Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
medicine
Prioritizing CD4 count monitoring in response to ART in resource-constrained settings: A retrospective application of prediction-based classification
PLoS Medicine, Volume 9, No. 4, Article e1001207, Year 2012
Notification
URL copied to clipboard!
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/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. © 2012 Azzoni et al.
Available Materials
https://efashare.b-cdn.net/share/pmc/articles/PMC3328436/bin/pmed.1001207.s001.doc
Authors & Co-Authors
Azzoni, Livio
United States, Philadelphia
The Wistar Institute
Foulkes, Andrea
United States, Amherst
University of Massachusetts Amherst
Liu, Yan
United States, Amherst
University of Massachusetts Amherst
Li, Xiaohong
United States, Waltham
Bg Medicine
Johnson, Margaret A.
United Kingdom, London
Royal Free London Nhs Foundation Trust
Smith, Collette
United Kingdom, London
Ucl Medical School
Kamarulzaman, Adeeba Binti
Malaysia, Kuala Lumpur
Universiti Malaya
Montaner, Julio S.G.
Canada, Vancouver
The University of British Columbia
Mounzer, Karam C.
United States, Philadelphia
Philadelphia Fight
Saag, Michael S.
United States, Tuscaloosa
The University of Alabama
Cahn, Pedro Enrique
Argentina, Buenos Aires
Fundacion Huesped
Cesar, Carina
Argentina, Buenos Aires
Fundacion Huesped
Krolewiecki, Alejandro Javier
Argentina, Buenos Aires
Fundacion Huesped
Sanne, Ian
South Africa, Johannesburg
University of the Witwatersrand
Montaner, Luis J.
United States, Philadelphia
The Wistar Institute
Statistics
Citations: 15
Authors: 15
Affiliations: 11
Identifiers
Doi:
10.1371/journal.pmed.1001207
ISSN:
15491277
e-ISSN:
15491676
Research Areas
Health System And Policy
Infectious Diseases
Study Design
Cohort Study