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Hu Y, Zhang X, Wei M, Yang T, Chen J, Wu X, Zhu Y, Chen X, Lou S, Zhu J. Using machine learning to predict the bleeding risk for patients with cardiac valve replacement treated with warfarin in hospitalized. Pharmacoepidemiol Drug Saf 2024; 33:e5756. [PMID: 38357810 DOI: 10.1002/pds.5756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 08/14/2023] [Accepted: 01/08/2024] [Indexed: 02/16/2024]
Abstract
BACKGROUND Distinguishing warfarin-related bleeding risk at the bedside remains challenging. Studies indicate that warfarin therapy should be suspended when international normalized ratio (INR) ≥ 4.5, or it may sharply increase the risk of bleeding. We aim to develop and validate a model to predict the high bleeding risk in valve replacement patients during hospitalization. METHOD Cardiac valve replacement patients from January 2016 to December 2021 across Nanjing First Hospital were collected. Five different machine-learning (ML) models were used to establish the prediction model. High bleeding risk was an INR ≥4.5. The area under the receiver operating characteristic curve (AUC) was used for evaluating the prediction performance of different models. The SHapley Additive exPlanations (SHAP) was used for interpreting the model. We also compared ML with ATRIA score and ORBIT score. RESULTS A total of 2376 patients were finally enrolled in this model, 131 (5.5%) of whom experienced the high bleeding risk after anticoagulation therapy of warfarin during hospitalization. The extreme gradient boosting (XGBoost) exhibited the best overall prediction performance (AUC: 0.882, confidence interval [CI] 0.817-0.946, Brier score, 0.158) compared to other prediction models. It also shows superior performance compared with ATRIA score and ORBIT score. The top 5 most influential features in XGBoost model were platelet, thyroid stimulation hormone, body surface area, serum creatinine and white blood cell. CONCLUSION A model for predicting high bleeding risk in valve replacement patients who treated with warfarin during hospitalization was successfully developed by using machine learning, which may well assist clinicians to identify patients at high risk of bleeding and allow timely adjust therapeutic strategies in evaluating individual patient.
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Affiliation(s)
- Yixing Hu
- Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
- Department of Pharmacy, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xuemeng Zhang
- Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
- Department of Pharmacy, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Meng Wei
- Department of Clinical Pharmacy, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Tongtong Yang
- Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
- Department of Pharmacy, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Jinjin Chen
- Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
- Department of Pharmacy, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xia Wu
- Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
- Department of Pharmacy, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yifan Zhu
- Department of Cardio-Thoracic Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xin Chen
- Department of Cardio-Thoracic Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Sheng Lou
- Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
- Department of Pharmacy, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Junrong Zhu
- Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China
- Department of Pharmacy, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
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Gao W, Zhang Z, Guan Z, Chen W, Li Z. Developing Chinese race-specific warfarin dose prediction algorithms. Int J Clin Pharm 2023:10.1007/s11096-023-01565-1. [PMID: 36991222 DOI: 10.1007/s11096-023-01565-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Accepted: 02/24/2023] [Indexed: 03/31/2023]
Abstract
BACKGROUND Numerous genotype-guided warfarin dosing algorithms have been developed to individualize warfarin doses, but they can only explain 47-52% of the variability. AIM This study aimed to develop new warfarin algorithms suitable to predict the stable warfarin dose for the Chinese population and to compare their prediction performance with those of the most commonly used algorithms. METHOD Multiple linear regression analysis with the warfarin optimal dose (WOD), logarithm (log) WOD, 1/WOD, and [Formula: see text], respectively, as the dependent variables were performed to deduce a new warfarin algorithm (NEW-Warfarin). WOD was the stable dose that maintained the international normalized ratio (INR) within the target range (2.0-3.0). Three major genotype-guided warfarin dosing algorithms were selected and compared against NEW-Warfarin predictive performance using the mean absolute error (MAE). Furthermore, patients were divided into five groups according to warfarin indications [atrial fibrillation (AF), pulmonary embolism (PE), cardiac-related disease (CRD), deep vein thrombosis (DVT), and other diseases (OD)]. Multiple linear regression analyses were also performed for each group. RESULTS The regression equation with [Formula: see text] as the dependent variable had the highest coefficient of determination (R2 = 0.489). The NEW-Warfarin had the best predictive accuracy compared to the three algorithms selected. Group analysis, according to indications, showed that the R2 of the five groups were PE (0.902) > DVT (0.608) > CRD (0.569) > OD (0.436) > AF (0.424). CONCLUSION Dosing algorithms based on warfarin indications are more suitable for predicting warfarin doses. Our research provides a novel strategy to develop indication-specific warfarin dosing algorithms to improve the efficacy and safety of warfarin prescribing.
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Affiliation(s)
- Weiqi Gao
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, 99 Longcheng Street, Taiyuan, 030032, China
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Zhijiao Zhang
- School of Pharmacy, Shanxi Medical University, Taiyuan, 030001, China
| | - Zhaobo Guan
- School of Pharmacy, Shanxi Medical University, Taiyuan, 030001, China
| | - Weihong Chen
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, 99 Longcheng Street, Taiyuan, 030032, China
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Zhihong Li
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, 99 Longcheng Street, Taiyuan, 030032, China.
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
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Wang D, Yong L, Zhang Q, Chen H. Impact of CYP2C19 gene polymorphisms on warfarin dose requirement: a systematic review and meta-analysis. Pharmacogenomics 2022; 23:903-911. [PMID: 36222113 DOI: 10.2217/pgs-2022-0106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Background: Various genetic factors influence warfarin maintenance dose. Methods: A literature search was performed on PubMed, Embase and the Cochrane Library, and a meta-analysis to analyze the impact of CYP2C19 polymorphisms on warfarin maintenance dose was conducted. Results: From nine studies encompassing 1393 patients, three CYP2C19 SNPs were identified: rs4244285, rs4986893 and rs3814637. Warfarin maintenance dose was significantly reduced by 10% in individuals with the rs4986893 A allele compared with the GG carriers and was 34%, 16% and 18% lower in patients with rs3814637 TT and CT genotypes and T allele, respectively, than that in CC carriers. No significant dose difference was observed among the rs4244285 genotypes. Conclusion: CYP2C19 rs4986893 and rs3814637 are associated with significantly reduced warfarin dose requirements.
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Affiliation(s)
- Dongxu Wang
- Arrhythmia Center, National Center for Cardiovascular Diseases & Fuwai Hospital, CAMS & PUMC, Beijing, 100037, China
| | - Ling Yong
- Department of Pharmacy Administration & Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
| | - Qing Zhang
- Department of Cardiovascular, Beijing Hospital, National Centre of Gerontology, Beijing, 100730, China
| | - Hao Chen
- Department of Cardiovascular, Beijing Hospital, National Centre of Gerontology, Beijing, 100730, China
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