BACKGROUND: HIV-1 genotypic suceptibility 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. METHODS: We analyzed patients of the Italian ARCA cohort starting a new cART from 1999onwards either after virologic failure or as treatment-naive. The time to virologic failure was the endopoint, from the 90th day after tratment 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 dn 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. RESULTS: The dataset included 2337 regimesn from 2182 patients, of which 733 were previously treatment-naive. 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 yelded 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. CONCLUSIONS: GSSs of cART and several other covariates were investigated using linear and non-linear survival analysis. RSF models are a promising approach fo the development af a reliable system that predicts time to virologic failure better than Cox regression. Such models mitht represent a significant improvement over the current methods for monitoring and optimization of cART.
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