The HR provides an estimate of the instantaneous risk of experiencing a WHO event associated with having a VL at or above the specified cut-point. For example, the HR of 1.61 for the VL cut-point of 400 copies per milliliter indicates that subjects who had a VL measure at or above this cut-point were 61% more likely to experience a WHO event as those whose VL did not reach this level. The risk (HR) of WHO events was higher for the VL cutoffs associated with the lowest AIC scores (ie, best fit to the data): subjects with VL at or above the cut-point of 2600 copies per milliliter had a 2.0-fold higher risk (HR) for WHO events compared with those whose VL was below this level (P = 0.015); subjects with VL at or above the cut-point of 32,000 copies per milliliter had a 2.1 higher risk (HR) for WHO events compared with those whose VL was below this level (P = 0.0058). The HRs for CD4 level, HIV clinical stage, hemoglobin level, body size, and age were very similar when using either VL cutoff of 2600 or 32,000 copies per milliliter in the model (Table 2).
Prior analysis of this large cohort of perinatally HIV-infected Latin American children taking HAART for at least 6 months demonstrated that most recent VL greater than the WHO-defined virological failure threshold of 5000 copies per milliliter —but not above the 6-month virological failure threshold of 400 copies per milliliter used by US pediatric treatment guidelines at the time—was an independent predictor of WHO stage 3 and 4 events, even after adjusting for most recent CD4-defined immunosuppression, hemoglobin level, and other cofactors.6 By repeatedly using the same proportional hazards regression modeling with different VL cutoffs, we were able to demonstrate that 2 distinct VL cutoffs (2600 and 32,000 copies/mL) were the best-fitting models for predicting WHO events among VL cutoffs between 400 and 50,000 copies per milliliter.
It was somewhat expected that this analysis would yield a VL cutoff close to the 5000 copies per milliliter threshold reported in the original analysis. It was surprising, however, that the search for the VL cutoff that added the most independent value for predicting HIV-related clinical events would also yield a second, much higher VL cutoff that had such similar performance characteristics in the model. Furthermore, there was no evidence that the similar performance of these 2 VL cutoffs in the model was due to compensatory differences between the HR for CD4 level, hemoglobin level, age, and other factors included in the model. The VL cutoff of 32,000 copies per milliliter in the present analysis is similar to the 30,000 copies per milliliter threshold used in the Paediatric European Network for Treatment of AIDS 9/Pediatric AIDS Clinical Trials Group 390 (PENPACT-1) study that compared clinical and other outcomes among children randomized to switch to a new HAART regimen at a confirmed VL threshold of 1000 copies per milliliter versus at a threshold of 30,000 copies per milliliter.9 In that PENPACT-1 study, the clinical outcomes for children who switched at 30,000 copies per milliliter were similar to those for children who switched at the lower VL threshold. The finding in the current analysis of a maximum predictive value for clinical events when children had VL >32,000 copies per milliliter would be consistent with PENPACT-1 findings showing no clinical detriment in waiting until VL of 30,000 copies per milliliter to switch therapy in children.
Other factors besides HIV-related clinical events must also be considered when choosing a VL cutoff for treatment failure or as a trigger to switch to a new HAART regimen in children. In the PENPACT-1 study, there was a trend in children on nonnucleoside reverse transcriptase inhibitor–-based therapy who switched therapy after a VL of 30,000 copies per milliliter to accumulate more resistance mutations to the nucleoside reverse transcriptase inhibitors in the regimen. In contrast, there may be fewer second-line antiretroviral drugs tested or in formulations appropriate for children, limiting the feasibility of making a switch to a new HAART regimen at low VL cutoffs. In resource-rich settings where virological monitoring is routine, virological failure is suspected when adults and children who have been on HAART for at least 6 months have repeatedly detectable VL measurements (≥50–200 copies/mL).1,2 Assays that detect such low-level VL are unlikely to become available in low-resource settings. However, quantitative RNA assays from dried blood spots have been developed for use in low-resource settings with detection limits that should allow for detecting children who have VL of 30,000 copies per milliliter or even 2600 copies per milliliter.10 It will now be important to evaluate the clinical predictive value of a broad range of virological cutoffs using data from other cohorts of children on stable HAART to help confirm the best target for virological monitoring in pediatric HIV programs.
The 2011 NISDI PLACES protocol acknowledgment list: the principal investigators, co-principal investigators, study coordinators, data management center representatives, and NICHD staff are as follows: Brazil—Belo Horizonte: Jorge Pinto (principal investigator) and Flávia Faleiro (Universidade Federal de Minas Gerais); Caxias do Sul: Rosa Dea Sperhacke (principal investigator), Nicole Golin (co-principal investigator), and Sílvia Mariani Costamilan (Universidade de Caxias do Sul/Serviço Municipal de Infectologia); Nova Iguacu: Jose Pilotto (principal investigator), and co-principal investigators Beatriz Grinsztejn, Valdilea Veloso, Luis Felipe Moreira, and Ivete Gomes (Hospital Geral Nova de Iguacu—HIV Family Care Clinic); Porto Alegre: Rosa Dea Sperhacke (principal investigator), Breno Riegel Santos (co-principal investigator), and Rita de Cassia Alves Lira (Universidade de Caxias do Sul/Hospital Conceição); Rosa Dea Sperhacke (principal investigator), Mario Ferreira Peixoto (co-principal investigator), and Elizabete Teles (Universidade de Caxias do Sul/Hospital Fêmina); Rosa Dea Sperhacke (principal investigator), Marcelo Goldani (co-principal investigator), Carmem Lúcia Oliveira da Silva, and Margery Bohrer Zanetello (Universidade de Caxias do Sul/Hospital de Clínicas de Porto Alegre); Regis Kreitchmann (principal investigator), Marcelo Comerlato Scotta (co-principal investigator), and Debora Fernandes Coelho (Irmandade da Santa Casa de Misericordia de Porto Alegre); Ribeirão Preto: Marisa M. Mussi-Pinhata (principal investigator), Maria Célia Cervi (co-principal investigator), Márcia L. Isaac, Fernanda Tomé Sturzbecher, and Bento V. Moura Negrini (Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo); Rio de Janeiro: Ricardo Hugo S. Oliveira (principal investigator) and Maria C. Chermont Sapia (Instituto de Puericultura e Pediatria Martagão Gesteira); Esau Custodio Joao (principal investigator), Maria Leticia Cruz (co-principal investigator), Ana Paula Antunes, and Jacqueline Anita de Menezes (Hospital dos Servidores do Estado); São Paulo: Regina Celia de Menezes Succi (principal investigator) and Daisy Maria Machado (Escola Paulista de Medicina—Universidade Federal de São Paulo); Marinella Della Negra (principal investigator), and co-principal investigators Wladimir Queiroz and Yu Ching Lian (Instituto de Infectologia Emilio Ribas). Mexico—Mexico City: Noris Pavía-Ruz (principal investigator), co-principal investigators Dulce Morales-Pérez and Jorge Gamboa-Cardeña (Hospital Infantil de México Federico Gómez). Peru—Lima: Jorge Alarcón Villaverde (principal investigator) (Instituto de Medicina Tropical “Daniel Alcides Carrión”—Sección de Epidemiologia, Universidad Nacional Mayor de San Marcos), María Castillo Díaz (co-principal investigator) (Instituto Nacional de Salud del Niño), and Mary Felissa Reyes Vega (Instituto de Medicina Tropical “Daniel Alcides Carrión”—Sección de Epidemiologia, Universidad Nacional Mayor de San Marcos). Data Management and Statistical Center: Yolanda Bertucci, Laura Freimanis Hance, René Gonin, D. Robert Harris, Roslyn Hennessey, Margot Krauss, James Korelitz, Kathryn Miller, Sharon Sothern de Sanchez, Sonia K. Stoszek (Westat, Rockville, MD); NICHD: Rohan Hazra (principal investigator), Lynne M. Mofenson, George K. Siberry (Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland).
3. Shearer WT, Quinn TC, LaRussa P, et al.. Viral load and disease progression in infants infected with human immunodeficiency virus type 1. Women and Infants Transmission Study Group. N Engl J Med. 1997;336:1337–1342.
4. Palumbo PE, Raskino C, Fiscus S, et al.. Predictive value of quantitative plasma HIV RNA and CD4+ lymphocyte count in HIV-infected infants and children. JAMA. 1998;279:756–761.
5. Mofenson LM, Korelitz J, Meyer WA III, et al.. The relationship between serum human immunodeficiency virus type 1 (HIV-1) RNA level, CD4 lymphocyte percent, and long-term mortality risk in HIV-1-infected children. National Institute of Child Health and Human Development Intravenous Immunoglobulin Clinical Trial Study Group. J Infect Dis. 1997;175:1029–1038.
6. Oliveira R, Krauss M, Essama-Bibi S, et al.. NISDI Pediatric Study Group 2010. Viral load predicts new world health organization stage 3 and 4 events in HIV-infected children receiving highly active antiretroviral therapy, independent of CD4 T lymphocyte value. Clin Infect Dis. 2010;51:1325–1333.
8. Akaike H. Likelihood of a model and information criteria. J Econom. 1981;16:3–14.
9. PENPACT-1 (PENTA 9/PACTG 390) Study TeamBabiker A, Castro nee Green H, Compagnucci A, et al.. First-line antiretroviral therapy with a protease inhibitor versus non-nucleoside reverse transcriptase inhibitor and switch at higher versus low viral load in HIV-infected children: an open-label, randomised phase 2/3 trial. Lancet Infect Dis. 2011;11:273–283.
10. Johannessen A, Garrido C, Zahonero N, et al.. Dried blood spots perform well in viral load monitoring
of patients who receive antiretroviral treatment in rural Tanzania. Clin Infect Dis. 2009;49:976–981.
Keywords:© 2012 Lippincott Williams & Wilkins, Inc.
pediatric HIV infection; viral load monitoring; viral load threshold; Latin America