BACKGROUND: Blood transfusion centers around the world have introduced minipool NAT to reduce the risk of HBV, HCV, and HIV transmission by blood donations drawn in the infectious window phase. What would be the reduction in the residual risk when minipool NAT would be replaced by single-donation NAT? STUDY DESIGN AND METHODS: A mathematic model was developed to estimate the probability of virus transmission by blood transfusion when NAT screening methods are used for virologic safety testing. The major assumptions used are threefold: 1) The viral nucleic acid concentrations in the early window phase of infection double in 2.8 (HBV), 0.74 (HCV), and 0.90 (HIV) days. 2) The detectability of low copy numbers of viral DNA or RNA by the screening assay can be described with a probit model. 3) The probability of infection depends linearly on the logarithm of the administered dose, with 50-percent infectivity rates at 10 (HBV and HCV) or 1000 (HIV) viral nucleic acid copies per transfusion unit (estimates based on NAT studies with samples of known infectivity in chimpanzees). RESULTS: A reasonably simple equation was obtained that allows studying the effect of the sensitivity of the NAT assay and of the pool size used for screening on the residual risk of transfusion-transmitted infection. The computations are illustrated by using observed sensitivity estimates of various NAT methods. By using epidemiologic data among European donors over 1997 as baseline, the calculations predict that the incidence of virus transmission per 10-million RBC transfusions reduces with the following numbers when lowering the test pool size from 96 to 1 (single-donation testing): HBV from 11 to 13 to 3.3 to 5.1, HCV from 1.7 to 2.0 to 0.5 to 0.8, and HIV from 0.47 to 0.62 to 0.010 to 0.045 (ranges for the different NAT screening methods). CONCLUSION: A proper mathematic model for the calculation of residual infection risk by blood transfusion helps understand the impact of introducing new NAT methods for blood safety testing.
BACKGROUND: Blood transfusion centers around the world have introduced minipool NAT to reduce the risk of HBV, HCV, and HIV transmission by blood donations drawn in the infectious window phase. What would be the reduction in the residual risk when minipool NAT would be replaced by single-donation NAT? STUDY DESIGN AND METHODS: A mathematic model was developed to estimate the probability of virus transmission by blood transfusion when NAT screening methods are used for virologic safety testing. The major assumptions used are threefold: 1) The viral nucleic acid concentrations in the early window phase of infection double in 2.8 (HBV), 0.74 (HCV), and 0.90 (HIV) days. 2) The detectability of low copy numbers of viral DNA or RNA by the screening assay can be described with a probit model. 3) The probability of infection depends linearly on the logarithm of the administered dose, with 50-percent infectivity rates at 10 (HBV and HCV) or 1000 (HIV) viral nucleic acid copies per transfusion unit (estimates based on NAT studies with samples of known infectivity in chimpanzees). RESULTS: A reasonably simple equation was obtained that allows studying the effect of the sensitivity of the NAT assay and of the pool size used for screening on the residual risk of transfusion-transmitted infection. The computations are illustrated by using observed sensitivity estimates of various NAT methods. By using epidemiologic data among European donors over 1997 as baseline, the calculations predict that the incidence of virus transmission per 10-million RBC transfusions reduces with the following numbers when lowering the test pool size from 96 to 1 (single-donation testing): HBV from 11 to 13 to 3.3 to 5.1, HCV from 1.7 to 2.0 to 0.5 to 0.8, and HIV from 0.47 to 0.62 to 0.010 to 0.045 (ranges for the different NAT screening methods). CONCLUSION: A proper mathematic model for the calculation of residual infection risk by blood transfusion helps understand the impact of introducing new NAT methods for blood safety testing.
Authors: Jefferson M Jones; Brian M Gurbaxani; Alice Asher; Stephanie Sansom; Pallavi Annambhotla; Anne C Moorman; Saleem Kamili; John T Brooks; Sridhar V Basavaraju Journal: Am J Transplant Date: 2019-05-10 Impact factor: 8.086
Authors: Pallavi D Annambhotla; Brian M Gurbaxani; Matthew J Kuehnert; Sridhar V Basavaraju Journal: Transpl Infect Dis Date: 2017-03-27 Impact factor: 2.228