Quantitative interpretation of Sedia LAg Assay test results after HIV diagnosis

Abstract

Background Testing for recent HIV infection is common in surveillance, where only population-level estimates (of incidence) are reported. Typically, recent infection is a category, obtained by applying a threshold on an underlying continuous biomarker from some laboratory assay(s). Somehow interpreting the biomarker values obtained for individual subjects, for example, interpreting them as estimates of the date of infection, has obvious potential applications in the context of studies of early infection, and has also for some years attracted significant interest as an extra component of post-test counselling and treatment initiation. The applicable analyses have typically run aground on the complexity of the full biomarker growth model, which is in principle a non-linear mixed-effects model of unknown structure, the fitting of which seems infeasible from realistically obtainable data. Methods It is known that to estimate Mean Duration of Recent Infection (MDRI) at a given value of the recent/non-recent -infection discrimination threshold, one may compress the full biomarker growth model into a relation capturing the probability of a recent test result as a function of time since infection. Noting that the time-derivative (gradient) of this curve (for a value of threshold — h) is identical to the formal likelihood relevant to Bayesian inference of the infection date, for a subject yielding an assay result * h * on the date of their first positive HIV test. This observation bypasses the need for fitting a complex detailed biomarker growth model. Using publicly available data from the CEPHIA collaboration, we calculated curves for a range of thresholds for the Sedia Lag assay and performed Bayesian inference of infection data, given a uniform prior implied by a last negative and first positive test. Results We demonstrate the generation of posteriors for infection date, for patients with various delays between their last negative and first positive HIV test, and a range of LAg assay results (ODn) hypothetically obtained on the date of the first positive result. Conclusion Depending on the last-negative / first-positive interval, there is a range of ODn values that yields posteriors significantly different from the uniform prior one would be left with based merely on interval censoring. Hence, a LAg ODn obtained on the date of, or soon after, diagnosis contains potentially significant information about infection dating. It seems worth analysing other assays with meaningful dynamic range, especially tests already routinely used in primary HIV diagnosis (for example chemiluminescent assays and reader/cartridge lateral flow tests which admit objective variable line intensity readings) which have a sufficient dynamic range that corresponds to a clinically meaningful range of times-since-infection that are worth distinguishing from each other.