The HIV Resistance Response Database Initiative (RDI), London, United Kingdom.
Servicio de Enfermedades Infecciosas, Hospital Ramón y Cajal, Madrid, Spain.
J Acquir Immune Defic Syndr. 2019 Jun 1;81(2):207-215. doi: 10.1097/QAI.0000000000001989.
Definitions of virological response vary from <50 up to 1000 copies of HIV-RNA/mL. Our previous models estimate the probability of HIV drug combinations reducing the viral load to <50 copies/mL, with no indication of whether higher thresholds of response may be achieved. Here, we describe the development of models that predict absolute viral load over time.
Two sets of random forest models were developed using 50,270 treatment change episodes from more than 20 countries. The models estimated viral load at different time points following the introduction of a new regimen from variables including baseline viral load, CD4 count, and treatment history. One set also used genotypes in their predictions. Independent data sets were used for evaluation.
Both models achieved highly significant correlations between predicted and actual viral load changes (r = 0.67-0.68, mean absolute error of 0.73-0.74 log10 copies/mL). The models produced curves of virological response over time. Using failure definitions of <100, 400, or 1000 copies/mL, but not 50 copies/mL, both models were able to identify alternative regimens they predicted to be effective for the majority of cases where the new regimen prescribed in the clinic failed.
These models could be useful for selecting the optimum combination therapy for patients requiring a change in therapy in settings using any definition of virological response. They also give an idea of the likely response curve over time. Given that genotypes are not required, these models could be a useful addition to the HIV-TRePS system for those in resource-limited settings.
病毒学反应的定义从<50 到 1000 个 HIV-RNA/ml 不等。我们之前的模型估计了 HIV 药物组合将病毒载量降低到<50 拷贝/ml 的概率，但没有表明是否可以达到更高的反应阈值。在这里，我们描述了开发能够预测病毒载量随时间变化的模型的过程。
使用来自 20 多个国家的 50270 个治疗变化期，开发了两组随机森林模型。这些模型从基线病毒载量、CD4 计数和治疗史等变量出发，估计了引入新方案后不同时间点的病毒载量。一组模型还在预测中使用了基因型。使用独立的数据集进行评估。
两个模型在预测和实际病毒载量变化之间都达到了高度显著的相关性（r = 0.67-0.68，平均绝对误差为 0.73-0.74 log10 拷贝/ml）。这些模型生成了病毒学反应随时间的变化曲线。使用<100、400 或 1000 拷贝/ml 的失败定义，但不是 50 拷贝/ml 的失败定义，两个模型都能够识别出它们预测为大多数新方案在诊所规定失败的情况下有效的替代方案。
这些模型在使用任何病毒学反应定义的情况下，对需要改变治疗方案的患者选择最佳的联合治疗方案可能有用。它们还提供了一个关于随时间推移可能的反应曲线的概念。由于不需要基因型，这些模型可以成为 HIV-TRePS 系统在资源有限的环境中的有用补充。
