Machine learning guided prediction of warfarin blood levels for personalized medicine based on clinical longitudinal data from cardiac surgery patients: a prospective observational study

  1. Xue, Ling 38
  2. He, Shan 67
  3. Singla, Rajeev K. 59
  4. Qin, Qiong 3
  5. Ding, Yinglong 12
  6. Liu, Linsheng 3
  7. Ding, Xiaoliang 3
  8. Bediaga-Bañeres, Harbil 6
  9. Arrasate, Sonia 7
  10. Durado-Sanchez, Aliuska 611
  11. Zhang, Yuzhen 10
  12. Shen, Zhenya 12
  13. Shen, Bairong 5
  14. Miao, Liyan 34
  15. González-Díaz, Humberto 71213
  1. 1 Department of Cardiovascular Surgery, the First Affiliated Hospital of Soochow University, Suzhou, China
  2. 2 Institute for Cardiovascular Science, Soochow University, Suzhou, China
  3. 3 Department of Pharmacy, the First Affiliated Hospital of Soochow University, Suzhou, China
  4. 4 Institute for Interdisciplinary Drug Research and Translational Sciences, Soochow University, Suzhou, China
  5. 5 Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
  6. 6 IKERDATA S.L., ZITEK, University of The Basque Country (UPV/EHU), Bilbao, Basque Country, Spain
  7. 7 Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), Bilbao, Basque Country, Spain
  8. 8 Department of Pharmacology, Faculty of Medicine, University of The Basque Country (UPV/EHU), Bilbao, Basque Country, Spain
  9. 9 School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, Punjab-144411, India
  10. 10 Department of Cardiology, the First Affiliated Hospital of Soochow University, Suzhou, China
  11. 11 Department of Public Law, Faculty of Law, University of The Basque Country (UPV/EHU), Bilbao, Basque Country, Spain
  12. 12 BIOFISIKA: Basque Center for Biophysics CSIC, University of The Basque Country (UPV/EHU), Bilbao, Basque Country, Spain
  13. 13 IKERBASQUE, Basque Foundation for Science, Bilbao, Basque Country, Spain
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Revista:
International Journal of Surgery

ISSN: 1743-9159

Año de publicación: 2024

Tipo: Artículo

DOI: 10.1097/JS9.0000000000001734 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: International Journal of Surgery

Resumen

Background: Warfarin is a common oral anticoagulant, and its effects vary widely among individuals. Numerous dose-prediction algorithms have been reported based on cross-sectional data generated via multiple linear regression or machine learning. This study aimed to construct an information fusion perturbation theory and machine learning prediction model of warfarin blood levels based on clinical longitudinal data from cardiac surgery patients.Methods and Material: The data of 246 patients were obtained from electronic medical records. Continuous variables were processed by calculating the distance of the raw data with the moving average (MA ∆vki(sj)), and categorical variables in different attribute groups were processed using Euclidean distance (ED ǁ∆vk(sj)ǁ). Regression and classification analyses were performed on the raw data, MA ∆vki(sj), and ED ǁ∆vk(sj)ǁ. Different machine-learning algorithms were chosen for the STATISTICA and WEKA software.Results: The random forest (RF) algorithm was the best for predicting continuous outputs using the raw data. The correlation coefficients of the RF algorithm were 0.978 and 0.595 for the training and validation sets, respectively, and the mean absolute errors were 0.135 and 0.362 for the training and validation sets, respectively. The proportion of ideal predictions of the RF algorithm was 59.0%. General discriminant analysis (GDA) was the best algorithm for predicting the categorical outputs using the MA ∆vki(sj) data. The GDA algorithm’s total true positive rate (TPR) was 95.4% and 95.6% for the training and validation sets, respectively, with MA ∆vki(sj) data.Conclusions: An information fusion perturbation theory and machine learning model for predicting warfarin blood levels was established. A model based on the RF algorithm could be used to predict the target international normalized ratio (INR), and a model based on the GDA algorithm could be used to predict the probability of being within the target INR range under different clinical scenarios.