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.

Trying to link ward-level hand hygiene compliance and healthcare-associated infections

I've spent 15 years studying hand hygiene, so I obviously think it is critical to safe healthcare delivery. As I mentioned in my prior post on hand hygiene and HAI, sometimes these posts are difficult to write. The difficulty stems from the inertia required to confront dogma, while simultaneously bracing for the inevitable criticism. And of course, I could be completely wrong. Often times, dogma is correct.

But as I've gazed out of my office in the old Singapore CDC (soon to be replaced by a shiny new NCID), I've had moments to consider various causal diagrams linking hand hygiene to various outcomes, like CLABSI. (see below or source) If you carefully examine (click to expand) this or other causal models you see that hand hygiene is there, but it is only one of many possible causes of CLABSI. So, strictly speaking hand hygiene is in the causal pathway to CLABSI development. That's the dogma and it's true, to a point.

But let's move on to my contention: ward-level or ICU-level hand hygiene compliance changes can't be linked to reductions in HAI. For example, no amount of raising hand hygiene from 0% to 100% can be associated with reductions in HAI, such as CLABSI. It's just not mathematically possible. Sure, some study might show such an association, but I wouldn't believe it.

So to borrow a strategy used often by fellow blogger Mike, I'll use math(s).

Most facilities monitor hand hygiene compliance with direct observation. On the ward level, we reported that less than 30 opportunities/ward/month are collected. That was in 2012, so let's say things are much better and we observe 100 opportunities. This actually doesn't matter - you could observe 10,000 per month with an automated system, but let's stick with 100 opportunities.

Now, let's estimate how many opportunities are related to HH moment #2 (before aseptic procedure). Most estimates that I've seen are close to 10%. And how many of HH moment #2 involve directly manipulating a central venous catheter - let's go with 2%. You can estimate a lower or higher rate depending on ward acuity, but I'm going to stick with 2% since the vast majority involve peripheral lines. So, 2% of 10% is 0.2% or 0.002.

So, how many months of 100 observations/month are required before we witness one opportunity where the HCW touches a CVC? Answer: 5 months.

Now over those 5 months, let's assume we have observed a hand hygiene compliance of 50%, so 250/500. Let's also assume the worst and say that HCW were 0% complaint with CVC-related moment #2 in those 5 months. Now, let's assume they were 100% compliant over the next 5 months after we targeted a hand hygiene education program to moment #2. Our compliance would increase to 251/500 or 50.2%. Any other increases in hand hygiene would not be in the causal pathway for CLABSI, so even if compliance shot up to 80%, we would only care about the 0.2% increase.  In fact, this highlights why it's difficult to link ward-level hand hygiene compliance to reduced CLABSI, since most of the increase does not involve CVC-related moment #2. It's almost all noise.

And if you still want to install the automated monitoring system, you can multiply the numerator and denominator by 100, and still have 25000/50000 (50%) with an increase to 25100/50000 (50.2%). And if your hand hygiene education was super successful and compliance increased to 80% (40000/50000), it would still be true that only 100 of the 15000 additional compliant opportunities would be CVC related. 100/15000 is 0.67%. Thus, CVC related hand hygiene opportunities are a needle in a haystack.

I encourage you to check my math, choose different rates or numbers and correct me in the comments below or on Twitter. Sadly, it's hard to link ward (or ICU) level hand hygiene compliance to ward-level CLABSI rates. But as I've said before, keep washing your hands and monitoring hand hygiene compliance in your hospital. No one wants a CRE outbreak.

Is the CLABSI metric now doing more harm than good?

It’s a good news, bad news situation. 

The good news is that we’ve got an effective bundle of practices that can prevent most central line associated bloodstream infections (CLABSIs). Public reporting and pay-for-performance have added to the incentive for hospitals to implement these practices, leading to a dramatic nationwide decline in CLABSI rates. 

The bad news for most hospitals is that the remaining events that meet the NHSN CLABSI definition represent a motley assortment of “one-offs” that aren’t clearly preventable (see this great post from Scott Fridkin for more detail, and to share your “one-offs”). Given how low the CLABSI baseline now is, just a slight “blip” in these one-offs can push a hospital’s SIR above 1 and contribute to financial penalties (for some perspective, the post-intervention CLABSI rate in the widely cited Keystone ICU project (1.4 per 1K line days) now translates to an SIR of >1 at many hospitals). 

These blips result in a tremendous amount of effort by unit leadership and infection prevention programs, as they examine each event for preventability or for common themes—often concluding that the events are either secondary or mucosal-barrier injury (MBI) attributable, but don’t meet the requisite NHSN definition (so must be reported as primary CLABSIs).

HAI definitions, like lab diagnostics, perform best when applied to individuals with moderate-to-high pretest likelihood of disease (or, populations in which the disease is prevalent). This leads to a paradox for HAI surveillance—as prevention approaches improve and HAI rates fall, the positive predictive value of surveillance definitions also declines (if one defines a “true positive” as an event that meets an intuitive clinical definition of the HAI in question—e.g. an old-fashioned primary CLABSI likely to be due to breaches in the process of line insertion and care).

Aside from the time and energy spent chasing one-offs, the continued pressure on the CLABSI metric is going to result in some counterproductive approaches to reach “zero”. After all, there are really only two ways to eliminate CLABSI as it is currently defined: (1) stop using central lines, and/or (2) stop obtaining blood cultures in patients who have central lines.

Safer.

AHRQ.

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.

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.

It's my opinion that this treatment won't kill you directly, so....

Iowa recently passed a law that allows physicians to “expand upon the traditional standards of care for Lyme patients”, provided they get informed consent from the patient and provided that “in the opinion of the licensee, [the treatment] will not result in the direct and proximate death of or serious bodily injury to the patient”. If you don’t believe me, check it out for yourself! 

Thanks to the CDC and others (MMWR June 16, 2017) for reminding us that the opinion of a provider, no matter how well-meaning he or she may be, is no guarantee that a protracted course of antibiotics or immunotherapy will not, in fact, lead to “death or serious bodily injury”.

As more states pass laws that protect health care providers from sanctions (or ensure insurance companies must reimburse) for the use of unproven and dangerous therapy for “chronic Lyme disease”, this will become an increasingly important patient safety issue.  Publishing periodic case reports or case series of the unintended but devastating consequences of these treatments isn’t enough—we should establish a registry to track these adverse outcomes.

And finally, the most important roles for the physicians seeing patients who carry a “chronic Lyme” diagnosis are (1) to perform a complete and unbiased evaluation to assess for other causes of the patient’s very real suffering, and (2) to educate in order to protect patients from unproven therapy that will place them at risk for serious infection and death.

Playing Nice: Infection Control and Clinical Microbiology in the Pay-For-Performance Era

As it's probably clear, it's been a great honor for me to work (and blog) with Dan and Mike over the past 8! years. One of the things that stands out when talking shop is their ability to see both sides of an argument even while pushing for the changes they support. Many times, their ability to see both sides clearly is possible because they've lived both sides - Mike has been an ID chief and hospital epidemiologist and is now our CQO and Dan is an ID chief, hospital epidemiologist and clinical microbiologist. You know, if I was a fellow or faculty member looking for a hospital epidemiologist position with great mentorship and support, I would move to Iowa...but I digress.

One specific area where understanding competing goals is critically important is the interplay between the increasing sensitivity and precision of microbiologic tests and the growing pressure to reduce HAI. As you can imagine, with 3% of CMS payments potentially at risk, anything that could impact HAI rates in a negative fashion is bound to be a flashpoint for hospital administrators. With that in mind, I point you to Dan's excellent commentary just published in JCM that examines the implications of advances in microbiological testing on HAI rates and provides specific suggestions for how hospital epi programs and clinical microbiology labs can work together to respond to these changes.

Initially, Dan provides three scenarios where changes in the micro lab could directly impact HAI rates (1) The effect of MALDI-TOF on CLABSI rates (2) The shift from EIA to nucleic-acid amplification tests (NAAT) for C. difficile detection and (3) Pressure to block urine culture ordering to reduce CAUTI. After delving into the current CMS reimbursement landscape, the unintended consequences of improvements in diagnostic testing and the use/misuse of surveillance definitions, he provides six valuable recommendations that clinical microbiology labs (CML) and infection prevention programs (IPP) should consider:

(1) CML leadership should select diagnostic approaches with the goal of improving individual patient outcomes

(2) Hospital and IPP leadership should not pressure the CML to alter diagnostic practices based on the need to demonstrate lower HAI rates for pay-for-performance measures. 

(3) Public health authorities (CDC/NHSN) must be proactive in adjusting HAI metrics to changing CML technology

For recommendations 4-6, you're gonna have to read his commentary. But a hint at #6 -  CML and IPP leadership need to collaborate and advocate for their needs, because, unlike at Iowa, both sides aren't always present in the mind of a single person.

Retractions - One now, perhaps more in the future

We've written before about article retractions and why high-profile journals like NEJM might have the highest retraction indices (figure). Yet, retractions in infection prevention are very infrequent. In fact, the one major retraction we mentioned during 7 years of blogging was not strictly a retraction, but a re-analysis of N-95 vs surgical masks.

Which brings us to a rare retraction in infection prevention, namely this 2014 AJIC study looking at zero fluid displacement intravenous needless connector and CLABSI prevention. As reported this week in Retraction Watch, the study was initially questioned because of a failure to report conflicts of interest but has now been fully retracted by AJIC since an investigation found problems with the "consistency of the statistics over various study periods as well as the methods by which study sites were chosen." Fortunately, since this study was published less than 2-years ago and only cited once, this retractions won't be particularly damaging to the field.

However, with the recent announcement that NIH is providing an additional $100 million for antimicrobial resistance (AMR) along with increases in CDC and industry funding, there are now resources to fund high-impact studies that will appear in high-impact journals. And as if those pressures are not enough, the US Government has been tasked to reduce C. difficile infection by 50%, CRE by 60% and MRSA by 50% by 2020 compared to 2011. With the unprecedented increase in funding combined with difficult to achieve targets, there will be unbelievable pressure to cut corners in study design and analysis and over-promise or over-promote results. This all represents a potential recipe for disaster if our field isn't careful. On the bright side, societies like SHEA have very strict conflict of interest reporting, so we have some checks in place to identify conflicts and bias. Of course, this won't be enough. Be careful out there.

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.

Will you trade me a CLABSI for a pneumothorax?

There was an interesting new study published today in the NEJM by Jean-Jacques Parienti and colleagues and funded by the French Ministry of Health. The study aimed to compare the rates of catheter-related bloodstream infection and symptomatic deep-vein thrombosis (DVT) in 2532 adult ICU patients randomized to subclavian (N=843), jugular (N=845) or femoral vein (844) sites for nontunneled central venous catheter insertion. Baseline characteristics were very similar across the three groups.

The results are not very surprising with higher rates of infection and symptomatic DVT when catheters were inserted using the jugular and femoral veins compared to the subclavian veins but these were almost cancelled out by a higher risk of pneumothorax when using the subclavian approach. (see figure below).

Here are my thoughts on the paper:

1. This is a very nice study that confirms the results of prior smaller studies in the field. 
2. The overall complication rate is low with approximately 97% of catheters in each group inserted safely without a pneumothorax and without infection or DVT. 
3. When all complications are included, it doesn’t appear there is a preferred site for catheter insertion. However, since hospitals are penalized for infections and not other complications**, there has been a preference for chosing the subclavian site, since it is associated with fewer infections. Unfortunately, the subclavian site had a much higher rate of pneumothorax. 
4. One important comment that the authors make is that how long the catheter remains in place can greatly increase the rate of infections and blood clots. So, if the doctor expects the catheter to remain in for a short period of time, she might chose a femoral or jugular vein approach to limit the pneumothorax risk, with very little infection or clotting risk since the catheter will be removed before the complication can occur. However, if the catheter is to remain in place for many days, it is probably worth the higher risk of pneumothorax associated with the subclavian site, which only occurs when the catheter is inserted, in order to reduce the long term infection and DVT risks. 
5. The study sites did not use daily chlorhexidine bathing and did not place chlorhexidine-impregnated dressings at the catheter insertion site. Both of these interventions have been shown to reduce catheter-related bloodstream infections. Thus, these results might not be generalizable to hospitals that use chlorhexidine bathing and/or chlorhexidine dressings. It's possible that either of these interventions or both could mitigate the infection risk rendering femoral or jugular vein approaches safer than they appear in this study.
6. Overall, it looks like fears of using the femoral vein are exaggerated, especially when you consider the rates of other complications like pneumothorax. It may be that in our efforts to get to zero CLABSI, we're putting patients at higher risk for other complications. Perhaps a more nuanced target of "any complications per catheter inserted" could replace CLABSI as a quality metric?

**Edit: Mike kindly informed me that hospitals are also penalized for iatrogenic pneumothorax through an AHRQ patient safety indicator, which is included in the CMS Hospital-Acquired Condition Reduction Program. Thus, selecting a subclavian site could lower your CLABSI penalty but raise your pneumothorax penalty. Of course!

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.

Where did all the pediatric HAIs go?

In keeping with the pediatric theme this week, there is a nice study just published in Pediatrics outlining the general trends in Pediatric HAIs between 2007 and 2012. Stephen Patrick and colleagues from Vanderbilt and Boston Children's Hospital used CDC NHSN data from 173 NICUs and 64 PICUs to track CLABSI, VAP and CAUTI rates.

The good news is that CLABSIs declined from 4.9 to 1.5 per 1000 central-line days in the NICU and from 4.7 to 1.0 per 1000 CL-days in the PICU. There were also significant reductions seen in VAP in both NICUs and PICUs. CAUTIs were not adequately reported in NICUs; however, in PICUs the authors found that CAUTI rates did not change significantly over the 6-year study period. I've included the NICU and PICU figures below. Importantly,  CLABSI rates were twice as high and VAP rates were over three times as high in very low birth weight ( < 1500g) infants.

Apart from highlighting recent successes in reducing HAI, I think the main messages (as I've already stated elsewhere) are that HAI are still very common in VLBW infants and that rates remain above zero in all pediatric populations. Thus, despite cultural changes, implementation of insertion bundles and technological innovations, such as antimicrobial-coated catheters, we still need additional evidence-based methods to prevent HAI in children. While we might take this occasion to rest on our laurels and celebrate past successes, we should instead increase funding for development of HAI prevention interventions, particularly in VLBW populations.

And while policymakers might think we can mandate further HAI reductions through such things pay-for-performance, it is clear from these data that those tools have already done their job yet there's much more hard work to do. Notice the almost flat rates in both figures over the past 3-4 years. We need more than quality-improvement goals. Without scientifically proven methods to further reduce HAI, I suspect rates will remain stagnant. That would be unfortunate.

More on CLABSI validation

A new study in the American Journal of Infection Control looks at validation of publicly reported CLABSI (central line associated bloodstream infection) data in New York. To my knowledge, New York has done the most work on validation, so it's worth taking a look at this paper. The authors analyzed CLABSI data from 2007 through 2010. By year, the sensitivity of reported data varied from 68% to 74%, and for the entire time period was 71%. Of concern, there appeared to be no improvement over time, and for 2010 sensitivity for medical ICUs was only 46%. Specificity ranged from 90 to 99%. So, about 30% of CLABSIs are not reported in a state that has focused on validity. It makes you wonder how bad validity may be in states that have done little work on this. It's interesting that consumer advocacy groups don't seem to be too concerned about the validity issue, and to some degree neither do payers. A consumer advocate once told me, "Just give us all the data. We don't care if it's correct." Caveat emptor!

Graphic: WhyWeSuffer.com

Why validation is important

A new study in the American Journal of Infection Control describes the validation of publicly reported central line associated bloodstream infection (CLABSI) data in Colorado. The study encompassed a review of data from ICUs at 35 acute care hospitals and 8 LTACHs for the first quarter of 2010. Charts of 519 patients with positive blood cultures were reviewed. Results of this study, as well as 3 other statewide CLABSI validation projects are summarized below.

	Sensitivity	Specificity
Connecticut	48%	99%
New York	74%	95%
Oregon	72%	99%
Colorado	83%	99%

These 4 studies show that 17-53% of CLABSI cases were misclassified as non-CLABSIs, primarily due to labeling CLABSIs as secondary bacteremias. Interestingly, the Colorado study attempted to delineate the underlying causes of misclassification. Two reasons were found to be significant: lack of an electronic medical record or data mining software, and review of potential cases by an infectious diseases physician (i.e., post-surveillance certification).

Given all the attention paid to publicly reported HAI data and the important implications with regards to reimbursement, it's imperative that hospitals produce valid data. However, validation projects remain infrequent due to their cost. We have a long way to go...