Centro Diagnostico Italiano, Milan, Italy.
Pharmacogenomics. 2014 Jan;15(1):29-37. doi: 10.2217/pgs.13.212.
In recent years, pharmacogenetic algorithms were developed for estimating the appropriate dose of vitamin K antagonists.
To evaluate the performance of new generation artificial neural networks (ANNs) to predict the warfarin maintenance dose.
Demographic, clinical and genetic data (CYP2C9 and VKORC1 polymorphisms) from 377 patients treated with warfarin were used. The final prediction model was based on 23 variables selected by TWIST® system within a bipartite division of the data set (training and testing) protocol.
The ANN algorithm reached high accuracy, with an average absolute error of 5.7 mg of the warfarin maintenance dose. In the subset of patients requiring ≤21 mg and 21-49 mg (45 and 51% of the cohort, respectively) the absolute error was 3.86 mg and 5.45 with a high percentage of subjects being correctly identified (71 and 73%, respectively).
ANN appears to be a promising tool for vitamin K antagonist maintenance dose prediction.
近年来，出现了用于估算维生素 K 拮抗剂适当剂量的药物遗传学算法。
评估新一代人工神经网络 (ANN) 预测华法林维持剂量的性能。
使用了 377 名接受华法林治疗的患者的人口统计学、临床和遗传数据（CYP2C9 和 VKORC1 多态性）。最终预测模型基于 TWIST®系统在数据集（训练和测试）协议的二分划分中选择的 23 个变量。
ANN 算法达到了很高的准确性，华法林维持剂量的平均绝对误差为 5.7 毫克。在需要 ≤21 毫克和 21-49 毫克的患者亚组中（分别占队列的 45%和 51%），绝对误差分别为 3.86 毫克和 5.45 毫克，并且有很大比例的患者被正确识别（分别为 71%和 73%）。
ANN 似乎是预测维生素 K 拮抗剂维持剂量的有前途的工具。
