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How to Measure the Impacts of Antibiotic Resistance and Antibiotic Development on Empiric Therapy: New Composite Indices

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How to Measure the Impacts of Antibiotic Resistance and Antibiotic Development on Empiric Therapy: New Composite Indices Open Access Research BMJ Open: first published as 10.1136/bmjopen-2016-012040 on 16 December 2016. Downloaded from How to measure the impacts of antibiotic resistance and antibiotic development on empiric therapy: new composite indices Josie S Hughes,1 Amy Hurford,2 Rita L Finley,3 David M Patrick,4,5 Jianhong Wu,1 Andrew M Morris6,7 To cite: Hughes JS, ABSTRACT et al Strengths and limitations of this study Hurford A, Finley RL, . Objectives: We aimed to construct widely useable How to measure the impacts summary measures of the net impact of antibiotic ▪ of antibiotic resistance and Provides novel and clinically meaningful resistance on empiric therapy. Summary measures are antibiotic development on summary measures of our current ability to empiric therapy: needed to communicate the importance of resistance, provide empiric coverage, and of the available new composite indices. BMJ plan and evaluate interventions, and direct policy and stock of antibiotics. Open 2016;6:e012040. investment. ▪ Uses widely available cumulative antibiogram doi:10.1136/bmjopen-2016- Design, setting and participants: As an example, data. 012040 we retrospectively summarised the 2011 cumulative ▪ Accounts for variation in resistance, the relative antibiogram from a Toronto academic intensive care clinical importance of pathogens, and the avail- ▸ Prepublication history and unit. ability of drugs among infection types and additional material is Outcome measures: We developed two contexts. available. To view please visit complementary indices to summarise the clinical impact ▪ Does not account for variation in toxicity, cost, the journal (http://dx.doi.org/ of antibiotic resistance and drug availability on empiric or in vivo efficacy among drugs that are consid- 10.1136/bmjopen-2016- therapy. The Empiric Coverage Index (ECI) measures ered appropriate for empiric therapy. 012040). susceptibility of common bacterial infections to available ▪ Does not account for variation in the propensity empiric antibiotics as a percentage. The Empiric Options of pathogens to acquire resistance. Index (EOI) varies from 0 to ‘the number of treatment Received 24 March 2016 ’ http://bmjopen.bmj.com/ Revised 4 August 2016 options available , and measures the empiric value of Accepted 3 October 2016 the current stock of antibiotics as a depletable resource. The indices account for drug availability and the relative INTRODUCTION clinical importance of pathogens. We demonstrate Efforts to understand the epidemiology and meaning and use by examining the potential impact of burden of antibiotic resistance have largely new drugs and threatening bacterial strains. focused on a few sentinel multidrug-resistant – Conclusions: In our intensive care unit coverage of organisms,1 3 but this strategy is increasingly device-associated infections measured by the ECI unfeasible given the increasing prevalence remains high (98%), but 37–44% of treatment potential and diversity of resistance. There is a need to on September 30, 2021 by guest. Protected copyright. measured by the EOI has been lost. Without reserved summarise and understand the net clinical – β drugs, the ECI is 86 88%. New cephalosporin/ - impact of resistance—accounting for vari- lactamase inhibitor combinations could increase the ation in strain prevalence and drug availabi- EOI, but no single drug can compensate for losses. — Increasing methicillin-resistant Staphylococcus aureus lity to focus attention on the most pressing (MRSA) prevalence would have little overall impact problems. (ECI=98%, EOI=4.8–5.2) because many Gram-positives Strategies for minimising the impact of are already resistant to β-lactams. Aminoglycoside antibiotic resistance on patients include resistance, however, could have substantial clinical introducing new drugs, preventing the impact because they are among the few drugs that spread of threatening strains, and reducing – provide coverage of Gram-negative infections unnecessary antibiotic use.3 9 A central chal- (ECI=97%, EOI=3.8–4.5). Our proposed indices lenge is that strategies may be effective and For numbered affiliations see summarise the local impact of antibiotic resistance on end of article. appropriate in one context, but not in empiric coverage (ECI) and available empiric treatment another, depending on the incidence and options (EOI) using readily available data. Policymakers virulence of resistant strains, the type of infec- Correspondence to and drug developers can use the indices to help Dr Andrew M Morris; evaluate and prioritise initiatives in the effort against tions, the consequences of failed treatment, andrew.morris@ antimicrobial resistance. and the availability of treatment options. In sinaihealthsystem.ca order to better understand variation in the Hughes JS, et al. BMJ Open 2016;6:e012040. doi:10.1136/bmjopen-2016-012040 1 Open Access BMJ Open: first published as 10.1136/bmjopen-2016-012040 on 16 December 2016. Downloaded from effectiveness of stewardship strategies, we need to better We propose two complementary indices: an Empiric understand variation in the impact of resistance. We Coverage Index (ECI) to measure available empiric – propose two indices10 12 that can be used to summarise coverage of common infections, and an Empiric the local impact of resistance on empiric treatment of Options Index (EOI) to measure the empiric value of common bacterial infections, and to assess threats and the current stock of antibiotics on the understanding interventions in that context. The indices account for that antibiotic resources are inevitably diminished by resistance, drug availability and the relative importance use.19 Together, the ECI and EOI measure current diffi- of pathogens, and can be easily calculated from readily culties and vulnerability to increasing resistance, provid- available cumulative antibiogram data.13 ing a clinically meaningful summary of antibiotic We concentrate our attention on the failure of resistance that can be used to assess the real and poten- empiric therapy because it is the most common problem tial impacts of threats and interventions. As an example, caused by resistance in many contexts.14 15 Empiric we consider device-associated infections in North therapy may fail microbiologically (ie, related to American intensive care units (ICUs), but the indices bug-drug mismatch) because resistance is prevalent, can be adapted for any population for which cumulative because clinicians prescribe antibiotics contrary to avail- antibiogram data are available.13 able evidence, or because patients do not follow pre- scriptions. Although poor treatment decisions, patient adherence and resistance each deserve attention, the MATERIALS AND METHODS problems require different solutions, so we do not Common infections and their causes include poor treatment or adherence in our indices. The impact of resistance depends on the incidence of Our approach differs in this respect from the drug resist- infections and their causes, but these data are not ance index (DRI) proposed by Laxminarayan and readily available at most institutions. Instead, we have a Klugman.10 The DRI can be interpreted as the probabil- general understanding of the causes of infections. ity of inadequate treatment given observed drug use.11 Economists solve an analogous problem using classic In contrast, we estimate potential empiric coverage by fixed-basket consumer price indices.20 These indices assuming that the empiric therapy that is most likely to measure the average price of a standard basket of con- succeed is selected, informed by cumulative antibio- sumer goods weighted by the relative importance of grams, Gram stains, and knowledge of the likely causa- each good, ignoring variation in the set of goods con- – tive organism(s) for each clinical syndrome.16 18 This sumed in order to focus on price variation. Following a allows investigation of new drugs, new resistant strains similar logic, we construct a standard basket of infections and other cases in which drug-use data are not available. in order to examine variation in resistance. Our basket Figure 1 A basket of http://bmjopen.bmj.com/ device-associated bacterial infections. The height of each bar shows the relative frequency of common species associated with CLABSIs, VAPs, and CAUTIs in US acute care hospitals.23 The width of each bar indicates the relative frequency of each infection type in adult critical-care on September 30, 2021 by guest. Protected copyright. units.23 The area of each bar therefore shows the proportion of device-associated infections that are of a particular type, caused by a particular species or species group. CoNS are coagulase- negative Staphylococci. CAUTIs, catheter-associated urinary tract infections; CLABSIs, central-line associated bloodstream infections; VAPs, ventilator- associated pneumonia. 2 Hughes JS, et al. BMJ Open 2016;6:e012040. doi:10.1136/bmjopen-2016-012040 Open Access BMJ Open: first published as 10.1136/bmjopen-2016-012040 on 16 December 2016. Downloaded from consists of common infections, with weights indicating antibiogram includes the first clinical isolate from each infection frequency. patient, as recommended by CLSI to guide initial Proceeding with a critical care example, we construct empiric therapy.13 Blood, respiratory, urine and skin and a basket of device-associated infections consisting of soft-tissue infections were included. Resistance data are central-line associated bloodstream infections (CLABSI), widely available in this format.13 catheter-associated urinary tract
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