Prioritizing CD4 count monitoring in response to ART in resource-constrained settings: a retrospective application of prediction-based classification
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...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science
2012
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| Online Access: | http://eprints.um.edu.my/9596/1/00000544_85598.pdf |
| _version_ | 1825719732290977792 |
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| author | Azzoni, Livio Foulkes, Andrea S. Liu, Yan Johnson, Margaret Smith, Collette Kamarulzaman, Adeeba Montaner, Julio Mounzer, Karam Saag, Michael Cahn, Pedro Cesar, Carina Krolewiecki, Alejandro Sanne, Ian Montaner, Luis J. |
| author_facet | Azzoni, Livio Foulkes, Andrea S. Liu, Yan Johnson, Margaret Smith, Collette Kamarulzaman, Adeeba Montaner, Julio Mounzer, Karam Saag, Michael Cahn, Pedro Cesar, Carina Krolewiecki, Alejandro Sanne, Ian Montaner, Luis J. |
| author_sort | Azzoni, Livio |
| collection | UM |
| description | 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/ml). The algorithm correctly classified 90% (cross-validation estimate = 91.5%, standard deviation [SD] = 4.5%) of CD4 count measurements ,200 cells/ml
in the first year of follow-up; if laboratory testing is applied only to patients predicted to be below the 200-cells/ml 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/ml. 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. |
| first_indexed | 2024-03-06T05:24:14Z |
| format | Article |
| id | um.eprints-9596 |
| institution | Universiti Malaya |
| language | English |
| last_indexed | 2024-03-06T05:24:14Z |
| publishDate | 2012 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | um.eprints-95962018-10-12T01:07:57Z http://eprints.um.edu.my/9596/ Prioritizing CD4 count monitoring in response to ART in resource-constrained settings: a retrospective application of prediction-based classification Azzoni, Livio Foulkes, Andrea S. Liu, Yan Johnson, Margaret Smith, Collette Kamarulzaman, Adeeba Montaner, Julio Mounzer, Karam Saag, Michael Cahn, Pedro Cesar, Carina Krolewiecki, Alejandro Sanne, Ian Montaner, Luis J. R Medicine 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/ml). The algorithm correctly classified 90% (cross-validation estimate = 91.5%, standard deviation [SD] = 4.5%) of CD4 count measurements ,200 cells/ml in the first year of follow-up; if laboratory testing is applied only to patients predicted to be below the 200-cells/ml 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/ml. 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. Public Library of Science 2012-04 Article PeerReviewed application/pdf en http://eprints.um.edu.my/9596/1/00000544_85598.pdf Azzoni, Livio and Foulkes, Andrea S. and Liu, Yan and Johnson, Margaret and Smith, Collette and Kamarulzaman, Adeeba and Montaner, Julio and Mounzer, Karam and Saag, Michael and Cahn, Pedro and Cesar, Carina and Krolewiecki, Alejandro and Sanne, Ian and Montaner, Luis J. (2012) Prioritizing CD4 count monitoring in response to ART in resource-constrained settings: a retrospective application of prediction-based classification. PLoS Medicine, 9 (4). e1001207. ISSN 1549-1277, DOI https://doi.org/10.1371/journal.pmed.1001207 <https://doi.org/10.1371/journal.pmed.1001207>. https://doi.org/10.1371/journal.pmed.1001207 doi:10.1371/journal.pmed.1001207 |
| spellingShingle | R Medicine Azzoni, Livio Foulkes, Andrea S. Liu, Yan Johnson, Margaret Smith, Collette Kamarulzaman, Adeeba Montaner, Julio Mounzer, Karam Saag, Michael Cahn, Pedro Cesar, Carina Krolewiecki, Alejandro Sanne, Ian Montaner, Luis J. Prioritizing CD4 count monitoring in response to ART in resource-constrained settings: a retrospective application of prediction-based classification |
| title | Prioritizing CD4 count monitoring in response to ART in
resource-constrained settings: a retrospective application of prediction-based classification |
| title_full | Prioritizing CD4 count monitoring in response to ART in
resource-constrained settings: a retrospective application of prediction-based classification |
| title_fullStr | Prioritizing CD4 count monitoring in response to ART in
resource-constrained settings: a retrospective application of prediction-based classification |
| title_full_unstemmed | Prioritizing CD4 count monitoring in response to ART in
resource-constrained settings: a retrospective application of prediction-based classification |
| title_short | Prioritizing CD4 count monitoring in response to ART in
resource-constrained settings: a retrospective application of prediction-based classification |
| title_sort | prioritizing cd4 count monitoring in response to art in resource constrained settings a retrospective application of prediction based classification |
| topic | R Medicine |
| url | http://eprints.um.edu.my/9596/1/00000544_85598.pdf |
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