Prioritizing CD4 count monitoring in response to ART in resource-constrained settings: a retrospective application of prediction-based classification.

AZZONI L; FOULKES AS; LIU Y; LI X; JOHNSON M; SMITH C; KAMARULZAMAN AB; MONTANER J; MOUNZER K; SAAG M; CAHN P; CESAR C; KROLEWIECKI A; SANNE I; MONTANER LJ

PLOS MEDICINE

PUBLIC LIBRARY SCIENCE

Lugar: San Francisco; Año: 2012 vol. 9 p. 1 - 11

1549-1277

Background Global programs of anti-HIV treatment depend on sustained laboratory capacity to assess treatment initiation thresholds and treatment response over time. Currently, there is no valid alternative to CD4 count testing for monitoring immunologic responses to treatment, but laboratory cost and capacity limit access to CD4 testing in resource-constrained settings. Thus, methods to prioritize patients for CD4 count testing could improve treatment monitoring by optimizing resource allocation. Methods and Findings Using a prospective cohort of HIV-infected patients (n=1,956) monitored upon antiretroviral therapy initiation in seven clinical sites with distinct geographical and socio-economic settings, we retrospectively apply a novel prediction-based classification (PBC) modeling method. The model uses repeatedly measured biomarkers (white blood cell count and lymphocyte percent) to predict CD4+ T cell outcome through first-stage modeling and subsequent classification based on clinically relevant thresholds (CD4+ T cell count of 200 or 350 cells/µl). The algorithm correctly classified 90% (cross-validation estimate=91.5%, standard deviation [SD]=4.5%) of CD4 count measurements