Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea

作者：Heejung Choi, Hee Jun Kang, Imjin Ahn, Hansle Gwon, Yunha Kim, Yunha Kim, Hyeram Seo, Ha Na Cho, Jiye Han, Minkyoung Kim, Gaeun Kee, Seohyun Park, Osung Kwon, Jae‐Hyung Roh, Ju Hyeon Kim, Ju Hyeon Kim, Tae Joon Jun, Young‐Hak Kim, Young‐Hak Kim · 发表于：Scientific Reports · 年份：2023 · DOI：10.1038/s41598-023-49831-6 · 被引用次数：19 · 研究领域：Machine Learning in Healthcare、Blood Pressure and Hypertension Studies、Sepsis Diagnosis and Treatment

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 fo...