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


RESULTS
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.
Clinic of Infectious Diseases, Catholic University of Sacred Heart, Rome, Italy. ahnven@yahoo.it
Mattia C F
MC
Prosperi
Michal
M
Rosen-Zvi
André
A
Altmann
Maurizio
M
Zazzi
Simona
S
Di Giambenedetto
Rolf
R
Kaiser
Eugen
E
Schülter
Daniel
D
Struck
Peter
P
Sloot
David A
DA
van de Vijver
Anne-Mieke
AM
Vandamme
Anders
A
Sönnerborg
D000328:Adult; D019380:Anti-HIV Agents / Q000627:therapeutic use*; D024882:Drug Resistance, Viral / Q000235:genetics*; D005260:Female; D005838:Genotype; D015658:HIV Infections / Q000188:drug therapy*; D015497:HIV-1 / Q000235:genetics; D006801:Humans; D008297:Male; D008875:Middle Aged; D012367:RNA, Viral / Q000097:blood; D012372:ROC Curve
D019380:Anti-HIV Agents; D012367:RNA, Viral