Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,087 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,005 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».