Koller M., Fatti G., Chi B.H., Keiser O., Hoffmann C.J., Wood R., Prozesky H., Stinson K., Giddy J., Mutevedzi P., Fox M.P., Law M., Boulle A., Egger M.
Institute of Social and Preventive Medicine, University of Bern, Finkenhubelweg 11, Bern, Switzerland; Kheth'Impilo, Cape Town, South Africa; Centre for Infectious Disease Research in Zambia, Lusaka, Zambia; Aurum Institute for Health Research, Johannesburg, South Africa; Gugulethu ART Programme and Desmond Tutu HIV Centre, University of Cape Town, Cape Town, South Africa; Division of Infectious Diseases, Department of Medicine, University of Stellenbosch, Tygerberg Academic Hospital, Cape Town, South Africa; Médecins Sans Frontières, Khayelitsha, Cape Town, South Africa; Sinikithemba Clinic, McCord Hospital, Durban, South Africa; Africa Centre for Health and Population Studies, University of KwaZulu-Natal, Somkhele, South Africa; Health Economics and Epidemiology Research Office, University of the Witwatersrand, Johannesburg, South Africa; Center for Global Health and Development, Department of Epidemiology, Boston University, Boston, MA, United States; Biostatistics and Databases Program, Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia; Centre for Infectious Disease Epidemiology and Research, School of Public Health and Family Medicine, University of Cape Town, South Africa
Koller, M., Institute of Social and Preventive Medicine, University of Bern, Finkenhubelweg 11, Bern, Switzerland; Fatti, G., Kheth'Impilo, Cape Town, South Africa; Chi, B.H., Centre for Infectious Disease Research in Zambia, Lusaka, Zambia; Keiser, O., Institute of Social and Preventive Medicine, University of Bern, Finkenhubelweg 11, Bern, Switzerland; Hoffmann, C.J., Aurum Institute for Health Research, Johannesburg, South Africa; Wood, R., Gugulethu ART Programme and Desmond Tutu HIV Centre, University of Cape Town, Cape Town, South Africa; Prozesky, H., Division of Infectious Diseases, Department of Medicine, University of Stellenbosch, Tygerberg Academic Hospital, Cape Town, South Africa; Stinson, K., Médecins Sans Frontières, Khayelitsha, Cape Town, South Africa, Centre for Infectious Disease Epidemiology and Research, School of Public Health and Family Medicine, University of Cape Town, South Africa; Giddy, J., Sinikithemba Clinic, McCord Hospital, Durban, South Africa; Mutevedzi, P., Africa Centre for Health and Population Studies, University of KwaZulu-Natal, Somkhele, South Africa; Fox, M.P., Health Economics and Epidemiology Research Office, University of the Witwatersrand, Johannesburg, South Africa, Center for Global Health and Development, Department of Epidemiology, Boston University, Boston, MA, United States; Law, M., Biostatistics and Databases Program, Kirby Institute, Faculty of Medicine, University of New South Wales, Sydney, Australia; Boulle, A., Centre for Infectious Disease Epidemiology and Research, School of Public Health and Family Medicine, University of Cape Town, South Africa; Egger, M., Institute of Social and Preventive Medicine, University of Bern, Finkenhubelweg 11, Bern, Switzerland, Centre for Infectious Disease Epidemiology and Research, School of Public Health and Family Medicine, University of Cape Town, South Africa
Background: HIV-1 RNA viral load (VL) testing is recommended to monitor antiretroviral therapy (ART) but not available in many resource-limited settings. We developed and validated CD4-based risk charts to guide targeted VL testing. Methods: We modeled the probability of virologic failure up to 5 years of ART based on current and baseline CD4 counts, developed decision rules for targeted VL testing of 10%, 20%, or 40% of patients in 7 cohorts of patients starting ART in South Africa, and plotted cutoffs for VL testing on colour-coded risk charts. We assessed the accuracy of risk chart-guided VL testing to detect virologic failure in validation cohorts from South Africa, Zambia, and the Asia-Pacific. Results: In total, 31,450 adult patients were included in the derivation and 25,294 patients in the validation cohorts. Positive predictive values increased with the percentage of patients tested: from 79% (10% tested) to 98% (40% tested) in the South African cohort, from 64% to 93% in the Zambian cohort, and from 73% to 96% in the Asia-Pacific cohort. Corresponding increases in sensitivity were from 35% to 68% in South Africa, from 55% to 82% in Zambia, and from 37% to 71% in Asia-Pacific. The area under the receiver operating curve increased from 0.75 to 0.91 in South Africa, from 0.76 to 0.91 in Zambia, and from 0.77 to 0.92 in Asia-Pacific. Conclusions: CD4-based risk charts with optimal cutoffs for targeted VL testing maybe useful to monitor ART in settings where VL capacity is limited. Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.