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A Prediction Model of Pretreatment HIV RNA Levels in Naïve Thai HIV-infected Patients: An Application for Resource-limited Settings

BACKGROUND: The use of abacavir (ABC) and rilpivirine (RPV) in the first-line regimen for naïve HIV-infected patients with pretreatment HIV RNA >100,000 copies/mL is not recommended due to a high rate of treatment failure. If a model could accurately predict pretreatment HIV RNA levels, it would...

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Autores principales: Pongpech, Nisha, Avihingsanon, Anchalee, Chaiwarith, Romanee, Kantipong, Pacharee, Boettiger, David, Ross, Jeremy, Kiertiburanakul, Sasisopin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5630810/
http://dx.doi.org/10.1093/ofid/ofx163.1097
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author Pongpech, Nisha
Avihingsanon, Anchalee
Chaiwarith, Romanee
Kantipong, Pacharee
Boettiger, David
Ross, Jeremy
Kiertiburanakul, Sasisopin
author_facet Pongpech, Nisha
Avihingsanon, Anchalee
Chaiwarith, Romanee
Kantipong, Pacharee
Boettiger, David
Ross, Jeremy
Kiertiburanakul, Sasisopin
author_sort Pongpech, Nisha
collection PubMed
description BACKGROUND: The use of abacavir (ABC) and rilpivirine (RPV) in the first-line regimen for naïve HIV-infected patients with pretreatment HIV RNA >100,000 copies/mL is not recommended due to a high rate of treatment failure. If a model could accurately predict pretreatment HIV RNA levels, it would be a useful tool for the selection ABC or RPV in the first-line regimen. METHODS: Thai HIV-infected adults enrolled in the TREAT Asia HIV Observational Database (TAHOD) and additional patients of Ramathibodi Hospital were eligible if they had an HIV RNA result at the time of antiretroviral therapy initiation. Factors associated with pretreatment HIV RNA <100,000 copies/mL were determined by logistic regression. Based on the results of the final model, a prediction model was created. RESULTS: A total of 1,223 patients were included in the analysis. Among those in the derivation data set, median [interquartile range (IQR)] age was 36.3 (30.5–42.9) years, median (IQR) CD4 count was 122 (39–216) cells/mm(3), and pretreatment HIV RNA was 100,000 (32,449–229,777) copies/mL. Factors associated with pretreatment HIV RNA <100,000 copies/mL were anemia [odds ratio (OR) 2.05 vs. no anemia; 95% confidence interval (CI) 1.28–3.27], CD4 count >200 cells/mm(3) (OR 3.00 vs. CD4 count <200 cells/mm(3); 95% CI 2.08–4.33), and non-heterosexual HIV exposure (OR 1.61 vs. heterosexual HIV exposure; 95% CI 1.07–2.43). No AIDS-defining illness (11.5), no anemia (18.5), age <35 years (11), CD4 count >200 cells/mm(3) (27), duration of HIV infection >1 year (9), and weight >50 years (11) were included in the clinical prediction tool scores. A score ≥45 yielded a sensitivity of 45.3%, specificity of 76.7%, positive predictive value of 68.1%, and negative predictive value of 56.1% among patients in the derivation. The area under the receiver-operator characteristic curve was 0.655 (95% CI 0.614- 0.696) and 0.600 (95% CI 0.533–0.667) in the derivation and validation patients, respectively. CONCLUSION: Our final prediction model had poor sensitivity and specificity for predicting HIV RNA <100,000 copies/mL. Further study on a larger population with a greater diversity of data variables available is necessary to improve the model. Pretreatment HIV RNA remains necessary before ABC or RPV initiation for naïve Thai HIV-infected patients. DISCLOSURES: All authors: No reported disclosures.
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spelling pubmed-56308102017-11-07 A Prediction Model of Pretreatment HIV RNA Levels in Naïve Thai HIV-infected Patients: An Application for Resource-limited Settings Pongpech, Nisha Avihingsanon, Anchalee Chaiwarith, Romanee Kantipong, Pacharee Boettiger, David Ross, Jeremy Kiertiburanakul, Sasisopin Open Forum Infect Dis Abstracts BACKGROUND: The use of abacavir (ABC) and rilpivirine (RPV) in the first-line regimen for naïve HIV-infected patients with pretreatment HIV RNA >100,000 copies/mL is not recommended due to a high rate of treatment failure. If a model could accurately predict pretreatment HIV RNA levels, it would be a useful tool for the selection ABC or RPV in the first-line regimen. METHODS: Thai HIV-infected adults enrolled in the TREAT Asia HIV Observational Database (TAHOD) and additional patients of Ramathibodi Hospital were eligible if they had an HIV RNA result at the time of antiretroviral therapy initiation. Factors associated with pretreatment HIV RNA <100,000 copies/mL were determined by logistic regression. Based on the results of the final model, a prediction model was created. RESULTS: A total of 1,223 patients were included in the analysis. Among those in the derivation data set, median [interquartile range (IQR)] age was 36.3 (30.5–42.9) years, median (IQR) CD4 count was 122 (39–216) cells/mm(3), and pretreatment HIV RNA was 100,000 (32,449–229,777) copies/mL. Factors associated with pretreatment HIV RNA <100,000 copies/mL were anemia [odds ratio (OR) 2.05 vs. no anemia; 95% confidence interval (CI) 1.28–3.27], CD4 count >200 cells/mm(3) (OR 3.00 vs. CD4 count <200 cells/mm(3); 95% CI 2.08–4.33), and non-heterosexual HIV exposure (OR 1.61 vs. heterosexual HIV exposure; 95% CI 1.07–2.43). No AIDS-defining illness (11.5), no anemia (18.5), age <35 years (11), CD4 count >200 cells/mm(3) (27), duration of HIV infection >1 year (9), and weight >50 years (11) were included in the clinical prediction tool scores. A score ≥45 yielded a sensitivity of 45.3%, specificity of 76.7%, positive predictive value of 68.1%, and negative predictive value of 56.1% among patients in the derivation. The area under the receiver-operator characteristic curve was 0.655 (95% CI 0.614- 0.696) and 0.600 (95% CI 0.533–0.667) in the derivation and validation patients, respectively. CONCLUSION: Our final prediction model had poor sensitivity and specificity for predicting HIV RNA <100,000 copies/mL. Further study on a larger population with a greater diversity of data variables available is necessary to improve the model. Pretreatment HIV RNA remains necessary before ABC or RPV initiation for naïve Thai HIV-infected patients. DISCLOSURES: All authors: No reported disclosures. Oxford University Press 2017-10-04 /pmc/articles/PMC5630810/ http://dx.doi.org/10.1093/ofid/ofx163.1097 Text en © The Author 2017. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Abstracts
Pongpech, Nisha
Avihingsanon, Anchalee
Chaiwarith, Romanee
Kantipong, Pacharee
Boettiger, David
Ross, Jeremy
Kiertiburanakul, Sasisopin
A Prediction Model of Pretreatment HIV RNA Levels in Naïve Thai HIV-infected Patients: An Application for Resource-limited Settings
title A Prediction Model of Pretreatment HIV RNA Levels in Naïve Thai HIV-infected Patients: An Application for Resource-limited Settings
title_full A Prediction Model of Pretreatment HIV RNA Levels in Naïve Thai HIV-infected Patients: An Application for Resource-limited Settings
title_fullStr A Prediction Model of Pretreatment HIV RNA Levels in Naïve Thai HIV-infected Patients: An Application for Resource-limited Settings
title_full_unstemmed A Prediction Model of Pretreatment HIV RNA Levels in Naïve Thai HIV-infected Patients: An Application for Resource-limited Settings
title_short A Prediction Model of Pretreatment HIV RNA Levels in Naïve Thai HIV-infected Patients: An Application for Resource-limited Settings
title_sort prediction model of pretreatment hiv rna levels in naïve thai hiv-infected patients: an application for resource-limited settings
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5630810/
http://dx.doi.org/10.1093/ofid/ofx163.1097
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