Frontier Science Foundation, Amherst, NY.
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.
J Acquir Immune Defic Syndr. 2018 Sep 1;79(1):e21-e29. doi: 10.1097/QAI.0000000000001776.
The Stanford HIV-1 genotypic resistance interpretation algorithm has changed substantially over its lifetime. In many studies, the algorithm version used is not specified. It is easy to assume that results across versions are comparable, but the effects of version changes on resistance calls are unknown. We evaluate these effects for 20 antiretroviral drugs.
We calculated resistance interpretations for the same 5993 HIV-1 sequences, from participants in AIDS Clinical Trials Group studies, under 14 versions of the Stanford algorithm from 2002 to 2017. Trends over time were assessed using repeated-measures logistic regression. Changes in rule structure and scoring were examined.
For most drugs, the proportion of high-level resistance calls on the same sequences was greater using more recent algorithm versions; 16/20 drugs showed significant upward trends. Some drugs, especially tenofovir, had a substantial increase. Only darunavir had a decrease. Algorithm changes impacted calls for subtype C more than B. For intermediate and high-level resistance combined, effects were weaker and more varied. Over time, rules in the Stanford algorithm have become more complex and contain more subrules. The types of rule changes responsible for trends varied widely by drug.
Reporting the Stanford algorithm version used for resistance analysis is strongly recommended. Caution should be used when comparing results between studies, unless the same version of the algorithm was used. Comparisons using different Stanford versions may be valid for drugs with few changes over time, but for most comparisons, version matters, and for some drugs, the impact is large.
斯坦福 HIV-1 基因型耐药性解释算法在其整个生命周期中发生了重大变化。在许多研究中，未指定使用的算法版本。人们很容易假设不同版本的结果是可比的，但版本变化对耐药性检测的影响尚不清楚。我们评估了 20 种抗逆转录病毒药物的这些影响。
我们根据斯坦福算法的 14 个版本，对来自 AIDS 临床试验组研究参与者的 5993 个 HIV-1 序列进行了相同的耐药性解释，这些版本的算法时间跨度从 2002 年到 2017 年。使用重复测量逻辑回归评估了随时间的变化趋势。还检查了规则结构和评分的变化。
对于大多数药物，在相同的序列上，使用较新版本的算法时，高水平耐药性检测的比例更高；16/20 种药物显示出显著的上升趋势。一些药物，特别是替诺福韦，有大幅增加。只有达芦那韦有下降。算法变化对 C 亚型的影响比对 B 亚型的影响更大。对于中间和高水平耐药性的组合，影响较弱且更加多样化。随着时间的推移，斯坦福算法中的规则变得更加复杂，并包含更多的子规则。导致趋势变化的规则类型因药物而异。
强烈建议报告用于耐药性分析的斯坦福算法版本。除非使用相同的算法版本，否则应谨慎比较研究结果。对于大多数药物来说，使用不同的斯坦福版本进行比较可能是有效的，但对于一些药物来说，版本是重要的，而且对于某些药物来说，影响是巨大的。
