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PLoS One. 2018 Apr 3;13(4):e0192785. doi: 10.1371/journal.pone.0192785. eCollection 2018.

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

Author information

1
Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
2
International AIDS Vaccine Initiative, New York, New York, United States of America.
3
Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America.
4
Project San Francisco, Kigali, Rwanda.
5
Uganda Research Unit on AIDS, Uganda Virus Research Institute, Entebbe, Uganda.
6
Zambia-Emory Research Project, Lusaka & Copperbelt, Zambia.
7
Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia, United States of America.
8
Desmond Tutu HIV Centre, University of Cape Town, Cape Town, South Africa.
9
KAVI-ICR University of Nairobi, Nairobi, Kenya.
10
The Aurum Institute, Johannesburg, South Africa.
11
Imperial College of Science, Technology and Medicine, London, United Kingdom.
12
Kenya Medical Research Institute, Kilifi, Kenya.
13
University of Oxford, Headington, United Kingdom.
14
University of Amsterdam, Amsterdam, The Netherlands.

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 prior to seroconversion, or the mid-point between negative and positive serological tests. EHV was defined as mean untreated viral load ≥5 log10 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 log10 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.

PMID:
29614069
PMCID:
PMC5882095
DOI:
10.1371/journal.pone.0192785
[Indexed for MEDLINE]
Free PMC Article

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