Should surgeons be allowed to prescribe antibiotics without assistance?

It's the end of a long day on the ID consult service. You and the team have decided to recommend switching antibiotics on a post-op cardiac surgery patient since the S. aureus susceptibilities have returned and you'd prefer cefazolin over vancomycin for her MSSA bacteremia. The team text messages the primary surgical team and the intern meets the team in the ICU. You overhear the ID fellow's discussion with the surgical intern, who appears to not know the patient and who can't get approval from the senior resident, CT surgery fellow or the attending to make the antibiotic change since the whole team is scrubbed in the OR.

The above scenario is all too familiar to those who practice infectious diseases, and to be fair it could apply to other procedure-based subspecialties. But the question arrises, if only oncologists can prescribe chemotherapy, why is it that everyone is allowed to prescribe antibiotics? Is this really what is best for our patients? Yes, this is currently a controversial topic but these are the types of questions we need to ask if we're going to respond to the antimicrobial resistance crisis.

A group of researchers in the UK led by Esmita Charani and Alison Holmes began exploring the effects of culture and team dynamics on antimicrobial prescribing during surgical ward rounds and the results of their ethnographic study left me convinced that we must develop ways to improve antimicrobial prescribing on surgical services.

The research team observed the antimicrobial prescribing decision making of six surgical teams over a 3-month period. These included observation of 30 ward rounds and face-to-face, semi-structured interviews of 13 clinicians (5 consultant/attending surgeons, 3 registrars/residents, 2 nurses, 2 junior doctors/interns and the ward pharmacist). The qualitative analysis identified 4 key themes that influence antibiotic prescribing: (1) working in a constant state of flux; (2) communication jigsaw; (3) delegating antibiotic management; and (4) the need for an intervention. Here are a few quotes from the study:

Constant flux: There is a hierarchy as to who leads ward rounds (WR), but this is a shifting hierarchy whereby people are promoted or demoted from their position based on who is present on the WR...if the surgeon leading the WR is called away, for example to the OR, the line of authority shifts downwards and people must act up, for example the registrar takes on the role of the surgeon, the junior doctor ‘becomes’ the registrar and the medical student ‘becomes’ the junior doctor.

Communication jigsaw: WRs are often rushed, interrupted and dispersed and reconvened because of demands for the senior team to be in the OR. The constant disruption and people leaving and joining the WR means that members of staff will rarely be present for the entire WR. Because of being constantly split between the OR and the ward, communication within the surgical team occurs across different platforms. Key decisions are made, recorded and communicated not necessarily in medical health records but on handover sheets, text messaging, and applications on smartphones (e.g. WhatsApp). On many occasions a patient was thought to be on antibiotics by the team, and after further queries in notes and charts was found not to be on them, and vice versa.

Delegating antibiotic management: Surgeons tended to see the core elements of their role as relating to the surgical management of their patients, a role that is performed in the OR. The lack of priority given to antibiotic decision making is compounded by a lack of expertise, resulting in responsibility for antibiotic decisions being commonly delegated to others.

The need for intervention: The need and expectation to intervene means that often antibiotics are initiated for patients with no or little evidence of infection, but a high plausibility of infection in the minds of the surgeons. This process is rationalized by the surgeons as being an extension of their roles as ‘interventionists’. In the absence of evidence of infection what drives antibiotic decision making is a risk of failure, and a risk of blame. What is considered unique in surgery is that a patient has to be well enough to be able to undergo an operation, therefore any deterioration postoperatively is assumed to be a consequence of the surgery, and the decisions of the surgeon, and not the patient's underlying illness. These concerns drive a more conservative approach to antibiotic decision making leading to unnecessary and prolonged courses of antibiotics.

None of these points will appear very surprising to anyone who has cared for patients on a surgical service. However, the authors are to be commended for the care with which they completed this study and the wonderful structure they provided to the domains that influence antimicrobial prescribing. I agree with their assessment that "there is a need to explicitly assign the responsibility for antibiotic management of the surgical patient to a responsible, individual with necessary expertise... Diagnosis and treatment of infections is a specialty that requires expertise and training, therefore this is an opportunity to develop, with support from specialist microbiology laboratory and staff, a role for a clinician(s) responsible for perioperative antibiotic management. This will help to strengthen the antibiotic management for surgical patients and has the potential to facilitate continuity of care and to help overcome the substantial gaps in communication that have been identified in this study...The time is right to question whether we need to address the gap in antibiotic prescribing for surgical patients by developing this specific perioperative clinician role to manage infections. This is of critical importance considering the rising challenge of antibiotic resistance in postoperative patients."

They will never stop...

....trying to kill the Prevention and Public Health fund. We’ve blogged about this before, here, here, here, here, here, and sadly, again today.

The House of Representatives just voted to reauthorize the Children’s Health Insurance Program (CHIP), which is a good thing—but decided to pay for it with cuts to Medicare and with a huge slash to the Prevention and Public Health fund, which is a very bad thing that will harm many effective prevention efforts. Here is a list of what this fund supports, including expansion of laboratory capacity to detect and respond to emerging infections and antimicrobial resistance, and improvement of state public health infrastructure to detect and prevent healthcare-associated infections across all health care settings.

Meanwhile, our Congress is also planning a massive reduction in corporate taxes with no plan to pay for it (besides adding to the deficit).

Priorities!

De-implementation and Noninferiority in Infection Control Studies

De-implementation or "stopping practices that lack supporting evidence" is a popular topic in infection control circles. In fact, just yesterday I read a discussion where the authors suggested we no longer need to practice hand hygiene after removing gloves when caring for patients with CDI. I guess there aren't randomized trials - you can't be serious!

Which brings me to a recent review in the NEJM by Laura Mauri and Ralph D'Agostino titled "Challenges in the Design and Interpretation of Noninferiority Trials."  This review is very well written - perhaps required reading for epidemiology students well written. In infection control, it is important to recognize that most de-implementation studies are really non-inferiority trials. For example, when we discontinue contact precautions, we are really suggesting that "stopping contact precautions" is non-inferior to continuing contact precautions in preventing MDRO transmission - of course ignoring that compliance with contact precautions is probably so poor that they are basically the same intervention!

In the contact precautions example, we would be testing whether stopping contact precautions "is not worse than the control (continuing contact precautions) by an acceptably small amount, with a given degree of confidence." The null hypothesis would be that discontinuing contact precautions leads to higher transmission of MDRO (i.e. is worse) and rejection of the null hypothesis is used to support the claim that discontinuing CP is noninferior. Here I suggest you stare at Figure 1 for a bit (probably easier to read in the paper with the description of each condition, but I have included it below anyway)

Further discussion about the design and analysis of these trials is way beyond the scope of a humble blog post; however, the authors include nice descriptions of methods for deriving noninferiority margins, the "constancy assumption" and statistical analysis approaches. But their 6th and 7th components of noninferiority trials are worth mentioning from an infection control standpoint:

6) Adequate ascertainment of outcomes: The authors write that "incomplete or inaccurate ascertainment of outcomes, as a result of loss to follow-up, treatment crossover or nonadherence, or outcomes that are difficult to measure or subjective, may cause the treatments being compared to falsely appear similar."  I would suggest that studies that seek to de-implement contact precautions that do not include admission/discharge surveillance cultures seeking to detect transmission events fail this criteria.

7) Issues with "Intention-to-Treat" in noninferiority designs: In a superiority studies (typical RCTs), intention-to-treat analysis, where anyone who receives the treatment is included even if they get one dose, is the gold standard. The authors write: "In a noninferiority study, however, if some patients did not receive the full course of the assigned treatment, an intention-to-treat analysis may produce a bias toward a false positive conclusion of noninferiority by narrowing the difference between the treatments. In some instances, a per-protocol analysis, which excludes patients who did not meet the inclusion criteria or did not receive the randomized, per-protocol assignment, may be preferable in a noninferiority trial. However, a per-protocol analysis may include fewer participants and introduce postrandomization bias. In general, both the intention-to-treat and per-protocol data sets are important. We suggest analyzing both sets and examining the results for consistency."

Just some things to think about as we read the coming wave of de-implementation studies in infection control including diagnostic stewardship.

Wrong answer

This morning I stumbled upon this piece, Wrong Answer (free full text here), by Rachel Aviv in The New Yorker. It's an old article from 2014, but a wonderfully written, compelling, sad tale. It's the story of how high stakes standardized testing of middle school students in economically disadvantaged neighborhoods in Atlanta led to cheating by teachers. It focuses on Damany Lewis, a superb teacher totally committed to his students, who tirelessly worked to improve his students' math knowledge and was successful in doing so, but not successful enough to hit an unreachable goal. Responding to increasing pressure to raise testing scores, he and other teachers began to change the answers on students' tests. We all know that cheating is unethical, and at first glance I bet most of us would argue to punish those involved, but read this entire piece (warning: it's long), and you're likely to soften your stance. The consequences of not meeting unreasonable targets were so severe that the teachers felt compelled to cheat in the best interest of their students.

Now take this article from the education setting into the world of healthcare epidemiology, and if you're like me, there will be chills going down your spine as you read it. It should be required reading for anyone who works in healthcare quality or the key stakeholders in this space, from those at the front lines to those who work in professional societies, and to those who create policy at the state or national level. There are also lessons here for patients and patient advocates.

Donald Campbell

What happened in Atlanta shouldn't surprise us. In 1979, Donald Campbell, a psychologist, published a paper, the crux of which has become known as Campbell's law. I wasn't aware of this until I read Aviv's article. It states: "The more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor." Moving this to our world, you can delete the word "social" in Campbell's law and take a look at Dan Sexton's commentary, Casablanca Redux, from 2012. Here's an excerpt:

Our informal discussions with other hospital epidemiologists, our experience in evaluating the source of infection in hundreds of bacteremic intensive care unit (ICU) patients, and common sense have led us to suspect that many hospitals do not accurately report their true rates of CLABSI, using current NHSN definitions. In some cases this may reflect an unwillingness of local staff to accept these definitions as accurate or fair; in other situations it may reflect an unconscious desire to hedge or reduce their rate of CLABSI to avoid criticism and negative consequences from their local supervisors in the press, clinicians, or the general public who review their publicly reported data... If clinicians inappropriately or illogically fear or anticipate negative feedback about the rate of CLABSI in their institutions, they may consciously or subconsciously fail to obtain blood culture results for every patient with a possible or likely BSI. Simply put: no culture equals no infection, using standard definitions of CLABSI. 

At some level, we are all complicit in this. And depending on the action, it may not be the wrong thing to do. In fact, it may benefit the patient. For example, better diagnostic stewardship in the form of appropriately ordering fewer urine cultures not only lowers CAUTI rates but reduces antibiotic utilization with several resultant benefits. Still it's important to note that the primary impetus for this was to lower HAI rates. We take into consideration how a new diagnostic test may impact HAI rates and may even allow that to impact the decision to implement (see an excellent paper by Dan on this here). We may allow clinicians to censor infections that infection preventionists have detected even though the cases meet NHSN definitions. And at the extreme, hospitals may engage in practices that may harm patients in order to reduce publicly reported HAI rates. In a recent publication on how physicians in training view quality initiatives, a dirty secret was elicited from a resident during a focus group at an academic medial center: “There’s like the central line infection protocols…. If you suspect that anybody has any type of bacteremia, you don’t do a blood culture, you just do a urine culture and pull the lines … we just don’t even test for it because the quality improvement then like marks you off.” 

While reading Rachel Aviv's paper, I wondered: Do we ever ignore results (i.e., infection rates) that seem too good to be true like the educational administrators in Atlanta did? Do we critically analyze surprisingly good results to the same degree as we do surprisingly bad results? In Damany Lewis' case did the end justify the means? Is there ever a situation where I could be pushed to a similar point as Lewis?

The Atlanta school system harmed students and teachers in a thoughtless quest to improve quality. There were no winners. Sadly, the response to the cheating scandal was to raise the stakes for test scores even higher. With pay for performance the same is happening in health care.

Chlorhexidine bathing outside the ICU: Await the ABATE!

Daily chlorhexidine (CHG) bathing has become routine in many ICUs. Given that more healthcare-associated infections (HAIs) (including more central-line associated bloodstream infections (CLABSIs)) occur outside of the ICU, many hospitals have also implemented this practice on general medicine and surgical wards. However, to this point there are few data to support the effectiveness of CHG bathing outside of ICUs. 

So I was very excited to hear Susan Huang present the results of the Active Bathing to Eliminate Infection (ABATE) study at IDWeek last week. Similar to the REDUCE MRSA study, this 53 hospital cluster-randomized trial took place in the Hospital Corporation of America (HCA) system. Units were randomized to routine care or decolonization (this consisted of daily CHG [4% rinse-off shower or 2% leave-on bed bath] with addition of mupirocin nasal ointment for 5 days if + for MRSA by history or culture/screen) for a 21-month intervention period (after collecting baseline data for 12 months). The primary outcome was MRSA or VRE clinical isolates, and the main secondary outcome was any bloodstream isolate attributed to the unit (for common commensals, 2 or more + cultures).

With the requisite reminder that it is always best to wait for the peer-reviewed publication to make firm conclusions, the results presented at IDWeek suggest the likely take-home from this study: No measurable benefit in the entire population, but significant reductions in MRSA/VRE clinical cultures and bloodstream infection in the subgroup with devices (central lines, midlines, and lumbar drains). This subgroup represented 12% of the study population but accounted for 34% of all MRSA/VRE events and 59% of bloodstream infections. The additional reductions in the decolonization arm for this subgroup (compared with routine care) were 32% for MRSA/VRE cultures and 28% for BSI (both highly statistically significant). 

Once published, these findings will leave infection prevention programs with some interesting decisions. A quick take might be, “OK, let’s just use CHG in those non-ICU patients with devices (or just central lines).” However, this isn’t what the ABATE trial evaluated—it showed a substantial reduction in MRSA/VRE/BSI outcomes in patients with devices when everyone else was also receiving CHG (+/- mupirocin). To assume that the decolonization of the non-device population had no beneficial effect on those with devices is to discount any potential role for reduction in pathogen transmission between non-device and device patients. Another tricky question has to do with the role of mupirocin—adding this agent to CHG for known MRSA carriers without knowing how important this component of the intervention was adds some logistical complexity, cost, and antimicrobial resistance concerns (the investigators are also doing the microbiology work to assess for CHG and mupirocin resistance emergence). 

We’ll revisit this study once it is published and all the details are available. For now we should congratulate Susan and the entire ABATE trial team for another tremendous contribution!

IDWeek 2017 is in the books

I'm back from IDWeek 2017 in San Diego -- always good to reconnect with old friends, meet new colleagues, and hear the latest in infection prevention and antibiotic stewardship science and practice.  This year I noted an increase in pro-con and clinical controversies sessions, which I think are so valuable for those of us struggling day-to-day with issues where the evidence base is absent or opinion is conflicting.   If you didn't get a chance to attend, fear not, as I am sure there will be more conference-related news emerging over the coming weeks (all 6 of your favorite bloggers made an appearance). 

Of course, it's never too early to think about next year -- IDWeek 2018 heads back to the left coast, this time it's San Francisco, October 3-7, 2018 (mark your calendars!).  I'm among the SHEA contingent for the planning committee (along with Hilary), and we're very interested in program suggestions -- please send them our way.

I'll leave you with one meeting tidbit -- Shelley Magill presented the results from the CDC's 2015 HAI Prevalence study, a follow up to their 2011 survey that was published in the NEJM.  In the 4 years since the first survey (using the same hospitals included previously):

• The HAI prevalence rate among hospitalized acute care patients fell from 4.1% to 3.2% (a 22% decrease)
• Central line and urinary catheter use were both significantly lower
• Healthcare-associated UTIs and SSIs significantly decreased

Nice to see some positive news to reflect the extensive HAI prevention efforts nationwide.  Of note, antimicrobial use stayed stable, reinforcing the need for increased antibiotic stewardship efforts.  Given the enhanced focus in this area, I am hopeful that the next prevalence survey will show improvement on that end as well.

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.