Predicting acute HIV infection in key populations

Targeted screening of at-risk adults for acute HIV-1 infection in sub-Saharan Africa.

Sanders EJ, Wahome E, Powers KA, Werner L, Fegan G, Lavreys L, Mapanje C, McClelland RS, Garrett N, Miller WC, Graham SM. AIDS. 2015 Dec;29 Suppl 3:S221-30. doi: 10.1097/QAD.0000000000000924.

Background: Patients with acute HIV-1 infection (AHI) have elevated infectivity, but cannot be diagnosed using antibody-based testing. Approaches to screen patients for AHI are urgently needed to enable counselling and treatment to reduce onward transmission.

Methods: We pooled data from four African studies of high-risk adults that evaluated symptoms and signs compatible with acute retroviral syndrome and tested for HIV-1 at each visit. AHI was defined as detectable plasma viral load or p24 antigen in an HIV-1-antibody-negative patient who subsequently seroconverted. Using generalized estimating equation, we identified symptoms, signs, and demographic factors predictive of AHI, adjusting for study site. We assigned a predictor score to each statistically significant predictor based on its beta coefficient, summing predictor scores to calculate a risk score for each participant. We evaluated the performance of this algorithm overall and at each site.

Results: We compared 122 AHI visits with 45 961 visits by uninfected patients. Younger age (18-29 years), fever, fatigue, body pains, diarrhoea, sore throat, and genital ulcer disease were independent predictors of AHI. The overall area under the receiver operating characteristics curve (AUC) for the algorithm was 0.78, with site-specific AUCs ranging from 0.61 to 0.89. A risk score of at least 2 would indicate AHI testing for 5-50% of participants, substantially decreasing the number needing testing.

Conclusion: Our targeted risk score algorithm based on seven characteristics reduced the number of patients needing AHI testing and had good performance overall. We recommend this risk score algorithm for use by HIV programs in sub-Saharan Africa with capacity to test high-risk patients for AHI.

Abstract  Full-text [free] access

Editor’s notes: This analysis adds to the literature around the performance of risk score algorithms to guide testing for acute HIV infection (AHI). The four studies included in this analysis involved key populations in different African settings. In common with previous analyses, genital ulcer disease had by far the strongest association with AHI. The derived algorithm had modest accuracy overall and poor performance in South Africa, where symptoms and signs were particularly infrequent.

Most studies included in this analysis were cohort studies following key individuals. Whether or not algorithms based on recording of symptoms and signs during intensive follow-up for AHI can be translated for use in a real world situation of unselected people presenting for health care remains unproven. Ultimately, we really need evidence about the impact and cost-effectiveness of detecting AHI in different populations. This is in order to define the role of testing for AHI, and in particular whether rationalising testing with algorithms such as this is necessary (especially for key populations).   

Africa
Kenya, Malawi, South Africa
  • share