Improved detection of acute HIV-1 infection in sub-Saharan Africa: development of a risk score algorithm

Abstract

Individuals with acute (preseroconversion) HIV infection (AHI) are important in the spread of HIV. The identification of AHI requires the detection of viral proteins or nucleic acids with techniques that are often unaffordable for routine use. To facilitate the efficient use of these tests, we sought to develop a risk score algorithm for identifying likely AHI cases and targeting the tests towards those individuals. A cross-sectional study of 1448 adults attending a sexually transmitted infections (STI) clinic in Malawi. Using logistic regression, we identified risk behaviors, symptoms, HIV rapid test results, and STI syndromes that were predictive of AHI. We assigned a model-based score to each predictor and calculated a risk score for each participant. Twenty-one participants (1.45%) had AHI, 588 had established HIV infection, and 839 were HIV-negative. AHI was strongly associated with discordant rapid HIV tests and genital ulcer disease (GUD). The algorithm also included diarrhea, more than one sexual partner in 2 months, body ache, and fever. Corresponding predictor scores were 1 for fever, body ache, and more than one partner; 2 for diarrhea and GUD; and 4 for discordant rapid tests. A risk score of 2 or greater was 95.2% sensitive and 60.5% specific in detecting AHI. Using this algorithm, we could identify 95% of AHI cases by performing nucleic acid or protein tests in only 40% of patients. Risk score algorithms could enable rapid, reliable AHI detection in resource-limited settings.