Rega Institute for Medical Research, KU Leuven, Leuven, Belgium.
PLoS One. 2013 Apr 17;8(4):e61436. doi: 10.1371/journal.pone.0061436. Print 2013.
Clinically evaluating genotypic interpretation systems is essential to provide optimal guidance in designing potent individualized HIV-regimens. This study aimed at investigating the ability of the latest Rega algorithm to predict virological response on a short and longer period.
9231 treatment changes episodes were extracted from an integrated patient database. The virological response after 8, 24 and 48 weeks was dichotomized to success and failure. Success was defined as a viral load below 50 copies/ml or alternatively, a 2 log decrease from the baseline viral load at 8 weeks. The predictive ability of Rega version 8 was analysed in comparison with that of previous evaluated version Rega 5 and two other algorithms (ANRS v2011.05 and Stanford HIVdb v6.0.11). A logistic model based on the genotypic susceptibility score was used to predict virological response, and additionally, confounding factors were added to the model. Performance of the models was compared using the area under the ROC curve (AUC) and a Wilcoxon signed-rank test.
Per unit increase of the GSS reported by Rega 8, the odds on having a successful therapy response on week 8 increased significantly by 81% (OR = 1.81, CI = [1.76-1.86]), on week 24 by 73% (OR = 1.73, CI = [1.69-1.78]) and on week 48 by 85% (OR = 1.85, CI = [1.80-1.91]). No significant differences in AUC were found between the performance of Rega 8 and Rega 5, ANRS v2011.05 and Stanford HIVdb v6.0.11, however Rega 8 had the highest sensitivity: 76.9%, 76.5% and 77.2% on 8, 24 and 48 weeks respectively. Inclusion of additional factors increased the performance significantly.
Rega 8 is a significant predictor for virological response with a better sensitivity than previously, and with rules for recently approved drugs. Additional variables should be taken into account to ensure an effective regimen.
临床评估基因型解释系统对于提供最佳的个体化 HIV 治疗方案至关重要。本研究旨在探讨最新的 Rega 算法在短期和长期内预测病毒学反应的能力。
从一个综合患者数据库中提取了 9231 个治疗改变事件。将第 8、24 和 48 周的病毒学反应分为成功和失败。成功定义为病毒载量低于 50 拷贝/ml,或者在第 8 周时从基线病毒载量下降 2 个对数。分析了 Rega 版本 8 与之前评估的版本 Rega 5 以及另外两种算法(ANRS v2011.05 和斯坦福 HIVdb v6.0.11)的预测能力。使用基于基因型药敏评分的逻辑模型预测病毒学反应,并向模型中添加混杂因素。使用 ROC 曲线下面积(AUC)和 Wilcoxon 符号秩检验比较模型的性能。
Rega 8 报告的 GSS 每增加一个单位,第 8 周治疗反应成功的几率显著增加 81%(OR = 1.81,CI = [1.76-1.86]),第 24 周增加 73%(OR = 1.73,CI = [1.69-1.78]),第 48 周增加 85%(OR = 1.85,CI = [1.80-1.91])。Rega 8 与 Rega 5、ANRS v2011.05 和斯坦福 HIVdb v6.0.11 的 AUC 性能无显著差异,但 Rega 8 的灵敏度最高:第 8、24 和 48 周分别为 76.9%、76.5%和 77.2%。纳入额外因素可显著提高性能。
Rega 8 是病毒学反应的重要预测因子,其灵敏度高于以往,且具有最新批准药物的规则。应考虑额外的变量以确保有效的治疗方案。