This Article Full Text Full Text (PDF) Supplemental material Other Versions of this Article: JCM.00787-07v1 46/5/1785    most recent Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Reprints and Permissions Copyright Information Books from ASM Press MicrobeWorld Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Westreich, D. J. Articles by Pilcher, C. D. Search for Related Content PubMed PubMed Citation Articles by Westreich, D. J. Articles by Pilcher, C. D.

 Previous Article  |  Next Article 

Journal of Clinical Microbiology, May 2008, p. 1785-1792, Vol. 46, No. 5
0095-1137/08/$08.00+0     doi:10.1128/JCM.00787-07
Copyright © 2008, American Society for Microbiology. All Rights Reserved.

Optimizing Screening for Acute Human Immunodeficiency Virus Infection with Pooled Nucleic Acid Amplification Tests ^,

Department of Epidemiology,¹ Department of Biostatistics,² Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599,³ Department of Medicine, University of California at San Francisco School of Medicine, San Francisco, California 94110⁴

Received 12 April 2007/ Returned for modification 9 January 2008/ Accepted 27 February 2008

Recent studies have shown the public health importance of identifying individuals with acute human immunodeficiency virus infection (AHI); however, the cost of nucleic acid amplification testing (NAAT) makes individual testing of at-risk individuals prohibitively expensive in many settings. Pooled NAAT (or group testing) can improve efficiency and test performance of testing for AHI, but optimizing the pooling algorithm can be difficult. We developed simple, flexible biostatistical models of specimen pooling with NAAT for the identification of AHI cases; these models incorporate group testing theory, operating characteristics of biological assays, and a model of viral dynamics during AHI. Pooling algorithm sensitivity, efficiency (test kits used per individual specimen evaluated), and positive predictive value (PPV) were modeled and compared for three simple pooling algorithms: two-stage minipools (D2), three-stage hierarchical pools (D3), and square arrays with master pools (A2m). We confirmed the results by stochastic simulation and produced reference tables and a Web calculator to facilitate pooling by investigators without specific biostatistical expertise. All three pooling strategies demonstrated improved efficiency and PPV for AHI case detection compared to individual NAAT. D3 and A2m algorithms generally provided better efficiency and PPV than D2; additionally, A2m generally exhibited better PPV than D3. Used selectively and carefully, the simple models developed here can guide the selection of a pooling algorithm for the detection of AHI cases in a wide variety of settings.

Published ahead of print on 19 March 2008.

Supplemental material for this article may be found at http://jcm.asm.org/.