Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43 gil, Songpa-gu, Seoul, 05505, Republic of Korea.
Division of Cardiology, Asan Medical Center, 88, Olympicro 43gil, Songpagu, Seoul, 05505, Republic of Korea.
Sci Rep. 2023 Dec 18;13(1):22461. doi: 10.1038/s41598-023-49831-6.
As warfarin has a narrow therapeutic window and obvious response variability among individuals, it is difficult to rapidly determine personalized warfarin dosage. Adverse drug events(ADE) resulting from warfarin overdose can be critical, so that typically physicians adjust the warfarin dosage through the INR monitoring twice a week when starting warfarin. Our study aimed to develop machine learning (ML) models that predicts the discharge dosage of warfarin as the initial warfarin dosage using clinical data derived from electronic medical records within 2 days of hospitalization. During this retrospective study, adult patients who were prescribed warfarin at Asan Medical Center (AMC) between January 1, 2018, and October 31, 2020, were recruited as a model development cohort (n = 3168). Additionally, we created an external validation dataset (n = 891) from a Medical Information Mart for Intensive Care III (MIMIC-III). Variables for a model prediction were selected based on the clinical rationale that turned out to be associated with warfarin dosage, such as bleeding. The discharge dosage of warfarin was used the study outcome, because we assumed that patients achieved target INR at discharge. In this study, four ML models that predicted the warfarin discharge dosage were developed. We evaluated the model performance using the mean absolute error (MAE) and prediction accuracy. Finally, we compared the accuracy of the predictions of our models and the predictions of physicians for 40 data point to verify a clinical relevance of the models. The MAEs obtained using the internal validation set were as follows: XGBoost, 0.9; artificial neural network, 0.9; random forest, 1.0; linear regression, 1.0; and physicians, 1.3. As a result, our models had better prediction accuracy than the physicians, who have difficulty determining the warfarin discharge dosage using clinical information obtained within 2 days of hospitalization. We not only conducted the internal validation but also external validation. In conclusion, our ML model could help physicians predict the warfarin discharge dosage as the initial warfarin dosage from Korean population. However, conducting a successfully external validation in a further work is required for the application of the models.
华法林的治疗窗较窄，个体间反应差异明显，因此很难快速确定个体化的华法林剂量。华法林过量导致的药物不良反应（ADE）可能很严重，因此通常医生在开始使用华法林时，通过 INR 监测每周调整两次华法林剂量。我们的研究旨在开发机器学习（ML）模型，使用住院后 2 天内从电子病历中提取的临床数据预测华法林的出院剂量作为初始华法林剂量。在这项回顾性研究中，我们招募了 2018 年 1 月 1 日至 2020 年 10 月 31 日期间在 Asan 医疗中心（AMC）开出处方华法林的成年患者作为模型开发队列（n=3168）。此外，我们从医疗信息集市强化护理 III（MIMIC-III）创建了一个外部验证数据集（n=891）。用于模型预测的变量是根据与华法林剂量相关的临床原理选择的，例如出血。华法林的出院剂量被用作研究结果，因为我们假设患者在出院时达到了目标 INR。在这项研究中，我们开发了四种预测华法林出院剂量的 ML 模型。我们使用平均绝对误差（MAE）和预测准确性来评估模型性能。最后，我们比较了我们的模型和医生对 40 个数据点的预测准确性，以验证模型的临床相关性。使用内部验证集获得的 MAE 如下：XGBoost，0.9；人工神经网络，0.9；随机森林，1.0；线性回归，1.0；医生，1.3。结果表明，我们的模型比医生的预测更准确，医生很难根据住院后 2 天内获得的临床信息确定华法林的出院剂量。我们不仅进行了内部验证，还进行了外部验证。总之，我们的 ML 模型可以帮助医生从韩国人群中预测华法林的出院剂量作为初始华法林剂量。然而，为了模型的应用，还需要在进一步的工作中成功进行外部验证。
