Abstract
Estimating date of infection for HIV-1-infected patients is vital for disease tracking and informed public health decisions, but is difficult to obtain because most patients have an established infection of unknown duration at diagnosis. Previous studies have used HIV-1-specific immunoglobulin G (IgG) levels as measured by the IgG capture BED enzyme immunoassay (BED assay) to indicate if a patient was infected recently, but a time-continuous model has not been available. Therefore, we developed a logistic model of IgG production over time. We used previously published metadata from 792 patients for whom the HIV-1-specific IgG levels had been longitudinally measured using the BED assay. To account for patient variability, we used mixed effects modeling to estimate general population parameters. The typical patient IgG production rate was estimated at r = 6.72[approximate 95% CI 6.17,7.33]×10−3 OD-n units day−1, and the carrying capacity at K = 1.84[1.75,1.95] OD-n units, predicting how recently patients seroconverted in the interval ∧ t = (31,711) days. Final model selection and validation was performed on new BED data from a population of 819 Swedish HIV-1 patients diagnosed in 2002–2010. On an appropriate subset of 350 patients, the best model parameterization had an accuracy of 94% finding a realistic seroconversion date. We found that seroconversion on average is at the midpoint between last negative and first positive HIV-1 test for patients diagnosed in prospective/cohort studies such as those included in the training dataset. In contrast, seroconversion is strongly skewed towards the first positive sample for patients identified by regular public health diagnostic testing as illustrated in the validation dataset. Our model opens the door to more accurate estimates of date of infection for HIV-1 patients, which may facilitate a better understanding of HIV-1 epidemiology on a population level and individualized prevention, such as guidance during contact tracing.
Highlights
Estimating incidence of an infectious disease is vital for informed and targeted prevention, and knowing the date of infection per case is important for estimating the incidence in a population
Like human immunodeficiency virus type 1 (HIV-1) infection, it is more complicated to infer the date of infection because only rarely are persons diagnosed during primary HIV-1 infection (PHI)
When only modeling ODIgG(0) and r in the immunoglobulin G (IgG) growth phase, an ANOVA supported the observation that no pattern of correlation between intercepts ODIgG(0) and slopes r could be observed in the random effects (p,0.001, x2 = 134.72, df = 1; Table 1)
Summary
Estimating incidence of an infectious disease is vital for informed and targeted prevention, and knowing the date of infection per case is important for estimating the incidence in a population. Like human immunodeficiency virus type 1 (HIV-1) infection, it is more complicated to infer the date of infection because only rarely are persons diagnosed during primary HIV-1 infection (PHI). Due to the current problems with HIV-1 incidence estimation, there is considerable interest in the development of assays and biomarkers that can determine if an HIV-1 infection is recent, in order to allow for estimating HIV-1 incidence in a population [1,2,3,4,5,6,7,8]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
