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Abstract
The World Health Organization (WHO) guidelines for monitoring the effectiveness of human immunodeficiency virus (HIV) treatment in resource-limited settings are mostly based on clinical and immunological markers (e.g., CD4 cell counts). Recent research indicates that the guidelines are inadequate and can result in high error rates. Viral load (VL) is considered the “gold standard,” yet its widespread use is limited by cost and infrastructure. In this article, we propose a diagnostic algorithm that uses information from routinely collected clinical and immunological markers to guide a selective use of VL testing for diagnosing HIV treatment failure, under the assumption that VL testing is available only at a certain portion of patient visits. Our algorithm identifies the patient subpopulation, such that the use of limited VL testing on them minimizes a predefined risk (e.g., misdiagnosis error rate). Diagnostic properties of our proposed algorithm are assessed by simulations. For illustration, data from the Miriam Hospital Immunology Clinic (Providence, RI) are analyzed.
Acknowledgments
This research is funded by a 2009 developmental grant from the Lifespan/Tufts/Brown Center for AIDS Research. The project described is supported by grant number P30AI042853 from the National Institute of Allergy and Infectious Diseases (NIAID). The work of Dr. Kantor is also supported by a grant (number R01AI66922) from the National Institute of Health (NIH). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIAID or NIH. The authors are grateful for the helpful comments from reviewers, the associate editor, and the editor. The authors also thank Ms. Allison K. DeLong for discussions and comments on early versions of the article.
