Prediction of extended high viremia among newly HIV-1-infected persons in sub-Saharan Africa

• Authors: Kimberly A. Powers, University of North Carolina; Matthew A. Price, International AIDS Vaccine Initiative; Etienne Karita, Project San Francisco; Anatoli Kamali, International AIDS Vaccine Initiative; William Kilembe, Zambia-Emory Research Project; Susan A Allen, Emory University; Eric Hunter, Emory University; Linda-Gail Bekker, University of Cape Town; Shabir Lakhi, Zambia-Emory Research Project; Mubiana Inambao, Zambia-Emory Research Project; Omu Anzala, University of Nairobi; Mary H. Latka, The Aurum Institute; Patricia E. Fast, International AIDS Vaccine Initiative; Jill Gilmour, International AIDS Vaccine Initiative; Eduard J. Sanders, University of Oxford
• Journal Title: PLoS ONE
• Volume: Volume 13, Number 4
• Publisher: Public Library of Science | 2018-04-03, Pages e0192785-e0192785
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1371/journal.pone.0192785
• Subjects: Biology, Virology; Health Sciences, Public Health; Health Sciences, Epidemiology
• Keywords: Science & Technology; Multidisciplinary Sciences; Science & Technology - Other Topics; ACUTE RETROVIRAL SYNDROME; PRIMARY HIV-1 INFECTION; ANTIRETROVIRAL THERAPY; VIRAL LOAD; RNA LEVELS; SUBTYPE C; SET-POINT; PROGRESSION; SEROCONVERSION; SCORE; Viral load; HIV-1; Viremia; Algorithms; Antiretroviral therapy; Africa; Forecasting; Health services research

Abstract

Objective Prompt identification of newly HIV-infected persons, particularly those who are most at risk of extended high viremia (EHV), allows important clinical and transmission prevention benefits. We sought to determine whether EHV could be predicted during early HIV infection (EHI) from clinical, demographic, and laboratory indicators in a large HIV-1 incidence study in Africa. Design Adults acquiring HIV-1 infection were enrolled in an EHI study assessing acute retroviral syndrome (ARS) symptoms and viral dynamics. Methods Estimated date of infection (EDI) was based on a positive plasma viral load or p24 antigen test p rior to seroconversion, or the mid-point between negative and positive serological tests. EHV was defined as mean untreated viral load ≥5 log 10 copies/ml 130-330 days post-EDI. We used logistic regression to develop risk score algorithms for predicting EHV based on sex, age, number of ARS symptoms, and CD4 and viral load at diagnosis. Results Models based on the full set of five predictors had excellent performance both in the full population (c-statistic = 0.80) and when confined to persons with each of three HIV-1 subtypes(c-statistic = 0.80-0.83 within subtypes A, C, and D). Reduced models containing only 2-4 predictors performed similarly. In a risk score algorithm based on the final full-population model, predictor scores were one for male sex and enrollment CD4 < 350 cells/mm3, and two for having enrollment viral load > 4.9 log 10 copies/ml. With a risk score cut-point of two, this algorithm was 85% sensitive (95% CI: 76%-91%) and 61% specific (55%-68%) in predicting EHV. Conclusions Simple risk score algorithms can reliably identify persons with EHI in sub-Saharan Africa who are likely to sustain high viral loads if treatment is delayed. These algorithms may be useful for prioritizing intensified efforts around care linkage and retention, treatment initiation,adherence support, and partner services to optimize clinical and prevention outcomes.