prevention – I hope the users of this Atlas can help us make it better over time.

Our slow motion pandemic

    “We keep fantasizing about what will be the next biothreat, the next pandemic. It’s actually already here! We’re going to save our grandparents with triple bypass, but they’re going to die from pneumonia, because we will not have the right antibiotics to save them.”

Dr. Joanne Liu, International President of MSF, on Here’s the Thing.

Today the CDC releases it’s latest edition of Vital Signs, which is dedicated to the problem of antibiotic resistance (AR) among healthcare-associated pathogens. Using data from NHSN, CDC investigators estimate that the likelihood an HAI is caused by a targeted AR pathogen is one in seven in acute care facilities, and one in four in long term acute care.

There's good news in the report—the figure below shows impressive progress in reducing CLABSI rates, and to a lesser extent SSI and C. difficile. CAUTI, though, is a mixed bag (pun intended), and Mike’s covered this ground before. For reasons that Eli and our colleague Dan Livorsi outline here, it’s a shame that CAUTI has become such a prevention focus. Ironically, an unhealthy focus on CAUTI can drive testing and treatment practices that can result in antibiotic overuse, worsening the AR epidemic.

Today the CDC is also releasing the “AR Patient Safety Atlas”, a new web app with interactive data on HAIs caused by AR bacteria. I am about to post a more detailed item from Scott Fridkin about this exciting new development. Stay tuned!

Dollars, denominators, and risk adjustment

Because not everyone who reads this blog reads the comments, I wanted to highlight these particularly insightful observations about Mike’s post on denominators for CLABSI (emphasis mine):

"The thought experiment works with the assumption these two ICUs are indistinguishable except for the frequency of CVC use. Historically, I think the justification for comparative rates using CVC denominators was a no-brainer. These devices were critical to saving lives, and the variations in device utilization probably reflected differences in patient populations, even within similar types of locations. Accounting for the overwhelming primary risk (the CVC) made sense, since these devices were critical to care. The problem you’re outlining now is very real -- as the clinical environment has proven that a lot of the variations in CVC use may in fact be personal preference. Just like the argument with CAUTIs (where foley use is deemed less critical to care) to use a patient-day denominator is strong, we may be at a time where the CLABSI argument is as strong. Improving the classification of ICU types, by more objective criteria than currently used in NHSN (i.e. the 80% rule), would really advance the comparative metric substantially, and likely provide more valid risk adjustment with patient-day denominators than we currently have with these archaic classification schemes (e.g., "med-surg icu"). Advancing the use of composite administrative data to classify patient locations to a more objective, reliable, and granular level, based on fractions of patient-days that have key underlying diagnosis, procedures, etc. is greatly needed."

Given the millions of dollars that are now at stake based upon a hospital’s performance on healthcare-associated infection (HAI) metrics, it’s hard to overemphasize the pressure that is now being placed on the NHSN definitions, and the importance of ensuring that the definitions keep pace with evolving approaches to patient care. When I was a medical resident (yes, way back then), the presence of a CVC was a good indicator of severity of illness and likely served well as built-in risk adjustment for the broad categories of ICU. The same cannot be said now; the device utilization ratios (and percentile ranks compared across NHSN units) vary markedly between different ICU types in our hospital, and do not correlate well with illness severity. And as we’ve learned with CAUTI, the device days that are most amenable to reduction (the “low hanging fruit”) are always the lowest risk device days.

Denominators matter

Let's perform a thought experiment. At St. Eligius Hospital there are two ICUs. These two ICUs have the same number of beds, the same number of patient days (12,000/year), and the same case mix index. In fact, they're essentially identical, except that ICU A has an annual CLABSI rate of 2.7/1,000 central line days and ICU B has a CLABSI rate of 5.0/1,000 central line days. Which ICU is better performing with regards to CLABSI? Well, without any other data to consider, we'd be greatly tempted to conclude that ICU A is the better performer since it's CLABSI rate is nearly one-half that of ICU B. Now, let's add another piece of information: ICU B focused on reducing central line placement as a safety intervention--so at year's end, ICU A had 7,500 central line days and ICU B had 3,000 central line days. This means that ICU A finished the year with 20 CLABSIs, and ICU B had 15. Now it's clear that ICU B is the better performer despite having the higher rate.

This is not just a theoretical problem. During my first rotation on the Infectious Diseases Consultation Service at the University of Iowa last year, I was struck by the low prevalence of central lines in the medical ICU. Turns out my perception was spot on--when I looked at our NHSN data, I saw that 3 of our 5 adult ICUs have central line utilization ratios less than the 15th percentile nationally. This is not an accidental occurrence; clinicians in those ICUs have worked hard to avoid placement of devices that are associated with infection. The problem is that the central lines that do get placed in these units are concentrated in a group of patients that are sicker and more likely to develop CLABSI, since the less sick patients will be managed without a central line. Moreover, the denominator is reduced. And the result is higher CLABSI rates. Here, no good deed goes unpunished.

But there's an easy fix. Instead of using device days as the denominator, use patient days. In our thought experiment, we would see that ICU A would have a CLABSI rate of 1.7/1,000 patient days and ICU B would have a rate of 1.2/1,000 patient days. The better performer (ICU B) will now have the lower rate, as expected. Makes sense, no? CDC should move to address this given the financial penalties hospitals now face based on CLABSI rates. Changing the denominator would provide an incentive for hospitals to aggressively reduce device insertion. And since NHSN has collected patient days for decades, there would be no loss of long-term trending. Lastly, use of patient-days as a denominator produces a patient-centered metric. Think about it: do we really care at what rate catheters become infected? No! Our focus should be on what rate of and how many patients become infected, which is also more intuitive for providers at the sharp edge of patient care.

Punished for precision (or, too much information (TMI) from the micro lab!)

We recently had a patient's blood culture turn positive for a Gram-positive, catalase-positive, facultative diphtheroid. In the “pre-MALDI” era, we’d have called this isolate a “diphtheroid”. Taking into account other aspects of the case, the NHSN definition would have categorized this as a contaminant (diphtheroids being on the “common commensal” list maintained by NHSN). By virtue of the wonders of mass spectrometry, we are now able to identify the organism to species-level as Actinomyces neuii, an organism previously categorized as CDC group 1-like coryneform bacteria (also on the “common commensal” list). 

Actinomyces neuii isn’t anywhere on the NHSN organism lists. However, Actinomyces species (as a group) can be found on the “all organisms” list but NOT on the “common commensal” list. The NHSN rules tell us we have to categorize any organism on the “all organisms” list that isn’t also on the “common commensals” list as a pathogen, meaning this positive blood culture now helps define a central-line associated bloodstream infection (CLABSI).

And that’s the story of how a contaminated blood culture became a CLABSI. We’ve had other similar cases since we introduced MALDI-TOF. Before the CLABSI rate became worth millions of dollars to a hospital’s bottom line and reputation, this might have been easy to navigate. Now, though, it’s a much bigger deal. 

These and other issues regarding the impact of microbiological advances on infection prevention will be discussed at SHEA 2015 (ever heard of it?). Register now!

Clamoring for CLAMBI

Last month we blogged on updated NHSN surveillance definitions and we bemoaned the fact that CLAMBIs (central line associated mucosal barrier injury bloodstream infections) are not being separated from CLABSIs for public reporting, and more importantly for the CMS pay-for-performance programs. These infections are particularly common in patients with hematologic malignancies, are due to the translocation of enteric flora into the bloodstream, and unlike true CLABSIs are not preventable. A new paper in Infection Control and Hospital Epidemiology from Northwestern University demonstrates why this is important.

All cases of CLABSI were identified over a 14-month period on 2 inpatient hem/onc/BMT units (72 beds). The cases were further subdivided into "true" CLABSIs (i.e., not associated with mucosal barrier injury) and CLAMBIs. A total of 66 infections were identified, of which 47 (71%) were CLAMBIs. E. coli, enterococci and viridans streptococci accounted for 62% of the pathogens isolated.

The authors note that at the present time CLABSIs identified outside of ICUs are not publicly reported nationally; however, the CLAMBI patients spillover into ICUs. At Northwestern, 12% of ICU CLABSIs were determined to actually be CLAMBIs.

Is it any wonder that tertiary care hospitals are disproportionately affected by CMS penalties? This is just one of many reasons.

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.

Candida CAUTI: An oxymoron

While I was attending the SHEA Meeting last week, a list-serve that I follow had some disturbing comments regarding how some hospitals are addressing CAUTIs that are due to Candida spp. I'll address those comments a little later, but first want to address the concept of Candida CAUTI.

To meet the NHSN definition of CAUTI, a patient could have fever + 100,000 CFU/mL of Candida in the urine, or fever + 10 WBCs/mL + 1,000 CFU/mL of Candida. While this definition is appropriate for bacteria, it really isn't for Candida. There's an excellent paper (free full text here) by Carol Kauffman, one of the world's experts on fungal infections, that clearly outlines why capturing "Candida CAUTI" is inappropriate. Here are a few excerpts from that paper:

Candiduria is often observed in hospitalized patients. Candiduria is neither a symptom nor a sign, and it is clearly not a disease. The finding of yeast (almost always Candida species) in the urine could mean that the patient has pyelonephritis or cystitis. It also could mean that hematogenous seeding of the kidney cortex has occurred in the course of disseminated candidiasis. Finally, and most likely, the presence of candiduria may reflect only colonization of the bladder, perineum, or indwelling urinary catheter. The vast majority of patients with candiduria have no symptoms suggesting the presence of urinary tract infection (UTI); culture of the urine is done because of unexplained fever, elevated WBC count, or less cogent reasons, such as cloudy or smelly urine.

The diagnosis of bacterial UTIs relies on the findings of pyuria and bacteriuria, usually at a certain minimum number of colony-forming units, in a patient with appropriate symptoms. Guidelines exist for establishing the diagnosis of a bacterial UTI, and the existence of excellent diagnostic tests allows appropriate treatment to be evaluated and become standard practice. However, the diagnosis of a UTI due to Candida species is much more difficult. No studies have unequivocally established the importance of pyuria or quantitative urine cultures for UTI due to Candida.

The overwhelming body of evidence points to the fact that the presence of candiduria as an isolated observation generally does not portend subsequent invasive disease. This finding should make it less imperative to use antifungal agents to treat patients who have candiduria.

I took a look at my hospital's CAUTI data for 2013 and found that 41% of CAUTIs in our ICUs were due to Candida. In our medical ICU, 94% of CAUTIs were associated with Candida. The absurdity here is that we are expending enormous amounts of energy trying to prevent an infection that really isn't an infection. But even worse, as pointed out in list-serve comments, some hospitals are now changing urinary catheters before obtaining cultures. If you were a patient, would you want your catheter changed every time a urine culture is obtained?  Other hospitals are empirically treating catheterized patients with fluconazole to prevent candiduria. This will only result in non-albicans colonization and fluconazole resistance.

This could be easily fixed by simply removing Candida from the case definition. It's time to stop punishing hospitals and driving inappropriate treatment. Wake up, CDC!

Using NHSN C. difficile Infection Rates? Mind your denominator!

Over here in the US hinterland we're completing a systematic review of MDRO outcomes for CDC in cooperation with investigators in Salt Lake City. At the moment we're tackling C. difficile and are busily pouring through the literature. We've come across many good studies, such as an ICHE paper from early 2013 by Gase and colleagues from the New York State Dept. of Health that compared NY State CDI surveillance to NHSN in 30 hospitals. The authors noted an 80% agreement between the methods and thus recommended that NY State adopt the NHSN LabID method because of ease of implementation.

Building on that study, Haley and colleagues also from the NY State Dept of Health completed an analysis of the sources of bias in NHSN "Hospital Onset" CDI rate calculations using data from 124 NY hospitals. Their findings were published in the January 2014 issue of ICHE and were accompanied by a nice editorial by two of my former Maryland colleagues Jessina McGregor and Anthony Harris. The NY authors looked at how auditing, including outside labs, age adjustment and exclusion of "patient days not at risk in the denominator" would improve the calculation of hospital-onset CDI rates. As you can see by the portion of Table 2 that I pasted below, most of the corrections had minimal impact on the average hospital-onset CDI rates.  However, "exclusion of patient-days not at risk" had a huge impact on the calculated HO-CDI rate. The correct rate after controlling for all factors was 11.6/10,000 patient days; however, excluding auditing or outside labs, or age adjustment had minimal impact, whereas not excluding patient days not at risk from the denominator led to a rate that was 45% lower (6.4/10,000 pt-days).

The reason that eliminating "patient-days not at risk" from the denominator had such a huge impact is that the CDC NHS definition excludes CDI cases that occur in the first three days from the numerator but does not exclude patient-stays less than three days from the denominator. For example, a patient that stays only two days would not be at risk from contributing a HO-CDI case to the numerator but contributes their patient-days to the denominator.

This has several important implications.  One, not removing the patient days not at risk results in reported CDI rates that were much lower than they actually are. This occurs since many if not most patients have stays that are shorter than 4 days.  Second, as the authors state, "HO-CDI rates at hospitals with shorter LOS are biased downward more than the rates at hospitals with longer LOS." It seems to me that this artificially hurts the rates at tertiary-care and academic medical centers more than it would smaller community hospitals. We always hear how academic hospitals are falling behind, but it may have something to do with how rates are calculated, especially if we are including the wrong patient-days in the denominator.  It seems like this would be an easy fix - hospitals could just exclude the first three days from their patient-day calculations.  I hope this happens.

One way of Getting to Zero

Favorite quote: "I can disagree with gravity but it doesn't mean that it no longer applies to me."

 

Surveillance under pressure

There’s a great success story now published online in ICHE. The CDC, using National Nosocomial Infection Surveillance (NNIS) and National Healthcare Safety Network (NHSN) data, estimates that 100-200K central line associated bloodstream infections (CLABSIs) have been prevented since 1990 through implementation of evidence-based prevention practices. This accomplishment should be celebrated as a demonstration of the real progress that has been made in hospital infection prevention. As Mary Dixon-Woods and our fellow blogger Eli point out in an accompanying editorial, however, these results are also a time to reflect on how much surveillance has changed since 1990.

The CLABSI surveillance that we once performed exclusively to guide local prevention efforts is now used for much different purposes, with rates reported publicly and soon to have a real impact on each hospital’s bottom line. The pressure to bring CLABSI rates to “zero”, by any means necessary, gets passed along from hospital administrators to unit directors and infection prevention programs, turning CLABSI rates into what Mary and Eli correctly describe as a “reactive measure.” To quote their editorial,

“the more that organizations are incentivized by the prospect of shaming or financial penalties to decrease sensitivity—and thus not to find cases—the less certain it is that they are reporting a valid assessment of their infection rate”

It is instructive to examine what happens in other professions when intense pressure is brought to bear on a metric. Five minutes on The Google is enough to inform about what happens when law enforcement is under pressure to lower crime rates, or when teachers are under pressure to improve student test scores. Are police officers and teachers more inclined to “cheat” than are those tasked with counting infections in hospitals? Do officers who misclassify a burglary as a theft after receiving a call from a commander really have nothing in common with the IP program that misclassifies a primary CLABSI as secondary after a call from a unit director or hospital administrator?

This increased pressure is also felt at the CDC and NHSN, as a metric that was initially designed for one purpose is now appropriated for very different purposes. We recently performed an email survey of over 50 prominent hospital epidemiologists to gather their opinions about the direction of surveillance over the next decade. The results can be found here. Some of the highlights:

• Over 75% of those surveyed thought it likely or extremely likely that their local surveillance efforts will erode to focus only on those linked to payment policies or state/federal requirements.
• All thought that HAI surveillance metrics linked to payment policies and state/federal mandates would continue to grow to include more outcome and process measures.
• Respondents felt that pay-for-performance metrics were most likely to drive practice change (moreso than public disclosure of data, use of data by practitioners, or release of national summary statistics).
• Fewer than half thought it was likely that fully automated metrics from existing data elements would replace manual review of records for HAI determination.
• About half of respondents thought that the increased attention to HAI prevention from payment policies, mandates and public reporting has made patients safer (13% thought it hadn’t, and 40% thought the jury was still out).
• Almost 80% of respondents thought that infection prevention experts and clinical providers should have a much larger role in developing and modifying state/federal reporting requirements.

In other words, there is real concern in the HAI prevention community that the increased attention to HAIs, the drive to “zero”, the link to payment policies and public reporting requirements, is a double-edged sword. It has resulted in some tangible successes (CLABSI reductions being a prime example), but threatens to undermine our ability to respond nimbly to emerging local priorities by consuming all of our time and energy, and by producing data that no longer accurately reflect the true rate of adverse outcomes. To quote again from Mary and Eli:

"Undermining our surveillance system to serve ill-designed demands for accountability means that it may no longer be useful for monitoring and driving patient-safety improvements. That would truly be a shame."

Working the refs

As the Miami Heat and Oklahoma City Thunder prepare to do battle in the NBA finals, we should recognize the importance of home court advantage and the pressure placed on referees by coaches and players. There are data that home teams benefit from fewer fouls, and everyone knows coaches and players who are good at “working the refs” to influence their calls. There are also data that as the pressure mounts (game 7 in a 7 game series), referee influence is even greater.

Now, as a mental exercise, picture your infection preventionists (IPs) in referee outfits. It shouldn’t be surprising that as the pressure mounts for hospitals to eliminate reportable HAIs, the refs (those who “make the calls” as to whether an event meets the NHSN criteria for an HAI) are under increasing pressure. We’ll soon publish survey data revealing how commonly hospitals use “consensus methods” (e.g. adjudication panels that include clinical leaders and/or hospital administrators) or even allow clinicians to “veto” HAI calls. These approaches all drive HAI rates lower, as I know of no hospitals where clinicians or hospital administrators bring cases to the IPs to ask why they failed to report them as HAIs. They are also corrosive of the prevention culture, and contribute to IP burnout.

However, even if all hospitals stopped these practices immediately, the increasing pressure to demonstrate HAI elimination would remain a problem. The celebrations units have when they reach “zero” for a period of time, and the massive disappointment when a single VAP or CLABSI ruins the celebration, are akin to the crowds and coaches during game 7, cheering a call for the home team and booing a call for the opposition. And in those centers where financial rewards and penalties accrue to unit directors based upon HAI rates, the pressure is even greater. 

The answer? Eliminate subjectivity in HAI definitions, and move to objective definitions that are amenable to electronic reporting. These HAI events may no longer correlate well with the infectious disease syndromes we diagnose and treat at the bedside, but as long as they are associated with important adverse outcomes (length of stay, mortality) and are preventable, they should suffice (see VAC vs. VAP).

Central line + positive blood culture = CLABSI (not!)

There's a thoughtful commentary in a recent issue of Clinical Infectious Diseases by Tom Fraser and Steve Gordon at Cleveland Clinic on problems related to CDC's central line associated bloodstream infection (CLABSI) case definition. We've blogged about this before. The definition is old and was designed to maximize sensitivity long before anyone thought about public reporting. But the issues of poor specificity of this definition are haunting many of us, particularly those who work at hospitals with cancer centers. Unfortunately, neutropenic cancer patients not uncommonly have translocation of enteric flora across their intestinal mucosa and the resulting bloodstream infection in the presence of a central line forces us to label these as CLABSIs, even though these infections are not at all related to the central line. Ten years ago no one cared about this surveillance technicality. Now, in the era of public reporting this is a big problem. In fact, nearly every "CLABSI" in the medical ICU of my hospital falls into this category. Fraser and Gordon show us how this is handled at their hospital with a modification to the CDC definition that is used for internal purposes. Hopefully relief is on the way. CDC is very interested in this issue and has assembled a committee that is actively evaluating the issue.