Comparison of viral load and human leukocyte antigen statistical and neural network predictive models for the rate of HIV-1 disease progression across two cohorts of homosexual men.

Abstract

We compared the performance of HIV-1 RNA and models based on human leukocyte antigen (HLA) in predicting the rate of HIV-1 disease progression using both linear regression and neural network models across two different cohorts of homosexual men. In all, 139 seroconverters from the Multicenter AIDS Cohort Study were used as the training set and 97 seroconverters from the District of Columbia Gay (DCG) cohort were used for validation to assess the generalizability of trained predictive models. Both viral load and HLA markers were strongly predictive of disease progression (p < .0001 and p = .001, respectively), with viral load superior to HLA (change in -2 log likelihood [-2LL] 26.7 and 10.2, respectively, in proportional hazards models). Consideration of both HLA markers and viral load offered no significant predictive advantage over viral load alone in most cases; however, HLA-based predictions obtained from neural networks modeling improved the discrimination among patients with high viral load (p = .02). Viral load, HLA scores, and rapid disease progression were moderately correlated (p < .01 for all three pairs of these variables). The median viral load was 10(3.70) copies/ml among DCG patients who had more favorable than unfavorable HLA markers and 10(4.66) copies/ml among patients with more unfavorable than favorable HLA markers. Viral load is a simpler, stronger predictor of disease progression than early developed HLA models, but neural network methods and further refined HLA models may offer additional prognostic information, especially for rapid progressors. The correlation between viral load and HLA markers suggests a possible HLA effect on setting viral load levels.