Mostrar el registro sencillo del ítem
dc.contributor.author
Azzoni, Livio
dc.contributor.author
Foulkes, Andrea S.
dc.contributor.author
Liu, Yan
dc.contributor.author
Li, Xiaohong
dc.contributor.author
Johnson, Margaret
dc.contributor.author
Smith, Collette
dc.contributor.author
Kamarulzaman, Adeebabte
dc.contributor.author
Montaner, Julio
dc.contributor.author
Mounzer, Karam
dc.contributor.author
Saag, Michael
dc.contributor.author
Cahn, Pedro
dc.contributor.author
Cesar, Carina
dc.contributor.author
Krolewiecki, Alejandro Javier
dc.contributor.author
Sanne, Ian
dc.contributor.author
Montaner, Luis J.
dc.date.available
2018-01-09T14:15:49Z
dc.date.issued
2012-04
dc.identifier.citation
Montaner, Luis J.; Sanne, Ian; Cesar, Carina; Cahn, Pedro; Saag, Michael; Montaner, Julio; et al.; Prioritizing CD4 count monitoring in response to ART in resource-constrained settings: a retrospective application of prediction-based classification.; Public Library Of Science; Plos Medicine; 9; 4; 4-2012; 1-11
dc.identifier.issn
1549-1277
dc.identifier.uri
http://hdl.handle.net/11336/32641
dc.description.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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Public Library Of Science
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Cd4
dc.subject
Hiv
dc.subject
Monitoreo
dc.subject
Modelo Predictivo
dc.subject.classification
Salud Ocupacional
dc.subject.classification
Ciencias de la Salud
dc.subject.classification
CIENCIAS MÉDICAS Y DE LA SALUD
dc.title
Prioritizing CD4 count monitoring in response to ART in resource-constrained settings: a retrospective application of prediction-based classification.
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2017-05-29T15:37:33Z
dc.journal.volume
9
dc.journal.number
4
dc.journal.pagination
1-11
dc.journal.pais
Estados Unidos
dc.journal.ciudad
San Francisco
dc.description.fil
Fil: Azzoni, Livio. Wistar Institute, University Of Pennsylvania;
dc.description.fil
Fil: Foulkes, Andrea S.. University of Massachussets; Estados Unidos
dc.description.fil
Fil: Liu, Yan. University of Massachussets; Estados Unidos
dc.description.fil
Fil: Li, Xiaohong. Bg Medicine, Walthman; Estados Unidos
dc.description.fil
Fil: Johnson, Margaret. Royal Free Hampstead Nhs Trust;
dc.description.fil
Fil: Smith, Collette. Ucl Medical School,;
dc.description.fil
Fil: Kamarulzaman, Adeebabte. University Of Malaya;
dc.description.fil
Fil: Montaner, Julio. University Of British Columbia; Canadá
dc.description.fil
Fil: Mounzer, Karam. Philadelphia Fight;
dc.description.fil
Fil: Saag, Michael. University Of Alabama At Tuscaloosa;
dc.description.fil
Fil: Cahn, Pedro. Fundación Huésped; Argentina
dc.description.fil
Fil: Cesar, Carina. Fundación Huésped; Argentina
dc.description.fil
Fil: Krolewiecki, Alejandro Javier. Fundación Huésped; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Patología Experimental. Universidad Nacional de Salta. Facultad de Ciencias de la Salud. Instituto de Patología Experimental; Argentina
dc.description.fil
Fil: Sanne, Ian. University Of The Witwatersrand;
dc.description.fil
Fil: Montaner, Luis J.. Wistar Institute, University Of Pennsylvania;
dc.journal.title
Plos Medicine
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3328436/?tool=pubmed
Archivos asociados