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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.

Titel:	Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea.
Autor/in / Beteiligte Person:	Choi, H ; Kang, HJ ; Ahn, I ; Gwon, H ; Kim, Y ; Seo, H ; Cho, HN ; Han, J ; Kim, M ; Kee, G ; Park, S ; Kwon, O ; Roh, JH ; Kim, AR ; Kim, JH ; Jun, TJ ; Kim, YH
Link:	Zum Volltext
Zeitschrift:	Scientific reports, Jg. 13 (2023-12-18), Heft 1, S. 22461
Veröffentlichung:	London : Nature Publishing Group, copyright 2011-, 2023
Medientyp:	academicJournal
ISSN:	2045-2322 (electronic)
DOI:	10.1038/s41598-023-49831-6
Schlagwort:	Adult Humans Retrospective Studies Inpatients Anticoagulants adverse effects Machine Learning Warfarin adverse effects Patient Discharge
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [Sci Rep] 2023 Dec 18; Vol. 13 (1), pp. 22461. <i>Date of Electronic Publication: </i>2023 Dec 18. MeSH Terms: Warfarin* / adverse effects ; Patient Discharge* ; Adult ; Humans ; Retrospective Studies ; Inpatients ; Anticoagulants / adverse effects ; Machine Learning References: Pirmohamed, M. Warfarin: Almost 60 years old and still causing problems. Br. J. Clin. Pharmacol. 62, 509 (2006). (PMID: 10.1111/j.1365-2125.2006.02806.x170619591885167) ; Pirmohamed, M., Kamali, F., Daly, A. K. & Wadelius, M. Oral anticoagulation: A critique of recent advances and controversies. Trends Pharmacol. Sci. 36, 153–163 (2015). (PMID: 10.1016/j.tips.2015.01.00325698605) ; Glurich, I., Burmester, J. K. & Caldwell, M. D. Understanding the pharmacogenetic approach to warfarin dosing. Heart Fail. Rev. 15, 239–248 (2010). (PMID: 10.1007/s10741-008-9115-918998206) ; Gage, B. F., Fihn, S. D. & White, R. 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Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea.