Antiretroviral therapy optimisation without genotype resistance testing: a perspective on treatment history based models.

<h4>Background</h4>Although genotypic resistance testing (GRT) is recommended to guide combination antiretroviral therapy (cART), funding and/or facilities to perform GRT may not be available in low to middle income countries. Since treatment history (TH) impacts response to subsequent t...

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Autores principales:	Mattia C F Prosperi, Michal Rosen-Zvi, André Altmann, Maurizio Zazzi, Simona Di Giambenedetto, Rolf Kaiser, Eugen Schülter, Daniel Struck, Peter Sloot, David A van de Vijver, Anne-Mieke Vandamme, Anders Sönnerborg, EuResist study group, Virolab study group
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/133459cb09394a9d94e80aa53ce3dc4f
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spelling	oai:doaj.org-article:133459cb09394a9d94e80aa53ce3dc4f2021-11-18T07:02:37ZAntiretroviral therapy optimisation without genotype resistance testing: a perspective on treatment history based models.1932-620310.1371/journal.pone.0013753https://doaj.org/article/133459cb09394a9d94e80aa53ce3dc4f2010-10-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21060792/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Although genotypic resistance testing (GRT) is recommended to guide combination antiretroviral therapy (cART), funding and/or facilities to perform GRT may not be available in low to middle income countries. Since treatment history (TH) impacts response to subsequent therapy, we investigated a set of statistical learning models to optimise cART in the absence of GRT information.<h4>Methods and findings</h4>The EuResist database was used to extract 8-week and 24-week treatment change episodes (TCE) with GRT and additional clinical, demographic and TH information. Random Forest (RF) classification was used to predict 8- and 24-week success, defined as undetectable HIV-1 RNA, comparing nested models including (i) GRT+TH and (ii) TH without GRT, using multiple cross-validation and area under the receiver operating characteristic curve (AUC). Virological success was achieved in 68.2% and 68.0% of TCE at 8- and 24-weeks (n = 2,831 and 2,579), respectively. RF (i) and (ii) showed comparable performances, with an average (st.dev.) AUC 0.77 (0.031) vs. 0.757 (0.035) at 8-weeks, 0.834 (0.027) vs. 0.821 (0.025) at 24-weeks. Sensitivity analyses, carried out on a data subset that included antiretroviral regimens commonly used in low to middle income countries, confirmed our findings. Training on subtype B and validation on non-B isolates resulted in a decline of performance for models (i) and (ii).<h4>Conclusions</h4>Treatment history-based RF prediction models are comparable to GRT-based for classification of virological outcome. These results may be relevant for therapy optimisation in areas where availability of GRT is limited. Further investigations are required in order to account for different demographics, subtypes and different therapy switching strategies.Mattia C F ProsperiMichal Rosen-ZviAndré AltmannMaurizio ZazziSimona Di GiambenedettoRolf KaiserEugen SchülterDaniel StruckPeter SlootDavid A van de VijverAnne-Mieke VandammeAnne-Mieke VandammeAnders SönnerborgEuResist study groupVirolab study groupPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 10, p e13753 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mattia C F Prosperi Michal Rosen-Zvi André Altmann Maurizio Zazzi Simona Di Giambenedetto Rolf Kaiser Eugen Schülter Daniel Struck Peter Sloot David A van de Vijver Anne-Mieke Vandamme Anne-Mieke Vandamme Anders Sönnerborg EuResist study group Virolab study group Antiretroviral therapy optimisation without genotype resistance testing: a perspective on treatment history based models.
description	<h4>Background</h4>Although genotypic resistance testing (GRT) is recommended to guide combination antiretroviral therapy (cART), funding and/or facilities to perform GRT may not be available in low to middle income countries. Since treatment history (TH) impacts response to subsequent therapy, we investigated a set of statistical learning models to optimise cART in the absence of GRT information.<h4>Methods and findings</h4>The EuResist database was used to extract 8-week and 24-week treatment change episodes (TCE) with GRT and additional clinical, demographic and TH information. Random Forest (RF) classification was used to predict 8- and 24-week success, defined as undetectable HIV-1 RNA, comparing nested models including (i) GRT+TH and (ii) TH without GRT, using multiple cross-validation and area under the receiver operating characteristic curve (AUC). Virological success was achieved in 68.2% and 68.0% of TCE at 8- and 24-weeks (n = 2,831 and 2,579), respectively. RF (i) and (ii) showed comparable performances, with an average (st.dev.) AUC 0.77 (0.031) vs. 0.757 (0.035) at 8-weeks, 0.834 (0.027) vs. 0.821 (0.025) at 24-weeks. Sensitivity analyses, carried out on a data subset that included antiretroviral regimens commonly used in low to middle income countries, confirmed our findings. Training on subtype B and validation on non-B isolates resulted in a decline of performance for models (i) and (ii).<h4>Conclusions</h4>Treatment history-based RF prediction models are comparable to GRT-based for classification of virological outcome. These results may be relevant for therapy optimisation in areas where availability of GRT is limited. Further investigations are required in order to account for different demographics, subtypes and different therapy switching strategies.
format	article
author	Mattia C F Prosperi Michal Rosen-Zvi André Altmann Maurizio Zazzi Simona Di Giambenedetto Rolf Kaiser Eugen Schülter Daniel Struck Peter Sloot David A van de Vijver Anne-Mieke Vandamme Anne-Mieke Vandamme Anders Sönnerborg EuResist study group Virolab study group
author_facet	Mattia C F Prosperi Michal Rosen-Zvi André Altmann Maurizio Zazzi Simona Di Giambenedetto Rolf Kaiser Eugen Schülter Daniel Struck Peter Sloot David A van de Vijver Anne-Mieke Vandamme Anne-Mieke Vandamme Anders Sönnerborg EuResist study group Virolab study group
author_sort	Mattia C F Prosperi
title	Antiretroviral therapy optimisation without genotype resistance testing: a perspective on treatment history based models.
title_short	Antiretroviral therapy optimisation without genotype resistance testing: a perspective on treatment history based models.
title_full	Antiretroviral therapy optimisation without genotype resistance testing: a perspective on treatment history based models.
title_fullStr	Antiretroviral therapy optimisation without genotype resistance testing: a perspective on treatment history based models.
title_full_unstemmed	Antiretroviral therapy optimisation without genotype resistance testing: a perspective on treatment history based models.
title_sort	antiretroviral therapy optimisation without genotype resistance testing: a perspective on treatment history based models.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/133459cb09394a9d94e80aa53ce3dc4f
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Antiretroviral therapy optimisation without genotype resistance testing: a perspective on treatment history based models.

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