Department of Thoracic and Cardiovascular Surgery, Sejong General Hospital, Bucheon-si, Gyeonggi-do, Republic of Korea.
Department of Anesthesiology and Pain Medicine, Anesthesia and Pain Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
Sci Rep. 2021 Jul 20;11(1):14745. doi: 10.1038/s41598-021-94305-2.
The first aim of this study was to develop a prothrombin time international normalized ratio (PT INR) prediction model. The second aim was to develop a warfarin maintenance dose decision support system as a precise warfarin dosing platform. Data of 19,719 inpatients from three institutions was analyzed. The PT INR prediction algorithm included dense and recurrent neural networks, and was designed to predict the 5th-day PT INR from data of days 1-4. Data from patients in one hospital (n = 22,314) was used to train the algorithm which was tested with the datasets from the other two hospitals (n = 12,673). The performance of 5th-day PT INR prediction was compared with 2000 predictions made by 10 expert physicians. A generator of individualized warfarin dose-PT INR tables which simulated the repeated administration of varying doses of warfarin was developed based on the prediction model. The algorithm outperformed humans with accuracy terms of within ± 0.3 of the actual value (machine learning algorithm: 10,650/12,673 cases (84.0%), expert physicians: 1647/2000 cases (81.9%), P = 0.014). In the individualized warfarin dose-PT INR tables generated by the algorithm, the 8th-day PT INR predictions were within 0.3 of actual value in 450/842 cases (53.4%). An artificial intelligence-based warfarin dosing algorithm using a recurrent neural network outperformed expert physicians in predicting future PT INRs. An individualized warfarin dose-PT INR table generator which was constructed based on this algorithm was acceptable.
本研究的首要目的是开发一个凝血酶原时间国际标准化比值（PT INR）预测模型。第二个目的是开发一个华法林维持剂量决策支持系统，作为一个精确的华法林给药平台。分析了来自三个机构的 19719 名住院患者的数据。PT INR 预测算法包括密集和递归神经网络，旨在根据第 1-4 天的数据预测第 5 天的 PT INR。来自一家医院（n=22314）的患者数据用于训练算法，然后在另外两家医院（n=12673）的数据集上进行测试。将第 5 天的 PT INR 预测性能与 10 位专家医生的 2000 次预测进行比较。根据预测模型开发了一种个体化华法林剂量-PT INR 表的生成器，该生成器模拟了不同剂量华法林的重复给药。该算法的准确性术语在实际值的±0.3 以内，优于人类（机器学习算法：10650/12673 例（84.0%），专家医生：1647/2000 例（81.9%），P=0.014）。在算法生成的个体化华法林剂量-PT INR 表中，450/842 例（53.4%）第 8 天的 PT INR 预测值在实际值的 0.3 以内。一种基于人工智能的使用递归神经网络的华法林给药算法在预测未来的 PT INRs 方面优于专家医生。基于该算法构建的个体化华法林剂量-PT INR 表生成器是可以接受的。
