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

Abstract

Objective: 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.

Design: A cross-sectional study of 1448 adults attending a sexually transmitted infections (STI) clinic in Malawi.

Methods: 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.

Results: 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.

Conclusion: 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.

Fig. 1. Sensitivity, specificity, and percentage to be referred for RNA/p24 tests

The horizontal axis displays all possible risk score cut-offs that could be chosen for clinical implementation of the algorithm. These scores correspond to the range of risk scores observed across individuals in this study, based on each individual’s predictor profile. In clinical implementation of the algorithm, all antibody-negative or discordant individuals with risk scores at or above a chosen cut-off would be referred for RNA or p24 tests. ‘RNA or p24 tests’ () refers to the proportion of all antibody-negative or discordant patients who would be referred for RNA or p24 testing at a given cut-off (i.e. the proportion of all antibody-negative or discordant patients with risk scores at or above a given cut-off). As the risk score cut-off increases, fewer patients [both with acute HIV infection (AHI) and HIV negative] have risk scores at or above that value, such that fewer patients are indicated for RNA or p24 testing. ‘Sensitivity’ () refers to the proportion of AHI cases with risk scores at or above a given cut-off. As the risk score cut-off increases, fewer AHI cases will be referred for RNA or p24 testing, so they will not be detected as AHI cases (sensitivity decreases). ‘Specificity’ () refers to the proportion of HIV-negative participants with risk scores lower than a given cut-off (i.e. the proportion of HIV-negative individuals ruled out as AHI cases). As the risk score cut-off increases, more HIV-negative participants have scores less than the cut-off, so specificity increases. The vertical lines with horizontal bars around each point estimate represent 95% confidence intervals.