Highly Predictive Model for a Protective Immune Response to the A(H1N1)pdm2009 Influenza Strain after Seasonal Vaccination

Understanding the immune response after vaccination against new influenza strains is highly important in case of an imminent influenza pandemic and for optimization of seasonal vaccination strategies in high risk population groups, especially the elderly. Models predicting the best sero-conversion response among the three strains in the seasonal vaccine were recently suggested. However, these models use a large number of variables and/or information post- vaccination. Here in an exploratory pilot study, we analyzed the baseline immune status in young (+ T cells, and amongst them particularly naive CD4+ T cells, as the best correlates for a successful A(H1N1)pdm09 immune response. Moreover, the number of influenza strains a donor was sero-negative to at baseline (NSSN) in addition to age, as expected, were important predictive factors. Age, NSSN and CD4+ T cell count at baseline together predicted sero-protection (HAI≥40) to A(H1N1)pdm09 with a high accuracy of 89% (p-value = 0.00002). An additional validation study (N = 43 vaccinees sero-negative to A(H1N1)pdm09) has confirmed the predictive value of age, NSSN and baseline CD4+ counts (accuracy = 85%, p-value = 0.0000004). Furthermore, the inclusion of donors at ages 31–50 had shown that the age predictive function is not linear with age but rather a sigmoid with a midpoint at about 50 years. Using these results we suggest a clinically relevant prediction model that gives the probability for non-protection to A(H1N1)pdm09 influenza strain after seasonal multi-valent vaccination as a continuous function of age, NSSN and baseline CD4 count.