Revell A D, Boyd M A, Wang D, Emery S, Gazzard B, Reiss P, van Sighem A I, Montaner J S, Lane H C, Larder B A
The HIV Resistance Response Database Initiative (RDI), London, UK.
HIV Med. 2014 Aug;15(7):442-8. doi: 10.1111/hiv.12156. Epub 2014 Apr 15.
We compared the use of computational models developed with and without HIV genotype vs. genotyping itself to predict effective regimens for patients experiencing first-line virological failure.
Two sets of models predicted virological response for 99 three-drug regimens for patients on a failing regimen of two nucleoside/nucleotide reverse transcriptase inhibitors and one nonnucleoside reverse transcriptase inhibitor in the Second-Line study. One set used viral load, CD4 count, genotype, plus treatment history and time to follow-up to make its predictions; the second set did not include genotype. Genotypic sensitivity scores were derived and the ranking of the alternative regimens compared with those of the models. The accuracy of the models and that of genotyping as predictors of the virological responses to second-line regimens were compared.
The rankings of alternative regimens by the two sets of models were significantly correlated in 60-69% of cases, and the rankings by the models that use a genotype and genotyping itself were significantly correlated in 60% of cases. The two sets of models identified alternative regimens that were predicted to be effective in 97% and 100% of cases, respectively. The area under the receiver-operating curve was 0.72 and 0.74 for the two sets of models, respectively, and significantly lower at 0.55 for genotyping.
The two sets of models performed comparably well and significantly outperformed genotyping as predictors of response. The models identified alternative regimens predicted to be effective in almost all cases. It is encouraging that models that do not require a genotype were able to predict responses to common second-line therapies in settings where genotyping is unavailable.
我们比较了使用有和没有HIV基因型的计算模型与基因分型本身来预测一线病毒学失败患者的有效治疗方案。
在二线研究中，两组模型预测了99种三联药物方案对正在接受两种核苷/核苷酸逆转录酶抑制剂和一种非核苷逆转录酶抑制剂失败方案治疗的患者的病毒学反应。一组模型使用病毒载量、CD4细胞计数、基因型、治疗史和随访时间进行预测；第二组模型不包括基因型。得出基因分型敏感性评分，并将替代方案的排名与模型的排名进行比较。比较了模型和基因分型作为二线治疗方案病毒学反应预测指标的准确性。
两组模型对替代方案的排名在60%-69%的病例中显著相关，使用基因型的模型与基因分型本身的排名在60%的病例中显著相关。两组模型分别识别出预计在97%和100%的病例中有效的替代方案。两组模型的受试者工作特征曲线下面积分别为0.72和0.74，而基因分型的该面积显著较低，为0.55。
作为反应预测指标，两组模型表现相当，且显著优于基因分型。模型识别出几乎在所有病例中预计有效的替代方案。令人鼓舞的是，在无法进行基因分型的情况下，不需要基因型的模型能够预测对常见二线治疗的反应。
