ArticlePloS one2018
Prediction of extended high viremia among newly HIV-1-infected persons in sub-Saharan Africa.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 17 citations in OpenAlex.
- A prediction model for virologic failure in adolescents living with HIV in Uganda: Findings from the Suubi+Adherence study.Public health · 2025Trial
- Early HIV viral suppression associated with subsequent 12-month treatment success among people living with HIV in South Africa.HIV medicine · 2024Trial
- Socio-economic condition and lack of virological suppression among adults and adolescents receiving antiretroviral therapy in Ethiopia.PloS one · 2020Trial
- Web-Based Risk Prediction Tool for an Individual's Risk of HIV and Sexually Transmitted Infections Using Machine Learning Algorithms: Development and External Validation Study.Journal of medical Internet research · 2022Article
- Development and Validation of a Risk Prediction Tool to Identify People with HIV Infection Likely Not to Achieve Viral Suppression.AIDS patient care and STDs · 2020Article
Corrections and comments
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Authors and funding
15 authors at 7 institutions in 6 countries.
Funding
Abstract
objectivePrompt 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.
designAdults acquiring HIV-1 infection were enrolled in an EHI study assessing acute retroviral syndrome (ARS) symptoms and viral dynamics.
methodsEstimated 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.
resultsModels 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.
conclusionsSimple 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.
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