Department of Clinical Pharmacy, School of Pharmacy, 521 Parnassus Avenue, University of California, San Francisco, CA 94143-90622, USA.
Pharmacogenomics. 2011 Jan;12(1):125-34. doi: 10.2217/pgs.10.168.
Multiple warfarin pharmacogenetic dosing algorithms have been reported to date. However, there is only limited information available on the performance of the algorithms that can be used with the results of a US FDA-cleared warfarin pharmacogenetic test. We compared the performance of warfarin pharmacogenetic dosing algorithms in a large racially diverse cohort.
MATERIALS & METHODS: Warfarin pharmacogenetic dosing algorithms were identified using the PubMed database. Patient information from the International Warfarin Pharmacogenetics Consortium database was used to predict therapeutic warfarin doses according to each algorithm. By using bootstrapping analysis, the performance of algorithms was tested by comparing the mean absolute error and mean percentage of patients whose predicted dose fell within 20% of actual dose (percentage within 20%) in the entire cohort, and by race and therapeutic dose range.
A total of 13 algorithms and 1940 patients were included in the study. Overall, all the algorithms had similar performances (mean absolute error: 10.3 mg/week and mean percentage within 20%-41.4%). However, algorithms derived from racially mixed populations tended to perform better than those derived from single race populations. Mixed population algorithms had the lowest mean absolute error and the highest percentage within 20% across the racial groups. Most algorithms performed better in the intermediate-dose range (between 21 and 49 mg/week) than in the low (≤21 mg/week) or high-(≥49 mg/week) range.
Published warfarin pharmacogenetic algorithms performed similarly, although mixed population algorithms tended to perform better than race-specific algorithms.
迄今为止，已有多种华法林药物基因组学剂量算法被报道。然而，可用的算法有限，且这些算法只能与美国 FDA 批准的华法林药物基因组学检测结果结合使用。我们比较了这些算法在一个大型、种族多样化队列中的表现。
我们使用 PubMed 数据库来确定华法林药物基因组学剂量算法。国际华法林药物基因组学联合会数据库中的患者信息被用于根据每种算法预测治疗剂量的华法林。通过bootstrap 分析，我们通过比较整个队列中预测剂量与实际剂量相差 20%的患者的平均绝对误差和平均百分比（20%内的百分比），以及按种族和治疗剂量范围来测试算法的性能。
共有 13 种算法和 1940 名患者被纳入研究。总的来说，所有的算法都有相似的表现（平均绝对误差：10.3mg/周和 20%-41.4%内的平均百分比）。然而，源自混合种族人群的算法往往比源自单一种族人群的算法表现更好。混合人群算法在所有种族群体中具有最低的平均绝对误差和最高的 20%内的百分比。大多数算法在中剂量范围（21-49mg/周）的表现优于低剂量范围（≤21mg/周）或高剂量范围（≥49mg/周）。
已发表的华法林药物基因组学算法表现相似，尽管混合人群算法的表现往往优于特定种族的算法。
