Tao Huan, Li Qian, Zhou Qin, Chen Jie, Fu Bo, Wang Jing, Qin Wenzhe, Hou Jianglong, Chen Jin, Dong Li
Department of Evidence-based Medicine and clinical epidemiology, West China Hospital, Sichuan University, 37 Guo Xue Xiang, ChengDu, 610041, China.
Department of Nutrition, The Second affiliated hospital of Chongqing medical university, Chongqing, China.
BMC Surg. 2018 Feb 15;18(1):10. doi: 10.1186/s12893-018-0343-1.
It's difficult but urgent to achieve the individualized rational medication of the warfarin, we aim to predict the individualized warfarin stable dose though the artificial intelligent Adaptive neural-fuzzy inference system (ANFIS).
Our retrospective analysis based on a clinical database, involving 21,863 patients from 15 Chinese provinces who receive oral warfarin after the heart valve replacement. They were allocated into four groups: the external validation group (A group), the internal validation group (B group), training group (C group) and stratified training group (D group). We used a univariate analysis of general linear models(GLM-univariate) to select the input variables and construct two prediction models by the ANFIS with the training and stratified training group, and then verify models with two validation groups by the mean squared error(MSE), mean absolute error(MAE) and the ideal predicted percentage.
A total of 13,639 eligible patients were selected, including 1639 in A group, 3000 in B group, 9000 in C group, and 3192 in D group. Nine input variables were selected out and two five-layered ANFIS models were built. ANFIS model achieved the highest total ideal predicted percentage 63.7%. In the dose subgroups, all the models performed best in the intermediate-dose group with the ideal predicted percentage 82.4~ 86.4%, and the use of the stratified training group slightly increased the prediction accuracy in low-dose group by 8.8 and 5.2%, respectively.
As a preliminary attempt, ANFIS model predicted the warfarin stable dose properly after heart valve surgery among Chinese, and also proved that Chinese need lower anticoagulation intensity INR (1.5-2.5) to warfarin by reference to the recommended INR (2.5-3.5) in the developed countries.
实现华法林的个体化合理用药困难但迫切，我们旨在通过人工智能自适应神经模糊推理系统（ANFIS）预测华法林个体化稳定剂量。
我们基于临床数据库进行回顾性分析，纳入来自中国15个省份的21863例心脏瓣膜置换术后接受口服华法林治疗的患者。他们被分为四组：外部验证组（A组）、内部验证组（B组）、训练组（C组）和分层训练组（D组）。我们使用一般线性模型单变量分析（GLM-单变量）选择输入变量，并通过ANFIS分别利用训练组和分层训练组构建两个预测模型，然后用均方误差（MSE）、平均绝对误差（MAE）和理想预测百分比对两个验证组的模型进行验证。
共纳入13639例符合条件的患者，其中A组1639例，B组3000例，C组9000例，D组3192例。筛选出9个输入变量并构建了两个五层ANFIS模型。ANFIS模型获得的总理想预测百分比最高，为63.7%。在剂量亚组中，所有模型在中剂量组表现最佳，理想预测百分比为82.4%~86.4%，分层训练组的使用使低剂量组的预测准确率分别略有提高8.8%和5.2%。
作为初步尝试，ANFIS模型在中国人群心脏瓣膜置换术后对华法林稳定剂量进行了合理预测，也证明相对于发达国家推荐的国际标准化比值（INR）（2.5 - 3.5），中国人使用华法林时需要较低的抗凝强度INR（1.5 - 2.5）。
