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
Complete blood cell count as a surrogate CD4 cell marker for HIV monitoring in resource-limited settings
Journal of Acquired Immune Deficiency Syndromes, Volume 44, No. 5, Year 2007
Notification
URL copied to clipboard!
Description
BACKGROUND: A total lymphocyte count (TLC) of 1200 cells/mL has been used as a surrogate for a CD4 count of 200 cells/μL in resource-limited settings with varying results. We developed a more effective method based on a decision tree algorithm to classify subjects. METHODS: A decision tree was used to develop models with the variables TLC, hemoglobin, platelet count, gender, body mass index, and antiretroviral treatment status of subjects from the University of Alabama at Birmingham (UAB) observational database. Models were validated on data from the Birmingham Veterans Affairs Medical Center (BVAMC) and Zambia, with primary decision trees also generated from these data. RESULTS: A total of 1189 patients from the UAB observational database were included. The UAB decision tree classified a CD4 count ≤200 cells/μL as better than a TLC cut-point of 1200 cells/mL, based on the area under the curve of the receiver-operator characteristic curve (P < 0.0001). When applied to data from the BVAMC and Zambia, the UAB-based decision tree performed better than the TLC cut-point of 1200 cells/mL (BVAMC: P < 0.0001; Zambia: P = 0.0009) but worse than a decision tree based on local data (BVAMC: P ≤ 0.0001; Zambia: P ≤ 0.0001). CONCLUSION: A decision tree algorithm based on local data identifies low CD4 cell counts better than one developed from a different population or a TLC cut-point of 1200 cells/mL. © 2007 Lippincott Williams & Wilkins, Inc.
Authors & Co-Authors
Chen, Ray Y.
United States, Birmingham
The University of Alabama at Birmingham
United States, Birmingham
Birmingham va Medical Center
United States, Bethesda
National Institutes of Health Nih
Westfall, Andrew O.
United States, Birmingham
The University of Alabama at Birmingham
Stringer, Jeffrey S.A.
United States, Birmingham
The University of Alabama at Birmingham
Raper, James Luther
United States, Birmingham
The University of Alabama at Birmingham
Vermund, Sten Havlor
United States, Birmingham
The University of Alabama at Birmingham
United States, Nashville
Vanderbilt University School of Medicine
Gotuzzo, Eduardo H.
Peru, Lima
Hospital Nacional Cayetano Heredia
Allison, Jeroan J.
United States, Birmingham
The University of Alabama at Birmingham
Saag, Michael S.
United States, Birmingham
The University of Alabama at Birmingham
Statistics
Citations: 18
Authors: 8
Affiliations: 6
Identifiers
Doi:
10.1097/QAI.0b013e318032385e
ISSN:
15254135
Research Areas
Infectious Diseases
Study Design
Cross Sectional Study
Study Locations
Zambia