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Record W1537137282 · doi:10.1111/ajt.12638

Cost-Effectiveness of Routine Nucleic Acid Testing in Organ Donors

2014· letter· en· W1537137282 on OpenAlexaff
B. Kiberd

Bibliographic record

VenueAmerican Journal of Transplantation · 2014
Typeletter
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineNucleic acidBiochemistryBiology

Abstract

fetched live from OpenAlex

To the Editor: The medical decision article by Lai et al (1Lai JC Kahn JG Tavakol M Peters MG Roberts JP Reducing infection transmission in solid organ transplantation through donor nucleic acid testing: A cost-effectiveness analysis.Am J Transplant. 2013; 13: 2611-2618Abstract Full Text Full Text PDF PubMed Scopus (13) Google Scholar) demonstrating the cost-effectiveness of nucleic acid-amplification testing (NAT) for hepatitis C virus (HCV) bears further scrutiny. The issue at hand is the residual window period infection after serology testing in the general donor population. Although it is difficult to decipher, it appears the authors have used prevalent infection rather than the more appropriate incident infection rate in their analysis. Since these two numbers are >100-fold different the cost–benefit is considerably inaccurate. Potential deceased donors are screened by history, physical examination and laboratory testing. History is often from collaborative sources. Laboratory evaluation includes screening for viral infections with serology. As stated by the authors in their methods, they assume that all patients with prevalent infection determined by positive serology are excluded from donation. The remaining sero-negative subjects are potential donors. However, a small proportion of these will be infectious but missed by serology if the patient has been screened after they contracted a new infection but before they sero-convert. The time from infection to sero-conversion is known as the window period. The probability of missing a covert sero-negative infection is a function of the size of the window period (10 weeks for HCV) and the incidence rate (not prevalence) of a new infection in a susceptible naïve host. Although higher prevalence is associated with higher incidence rates, they are not the same and can be different by several orders of magnitude particularly if the infection persists and has a low mortality rate. For example in a study estimating the risk of HCV in tissue donors, men age 30–49 had a prevalence of confirmed HCV of 3.3%, yet the incidence rate of new infections was estimated to be only 15.67 per 100 000 patient years (0.01567%) (2Zou S Dodd RY Stramer SL Strong DM Tissue Safety Study GroupProbability of viremia with HBV, HCV, HIV, and HTLV among tissue donors in the United States.N Engl J Med. 2004; 351: 751-759Crossref PubMed Scopus (164) Google Scholar). It is not clear in the paper by Lai et al (1Lai JC Kahn JG Tavakol M Peters MG Roberts JP Reducing infection transmission in solid organ transplantation through donor nucleic acid testing: A cost-effectiveness analysis.Am J Transplant. 2013; 13: 2611-2618Abstract Full Text Full Text PDF PubMed Scopus (13) Google Scholar) what incidence rates were used since none were reported. I suspect the incidence rate used by Lai et al (1Lai JC Kahn JG Tavakol M Peters MG Roberts JP Reducing infection transmission in solid organ transplantation through donor nucleic acid testing: A cost-effectiveness analysis.Am J Transplant. 2013; 13: 2611-2618Abstract Full Text Full Text PDF PubMed Scopus (13) Google Scholar) was 3.5% per year for HCV; however, this is the prevalence in the population. Assuming three organ recipients from one donor, 2.34 lost quality adjusted life years (QALY) per infected recipient, and a window period infection that is reduced from 10 weeks to 0, the estimated increase in QALYs with NAT would be in the range of 0.0473 QALYs. For an incremental screening cost of $150, the incremental cost utility would be $3174/QALY, which is very close to Lai et al’s (1Lai JC Kahn JG Tavakol M Peters MG Roberts JP Reducing infection transmission in solid organ transplantation through donor nucleic acid testing: A cost-effectiveness analysis.Am J Transplant. 2013; 13: 2611-2618Abstract Full Text Full Text PDF PubMed Scopus (13) Google Scholar) value of $3290. The more likely risk of transmission of HCV from the general donor population screened by serology testing is 1/42 000 (2Zou S Dodd RY Stramer SL Strong DM Tissue Safety Study GroupProbability of viremia with HBV, HCV, HIV, and HTLV among tissue donors in the United States.N Engl J Med. 2004; 351: 751-759Crossref PubMed Scopus (164) Google Scholar,3Humar A Morris M Blumberg E et al.Nucleic acid testing (NAT) of organ donors: Is the ’best’ test the right test? A consensus conference report.Am J Transplant. 2010; 10: 889-899Abstract Full Text Full Text PDF PubMed Scopus (137) Google Scholar). Lai et al’s (1Lai JC Kahn JG Tavakol M Peters MG Roberts JP Reducing infection transmission in solid organ transplantation through donor nucleic acid testing: A cost-effectiveness analysis.Am J Transplant. 2013; 13: 2611-2618Abstract Full Text Full Text PDF PubMed Scopus (13) Google Scholar) rough estimate is probably 1/150. These are very different. Using the lower risk value catapults the incremental cost utility estimate to >$500 000/QALY assuming the test costs $150. I admit these calculations do not include the costs averted from transmitted infection, the lost benefit of false positive NAT tested organs that are not used and the residual infection even with NAT. It is important to point out that the residual risks for HCV used by Lai et al (1Lai JC Kahn JG Tavakol M Peters MG Roberts JP Reducing infection transmission in solid organ transplantation through donor nucleic acid testing: A cost-effectiveness analysis.Am J Transplant. 2013; 13: 2611-2618Abstract Full Text Full Text PDF PubMed Scopus (13) Google Scholar) are very appropriate in higher risk donors such as intravenous drug users (3Humar A Morris M Blumberg E et al.Nucleic acid testing (NAT) of organ donors: Is the ’best’ test the right test? A consensus conference report.Am J Transplant. 2010; 10: 889-899Abstract Full Text Full Text PDF PubMed Scopus (137) Google Scholar). In these circumstances, the cost/QALY gained with NAT for HCV would be very attractive and highly recommended (3Humar A Morris M Blumberg E et al.Nucleic acid testing (NAT) of organ donors: Is the ’best’ test the right test? A consensus conference report.Am J Transplant. 2010; 10: 889-899Abstract Full Text Full Text PDF PubMed Scopus (137) Google Scholar). It should be pointed out that the estimated costs per QALY for HCV NAT exceed $1 million for blood (4Jackson BR Busch MP Stramer SL AuBuchon JP The cost-effectiveness of NAT for HIV, HCV, and HBV in whole-blood donations.Transfusion. 2003; 43: 721-729Crossref PubMed Scopus (227) Google Scholar). The observed transmission of HCV to transplant recipients currently is far less that the 1/150 estimated by Lai et al (1Lai JC Kahn JG Tavakol M Peters MG Roberts JP Reducing infection transmission in solid organ transplantation through donor nucleic acid testing: A cost-effectiveness analysis.Am J Transplant. 2013; 13: 2611-2618Abstract Full Text Full Text PDF PubMed Scopus (13) Google Scholar) even with considerable underreporting (5Ison MG Hager J Blumberg E et al.Donor-derived disease transmission events in the United States: Data reviewed by the OPTN/UNOS Disease Transmission Advisory Committee.Am J Transplant. 2009; 9: 1929-1935Crossref PubMed Scopus (188) Google Scholar). Together with the above analysis, there is a reason to believe that the findings by the authors are inaccurate, will mislead policy makers and must be re-evaluated with specific attention and explicit details of the incident infection rates for HCV used in their model. The author of this manuscript has no conflicts of interest to disclose as described by the American Journal of Transplantation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.087
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.087
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0130.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.062
GPT teacher head0.347
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2014
Admission routes1
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