Azzoni, L; Foulkes, AS; Liu, Y; Li, X; Johnson, M; Smith, C; Kamarulzaman, AB; ... Montaner, LJ; + view all 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; - view fewer (2012) Prioritizing CD4 Count Monitoring in Response to ART in Resource-Constrained Settings: A Retrospective Application of Prediction-Based Classification. PLoS Medicine , 9 (4) , Article e1001207. 10.1371/journal.pmed.1001207.

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Abstract

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 <200 cells/µl in the first year of follow-up; if laboratory testing is applied only to patients predicted to be below the 200-cells/µl threshold, we estimate a potential savings of 54.3% (SD = 4.2%) in CD4 testing capacity. A capacity savings of 34% (SD = 3.9%) is predicted using a CD4 threshold of 350 cells/µl. Similar results were obtained over the 3 y of follow-up available (n = 619). Limitations include a need for future economic healthcare outcome analysis, a need for assessment of extensibility beyond the 3-y observation time, and the need to assign a false positive threshold. Conclusions Our results support the use of PBC modeling as a triage point at the laboratory, lessening the need for laboratory-based CD4+ T cell count testing; implementation of this tool could help optimize the use of laboratory resources, directing CD4 testing towards higher-risk patients. However, further prospective studies and economic analyses are needed to demonstrate that the PBC model can be effectively applied in clinical settings.

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