Abstract
BackgroundHIV-1 genotypic susceptibility scores (GSSs) were proven to be significant prognostic factors of fixed time-point virologic outcomes after combination antiretroviral therapy (cART) switch/initiation. However, their relative-hazard for the time to virologic failure has not been thoroughly investigated, and an expert system that is able to predict how long a new cART regimen will remain effective has never been designed.MethodsWe analyzed patients of the Italian ARCA cohort starting a new cART from 1999 onwards either after virologic failure or as treatment-naïve. The time to virologic failure was the endpoint, from the 90th day after treatment start, defined as the first HIV-1 RNA > 400 copies/ml, censoring at last available HIV-1 RNA before treatment discontinuation. We assessed the relative hazard/importance of GSSs according to distinct interpretation systems (Rega, ANRS and HIVdb) and other covariates by means of Cox regression and random survival forests (RSF). Prediction models were validated via the bootstrap and c-index measure.ResultsThe dataset included 2337 regimens from 2182 patients, of which 733 were previously treatment-naïve. We observed 1067 virologic failures over 2820 persons-years. Multivariable analysis revealed that low GSSs of cART were independently associated with the hazard of a virologic failure, along with several other covariates. Evaluation of predictive performance yielded a modest ability of the Cox regression to predict the virologic endpoint (c-index≈0.70), while RSF showed a better performance (c-index≈0.73, p < 0.0001 vs. Cox regression). Variable importance according to RSF was concordant with the Cox hazards.ConclusionsGSSs of cART and several other covariates were investigated using linear and non-linear survival analysis. RSF models are a promising approach for the development of a reliable system that predicts time to virologic failure better than Cox regression. Such models might represent a significant improvement over the current methods for monitoring and optimization of cART.
Highlights
HIV-1 genotypic susceptibility scores (GSSs) were proven to be significant prognostic factors of fixed time-point virologic outcomes after combination antiretroviral therapy switch/initiation
The prevalence of drug resistance seems to be decreasing in recent years [6], despite modern combination antiretroviral therapy (cART) options [7], drug resistance remains a concern in chronically infected patients with a long treatment history and in treatment-naïve patients who have been infected with drug resistant isolates [8]
Genotypic susceptibility scores (GSSs) have been developed for interpreting HIV-1 drug susceptibility based on the sequence of the virus genome coding for drug targets
Summary
HIV-1 genotypic susceptibility scores (GSSs) were proven to be significant prognostic factors of fixed time-point virologic outcomes after combination antiretroviral therapy (cART) switch/initiation. These machine learning methods explore a larger set of variables besides the viral genotype (such as viral load, CD4+ T cell counts, demographic information, treatment history), and include techniques such as artificial neural networks [12], mutagenetic trees [13], Bayesian networks [14], and random forests [15] Both GSSs and machine learning approaches have been proven to usefully predict virologic outcome at fixed time points (e.g. n-weeks) after cART initiation or switch [14,15,16,17,18]. The state-of-the art systems, available as free webservices, are able to select a set of suitable cARTs for a patient, given the patient’s viral genotype and other background information, ensuring the maximal probability of viral load reduction after n-weeks of uninterrupted treatment [19,20]
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