Weighted-incidence syndromic combination antibiograms to guide empiric treatment of critical care infections: a retrospective cohort study

Randhawa, Varinder K.; Sarwar, Syed; Walker, Sandra A N; Elligsen, Marion; Palmay, Lesley; Daneman, Nick

doi:10.1186/cc13901

Cited by 41 publications

(31 citation statements)

References 23 publications

(31 reference statements)

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“…The early use of antibiotics provides effective control of infections; however, at the same time, the use of broad-spectrum empiric antibiotics is causing an increasing emergence of antibiotic resistance [1]. A rise in multidrug-resistant bacteria is limiting the available therapeutic options for infections in the ICU and further reducing the likelihood that empiric treatment selections will offer adequate coverage for common ICU pathogens [2]. …”

Section: Introductionmentioning

confidence: 99%

Antibiogram of Medical Intensive Care Unit at Tertiary Care Hospital Setting of Pakistan

Qadeer¹,

Akhtar²,

Ain³

et al. 2016

Cureus

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Objective: To determine the frequency of micro-organisms causing sepsis as well as to determine the antibiotic susceptibility and resistance of microorganisms isolated in a medical intensive care unit.Materials and methods: This is a cross-sectional analysis of 802 patients from a medical intensive care unit (ICU) of Shifa International Hospital, Islamabad, Pakistan over a one-year period from August 2015 to August 2016. Specimens collected were from blood, urine, endotracheal secretions, catheter tips, tissue, pus swabs, cerebrospinal fluid, ascites, bronchoalveolar lavage (BAL), and pleural fluid. All bacteria were identified by standard microbiological methods, and antibiotic sensitivity/resistance was performed using the disk diffusion technique, according to Clinical and Laboratory Standards Institute (CLSI) guidelines. Data was collected using a critical care unit electronic database and data analysis was done by using the Statistical Package for Social Sciences (SPSS), version 20 (IBM SPSS Statistics, Armonk, NY).Results: Gram-negative bacteria were more frequent as compared to gram-positive bacteria. Most common bacterial isolates were Acinetobacter (15.3%), Escherichia coli (15.3%), Pseudomonas aeruginosa (13%), and Klebsiella pneumoniae (10.2%), whereas Enterococcus (7%) and methicillin-resistant staphylococcus aureus (MRSA) (6.2%) were the two most common gram-positive bacteria. For Acinetobacter, colistin was the most effective antibiotic (3% resistance). For E.coli, colistin (0%), tigecycline (0%), amikacin (7%), and carbapenems (10%) showed low resistance. Pseudomonas aeruginosa showed low resistance to colistin (7%). For Klebsiella pneumoniae, low resistance was seen for tigecycline (0%) and minocycline (16%). Overall, ICU mortality was 31.3%, including miscellaneous cases.Conclusion: Gram-negative infections, especially by multidrug-resistant organisms, are on the rise in ICUs. Empirical antibiotics should be used according to the local unit specific data. Constant evaluation of current practice on basis of trends in multidrug resistance and antibiotic consumption patterns are essential.

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Section: Introductionmentioning

confidence: 99%

Antibiogram of Medical Intensive Care Unit at Tertiary Care Hospital Setting of Pakistan

Qadeer¹,

Akhtar²,

Ain³

et al. 2016

Cureus

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“…50–54 We encourage users to choose an appropriate basket of infections for their context using local data where available, and caution against using our coverage estimates to guide clinical decisions. The weighted-incidence syndromic combination antibiogram (WISCA)15 41 42 is a more appropriate measure of coverage in a clinical context because it accounts for local variation in disease aetiology and reports the coverage provided by drug combinations. In vivo efficacy, side effects, previous drug exposure and other patient risk factors, previous culture results, rapid diagnostic results, administration route, costs and ecological impacts should also be considered 6…”

Section: Discussionmentioning

confidence: 99%

“…Resistance testing, surveillance, infection control, prevention and stewardship interventions are also costly 35–9 13 Empiric and definitive therapies are often inappropriate,14 15 and difficult multidrug-resistant infections do occur 1. Our resistance rates are conservative because they use microbiology data from first isolates only, and therefore under-report resistance acquired during ICU stay which may or may not be due to antimicrobial therapy 13.…”

Section: Discussionmentioning

confidence: 99%

“…Empiric therapy of severe device-associated infections often includes two drugs, but combination antibiograms15 41 42 are not readily available at most institutions. To get a rough, conservative measure of the coverage provided by drug combinations, we assume that treatment is informed by Gram stains 18.…”

Section: Methodsmentioning

confidence: 99%

“…15 Empiric therapy may fail microbiologically (ie, related to bug-drug mismatch) because resistance is prevalent, because clinicians prescribe antibiotics contrary to available evidence, or because patients do not follow prescriptions. Although poor treatment decisions, patient adherence and resistance each deserve attention, the problems require different solutions, so we do not include poor treatment or adherence in our indices.…”

Section: Introductionmentioning

confidence: 99%

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How to measure the impacts of antibiotic resistance and antibiotic development on empiric therapy: new composite indices

et al. 2016

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ObjectivesWe aimed to construct widely useable summary measures of the net impact of antibiotic resistance on empiric therapy. Summary measures are needed to communicate the importance of resistance, plan and evaluate interventions, and direct policy and investment.Design, setting and participantsAs an example, we retrospectively summarised the 2011 cumulative antibiogram from a Toronto academic intensive care unit.Outcome measuresWe developed two complementary indices to summarise the clinical impact of antibiotic resistance and drug availability on empiric therapy. The Empiric Coverage Index (ECI) measures susceptibility of common bacterial infections to available empiric antibiotics as a percentage. The Empiric Options Index (EOI) varies from 0 to ‘the number of treatment options available’, and measures the empiric value of the current stock of antibiotics as a depletable resource. The indices account for drug availability and the relative clinical importance of pathogens. We demonstrate meaning and use by examining the potential impact of new drugs and threatening bacterial strains.ConclusionsIn our intensive care unit coverage of device-associated infections measured by the ECI remains high (98%), but 37–44% of treatment potential measured by the EOI has been lost. Without reserved drugs, the ECI is 86–88%. New cephalosporin/β-lactamase inhibitor combinations could increase the EOI, but no single drug can compensate for losses. Increasing methicillin-resistant Staphylococcus aureus (MRSA) prevalence would have little overall impact (ECI=98%, EOI=4.8–5.2) because many Gram-positives are already resistant to β-lactams. Aminoglycoside resistance, however, could have substantial clinical impact because they are among the few drugs that provide coverage of Gram-negative infections (ECI=97%, EOI=3.8–4.5). Our proposed indices summarise the local impact of antibiotic resistance on empiric coverage (ECI) and available empiric treatment options (EOI) using readily available data. Policymakers and drug developers can use the indices to help evaluate and prioritise initiatives in the effort against antimicrobial resistance.

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Evaluation of the Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries

et al. 2020

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IMPORTANCE High levels of antimicrobial resistance in neonatal bloodstream isolates are being reported globally, including in Asia. Local hospital antibiogram data may include too few isolates to meaningfully examine the expected coverage of antibiotic regimens. OBJECTIVE To assess the coverage offered by 3 antibiotic regimens for empirical treatment of neonatal sepsis in Asian countries. DESIGN, SETTING, AND PARTICIPANTS A decision analytical model was used to estimate coverage of 3 prespecified antibiotic regimens according to a weighted-incidence syndromic combination antibiogram. Relevant data to parameterize the models were identified from a systematic search of Ovid MEDLINE and Embase. Data from Asian countries published from 2014 onward were of interest. Only data on blood culture isolates from neonates with sepsis, bloodstream infection, or bacteremia reported from the relevant setting were included. Data analysis was performed from April 2019 to July 2019. EXPOSURES The prespecified regimens of interest were aminopenicillin-gentamicin, thirdgeneration cephalosporins (cefotaxime or ceftriaxone), and meropenem. The relative incidence of different bacteria and their antimicrobial susceptibility to antibiotics relevant for determining expected concordance with these regimens were extracted. MAIN OUTCOMES AND MEASURES Coverage was calculated on the basis of a decision-tree model incorporating relative bacterial incidence and antimicrobial susceptibility of relevant isolates. Data on 7 bacteria most commonly reported in the included studies were used for estimating coverage, which was reported at the country level. RESULTS Data from 48 studies reporting on 10 countries and 8376 isolates were used. Individual countries reported 51 (Vietnam) to 6284 (India) isolates. Coverage varied considerably between countries. Meropenem was generally estimated to provide the highest coverage, ranging from 64.0% (95% credible interval [CrI], 62.6%-65.4%) in India to 90.6% (95% CrI, 86.2%-94.4%) in Cambodia, followed by aminopenicillin-gentamicin (from 35.9% [95% CrI, 27.7%-44.0%] in Indonesia to 81.0% [95% CrI, 71.1%-89.7%] in Laos) and cefotaxime or ceftriaxone (from 17.9% [95% CrI, 11.7%-24.7%] in Indonesia to 75.0% [95% CrI, 64.8%-84.1%] in Laos). Aminopenicillingentamicin coverage was lower than that of meropenem in all countries except Laos (81.0%; 95% CrI, 71.1%-89.7%) and Nepal (74.3%; 95% CrI, 70.3%-78.2%), where 95% CrIs for aminopenicillingentamicin and meropenem were overlapping. Third-generation cephalosporin coverage was lowest of the 3 regimens in all countries. The coverage difference between aminopenicillin-gentamicin and meropenem for countries with nonoverlapping 95% CrIs ranged from −15.9% in China to −52.9% in Indonesia. (continued) Key Points Question What is the antibiotic coverage offered by empirical neonatal sepsis treatment with aminopenicillingentamicin, third-generation cephalosporins (cefotaxime or ceftriaxone), and meropenem in Asian countries? Findings In this decision analytical model based on ...

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Weighted-incidence syndromic combination antibiograms to guide empiric treatment of critical care infections: a retrospective cohort study

Cited by 41 publications

References 23 publications

Antibiogram of Medical Intensive Care Unit at Tertiary Care Hospital Setting of Pakistan

Antibiogram of Medical Intensive Care Unit at Tertiary Care Hospital Setting of Pakistan

How to measure the impacts of antibiotic resistance and antibiotic development on empiric therapy: new composite indices

Evaluation of the Coverage of 3 Antibiotic Regimens for Neonatal Sepsis in the Hospital Setting Across Asian Countries

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