Altmann André, Däumer Martin, Beerenwinkel Niko, Peres Yardena, Schülter Eugen, Büch Joachim, Rhee Soo-Yon, Sönnerborg Anders, Fessel W Jeffrey, Shafer Robert W, Zazzi Maurizio, Kaiser Rolf, Lengauer Thomas
Max Planck Institute for Informatics, Saarbrücken, University of Cologne, Cologne, Germany.
J Infect Dis. 2009 Apr 1;199(7):999-1006. doi: 10.1086/597305.
Expert-based genotypic interpretation systems are standard methods for guiding treatment selection for patients infected with human immunodeficiency virus type 1. We previously introduced the software pipeline geno2pheno-THEO (g2p-THEO), which on the basis of viral sequence predicts the response to treatment with a combination of antiretroviral compounds by applying methods from statistical learning and the estimated potential of the virus to escape from drug pressure.
We retrospectively validated the statistical model used by g2p-THEO in approximately 7600 independent treatment-sequence pairs extracted from the EuResist integrated database, ranging from 1990 to 2007. Results were compared with the 3 most widely used expert-based interpretation systems: Stanford HIVdb, ANRS, and Rega.
The difference in receiver operating characteristic curves between g2p-THEO and expert-based approaches was significant (P < .001; paired Wilcoxon test). Indeed, at 80% specificity, g2p-THEO found 16.2%-19.8% more successful regimens than did the expert-based approaches. The increased performance of g2p-THEO was confirmed in a 2001-2007 data set from which most obsolete therapies had been removed.
Finding drug combinations that increase the chances of therapeutic success is the main reason for using decision support systems. The present analysis of a large data set derived from clinical practice demonstrates that g2p-THEO solves this task significantly better than state-of-the-art expert-based systems. The tool is available at http://www.geno2pheno.org.
基于专家的基因型解释系统是指导人类免疫缺陷病毒1型感染者治疗选择的标准方法。我们之前推出了软件管道geno2pheno-THEO（g2p-THEO），它基于病毒序列，通过应用统计学习方法和估计的病毒逃避药物压力的潜力，预测对抗逆转录病毒化合物联合治疗的反应。
我们从1990年至2007年的EuResist综合数据库中提取了约7600个独立的治疗序列对，对g2p-THEO使用的统计模型进行了回顾性验证。将结果与3种使用最广泛的基于专家的解释系统进行比较：斯坦福HIV数据库、法国国家艾滋病研究机构（ANRS）和雷加（Rega）。
g2p-THEO与基于专家的方法之间的受试者工作特征曲线差异显著（P <.001；配对威尔科克森检验）。事实上，在80%的特异性水平下，g2p-THEO比基于专家的方法发现了多16.2%-19.8%的成功治疗方案。g2p-THEO在一个已去除大多数过时疗法的2001-2007年数据集中表现出的性能提升得到了证实。
寻找能增加治疗成功几率的药物组合是使用决策支持系统的主要原因。目前对来自临床实践的大数据集的分析表明，g2p-THEO在解决这项任务方面比最先进的基于专家的系统显著更好。该工具可在http://www.geno2pheno.org获取。
