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. 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
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AMR surveillance: A solution
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Laboratory based
AMR surveillance
Conventional LQAS
Selection bias Representative
Population based
Copyright: Massachusetts Biotechnology Council
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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
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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
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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%