Francis Ben, Lane Steven, Pirmohamed Munir, Jorgensen Andrea
Department of Biostatistics, University of Liverpool, Liverpool, United Kingdom.
Department of Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, United Kingdom.
PLoS One. 2014 Dec 12;9(12):e114896. doi: 10.1371/journal.pone.0114896. eCollection 2014.
A number of a priori warfarin dosing algorithms, derived using linear regression methods, have been proposed. Although these dosing algorithms may have been validated using patients derived from the same centre, rarely have they been validated using a patient cohort recruited from another centre. In order to undertake external validation, two cohorts were utilised. One cohort formed by patients from a prospective trial and the second formed by patients in the control arm of the EU-PACT trial. Of these, 641 patients were identified as having attained stable dosing and formed the dataset used for validation. Predicted maintenance doses from six criterion fulfilling regression models were then compared to individual patient stable warfarin dose. Predictive ability was assessed with reference to several statistics including the R-square and mean absolute error. The six regression models explained different amounts of variability in the stable maintenance warfarin dose requirements of the patients in the two validation cohorts; adjusted R-squared values ranged from 24.2% to 68.6%. An overview of the summary statistics demonstrated that no one dosing algorithm could be considered optimal. The larger validation cohort from the prospective trial produced more consistent statistics across the six dosing algorithms. The study found that all the regression models performed worse in the validation cohort when compared to the derivation cohort. Further, there was little difference between regression models that contained pharmacogenetic coefficients and algorithms containing just non-pharmacogenetic coefficients. The inconsistency of results between the validation cohorts suggests that unaccounted population specific factors cause variability in dosing algorithm performance. Better methods for dosing that take into account inter- and intra-individual variability, at the initiation and maintenance phases of warfarin treatment, are needed.
已经提出了一些使用线性回归方法得出的华法林先验给药算法。尽管这些给药算法可能已在来自同一中心的患者中得到验证，但很少在从另一个中心招募的患者队列中进行验证。为了进行外部验证，使用了两个队列。一个队列由来自一项前瞻性试验的患者组成，另一个队列由欧盟-房颤导管消融治疗（EU-PACT）试验对照组的患者组成。在这些患者中，641名患者被确定已达到稳定给药，并形成了用于验证的数据集。然后将来自六个符合标准的回归模型预测的维持剂量与个体患者的稳定华法林剂量进行比较。参考包括决定系数（R平方）和平均绝对误差在内的几个统计量评估预测能力。这六个回归模型解释了两个验证队列中患者稳定维持华法林剂量需求的不同程度的变异性；调整后的R平方值范围为24.2%至68.6%。汇总统计的概述表明，没有一种给药算法可以被认为是最佳的。来自前瞻性试验的较大验证队列在六种给药算法中产生了更一致的统计数据。研究发现，与推导队列相比，所有回归模型在验证队列中的表现都更差。此外，包含药物遗传学系数的回归模型与仅包含非药物遗传学系数的算法之间几乎没有差异。验证队列之间结果的不一致表明，未考虑到的人群特定因素导致给药算法性能存在变异性。需要更好的给药方法，在华法林治疗的起始和维持阶段考虑个体间和个体内的变异性。
