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A generalizable method for estimating duration of HIV infections using clinical testing history and HIV test results

Pilcher, Christopher D.a; Porco, Travis C.a; Facente, Shelley N.a,b,c; Grebe, Eduardb,d; Delaney, Kevin P.e; Masciotra, Silvinae; Kassanjee, Reshmad,f; Busch, Michael P.a,b; Murphy, Garyg; Owen, S. Michelee; Welte, Alexd on behalf of the Consortium for the Evaluation and Performance of HIV Incidence Assays (CEPHIA)

doi: 10.1097/QAD.0000000000002190
EPIDEMIOLOGY AND SOCIAL
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Objective: To determine the precision of new and established methods for estimating duration of HIV infection.

Design: A retrospective analysis of HIV testing results from serial samples in commercially available panels, taking advantage of extensive testing previously conducted on 53 seroconverters.

Methods: We initially investigated four methods for estimating infection timing: method 1, ‘Fiebig stages’ based on test results from a single specimen; method 2, an updated ‘4th gen’ method similar to Fiebig stages but using antigen/antibody tests in place of the p24 antigen test; method 3, modeling of ‘viral ramp-up’ dynamics using quantitative HIV-1 viral load data from antibody-negative specimens; and method 4, using detailed clinical testing history to define a plausible interval and best estimate of infection time. We then investigated a ‘two-step method’ using data from both methods 3 and 4, allowing for test results to have come from specimens collected on different days.

Results: Fiebig and ‘4th gen’ staging method estimates of time since detectable viremia had similar and modest correlation with observed data. Correlation of estimates from both new methods (3 and 4), and from a combination of these two (’two-step method’) was markedly improved and variability significantly reduced when compared with Fiebig estimates on the same specimens.

Conclusion: The new ‘two-step’ method more accurately estimates timing of infection and is intended to be generalizable to more situations in clinical medicine, research, and surveillance than previous methods. An online tool is now available that enables researchers/clinicians to input data related to method 4, and generate estimated dates of detectable infection.

aUniversity of California, San Francisco

bVitalant Research Institute (formerly Blood Systems Research Institute), San Francisco

cFacente Consulting, Richmond, California, USA

dThe South African DST-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa

eCenters for Disease Control and Prevention, Atlanta, Georgia, USA

fDepartment of Statistical Sciences, University of Cape Town, Rondebosch, South Africa

gPublic Health England, London, United Kingdom.

Correspondence to Shelley N. Facente, MPH, Vitalant Research Institute, 270 Masonic Avenue, San Francisco, CA, 94118, USA. E-mail: sfacente@vitalant.org

Received 16 November, 2018

Revised 25 January, 2019

Accepted 31 January, 2019

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Copyright © 2019 Wolters Kluwer Health, Inc.