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首页> 外文期刊>BMC Medical Informatics and Decision Making >A Prognostic Model for Estimating the Time to Virologic Failure in HIV-1 Infected Patients Undergoing a New Combination Antiretroviral Therapy Regimen
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A Prognostic Model for Estimating the Time to Virologic Failure in HIV-1 Infected Patients Undergoing a New Combination Antiretroviral Therapy Regimen

机译：一种评估HIV-1感染患者接受新型抗逆转录病毒疗法联合治疗的病毒学失败时间的预​​后模型

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Background 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. 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 1999 onwards either after virologic failure or as treatment-na?ve. 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. Results The dataset included 2337 regimens from 2182 patients, of which 733 were previously treatment-na?ve. 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 Conclusions GSSs 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.
机译：事实证明，HIV-1基因型易感性评分（GSS）是联合抗逆转录病毒疗法（cART）转换/启动后固定时间点病毒学结局的重要预后因素。但是，它们对病毒学失败时间的相对危害尚未得到彻底研究，并且从未设计出能够预测新的cART方案将维持有效时间的专家系统。方法我们分析了意大利ARCA队列的患者，该患者从1999年起在病毒学衰竭或未接受治疗后开始新的cART。从治疗开始后的第90天起，达到病毒学衰竭的时间为终点，定义为第一个HIV-1 RNA> 400拷贝/ ml，对治疗终止前的最后可用HIV-1 RNA进行检查。我们根据不同的解释系统（Rega，ANRS和HIVdb）和其他协变量，通过Cox回归和随机生存森林（RSF）评估了GSS的相对危害/重要性。预测模型通过自举和c-index度量进行了验证。结果数据集包括来自2182例患者的2337方案，其中733例以前未接受过治疗。我们在2820人年内观察到1067例病毒学故障。多变量分析显示，cART的低GSS与其他几个协变量独立地与病毒学衰竭的危险相关。评估预测性能产生了Cox回归预测病毒学终点的适度能力（c-index≈0.70），而RSF表现出了更好的性能（c-index≈0.73，p）结论研究了cART和其他一些协变量的GSS线性和非线性生存分析：RSF模型是开发可靠系统的有前途的方法，该系统比Cox回归更好地预测了病毒性衰竭的时间，这种模型可能代表了对当前cART监测和优化方法的重大改进。

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