residual risk and waste in donated blood with pooled nucleic acid testing hrayer aprahamian, dr....
DESCRIPTION
The Challenges Screening tests are not perfectly reliable. FDA requires or recommends a set of infections for which the blood needs to be tested in the US, but does not specify a testing strategy. Nucleic Acid Tests (NAT) are more sensitive but are considerably more expensive and time-consuming: Individually testing using NAT might not be a feasible option. 3TRANSCRIPT
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Residual Risk and Waste in Donated Blood with Pooled Nucleic Acid
Testing
Hrayer Aprahamian, Dr. Ebru Bish, Dr. Douglas Bish
Virginia Polytechnic Institute and State University, Dept. of Industrial and Systems Engineering, Blacksburg, VA
Research supported by the National Science Foundation
October 8, 2015
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Motivation• Blood products are vital healthcare commodities.• Transfusion-transmitted infections (TTI) include:• HIV, HBV, HCV, WNV and Babesiosis (among others).
• Even with advances in testing technology, the risk of transmitting an infection through blood transfusion remains.• Effectively managing the limited resources to improve the
safety of blood is crucial.• Accurately measuring this risk is of utmost importance to
aid with strategic decision-making. 2
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The Challenges• Screening tests are not perfectly reliable.• FDA requires or recommends a set of infections for which
the blood needs to be tested in the US, but does not specify a testing strategy.• Nucleic Acid Tests (NAT) are more sensitive but are
considerably more expensive and time-consuming:• Individually testing using NAT might not be a feasible
option.
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The Challenges• Pooled testing is often used to reduce testing costs:• Dilution effect.
• Increases waste figures.
• In practice, failed samples are re-tested.• Numerous re-testing schemes are available.
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The Challenges• Residual risk alone does not provide the complete picture.
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Resid
ual r
isk
Testing cost
Expected waste
Testing strategy
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The Challenges• Individual variability in viral growth.• In sample test variability (i.e., testing the same sample
multiple times may yield different outcomes).• Infectivity of the blood sample: Low viral loads in blood
may not cause infection.
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Research Objective• Develop realistic and accurate expressions for the
performance metrics.
• Determine the best testing strategy:• Pooling size ()
• Re-test strategy
• Number of re-tests ()
• Screening test
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Viral Model
• Doubling time viral load model:
• To incorporate individual variability, we model as a random variable.
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• Infectivity of the viral load is modelled as a binomial with each viral particle having a probability of to cause infection.
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Infectivity
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Testing Error• Test sensitivity follows a probit model if unit is infected and in
the window period.
• Test specificity is assumed to be a constant and independent of the pooling size.
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Re-test Schemes• We examine four types of re-test schemes:
1) No re-test.
2) Pooled re-testing:The initial master pool is re-tested times.
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Re-test Schemes3) Dorfman-type (individual) re-testing:
Each individual sample in the master pool is re-tested times.
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Re-test Schemes4) Array-based re-testing:
The samples are placed in a matrix. pools, each of size , are constructed from samples in each row and column and tested. The process is repeated times.
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Re-test Schemes• Decision rule is crucial for determining the outcome of the
re-test:• We adopt the Believe the positive decision rule for all re-test
schemes (except array based).
• In the array re-test scheme a sample is rejected if at least one of the columns AND one of the rows fail corresponding to that node fails.
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Re-test Schemes
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Re-test #1 Re-test #2
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Events• : Sample is infected.• W: Infected sample is in the window period.• Let be the event that the overall test outcome is negative:
• Let be the event that blood sample is infectious.
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Residual Risk (RR)• Residual Risk = Probability of an infected and infectious blood unit being released into the blood pool.
• Waste= Number of infection-free blood units that are falsely discarded.18
Expected Waste
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Expected Number of Tests
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• Expected number of tests only depends on the outcome of the initial pool.
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Analysis – Residual Risk
Lemma 1.
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Analysis – Expected Tests
Lemma 2.
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Analysis – Expected WasteLemma 3.
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Case Study• We perform a case study using data available on South
Africa.• Some major findings are:• Performing re-tests increases both the risk and the expected number
of test.
• The benefits of re-tests is to reduce the expected waste.
• Adding a single re-test drastically reduces the waste figures.
• Array-based re-testing schemes outperforms other schemes when:• Testing costs are high.
• Prevalence rates are high23
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Cost-effectiveness
240 2 4 6 8 10 12 14 16
$19.00
$19.50
$20.00
$20.50
$21.00
$21.50
$22.00 Individual Re-test
m=0m=1m=2m=4m=8
Pool Size (Sp)
Cost
per
don
atio
n
Best solution
Current practice
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Cost-effectiveness
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• Switching to the best solution reduces cost by 3.5%.
• In 2013 16.1 million units collected and tested which translates to a saving of:
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Conclusions• Determining the “best” testing strategy is not a trivial
matter.
• The expressions derived provides an accurate approach to weight in the trade-offs being incurred to determine a suitable testing strategy.
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Reference and Contact Info.
Aprahamian, H., Bish, D., and Bish, E. “Residual Risk and Waste in Donated Blood with Pooled Nucleic Acid Testing ”, submitted for publication.
Email: [email protected]
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Thank you
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