Universität zu Köln

Prosperi, Mattia C. F., Rosen-Zvi, Michal, Altmann, Andre ORCID: 0000-0002-9265-2393, Zazzi, Maurizio ORCID: 0000-0002-0344-6281, Di Giambenedetto, Simona, Kaiser, Rolf, Schuelter, Eugen, Struck, Daniel, Sloot, Peter ORCID: 0000-0002-3848-5395, van de Vijver, David A., Vandamme, Anne-Mieke ORCID: 0000-0002-6594-2766 and Sonnerborg, Anders ORCID: 0000-0001-8928-3374 (2010). Antiretroviral Therapy Optimisation without Genotype Resistance Testing: A Perspective on Treatment History Based Models. PLoS One, 5 (10). SAN FRANCISCO: PUBLIC LIBRARY SCIENCE. ISSN 1932-6203

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Abstract

Background: 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. Methods and Findings: 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). Conclusions: 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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Prosperi, Mattia C. F. UNSPECIFIED UNSPECIFIED UNSPECIFIED Rosen-Zvi, Michal UNSPECIFIED UNSPECIFIED UNSPECIFIED Altmann, Andre UNSPECIFIED orcid.org/0000-0002-9265-2393 UNSPECIFIED Zazzi, Maurizio UNSPECIFIED orcid.org/0000-0002-0344-6281 UNSPECIFIED Di Giambenedetto, Simona UNSPECIFIED UNSPECIFIED UNSPECIFIED Kaiser, Rolf UNSPECIFIED UNSPECIFIED UNSPECIFIED Schuelter, Eugen UNSPECIFIED UNSPECIFIED UNSPECIFIED Struck, Daniel UNSPECIFIED UNSPECIFIED UNSPECIFIED Sloot, Peter UNSPECIFIED orcid.org/0000-0002-3848-5395 UNSPECIFIED van de Vijver, David A. UNSPECIFIED UNSPECIFIED UNSPECIFIED Vandamme, Anne-Mieke UNSPECIFIED orcid.org/0000-0002-6594-2766 UNSPECIFIED Sonnerborg, Anders UNSPECIFIED orcid.org/0000-0001-8928-3374 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-494074
DOI:	10.1371/journal.pone.0013753
Journal or Publication Title:	PLoS One
Volume:	5
Number:	10
Date:	2010
Publisher:	PUBLIC LIBRARY SCIENCE
Place of Publication:	SAN FRANCISCO
ISSN:	1932-6203
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language HIV-1 DRUG-RESISTANCE; FOLLOW-UP; VALIDATION; PREDICTION; MANAGEMENT; SYSTEMS; ASSAY Multiple languages Multidisciplinary Sciences Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/49407