title: Viral load criteria and threshold optimization to improve HIV incidence assay characteristics.
abstract: Assays for classifying HIV infections as 'recent' or 'nonrecent' for incidence surveillance fail to simultaneously achieve large mean durations of 'recent' infection (MDRIs) and low 'false-recent' rates (FRRs), particularly in virally suppressed persons. The potential for optimizing recent infection testing algorithms (RITAs), by introducing viral load criteria and tuning thresholds used to dichotomize quantitative measures, is explored.
  
  
  
  The Consortium for the Evaluation and Performance of HIV Incidence Assays characterized over 2000 possible RITAs constructed from seven assays (Limiting Antigen, BED, Less-sensitive Vitros, Vitros Avidity, BioRad Avidity, Architect Avidity, and Geenius) applied to 2500 diverse specimens.
  
  
  
  MDRIs were estimated using regression, and FRRs as observed 'recent' proportions, in various specimen sets. Context-specific FRRs were estimated for hypothetical scenarios. FRRs were made directly comparable by constructing RITAs with the same MDRI through the tuning of thresholds. RITA utility was summarized by the precision of incidence estimation.
  
  
  
  All assays produce high FRRs among treated patients and elite controllers (10-80%). Viral load testing reduces FRRs, but diminishes MDRIs. Context-specific FRRs vary substantially by scenario - BioRad Avidity and Limiting Antigen provided the lowest FRRs and highest incidence precision in scenarios considered.
  
  
  
  The introduction of a low viral load threshold provides crucial improvements in RITAs. However, it does not eliminate nonzero FRRs, and MDRIs must be consistently estimated. The tuning of thresholds is essential for comparing and optimizing the use of assays. The translation of directly measured FRRs into context-specific FRRs critically affects their magnitudes and our understanding of the utility of assays.
authors.affiliation: aThe South African DST/NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch bDepartment of Statistical Sciences, University of Cape Town, Cape Town, South Africa cUniversity of California dBlood Systems Research Institute, San Francisco, California, USA ePublic Health England, London, UK fInternational AIDS Vaccine Initiative (IAVI), Department of Medical Affairs, New York City, New York gDepartment of Medicine, University of California San Diego, San Diego, California, USA hDivision of Clinical Immunology and Allergy, School of Medicine, University of São Paulo, São Paulo, Brazil. *Christopher D. Pilcher and Alex Welte contributed equally to the writing of this article.
authors.forename: Reshma
authors.initials: R
authors.lastname: Kassanjee
authors.forename: Christopher D
authors.initials: CD
authors.lastname: Pilcher
authors.forename: Michael P
authors.initials: MP
authors.lastname: Busch
authors.forename: Gary
authors.initials: G
authors.lastname: Murphy
authors.forename: Shelley N
authors.initials: SN
authors.lastname: Facente
authors.forename: Sheila M
authors.initials: SM
authors.lastname: Keating
authors.forename: Elaine
authors.initials: E
authors.lastname: Mckinney
authors.forename: Kara
authors.initials: K
authors.lastname: Marson
authors.forename: Matthew A
authors.initials: MA
authors.lastname: Price
authors.forename: Jeffrey N
authors.initials: JN
authors.lastname: Martin
authors.forename: Susan J
authors.initials: SJ
authors.lastname: Little
authors.forename: Frederick M
authors.initials: FM
authors.lastname: Hecht
authors.forename: Esper G
authors.initials: EG
authors.lastname: Kallas
authors.forename: Alex
authors.initials: A
authors.lastname: Welte
mesh_terms: D015658:HIV Infections / Q000175:diagnosis* / Q000453:epidemiology / Q000821:virology*; D006801:Humans; D015994:Incidence; D019562:Viral Load / Q000379:methods*