Antiretroviral Therapy Optimisation without Genotype Resistance Testing: A Perspective on Treatment History Based Models

By Mattia C. F. Prosperi, Michal Rosen-Zvi, André Altmann, Maurizio Zazzi, S. Di Giambenedetto, R. Kaiser, Eugen Schülter, Daniel Struck, Peter M. A. Sloot, D. A. van de Vijver and 2 more. 2010

110 citations recorded by the source corpus.

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.

Subfields: Infectious Diseases, Virology

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In Infectious Diseases

1. Maurizio Zazzi
2. Jasmina Bogojeska

In Virology

1. Finale Doshi-Velez
2. Maurizio Zazzi
3. Jasmina Bogojeska
4. Sonali Parbhoo

Citations in the corpus

• Combining Kernel and Model Based Learning for HIV Therapy Selection (2017, methodology): “[2] use tree-based learning to predict a patient’s virological response using evolutionary information, while others [3, 4] predict virological response based on the patient’s treatment history.”