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Xue L, Singla RK, He S, Arrasate S, González-Díaz H, Miao L, Shen B. Warfarin-A natural anticoagulant: A review of research trends for precision medication. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2024; 128:155479. [PMID: 38493714 DOI: 10.1016/j.phymed.2024.155479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 01/29/2024] [Accepted: 02/22/2024] [Indexed: 03/19/2024]
Abstract
BACKGROUND Warfarin is a widely prescribed anticoagulant in the clinic. It has a more considerable individual variability, and many factors affect its variability. Mathematical models can quantify the quantitative impact of these factors on individual variability. PURPOSE The aim is to comprehensively analyze the advanced warfarin dosing algorithm based on pharmacometrics and machine learning models of personalized warfarin dosage. METHODS A bibliometric analysis of the literature retrieved from PubMed and Scopus was performed using VOSviewer. The relevant literature that reported the precise dosage of warfarin calculation was retrieved from the database. The multiple linear regression (MLR) algorithm was excluded because a recent systematic review that mainly reviewed this algorithm has been reported. The following terms of quantitative systems pharmacology, mechanistic model, physiologically based pharmacokinetic model, artificial intelligence, machine learning, pharmacokinetic, pharmacodynamic, pharmacokinetics, pharmacodynamics, and warfarin were added as MeSH Terms or appearing in Title/Abstract into query box of PubMed, then humans and English as filter were added to retrieve the literature. RESULTS Bibliometric analysis revealed important co-occuring MeShH and index keywords. Further, the United States, China, and the United Kingdom were among the top countries contributing in this domain. Some studies have established personalized warfarin dosage models using pharmacometrics and machine learning-based algorithms. There were 54 related studies, including 14 pharmacometric models, 31 artificial intelligence models, and 9 model evaluations. Each model has its advantages and disadvantages. The pharmacometric model contains biological or pharmacological mechanisms in structure. The process of pharmacometric model development is very time- and labor-intensive. Machine learning is a purely data-driven approach; its parameters are more mathematical and have less biological interpretation. However, it is faster, more efficient, and less time-consuming. Most published models of machine learning algorithms were established based on cross-sectional data sourced from the database. CONCLUSION Future research on personalized warfarin medication should focus on combining the advantages of machine learning and pharmacometrics algorithms to establish a more robust warfarin dosage algorithm. Randomized controlled trials should be performed to evaluate the established algorithm of warfarin dosage. Moreover, a more user-friendly and accessible warfarin precision medicine platform should be developed.
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Affiliation(s)
- Ling Xue
- 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; Department of Pharmacy, The First Affiliated Hospital of Soochow University, Suzhou, China; Department of Pharmacology, Faculty of Medicine, University of The Basque Country (UPV/EHU), Bilbao, Basque Country, Spain
| | - Rajeev K Singla
- 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; School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, Punjab-144411, India
| | - Shan He
- IKERDATA S.l., ZITEK, University of The Basque Country (UPVEHU), Rectorate Building, 48940, Bilbao, Basque Country, Spain; Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Basque Country, Spain
| | - Sonia Arrasate
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Basque Country, Spain
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, Faculty of Science and Technology, University of The Basque Country (UPV/EHU), P.O. Box 644, 48080, Bilbao, Basque Country, Spain; BIOFISIKA: Basque Center for Biophysics CSIC, University of The Basque Country (UPV/EHU), Barrio Sarriena s/n, Leioa, Bizkaia 48940, Basque Country, Spain; IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Basque Country, Spain
| | - Liyan Miao
- Department of Pharmacy, The First Affiliated Hospital of Soochow University, Suzhou, China; Institute for Interdisciplinary Drug Research and Translational Sciences, Soochow University, Suzhou, China; College of Pharmaceutical Sciences, Soochow University, Suzhou, China.
| | - Bairong Shen
- 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.
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Abdel-Hafez A, Scott IA, Falconer N, Canaris S, Bonilla O, Marxen S, Van Garderen A, Barras M. Predicting Therapeutic Response to Unfractionated Heparin Therapy: Machine Learning Approach. Interact J Med Res 2022; 11:e34533. [PMID: 35993617 PMCID: PMC9531006 DOI: 10.2196/34533] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 04/10/2022] [Accepted: 06/23/2022] [Indexed: 11/13/2022] Open
Abstract
Background Unfractionated heparin (UFH) is an anticoagulant drug that is considered a high-risk medication because an excessive dose can cause bleeding, whereas an insufficient dose can lead to a recurrent embolic event. Therapeutic response to the initiation of intravenous UFH is monitored using activated partial thromboplastin time (aPTT) as a measure of blood clotting time. Clinicians iteratively adjust the dose of UFH toward a target, indication-defined therapeutic aPTT range using nomograms, but this process can be imprecise and can take ≥36 hours to achieve the target range. Thus, a more efficient approach is required. Objective In this study, we aimed to develop and validate a machine learning (ML) algorithm to predict aPTT within 12 hours after a specified bolus and maintenance dose of UFH. Methods This was a retrospective cohort study of 3019 patient episodes of care from January 2017 to August 2020 using data collected from electronic health records of 5 hospitals in Queensland, Australia. Data from 4 hospitals were used to build and test ensemble models using cross-validation, whereas data from the fifth hospital were used for external validation. We built 2 ML models: a regression model to predict the aPTT value after a UFH bolus dose and a multiclass model to predict the aPTT, classified as subtherapeutic (aPTT <70 seconds), therapeutic (aPTT 70-100 seconds), or supratherapeutic (aPTT >100 seconds). Modeling was performed using Driverless AI (H2O), an automated ML tool, and 17 different experiments were iteratively conducted to optimize model accuracy. Results In predicting aPTT, the best performing model was an ensemble with 4x LightGBM models with a root mean square error of 31.35 (SD 1.37). In predicting the aPTT class using a repurposed data set, the best performing ensemble model achieved an accuracy of 0.599 (SD 0.0289) and an area under the receiver operating characteristic curve of 0.735. External validation yielded similar results: root mean square error of 30.52 (SD 1.29) for the aPTT prediction model, and accuracy of 0.568 (SD 0.0315) and area under the receiver operating characteristic curve of 0.724 for the aPTT multiclassification model. Conclusions To the best of our knowledge, this is the first ML model applied to intravenous UFH dosing that has been developed and externally validated in a multisite adult general medical and surgical inpatient setting. We present the processes of data collection, preparation, and feature engineering for replication.
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Affiliation(s)
- Ahmad Abdel-Hafez
- Clinical Informatics, Metro South Health, Queensland Health, Brisbane, Australia.,School of Public Health & Social Work, Queensland University of Technology, Brisbane, Australia
| | - Ian A Scott
- Department of Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Brisbane, Australia.,Greater Brisbane School of Clinical Medicine, University of Queensland, Brisbane, Australia
| | - Nazanin Falconer
- Department of Pharmacy, Princess Alexandra Hospital, Brisbane, Australia.,Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Stephen Canaris
- Clinical Informatics, Metro South Health, Queensland Health, Brisbane, Australia
| | - Oscar Bonilla
- Clinical Informatics, Metro South Health, Queensland Health, Brisbane, Australia
| | - Sven Marxen
- Pharmacy Service, Logan and Beaudesert Hospitals, Logan, Australia
| | - Aaron Van Garderen
- Clinical Informatics, Metro South Health, Queensland Health, Brisbane, Australia.,Pharmacy Service, Logan and Beaudesert Hospitals, Logan, Australia
| | - Michael Barras
- Department of Pharmacy, Princess Alexandra Hospital, Brisbane, Australia.,School of Pharmacy, University of Queensland, Brisbane, Australia
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Zhang F, Liu Y, Ma W, Zhao S, Chen J, Gu Z. Nonlinear Machine Learning in Warfarin Dose Prediction: Insights from Contemporary Modelling Studies. J Pers Med 2022; 12:jpm12050717. [PMID: 35629140 PMCID: PMC9147332 DOI: 10.3390/jpm12050717] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 04/26/2022] [Accepted: 04/28/2022] [Indexed: 02/01/2023] Open
Abstract
Objective: This study aimed to systematically assess the characteristics and risk of bias of previous studies that have investigated nonlinear machine learning algorithms for warfarin dose prediction. Methods: We systematically searched PubMed, Embase, Cochrane Library, Chinese National Knowledge Infrastructure (CNKI), China Biology Medicine (CBM), China Science and Technology Journal Database (VIP), and Wanfang Database up to March 2022. We assessed the general characteristics of the included studies with respect to the participants, predictors, model development, and model evaluation. The methodological quality of the studies was determined, and the risk of bias was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST). Results: From a total of 8996 studies, 23 were assessed in this study, of which 23 (100%) were retrospective, and 11 studies focused on the Asian population. The most common demographic and clinical predictors were age (21/23, 91%), weight (17/23, 74%), height (12/23, 52%), and amiodarone combination (11/23, 48%), while CYP2C9 (14/23, 61%), VKORC1 (14/23, 61%), and CYP4F2 (5/23, 22%) were the most common genetic predictors. Of the included studies, the MAE ranged from 1.47 to 10.86 mg/week in model development studies, from 2.42 to 5.18 mg/week in model development with external validation (same data) studies, from 12.07 to 17.59 mg/week in model development with external validation (another data) studies, and from 4.40 to 4.84 mg/week in model external validation studies. All studies were evaluated as having a high risk of bias. Factors contributing to the risk of bias include inappropriate exclusion of participants (10/23, 43%), small sample size (15/23, 65%), poor handling of missing data (20/23, 87%), and incorrect method of selecting predictors (8/23, 35%). Conclusions: Most studies on nonlinear-machine-learning-based warfarin prediction models show poor methodological quality and have a high risk of bias. The analysis domain is the major contributor to the overall high risk of bias. External validity and model reproducibility are lacking in most studies. Future studies should focus on external validity, diminish risk of bias, and enhance real-world clinical relevance.
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Affiliation(s)
- Fengying Zhang
- Department of Evidence-Based Medicine and Clinical Epidemiology, West China Hospital, Sichuan University, Chengdu 610041, China; (F.Z.); (W.M.); (S.Z.)
| | - Yan Liu
- Department of Clinical Pharmacy, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200092, China;
| | - Weijie Ma
- Department of Evidence-Based Medicine and Clinical Epidemiology, West China Hospital, Sichuan University, Chengdu 610041, China; (F.Z.); (W.M.); (S.Z.)
| | - Shengming Zhao
- Department of Evidence-Based Medicine and Clinical Epidemiology, West China Hospital, Sichuan University, Chengdu 610041, China; (F.Z.); (W.M.); (S.Z.)
| | - Jin Chen
- Department of Evidence-Based Medicine and Clinical Epidemiology, West China Hospital, Sichuan University, Chengdu 610041, China; (F.Z.); (W.M.); (S.Z.)
- Correspondence: (J.C.); (Z.G.)
| | - Zhichun Gu
- Department of Pharmacy, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
- Shanghai Anticoagulation Pharmacist Alliance, Shanghai Pharmaceutical Association, Shanghai 200040, China
- Correspondence: (J.C.); (Z.G.)
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Ma W, Li H, Dong L, Zhou Q, Fu B, Hou JL, Wang J, Qin W, Chen J. Warfarin maintenance dose prediction for Chinese after heart valve replacement by a feedforward neural network with equal stratified sampling. Sci Rep 2021; 11:13778. [PMID: 34215839 PMCID: PMC8253817 DOI: 10.1038/s41598-021-93317-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 06/23/2021] [Indexed: 02/05/2023] Open
Abstract
Patients requiring low-dose warfarin are more likely to suffer bleeding due to overdose. The goal of this work is to improve the feedforward neural network model's precision in predicting the low maintenance dose for Chinese in the aspect of training data construction. We built the model from a resampled dataset created by equal stratified sampling (maintaining the same sample number in three dose-groups with a total of 3639) and performed internal and external validations. Comparing to the model trained from the raw dataset of 19,060 eligible cases, we improved the low-dose group's ideal prediction percentage from 0.7 to 9.6% and maintained the overall performance (76.4% vs. 75.6%) in external validation. We further built neural network models on single-dose subsets to invest whether the subsets samples were sufficient and whether the selected factors were appropriate. The training set sizes were 1340 and 1478 for the low and high dose subsets; the corresponding ideal prediction percentages were 70.2% and 75.1%. The training set size for the intermediate dose varied and was 1553, 6214, and 12,429; the corresponding ideal prediction percentages were 95.6, 95.1%, and 95.3%. Our conclusion is that equal stratified sampling can be a considerable alternative approach in training data construction to build drug dosing models in the clinic.
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Affiliation(s)
- Weijie Ma
- Department of Evidence-Based Medicine and Clinical Epidemiology, School of Medicine/West China Hospital, Sichuan University, No. 17, Section 3, Renmin South Road, Chengdu, 610041, Sichuan, China
| | - Hongying Li
- College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Li Dong
- Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Qin Zhou
- Department of Nutrition, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Bo Fu
- Department of Cardiovascular Surgery, Tianjin Central Hospital, Tianjin, China
| | - Jiang-Long Hou
- Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jing Wang
- Department of Career Development Division, The Fourth Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Wenzhe Qin
- Department of Social Medicine and Health Management, Shandong University, Jinan, Shandong, China
| | - Jin Chen
- Department of Evidence-Based Medicine and Clinical Epidemiology, School of Medicine/West China Hospital, Sichuan University, No. 17, Section 3, Renmin South Road, Chengdu, 610041, Sichuan, China.
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Gu ZC, Huang SR, Dong L, Zhou Q, Wang J, Fu B, Chen J. An Adapted Neural-Fuzzy Inference System Model Using Preprocessed Balance Data to Improve the Predictive Accuracy of Warfarin Maintenance Dosing in Patients After Heart Valve Replacement. Cardiovasc Drugs Ther 2021; 36:879-889. [PMID: 33877502 DOI: 10.1007/s10557-021-07191-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/14/2021] [Indexed: 02/05/2023]
Abstract
BACKGROUND Tailoring warfarin use poses a challenge for physicians and pharmacists due to its narrow therapeutic window and substantial inter-individual variability. This study aimed to create an adapted neural-fuzzy inference system (ANFIS) model using preprocessed balance data to improve the predictive accuracy of warfarin maintenance dosing in Chinese patients undergoing heart valve replacement (HVR). METHODS This retrospective study enrolled patients who underwent HVR between June 1, 2012, and June 1, 2016, from 35 centers in China. The primary outcomes were the mean difference between predicted warfarin dose by ANFIS models and actual dose and the models' predictive accuracy, including the ideal predicted percentage, the mean absolute error (MAE), and the mean squared error (MSE). The eligible cases were divided into training, internal validation, and external validation groups. We explored input variables by univariate analysis of a general linear model and created two ANFIS models using imbalanced and balanced training sets. We finally compared the primary outcomes between the imbalanced and balanced ANFIS models in both internal and external validation sets. Stratified analyses were conducted across warfarin doses (low, medium, and high doses). RESULTS A total of 15,108 patients were included and grouped as follows: 12,086 in the imbalanced training set; 2820 in the balanced training set; 1511 in the internal validation set; and 1511 in the external validation set. Eight variables were explored as predictors related to warfarin maintenance doses, and imbalanced and balanced ANFIS models with multi-fuzzy rules were developed. The results showed a low mean difference between predicted and actual doses (< 0.3 mg/d for each model) and an accurate prediction property in both the imbalanced model (ideal prediction percentage, 74.39-78.16%; MAE, 0.37 mg/daily; MSE, 0.39 mg/daily) and the balanced model (ideal prediction percentage, 73.46-75.31%; MAE, 0.42 mg/daily; MSE, 0.43 mg/daily). Compared to the imbalanced model, the balanced model had a significantly higher prediction accuracy in the low-dose (14.46% vs. 3.01%; P < 0.001) and the high-dose warfarin groups (34.71% vs. 23.14%; P = 0.047). The results from the external validation cohort confirmed this finding. CONCLUSIONS The ANFIS model can accurately predict the warfarin maintenance dose in patients after HVR. Through data preprocessing, the balanced model contributed to improved prediction ability in the low- and high-dose warfarin groups.
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Affiliation(s)
- Zhi-Chun Gu
- Department of Pharmacy, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Shou-Rui Huang
- Department of Evidence-Based Medicine and Clinical epidemiology, West China Hospital, Sichuan University, Chengdu, China
| | - Li Dong
- Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Qin Zhou
- Department of Nutrition, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jing Wang
- Department of Career Development Division, The Fourth Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Bo Fu
- Department of Cardiovascular Surgery, Tianjin Central Hospital, Tianjin, China
| | - Jin Chen
- Department of Evidence-Based Medicine and Clinical epidemiology, West China Hospital, Sichuan University, Chengdu, China.
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Cao H, Jiang S, Lv M, Wu T, Chen W, Zhang J. Effectiveness of the Alfalfa App in Warfarin Therapy Management for Patients Undergoing Venous Thrombosis Prevention and Treatment: Cohort Study. JMIR Mhealth Uhealth 2021; 9:e23332. [PMID: 33650976 PMCID: PMC7967226 DOI: 10.2196/23332] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 12/13/2020] [Accepted: 01/08/2021] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Over the years, the internet has enabled considerable progress in the management of chronic diseases, especially hypertension and diabetes. It also provides novel opportunities in online anticoagulation management. Nevertheless, there is insufficient evidence regarding the effectiveness of online anticoagulation management. OBJECTIVE This study explored the effectiveness and safety of warfarin management via the Alfalfa app, so as to provide evidence in support of anticoagulant management through online services. METHODS In this retrospective, observational cohort study, 824 patients were included. In the offline group, patients went to the hospital clinic for warfarin management. In the Alfalfa app group, patients reported the dose of warfarin, current international normalized ratio (INR) value, and other related information through the Alfalfa app. Physicians or pharmacists used the app to adjust the dose of warfarin and determined the time for the next blood INR testing. Patients completed INR testing by point-of-care at home or hospital. The primary outcome of the study was the percentage of time in therapeutic range (TTR). Secondary outcomes included minor and major bleeding events, thrombotic events, warfarin-related emergency department visits, hospital admissions, and high INR values. RESULTS The TTR and percentage of INR values in the range were significantly higher in the Alfalfa app group than in the offline group (79.35% vs 52.38%, P<.001; 3314/4282, 77.39% vs 2005/4202, 47.72%, P<.001, respectively). Patients managed via the Alfalfa app had lower rates of subtherapeutic (172/4282, 4.02% vs 388/4202, 9.23%; P<.001), supratherapeutic (487/4282, 11.37% vs 882/4202, 20.99%; P<.001), and extreme subtherapeutic INR values (290/4282, 6.77% vs 910/4202, 21.66%; P<.001). Additionally, the Alfalfa app group had lower incidences of major bleeding (2/425, 0.5% vs 12/399, 3.0%; P=.005), warfarin-related emergency department visits (13/425, 3.1% vs 37/399, 9.3%; P<.001), and hospital admissions (1/425, 0.2% vs 12/399, 3.0%; P=.001) compared with the offline group. However, the Alfalfa app group had a higher incidence of minor bleeding than the offline group (45/425, 10.6% vs 20/399, 5.0%; P=.003). There were similar incidences in extreme supratherapeutic INR values (19/4282, 0.44% vs 17/4202, 0.40%; P=.78) and thromboembolic events (1/425, 0.2% vs 1/399, 0.3%; P=.53) between the two groups. CONCLUSIONS Warfarin management is superior via the Alfalfa app than via offline services in terms of major bleeding events, warfarin-related emergency department visits, and hospital admissions.
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Affiliation(s)
- Hua Cao
- Department of Cardiac Surgery, Fujian Medical University Union Hospital, Fuzhou, China
- Department of Cardiac Surgery, Fujian Maternity and Children Health Hospital, Fuzhou, China
| | - Shaojun Jiang
- Department of Pharmacy, Fujian Medical University Union Hospital, Fuzhou, China
- College of Pharmacy, Fujian Medical University, Fuzhou, China
| | - Meina Lv
- Department of Pharmacy, Fujian Medical University Union Hospital, Fuzhou, China
- College of Pharmacy, Fujian Medical University, Fuzhou, China
| | - Tingting Wu
- Department of Pharmacy, Fujian Medical University Union Hospital, Fuzhou, China
- College of Pharmacy, Fujian Medical University, Fuzhou, China
| | - Wenjun Chen
- Department of Pharmacy, Fujian Medical University Union Hospital, Fuzhou, China
- College of Pharmacy, Fujian Medical University, Fuzhou, China
| | - Jinhua Zhang
- Department of Pharmacy, Fujian Medical University Union Hospital, Fuzhou, China
- College of Pharmacy, Fujian Medical University, Fuzhou, China
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Zhang J, Zhou Q, Nelson G. Effect of Continuous Nursing Intervention of Artificial Intelligence on Discharged Patients after Heart Valve Replacement and Application of Omaha System (Preprint). JMIR Med Inform 2020. [DOI: 10.2196/18962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Zhang J, Wu T, Chen W, Fu J, Xia X, Chen L. Effect of Gene-Based Warfarin Dosing on Anticoagulation Control and Clinical Events in a Real-World Setting. Front Pharmacol 2020; 10:1527. [PMID: 32038232 PMCID: PMC6988825 DOI: 10.3389/fphar.2019.01527] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Accepted: 11/26/2019] [Indexed: 12/20/2022] Open
Abstract
The cytochrome P450 2C9 and vitamin K epoxide reductase complex subunit 1 genotypes are associated with anticoagulation control and the clinical events in warfarin therapy. However, the clinical utility of gene-based warfarin dosing (GBWD) is controversial. We compared the anticoagulation control and clinical events related to warfarin with GBWD to those with clinically fixed dosing (CFD). A retrospective cohort study was conducted in a real-world setting. Of the 915 patients who were reviewed, 844 patients met the study-entry criteria; 413 cases were guided by GBWD using the International Warfarin Pharmacogenetic Consortium algorithm; 431 cases were guided by CFD (2.5 mg/day). The primary outcomes were the time needed to achieve the therapeutic International Normalized Ratio (INR) and the time in the therapeutic range (TTR) during a 3-month timeframe. The time needed to achieve the therapeutic INR (in days) for patients in the GBWD group was shorter than that for patients in the CFD group (10.21 ± 4.68 vs. 14.31 ± 8.26, P < 0.001). The overall TTR (Day 4-90) was significantly different between the GBWD group and CFD group (56.86 ± 10.72 vs. 52.87 ± 13.92, P = 0.007).In subgroup analysis, the TTR was also significantly different between the GBWD group and CFD group during the first month of treatment (Day 4-14: 54.28 ± 21.90 vs. 47.01 ± 26.25, P = 0.012; Day 15-28: 59.60 ± 20.12 vs. 51.71 ± 18.96, P = 0.001). However, no significant difference in the TTR was observed after 29 days of treatment. These data suggest that GBWD provided clinical benefits.
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Affiliation(s)
- Jinhua Zhang
- Department of Pharmacy, Fujian Medical University Union Hospital, Fuzhou, China
- College of Pharmacy, Fujian Medical University, Fuzhou, China
| | - Tingting Wu
- Department of Pharmacy, Fujian Medical University Union Hospital, Fuzhou, China
- College of Pharmacy, Fujian Medical University, Fuzhou, China
| | - Wenjun Chen
- Department of Pharmacy, Fujian Medical University Union Hospital, Fuzhou, China
- College of Pharmacy, Fujian Medical University, Fuzhou, China
| | - Jinglan Fu
- Department of Pharmacy, Fujian Medical University Union Hospital, Fuzhou, China
- College of Pharmacy, Fujian Medical University, Fuzhou, China
| | - Xiaotong Xia
- Department of Pharmacy, Fujian Medical University Union Hospital, Fuzhou, China
- College of Pharmacy, Fujian Medical University, Fuzhou, China
| | - Liangwan Chen
- Department of Cardiovascular Surgery, Fujian Medical University Union Hospital, Fuzhou, China
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The Prediction Model of Warfarin Individual Maintenance Dose for Patients Undergoing Heart Valve Replacement, Based on the Back Propagation Neural Network. Clin Drug Investig 2019; 40:41-53. [DOI: 10.1007/s40261-019-00850-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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