Complete blood cell count as a surrogate CD4 cell marker for HIV monitoring in resource-limited settings
Authors
Chen, Ray Y.Westfall, Andrew O.
Hardin, J. Michael
Miller-Hardwick, Cassandra
Stringer, Jeffrey S. A.
Raper, James L
Vermund, Sten H
Gotuzzo, Eduardo
Allison, Jeroan J.
Saag, Michael S.
UMass Chan Affiliations
Department of Quantitative Health SciencesDocument Type
Journal ArticlePublication Date
2007-01-30Keywords
AdolescentAdult
Aged
Algorithms
*Blood Cell Count
*CD4 Lymphocyte Count
Databases, Factual
Decision Trees
Developing Countries
Female
HIV Infections
Hemoglobins
Humans
Male
Middle Aged
Models, Biological
Platelet Count
ROC Curve
Bioinformatics
Biostatistics
Epidemiology
Health Services Research
Metadata
Show full item recordAbstract
BACKGROUND: A total lymphocyte count (TLC) of 1200 cells/mL has been used as a surrogate for a CD4 count of 200 cells/microL 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 < or =200 cells/microL 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 < or = 0.0001; Zambia: P < or = 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.Source
J Acquir Immune Defic Syndr. 2007 Apr 15;44(5):525-30. Link to article on publisher's siteDOI
10.1097/QAI.0b013e318032385ePermanent Link to this Item
http://hdl.handle.net/20.500.14038/47687PubMed ID
17259910Related Resources
Link to Article in PubMedae974a485f413a2113503eed53cd6c53
10.1097/QAI.0b013e318032385e