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LQAS-based surveillance of antimicrobial resistance

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LQAS-based surveillance of antimicrobial resistance

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Empirical antibiotic treatment requires good insight in the prevalance of antimicrobial resistance (AMR). Conventional prevalence surveys are time consuming, costly, and provide aggregated estimates. The presentation introduces the LQAS methodology to rapidly classify well-defined populations as having high- or low prevalence of AMR, facilitating appropriate antibiotic treatment strategies at a local level.

Empirical antibiotic treatment requires good insight in the prevalance of antimicrobial resistance (AMR). Conventional prevalence surveys are time consuming, costly, and provide aggregated estimates. The presentation introduces the LQAS methodology to rapidly classify well-defined populations as having high- or low prevalence of AMR, facilitating appropriate antibiotic treatment strategies at a local level.

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LQAS-based surveillance of antimicrobial resistance

  1. 1. Lot Quality Assurance Sampling A tool for surveillance of antimicrobial resistance? F R AN K VAN L E T H A S S O C I A T E P R O F E S S O R O F G L O B A L H E A L T H A M S T E R D A M U N I V E R S I T Y M E D I C A L C E N T E R S , L O C A T I O N A M C , U N I V E R S I T Y O F A M S T E R D A M A M S T E R D A M I N S T I T U T E F O R G L O B A L H E A L T H A N D D E V E L O P M E N T
  2. 2. www.aighd.org Antimicrobial resistance kills
  3. 3. www.aighd.org Outline Measuring prevalence of antimicrobial resistance Concept of Lot Quality Assurance Sampling (LQAS) LQAS-based AMR survey in Indonesia
  4. 4. www.aighd.org AMR control needs measurement of AMR prevalence 4
  5. 5. www.aighd.org Current AMR surveillance largely laboratory-based 5
  6. 6. www.aighd.org AMR surveillance: current 6 Laboratory based AMR surveillance Conventional LQAS Selection bias Representative Population based
  7. 7. www.aighd.org Laboratory-based surveillance overestimates prevalence of AMR
  8. 8. www.aighd.org AMR surveillance: preferred 8 Laboratory based AMR surveillance Conventional LQAS Selection bias Representative Population based
  9. 9. www.aighd.org Repeated AMR surveys limited by large sample sizes 9
  10. 10. www.aighd.org AMR surveillance: A solution 10 Laboratory based AMR surveillance Conventional LQAS Selection bias Representative Population based Copyright: Massachusetts Biotechnology Council
  11. 11. LOT QUALITY ASSURANCE SAMPLING
  12. 12. www.aighd.org Lot Quality Assurance Sampling (LQAS) Derived from setting of manufacturing ◦ Quality assurance strategy Main question: Is the threshold for ”adequate quality” met? Assessment based on small samples from well-defined batches of goods 12
  13. 13. www.aighd.org The process of LQAS LOT Sample Decision rule Classification
  14. 14. www.aighd.org LQAS-based AMR surveillance Done in a classification framework Is the AMR prevalence in the population above or below x % Framework does not include issues on power or statistical significant difference ◦ Use concept of misclassification Lot can be any well-defined grouping ◦ Region ◦ Facility ◦ Subpopulation Classification “high AMR” should lead to an intervention 14
  15. 15. www.aighd.org Allowable misclassification True “low” prevalence classified as “high” and vice versa Misclassification unnecessarily implements or withholds intervention ◦ Both equal weight? 15
  16. 16. www.aighd.org Typical LQAS sample sizes LQAS definition Sample Size Decision rule Misclass L as H Misclass H as L Misclass Tot 2 - 10 76 4 6.6 4.7 11.3 5 - 20 44 5 6.7 4.4 11.1 10 - 20 112 16 9.2 4.7 13.9 10 - 25 55 9 9.5 4.5 13.9 15 - 25 139 27 9.3 5.0 14.3 15 - 30 70 15 9.4 4.1 13.5 16
  17. 17. TEST CHARACTERISTICS OF LQAS 17
  18. 18. www.aighd.org Theoretical lots from conventional surveillance 18 1 2 3 4 5 1 2 3 4 5
  19. 19. www.aighd.org LQAS classifications from repeated draws 19 1 LQAS classification Draw 1 Draw 2 Draw … Draw 999 Draw 1000 LQAS classification LQAS classification LQAS classification LQAS classification
  20. 20. www.aighd.org Operator curve: classification as high resistance 0 20406080 100 0 2 5 10 20 30 40 50 60 70 80 90 100 True prevalence of resistance (%) 2 - 10 5 - 20 10 - 20 10 - 30 20 - 50 30 - 50 20
  21. 21. www.aighd.org Test characteristics 21 LQAS definition Sensitivity Specificity 2 - 10 100 44.1 5 - 20 99.9 85.0 10 - 20 100 98.9 10 - 25 99.6 85.1 15 - 25 98.8 80.6 15 - 30 99.9 87.1
  22. 22. LQAS-BASED SURVEILLANCE 22
  23. 23. www.aighd.org Prospective LQAS-based AMR surveillance: identifying local variations outpatients inpatients 1 2 3 4 5 6 7 8 9 10 11 Ciprofloxacin Levofloxacin Trimethoprim- sulfamethoxazole Nitrofurantoin Amoxicillin/clavulanic acid Ampicillin/sulbactam Piperacillin/tazobactam Cefixime Ceftazidime Ceftriaxone Amikacin Gentamicin Fosfomycin$ Ertapenem Meropenem 23
  24. 24. www.aighd.org Titration: assessing multiple upper boundaries Clinic 1 Clinic 2 Clinic 3 Levofloxacin 2 - 10 high high 5- 20 high high high 10- 25 high high 15 - 30 high high Tigecycline 2 - 10 low high 5- 20 low low low 10- 25 low low 15 - 30 low low 24
  25. 25. www.aighd.org Titration: assessing multiple upper boundaries Clinic 1 Clinic 2 Clinic 3 Levofloxacin 2 - 10 high high 5- 20 high high high 10- 25 high high 15 - 30 high high Tigecycline 2 - 10 low high 5- 20 low low low 10- 25 low low 15 - 30 low low 25
  26. 26. CONCLUSION 26
  27. 27. www.aighd.org Conclusion LQAS promising approach to measure AMR resistance ◦ Timely ◦ Local variation ◦ Affordable Combining classifications lots into conventional prevalence estimates possible ◦ Needs careful design considerations Facilitates move from laboratory- to population-based surveillance ◦ Anticipated in GLASS 27
  28. 28. www.aighd.org Contact and slides Email: f.vanleth@aighd.org Twitter: @fvlscience Link to slides posted on frankvanleth.com

Editor's Notes

  • A critical issue in the control of antimicrobial resistance is the measurement of its prevalence. There is a loud call to improve AMR surveillance to inform policies and antimicrobial stewardship.
  • The discussion is guided by WHO that produces the GLASS guidelines. These guideline focus on a laboratory-based AMR surveillance, in which many countries now invest. Although the document mentions the need for population-based surveillance, its core is really on laboratory-based surveiilance
  • Laboratory-based surveillance is potentially biased. Strains for drug sensitivity testing come from clinical care, where submission of specimens is far from random. However, therapeutic guideliens, inclusign those for empirical treatment in the outpatient setting, are often defined by assessing the collective antibiograms form this laboratory surveillance system
  • Population-based surveillance in which submission of strains is systematic and from well-defined clinical syndromes, are much better placed for guideline development
  • However, the sample size is in general large and can increase rapidly with improved precision

    This preclused timely results and assessment of local variations in AMR prevalence
  • We therefore focuses on a sampling technique that could overcome this major hurdles

  • LQAS is a strategy that is already decades old and originates from the setting of manufacturing

    It is quality assurance strategy that assesses if a batch g goods passes a predefined quality threshold

    The assessment is based on small random samples from the larger batch
  • The batch or lot is a well defined grouping of items.
    A small sample is taken with a predetermined size
    All items in the sample are assessed for quality parameters
    If more items than a predefined number fail the quality threshold then the entire batch or lot is rejected
    If not, the entire batch or lot is accepted
  • This strategy is with an classification frame work
    It answers the question: …...........
    Discussions of statistically significant difference between lots and power are not in place
    Instead there is a strong emphasis on the probability of misclassification
    The wonderful thing is that a lot can be any well-defined grouping making it a very versatile strategy

    The underlying idea of this approach is that classification of high AMR should lead to an intervention. If you are not willing to do anything, then you do not need to measure it
  • A classification framework incorporates the concept of misclassification

    It should be determined how much that can be and whether classifying a true low resistance as high is just as undesired as classifying a true high resistance as low

    In all the following slides we perceived the later situation as less desirable and set a maximum of 5%, while for the former situation we set a maximum of 10%
  • Typical LQAS sample sizes in which the set assumptions on misclassification of 5% and 10% are met are listed here
    We have used these definition and sample sizes in the work that follows


  • We used a conventional AMR survey in outpatients in primary care in Indonesia to validate the LQAS methodology.

    The survey gave us a true AMR prevalence of 13 antibiotics.
    This can be interpreted as 13 LOTS with a known AMR prevalence
  • For each of these 13 lots we drew 1000 times the sample size required for different LQAS definitions
    We thereby obtained 1000 LQAS classifications (high or low) for each LOT and each LQAS definition
    Given that we knew the true AMR prevalence in the LOT, we could count the number of times the classification was correct
  • These are operator curves
    On the y-axis we plot how often is the lot classified as high resistance
    On the x-axis is the true prevalence of the LOT
    The curves correspond to different LQAS definition.

    With a true AMR prevalence below the lower limit of the LQAS definition, there is a very small probability classifying the lot as high prevalence
    Similarly, with a true AMR prevalence above the upper limit of the LQAs definition, there is a very large probability of classifying the lot as high prevalence
    In between there is potential misclassification.
  • Sensitivity is defined as correctly identifying high prevalence of AMR, while specificity if defined as correctly identifying low prevalence of AMR.

    These test characteristics show that LQAS can very well identify areas with high AMR prevalence
  • Each lot gets an LQAS classification for each antibiotic tested.
    This opens the door for the identification of local variations between lots.
    In a conventional AMR survey, you will have a single prevalence estimate for the entire survey population.

    The time for an LQAS classification varied between 45 and 100 days
  • Ensuring a sample size that accommodates different LQAS definition provides the opportunity for titration.

    Looking at levofloxacin, the lot is classified as high resistance in every LQAS definition

    That fits with the very high resistance found in the convectional survey of over 60%
  • Ensuring a sample size that accommodates different LQAS definition provides the opportunity for titration.

    Looking at levofloxacin, the lot is classified as high resistance in every LQAS definition

    That fits with the very high resistance found in the convectional survey of over 60%

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