Advertisement

 

 

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

Prediction of extended high viremia among newly HIV-1-infected persons in sub-Saharan Africa.
Author Information (click to view)

Powers KA, Price MA, Karita E, Kamali A, Kilembe W, Allen S, Hunter E, Bekker LG, Lakhi S, Inambao M, Anzala O, Latka MH, Fast PE, Gilmour J, Sanders EJ,


Powers KA, Price MA, Karita E, Kamali A, Kilembe W, Allen S, Hunter E, Bekker LG, Lakhi S, Inambao M, Anzala O, Latka MH, Fast PE, Gilmour J, Sanders EJ, (click to view)

Powers KA, Price MA, Karita E, Kamali A, Kilembe W, Allen S, Hunter E, Bekker LG, Lakhi S, Inambao M, Anzala O, Latka MH, Fast PE, Gilmour J, Sanders EJ,

Advertisement

PloS one 2018 04 0313(4) e0192785 doi 10.1371/journal.pone.0192785

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.

Submit a Comment

Your email address will not be published. Required fields are marked *

2 × 5 =

[ HIDE/SHOW ]