Factors associated with pre-treatment HIV RNA: Application for the use of abacavir and rilpivirine as the first-line regimen for HIV-infected patients in resource-limited settings

Sasisopin Kiertiburanakul; David Boettiger; Oon Tek Ng; Nguyen Kinh; Tuti Parwati Merati; Anchalee Avihingsanon; Wing Wai Wong; Man Po Lee; Romanee Chaiwarith; Adeeba Kamarulzaman; Pacharee Kantipong; Fujie J. Zhang; Jun Yong Choi; Nagalingeswaran Kumarasamy; Rossana Ditangco; Do Duy Cuong; Shinichi Oka; Benedict Lim Heng Sim; Winai Ratanasuwan; Penh Sun Ly; Evy Yunihastuti; Sanjay Pujari; Jeremy L. Ross; Matthew Law; Somnuek Sungkanuparph; V. Khol; H. X. Zhao; N. Han; P. C.K. Li; W. Lam; Y. T. Chan; S. Saghayam; C. Ezhilarasi; K. Joshi; S. Gaikwad; A. Chitalikar; D. N. Wirawan; F. Yuliana; D. Imran; A. Widhani; J. Tanuma; T. Nishijima; S. Na; J. M. Kim; Y. M. Gani; R. David; S. F. Syed Omar; S. Ponnampalavanar; I. Azwa; E. Uy; R. Bantique; W. W. Ku; P. C. Wu; P. L. Lim; L. S. Lee; P. S. Ohnmar; S. Gatechompol; P. Phanuphak; C. Phadungphon; L. Chumla; N. Sanmeema; T. Sirisanthana; W. Kotarathititum; J. Praparattanapan; P. Kambua; R. Sriondee; K. V. Nguyen; H. V. Bui; D. T.H. Nguyen; N. V. An; N. T. Luan; A. H. Sohn; B. Petersen; D. A. Cooper; A. Jiamsakul

doi:10.1186/s12981-017-0151-1

Factors associated with pre-treatment HIV RNA: Application for the use of abacavir and rilpivirine as the first-line regimen for HIV-infected patients in resource-limited settings

Sasisopin Kiertiburanakul, David Boettiger, Oon Tek Ng, Nguyen Kinh, Tuti Parwati Merati, Anchalee Avihingsanon, Wing Wai Wong, Man Po Lee, Romanee Chaiwarith, Adeeba Kamarulzaman, Pacharee Kantipong, Fujie J. Zhang, Jun Yong Choi, Nagalingeswaran Kumarasamy, Rossana Ditangco, Do Duy Cuong, Shinichi Oka, Benedict Lim Heng Sim, Winai Ratanasuwan, Penh Sun LyEvy Yunihastuti, Sanjay Pujari, Jeremy L. Ross, Matthew Law, Somnuek Sungkanuparph, V. Khol, H. X. Zhao, N. Han, P. C.K. Li, W. Lam, Y. T. Chan, S. Saghayam, C. Ezhilarasi, K. Joshi, S. Gaikwad, A. Chitalikar, D. N. Wirawan, F. Yuliana, D. Imran, A. Widhani, J. Tanuma, T. Nishijima, S. Na, J. M. Kim, Y. M. Gani, R. David, S. F. Syed Omar, S. Ponnampalavanar, I. Azwa, E. Uy, R. Bantique, W. W. Ku, P. C. Wu, P. L. Lim, L. S. Lee, P. S. Ohnmar, S. Gatechompol, P. Phanuphak, C. Phadungphon, L. Chumla, N. Sanmeema, T. Sirisanthana, W. Kotarathititum, J. Praparattanapan, P. Kambua, R. Sriondee, K. V. Nguyen, H. V. Bui, D. T.H. Nguyen, N. V. An, N. T. Luan, A. H. Sohn, B. Petersen, D. A. Cooper, A. Jiamsakul

Department of Internal Medicine

Research output: Contribution to journal › Article › peer-review

Abstract

Background: Abacavir and rilpivirine are alternative antiretroviral drugs for treatment-naïve HIV-infected patients. However, both drugs are only recommended for the patients who have pre-treatment HIV RNA <100,000 copies/mL. In resource-limited settings, pre-treatment HIV RNA is not routinely performed and not widely available. The aims of this study are to determine factors associated with pre-treatment HIV RNA <100,000 copies/mL and to construct a model to predict this outcome. Methods: HIV-infected adults enrolled in the TREAT Asia HIV Observational Database were eligible if they had an HIV RNA measurement documented at the time of ART initiation. The dataset was randomly split into a derivation data set (75% of patients) and a validation data set (25%). Factors associated with pre-treatment HIV RNA <100,000 copies/mL were evaluated by logistic regression adjusted for study site. A prediction model and prediction scores were created. Results: A total of 2592 patients were enrolled for the analysis. Median [interquartile range (IQR)] age was 35.8 (29.9-42.5) years; CD4 count was 147 (50-248) cells/mm³; and pre-treatment HIV RNA was 100,000 (34,045-301,075) copies/mL. Factors associated with pre-treatment HIV RNA <100,000 copies/mL were age <30 years [OR 1.40 vs. 41-50 years; 95% confidence interval (CI) 1.10-1.80, p = 0.01], body mass index >30 kg/m² (OR 2.4 vs. <18.5 kg/m²; 95% CI 1.1-5.1, p = 0.02), anemia (OR 1.70; 95% CI 1.40-2.10, p < 0.01), CD4 count >350 cells/mm³ (OR 3.9 vs. <100 cells/mm³; 95% CI 2.0-4.1, p < 0.01), total lymphocyte count >2000 cells/mm³ (OR 1.7 vs. <1000 cells/mm³; 95% CI 1.3-2.3, p < 0.01), and no prior AIDS-defining illness (OR 1.8; 95% CI 1.5-2.3, p < 0.01). Receiver-operator characteristic (ROC) analysis yielded area under the curve of 0.70 (95% CI 0.67-0.72) among derivation patients and 0.69 (95% CI 0.65-0.74) among validation patients. A cut off score >25 yielded the sensitivity of 46.7%, specificity of 79.1%, positive predictive value of 67.7%, and negative predictive value of 61.2% for prediction of pre-treatment HIV RNA <100,000 copies/mL among derivation patients. Conclusion: A model prediction for pre-treatment HIV RNA <100,000 copies/mL produced an area under the ROC curve of 0.70. A larger sample size for prediction model development as well as for model validation is warranted.

Original language	English
Article number	27
Journal	AIDS Research and Therapy
Volume	14
Issue number	1
DOIs	https://doi.org/10.1186/s12981-017-0151-1
Publication status	Published - 2017 May 5

Bibliographical note

Funding Information:
The TREAT Asia HIV Observational Database is an initiative of TREAT Asia, a program of amfAR, The Foundation for AIDS Research, with support from the US National Institutes of Health’s National Institute of Allergy and Infectious Diseases, the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Cancer Institute, the National Institute of Mental Health, and the National Institute on Drug Abuse, as part of the Inter‑ national Epidemiologic Databases to Evaluate AIDS (IeDEA; U01AI069907). The Kirby Institute is funded by the Australian Government Department of Health and Ageing, and is affiliated with the Faculty of Medicine, UNSW Australia. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of any of the governments or institutions mentioned above.

Publisher Copyright:
© 2017 The Author(s).

All Science Journal Classification (ASJC) codes

Molecular Medicine
Virology
Pharmacology (medical)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1186/s12981-017-0151-1

Cite this

Kiertiburanakul, S., Boettiger, D., Ng, O. T., Kinh, N., Merati, T. P., Avihingsanon, A., Wong, W. W., Lee, M. P., Chaiwarith, R., Kamarulzaman, A., Kantipong, P., Zhang, F. J., Choi, J. Y., Kumarasamy, N., Ditangco, R., Cuong, D. D., Oka, S., Sim, B. L. H., Ratanasuwan, W., ... Jiamsakul, A. (2017). Factors associated with pre-treatment HIV RNA: Application for the use of abacavir and rilpivirine as the first-line regimen for HIV-infected patients in resource-limited settings. AIDS Research and Therapy, 14(1), [27]. https://doi.org/10.1186/s12981-017-0151-1

@article{42c553d237c347b0b965149a454aa9b0,

title = "Factors associated with pre-treatment HIV RNA: Application for the use of abacavir and rilpivirine as the first-line regimen for HIV-infected patients in resource-limited settings",

abstract = "Background: Abacavir and rilpivirine are alternative antiretroviral drugs for treatment-na{\"i}ve HIV-infected patients. However, both drugs are only recommended for the patients who have pre-treatment HIV RNA <100,000 copies/mL. In resource-limited settings, pre-treatment HIV RNA is not routinely performed and not widely available. The aims of this study are to determine factors associated with pre-treatment HIV RNA <100,000 copies/mL and to construct a model to predict this outcome. Methods: HIV-infected adults enrolled in the TREAT Asia HIV Observational Database were eligible if they had an HIV RNA measurement documented at the time of ART initiation. The dataset was randomly split into a derivation data set (75% of patients) and a validation data set (25%). Factors associated with pre-treatment HIV RNA <100,000 copies/mL were evaluated by logistic regression adjusted for study site. A prediction model and prediction scores were created. Results: A total of 2592 patients were enrolled for the analysis. Median [interquartile range (IQR)] age was 35.8 (29.9-42.5) years; CD4 count was 147 (50-248) cells/mm3; and pre-treatment HIV RNA was 100,000 (34,045-301,075) copies/mL. Factors associated with pre-treatment HIV RNA <100,000 copies/mL were age <30 years [OR 1.40 vs. 41-50 years; 95% confidence interval (CI) 1.10-1.80, p = 0.01], body mass index >30 kg/m2 (OR 2.4 vs. <18.5 kg/m2; 95% CI 1.1-5.1, p = 0.02), anemia (OR 1.70; 95% CI 1.40-2.10, p < 0.01), CD4 count >350 cells/mm3 (OR 3.9 vs. <100 cells/mm3; 95% CI 2.0-4.1, p < 0.01), total lymphocyte count >2000 cells/mm3 (OR 1.7 vs. <1000 cells/mm3; 95% CI 1.3-2.3, p < 0.01), and no prior AIDS-defining illness (OR 1.8; 95% CI 1.5-2.3, p < 0.01). Receiver-operator characteristic (ROC) analysis yielded area under the curve of 0.70 (95% CI 0.67-0.72) among derivation patients and 0.69 (95% CI 0.65-0.74) among validation patients. A cut off score >25 yielded the sensitivity of 46.7%, specificity of 79.1%, positive predictive value of 67.7%, and negative predictive value of 61.2% for prediction of pre-treatment HIV RNA <100,000 copies/mL among derivation patients. Conclusion: A model prediction for pre-treatment HIV RNA <100,000 copies/mL produced an area under the ROC curve of 0.70. A larger sample size for prediction model development as well as for model validation is warranted.",

author = "Sasisopin Kiertiburanakul and David Boettiger and Ng, {Oon Tek} and Nguyen Kinh and Merati, {Tuti Parwati} and Anchalee Avihingsanon and Wong, {Wing Wai} and Lee, {Man Po} and Romanee Chaiwarith and Adeeba Kamarulzaman and Pacharee Kantipong and Zhang, {Fujie J.} and Choi, {Jun Yong} and Nagalingeswaran Kumarasamy and Rossana Ditangco and Cuong, {Do Duy} and Shinichi Oka and Sim, {Benedict Lim Heng} and Winai Ratanasuwan and Ly, {Penh Sun} and Evy Yunihastuti and Sanjay Pujari and Ross, {Jeremy L.} and Matthew Law and Somnuek Sungkanuparph and V. Khol and Zhao, {H. X.} and N. Han and Li, {P. C.K.} and W. Lam and Chan, {Y. T.} and S. Saghayam and C. Ezhilarasi and K. Joshi and S. Gaikwad and A. Chitalikar and Wirawan, {D. N.} and F. Yuliana and D. Imran and A. Widhani and J. Tanuma and T. Nishijima and S. Na and Kim, {J. M.} and Gani, {Y. M.} and R. David and {Syed Omar}, {S. F.} and S. Ponnampalavanar and I. Azwa and E. Uy and R. Bantique and Ku, {W. W.} and Wu, {P. C.} and Lim, {P. L.} and Lee, {L. S.} and Ohnmar, {P. S.} and S. Gatechompol and P. Phanuphak and C. Phadungphon and L. Chumla and N. Sanmeema and T. Sirisanthana and W. Kotarathititum and J. Praparattanapan and P. Kambua and R. Sriondee and Nguyen, {K. V.} and Bui, {H. V.} and Nguyen, {D. T.H.} and An, {N. V.} and Luan, {N. T.} and Sohn, {A. H.} and B. Petersen and Cooper, {D. A.} and A. Jiamsakul",

note = "Funding Information: The TREAT Asia HIV Observational Database is an initiative of TREAT Asia, a program of amfAR, The Foundation for AIDS Research, with support from the US National Institutes of Health{\textquoteright}s National Institute of Allergy and Infectious Diseases, the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Cancer Institute, the National Institute of Mental Health, and the National Institute on Drug Abuse, as part of the Inter‑ national Epidemiologic Databases to Evaluate AIDS (IeDEA; U01AI069907). The Kirby Institute is funded by the Australian Government Department of Health and Ageing, and is affiliated with the Faculty of Medicine, UNSW Australia. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of any of the governments or institutions mentioned above. Publisher Copyright: {\textcopyright} 2017 The Author(s).",

year = "2017",

month = may,

day = "5",

doi = "10.1186/s12981-017-0151-1",

language = "English",

volume = "14",

journal = "AIDS Research and Therapy",

issn = "1742-6405",

publisher = "BioMed Central",

number = "1",

}

Kiertiburanakul, S, Boettiger, D, Ng, OT, Kinh, N, Merati, TP, Avihingsanon, A, Wong, WW, Lee, MP, Chaiwarith, R, Kamarulzaman, A, Kantipong, P, Zhang, FJ, Choi, JY, Kumarasamy, N, Ditangco, R, Cuong, DD, Oka, S, Sim, BLH, Ratanasuwan, W, Ly, PS, Yunihastuti, E, Pujari, S, Ross, JL, Law, M, Sungkanuparph, S, Khol, V, Zhao, HX, Han, N, Li, PCK, Lam, W, Chan, YT, Saghayam, S, Ezhilarasi, C, Joshi, K, Gaikwad, S, Chitalikar, A, Wirawan, DN, Yuliana, F, Imran, D, Widhani, A, Tanuma, J, Nishijima, T, Na, S, Kim, JM, Gani, YM, David, R, Syed Omar, SF, Ponnampalavanar, S, Azwa, I, Uy, E, Bantique, R, Ku, WW, Wu, PC, Lim, PL, Lee, LS, Ohnmar, PS, Gatechompol, S, Phanuphak, P, Phadungphon, C, Chumla, L, Sanmeema, N, Sirisanthana, T, Kotarathititum, W, Praparattanapan, J, Kambua, P, Sriondee, R, Nguyen, KV, Bui, HV, Nguyen, DTH, An, NV, Luan, NT, Sohn, AH, Petersen, B, Cooper, DA & Jiamsakul, A 2017, 'Factors associated with pre-treatment HIV RNA: Application for the use of abacavir and rilpivirine as the first-line regimen for HIV-infected patients in resource-limited settings', AIDS Research and Therapy, vol. 14, no. 1, 27. https://doi.org/10.1186/s12981-017-0151-1

Factors associated with pre-treatment HIV RNA: Application for the use of abacavir and rilpivirine as the first-line regimen for HIV-infected patients in resource-limited settings. / Kiertiburanakul, Sasisopin; Boettiger, David; Ng, Oon Tek et al.
In: AIDS Research and Therapy, Vol. 14, No. 1, 27, 05.05.2017.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Factors associated with pre-treatment HIV RNA

T2 - Application for the use of abacavir and rilpivirine as the first-line regimen for HIV-infected patients in resource-limited settings

AU - Kiertiburanakul, Sasisopin

AU - Boettiger, David

AU - Ng, Oon Tek

AU - Kinh, Nguyen

AU - Merati, Tuti Parwati

AU - Avihingsanon, Anchalee

AU - Wong, Wing Wai

AU - Lee, Man Po

AU - Chaiwarith, Romanee

AU - Kamarulzaman, Adeeba

AU - Kantipong, Pacharee

AU - Zhang, Fujie J.

AU - Choi, Jun Yong

AU - Kumarasamy, Nagalingeswaran

AU - Ditangco, Rossana

AU - Cuong, Do Duy

AU - Oka, Shinichi

AU - Sim, Benedict Lim Heng

AU - Ratanasuwan, Winai

AU - Ly, Penh Sun

AU - Yunihastuti, Evy

AU - Pujari, Sanjay

AU - Ross, Jeremy L.

AU - Law, Matthew

AU - Sungkanuparph, Somnuek

AU - Khol, V.

AU - Zhao, H. X.

AU - Han, N.

AU - Li, P. C.K.

AU - Lam, W.

AU - Chan, Y. T.

AU - Saghayam, S.

AU - Ezhilarasi, C.

AU - Joshi, K.

AU - Gaikwad, S.

AU - Chitalikar, A.

AU - Wirawan, D. N.

AU - Yuliana, F.

AU - Imran, D.

AU - Widhani, A.

AU - Tanuma, J.

AU - Nishijima, T.

AU - Na, S.

AU - Kim, J. M.

AU - Gani, Y. M.

AU - David, R.

AU - Syed Omar, S. F.

AU - Ponnampalavanar, S.

AU - Azwa, I.

AU - Uy, E.

AU - Bantique, R.

AU - Ku, W. W.

AU - Wu, P. C.

AU - Lim, P. L.

AU - Lee, L. S.

AU - Ohnmar, P. S.

AU - Gatechompol, S.

AU - Phanuphak, P.

AU - Phadungphon, C.

AU - Chumla, L.

AU - Sanmeema, N.

AU - Sirisanthana, T.

AU - Kotarathititum, W.

AU - Praparattanapan, J.

AU - Kambua, P.

AU - Sriondee, R.

AU - Nguyen, K. V.

AU - Bui, H. V.

AU - Nguyen, D. T.H.

AU - An, N. V.

AU - Luan, N. T.

AU - Sohn, A. H.

AU - Petersen, B.

AU - Cooper, D. A.

AU - Jiamsakul, A.

N1 - Funding Information: The TREAT Asia HIV Observational Database is an initiative of TREAT Asia, a program of amfAR, The Foundation for AIDS Research, with support from the US National Institutes of Health’s National Institute of Allergy and Infectious Diseases, the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Cancer Institute, the National Institute of Mental Health, and the National Institute on Drug Abuse, as part of the Inter‑ national Epidemiologic Databases to Evaluate AIDS (IeDEA; U01AI069907). The Kirby Institute is funded by the Australian Government Department of Health and Ageing, and is affiliated with the Faculty of Medicine, UNSW Australia. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of any of the governments or institutions mentioned above. Publisher Copyright: © 2017 The Author(s).

PY - 2017/5/5

Y1 - 2017/5/5

N2 - Background: Abacavir and rilpivirine are alternative antiretroviral drugs for treatment-naïve HIV-infected patients. However, both drugs are only recommended for the patients who have pre-treatment HIV RNA <100,000 copies/mL. In resource-limited settings, pre-treatment HIV RNA is not routinely performed and not widely available. The aims of this study are to determine factors associated with pre-treatment HIV RNA <100,000 copies/mL and to construct a model to predict this outcome. Methods: HIV-infected adults enrolled in the TREAT Asia HIV Observational Database were eligible if they had an HIV RNA measurement documented at the time of ART initiation. The dataset was randomly split into a derivation data set (75% of patients) and a validation data set (25%). Factors associated with pre-treatment HIV RNA <100,000 copies/mL were evaluated by logistic regression adjusted for study site. A prediction model and prediction scores were created. Results: A total of 2592 patients were enrolled for the analysis. Median [interquartile range (IQR)] age was 35.8 (29.9-42.5) years; CD4 count was 147 (50-248) cells/mm3; and pre-treatment HIV RNA was 100,000 (34,045-301,075) copies/mL. Factors associated with pre-treatment HIV RNA <100,000 copies/mL were age <30 years [OR 1.40 vs. 41-50 years; 95% confidence interval (CI) 1.10-1.80, p = 0.01], body mass index >30 kg/m2 (OR 2.4 vs. <18.5 kg/m2; 95% CI 1.1-5.1, p = 0.02), anemia (OR 1.70; 95% CI 1.40-2.10, p < 0.01), CD4 count >350 cells/mm3 (OR 3.9 vs. <100 cells/mm3; 95% CI 2.0-4.1, p < 0.01), total lymphocyte count >2000 cells/mm3 (OR 1.7 vs. <1000 cells/mm3; 95% CI 1.3-2.3, p < 0.01), and no prior AIDS-defining illness (OR 1.8; 95% CI 1.5-2.3, p < 0.01). Receiver-operator characteristic (ROC) analysis yielded area under the curve of 0.70 (95% CI 0.67-0.72) among derivation patients and 0.69 (95% CI 0.65-0.74) among validation patients. A cut off score >25 yielded the sensitivity of 46.7%, specificity of 79.1%, positive predictive value of 67.7%, and negative predictive value of 61.2% for prediction of pre-treatment HIV RNA <100,000 copies/mL among derivation patients. Conclusion: A model prediction for pre-treatment HIV RNA <100,000 copies/mL produced an area under the ROC curve of 0.70. A larger sample size for prediction model development as well as for model validation is warranted.

AB - Background: Abacavir and rilpivirine are alternative antiretroviral drugs for treatment-naïve HIV-infected patients. However, both drugs are only recommended for the patients who have pre-treatment HIV RNA <100,000 copies/mL. In resource-limited settings, pre-treatment HIV RNA is not routinely performed and not widely available. The aims of this study are to determine factors associated with pre-treatment HIV RNA <100,000 copies/mL and to construct a model to predict this outcome. Methods: HIV-infected adults enrolled in the TREAT Asia HIV Observational Database were eligible if they had an HIV RNA measurement documented at the time of ART initiation. The dataset was randomly split into a derivation data set (75% of patients) and a validation data set (25%). Factors associated with pre-treatment HIV RNA <100,000 copies/mL were evaluated by logistic regression adjusted for study site. A prediction model and prediction scores were created. Results: A total of 2592 patients were enrolled for the analysis. Median [interquartile range (IQR)] age was 35.8 (29.9-42.5) years; CD4 count was 147 (50-248) cells/mm3; and pre-treatment HIV RNA was 100,000 (34,045-301,075) copies/mL. Factors associated with pre-treatment HIV RNA <100,000 copies/mL were age <30 years [OR 1.40 vs. 41-50 years; 95% confidence interval (CI) 1.10-1.80, p = 0.01], body mass index >30 kg/m2 (OR 2.4 vs. <18.5 kg/m2; 95% CI 1.1-5.1, p = 0.02), anemia (OR 1.70; 95% CI 1.40-2.10, p < 0.01), CD4 count >350 cells/mm3 (OR 3.9 vs. <100 cells/mm3; 95% CI 2.0-4.1, p < 0.01), total lymphocyte count >2000 cells/mm3 (OR 1.7 vs. <1000 cells/mm3; 95% CI 1.3-2.3, p < 0.01), and no prior AIDS-defining illness (OR 1.8; 95% CI 1.5-2.3, p < 0.01). Receiver-operator characteristic (ROC) analysis yielded area under the curve of 0.70 (95% CI 0.67-0.72) among derivation patients and 0.69 (95% CI 0.65-0.74) among validation patients. A cut off score >25 yielded the sensitivity of 46.7%, specificity of 79.1%, positive predictive value of 67.7%, and negative predictive value of 61.2% for prediction of pre-treatment HIV RNA <100,000 copies/mL among derivation patients. Conclusion: A model prediction for pre-treatment HIV RNA <100,000 copies/mL produced an area under the ROC curve of 0.70. A larger sample size for prediction model development as well as for model validation is warranted.

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UR - http://www.scopus.com/inward/citedby.url?scp=85018734109&partnerID=8YFLogxK

U2 - 10.1186/s12981-017-0151-1

DO - 10.1186/s12981-017-0151-1

M3 - Article

C2 - 28484509

AN - SCOPUS:85018734109

SN - 1742-6405

VL - 14

JO - AIDS Research and Therapy

JF - AIDS Research and Therapy

IS - 1

M1 - 27

ER -

Factors associated with pre-treatment HIV RNA: Application for the use of abacavir and rilpivirine as the first-line regimen for HIV-infected patients in resource-limited settings

Abstract

Bibliographical note

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UN SDGs

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