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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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Petch J, Nelson W, Wu M, Ghassemi M, Benz A, Fatemi M, Di S, Carnicelli A, Granger C, Giugliano R, Hong H, Patel M, Wallentin L, Eikelboom J, Connolly SJ. Optimizing warfarin dosing for patients with atrial fibrillation using machine learning. Sci Rep 2024; 14:4516. [PMID: 38402362 PMCID: PMC10894214 DOI: 10.1038/s41598-024-55110-9] [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: 05/02/2023] [Accepted: 02/20/2024] [Indexed: 02/26/2024] Open
Abstract
While novel oral anticoagulants are increasingly used to reduce risk of stroke in patients with atrial fibrillation, vitamin K antagonists such as warfarin continue to be used extensively for stroke prevention across the world. While effective in reducing the risk of strokes, the complex pharmacodynamics of warfarin make it difficult to use clinically, with many patients experiencing under- and/or over- anticoagulation. In this study we employed a novel implementation of deep reinforcement learning to provide clinical decision support to optimize time in therapeutic International Normalized Ratio (INR) range. We used a novel semi-Markov decision process formulation of the Batch-Constrained deep Q-learning algorithm to develop a reinforcement learning model to dynamically recommend optimal warfarin dosing to achieve INR of 2.0-3.0 for patients with atrial fibrillation. The model was developed using data from 22,502 patients in the warfarin treated groups of the pivotal randomized clinical trials of edoxaban (ENGAGE AF-TIMI 48), apixaban (ARISTOTLE) and rivaroxaban (ROCKET AF). The model was externally validated on data from 5730 warfarin-treated patients in a fourth trial of dabigatran (RE-LY) using multilevel regression models to estimate the relationship between center-level algorithm consistent dosing, time in therapeutic INR range (TTR), and a composite clinical outcome of stroke, systemic embolism or major hemorrhage. External validation showed a positive association between center-level algorithm-consistent dosing and TTR (R2 = 0.56). Each 10% increase in algorithm-consistent dosing at the center level independently predicted a 6.78% improvement in TTR (95% CI 6.29, 7.28; p < 0.001) and a 11% decrease in the composite clinical outcome (HR 0.89; 95% CI 0.81, 1.00; p = 0.015). These results were comparable to those of a rules-based clinical algorithm used for benchmarking, for which each 10% increase in algorithm-consistent dosing independently predicted a 6.10% increase in TTR (95% CI 5.67, 6.54, p < 0.001) and a 10% decrease in the composite outcome (HR 0.90; 95% CI 0.83, 0.98, p = 0.018). Our findings suggest that a deep reinforcement learning algorithm can optimize time in therapeutic range for patients taking warfarin. A digital clinical decision support system to promote algorithm-consistent warfarin dosing could optimize time in therapeutic range and improve clinical outcomes in atrial fibrillation globally.
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Affiliation(s)
- Jeremy Petch
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada.
- Population Health Research Institute, Hamilton, ON, Canada.
- Division of Cardiology, Department of Medicine, McMaster University, Hamilton, ON, Canada.
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.
| | - Walter Nelson
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | - Mary Wu
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Marzyeh Ghassemi
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Institute for Medical and Evaluative Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Vector Institute, Toronto, ON, Canada
| | - Alexander Benz
- Population Health Research Institute, Hamilton, ON, Canada
- Department of Cardiology, University Medical Center, Johannes Gutenberg University Mainz, Mainz, Germany
| | | | - Shuang Di
- Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Anthony Carnicelli
- Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, NC, USA
- Duke Clinical Research Institute, Duke University, Durham, NC, USA
| | - Christopher Granger
- Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, NC, USA
- Duke Clinical Research Institute, Duke University, Durham, NC, USA
| | - Robert Giugliano
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Hwanhee Hong
- Duke Clinical Research Institute, Duke University, Durham, NC, USA
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Manesh Patel
- Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, NC, USA
| | - Lars Wallentin
- Department of Medical Sciences, Cardiology, Uppsala University, Uppsala, Sweden
- Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | - John Eikelboom
- Population Health Research Institute, Hamilton, ON, Canada
- Division of Hematology and Thromboembolism, Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Stuart J Connolly
- Population Health Research Institute, Hamilton, ON, Canada
- Division of Cardiology, Department of Medicine, McMaster University, Hamilton, ON, Canada
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Dryden L, Song J, Valenzano TJ, Yang Z, Debnath M, Lin R, Topolovec-Vranic J, Mamdani M, Antoniou T. Evaluation of Machine Learning Approaches for Predicting Warfarin Discharge Dose in Cardiac Surgery Patients: Retrospective Algorithm Development and Validation Study. JMIR Cardio 2023; 7:e47262. [PMID: 38055310 PMCID: PMC10733832 DOI: 10.2196/47262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 09/28/2023] [Accepted: 10/04/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND Warfarin dosing in cardiac surgery patients is complicated by a heightened sensitivity to the drug, predisposing patients to adverse events. Predictive algorithms are therefore needed to guide warfarin dosing in cardiac surgery patients. OBJECTIVE This study aimed to develop and validate an algorithm for predicting the warfarin dose needed to attain a therapeutic international normalized ratio (INR) at the time of discharge in cardiac surgery patients. METHODS We abstracted variables influencing warfarin dosage from the records of 1031 encounters initiating warfarin between April 1, 2011, and November 29, 2019, at St Michael's Hospital in Toronto, Ontario, Canada. We compared the performance of penalized linear regression, k-nearest neighbors, random forest regression, gradient boosting, multivariate adaptive regression splines, and an ensemble model combining the predictions of the 5 regression models. We developed and validated separate models for predicting the warfarin dose required for achieving a discharge INR of 2.0-3.0 in patients undergoing all forms of cardiac surgery except mechanical mitral valve replacement and a discharge INR of 2.5-3.5 in patients receiving a mechanical mitral valve replacement. For the former, we selected 80% of encounters (n=780) who had initiated warfarin during their hospital admission and had achieved a target INR of 2.0-3.0 at the time of discharge as the training cohort. Following 10-fold cross-validation, model accuracy was evaluated in a test cohort comprised solely of cardiac surgery patients. For patients requiring a target INR of 2.5-3.5 (n=165), we used leave-p-out cross-validation (p=3 observations) to estimate model performance. For each approach, we determined the mean absolute error (MAE) and the proportion of predictions within 20% of the true warfarin dose. We retrospectively evaluated the best-performing algorithm in clinical practice by comparing the proportion of cardiovascular surgery patients discharged with a therapeutic INR before (April 2011 and July 2019) and following (September 2021 and May 2, 2022) its implementation in routine care. RESULTS Random forest regression was the best-performing model for patients with a target INR of 2.0-3.0, an MAE of 1.13 mg, and 39.5% of predictions of falling within 20% of the actual therapeutic discharge dose. For patients with a target INR of 2.5-3.5, the ensemble model performed best, with an MAE of 1.11 mg and 43.6% of predictions being within 20% of the actual therapeutic discharge dose. The proportion of cardiovascular surgery patients discharged with a therapeutic INR before and following implementation of these algorithms in clinical practice was 47.5% (305/641) and 61.1% (11/18), respectively. CONCLUSIONS Machine learning algorithms based on routinely available clinical data can help guide initial warfarin dosing in cardiac surgery patients and optimize the postsurgical anticoagulation of these patients.
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Affiliation(s)
| | | | | | - Zhen Yang
- Unity Health Toronto, Toronto, ON, Canada
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Li J, Chen T, Jie F, Xiang H, Huang L, Jiang H, Lu F, Zhu S, Wu L, Tang Y. Impact of VKORC1, CYP2C9, CYP1A2, UGT1A1, and GGCX polymorphisms on warfarin maintenance dose: Exploring a new algorithm in South Chinese patients accept mechanical heart valve replacement. Medicine (Baltimore) 2022; 101:e29626. [PMID: 35866816 PMCID: PMC9302374 DOI: 10.1097/md.0000000000029626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Warfarin is the most recommended oral anticoagulant after artificial mechanical valve replacement therapy. However, the narrow therapeutic window and varying safety and efficacy in individuals make dose determination difficult. It may cause adverse events such as hemorrhage or thromboembolism. Therefore, advanced algorithms are urgently required for the use of warfarin. OBJECTIVE To establish a warfarin dose model for patients after prosthetic mechanical valve replacement in southern China in combination with clinical and genetic variables, and to improve the accuracy and ideal prediction percentage of the model. METHODS Clinical data of 476 patients were tracked and recorded in detail. The gene polymorphisms of VKORC1 (rs9923231, rs9934438, rs7196161, and rs7294), CYP2C9 (rs1057910), CYP1A2 (rs2069514), GGCX (rs699664), and UGT1A1 (rs887829) were determined using Sanger sequencing. Multiple linear regressions were used to analyze the gene polymorphisms and the contribution of clinical data variables; the variables that caused multicollinearity were screened stepwise and excluded to establish an algorithm model for predicting the daily maintenance dose of warfarin. The ideal predicted percentage was used to test clinical effectiveness. RESULTS A total of 395 patients were included. Univariate linear regression analysis suggested that CYP1A2 (rs2069514) and UGT1A1 (rs887829) were not associated with the daily maintenance dose of warfarin. The new algorithm model established based on multiple linear regression was as follows: Y = 1.081 - 0.011 (age) + 1.532 (body surface area)-0.807 (rs9923231 AA) + 1.788 (rs9923231 GG) + 0.530 (rs1057910 AA)-1.061 (rs1057910 AG)-0.321 (rs699664 AA). The model accounted for 61.7% of individualized medication differences, with an ideal prediction percentage of 69%. CONCLUSION GGCX (rs699664) may be a potential predictor of warfarin dose, and our newly established model is expected to guide the individualized use of warfarin in clinical practice in southern China.
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Affiliation(s)
- Jin Li
- Emergency Department of the Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Tao Chen
- School of Science, Nanchang University, Nanchang, China
| | - Fangfang Jie
- School of Science, Nanchang University, Nanchang, China
| | - Haiyan Xiang
- Department of Cardiovascular Surgery, the Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Li Huang
- Department of Cardiovascular Surgery, the Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Hongfa Jiang
- Department of Cardiothoracic Surgery, Jiangxi Chest Hospital, Nanchang, China
| | - Fei Lu
- Comprehensive Intervention Department of the Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Shuqiang Zhu
- Department of Cardiovascular Surgery, the Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Lidong Wu
- Emergency Department of the Second Affiliated Hospital of Nanchang University, Nanchang, China
- * Correspondence: Lidong Wu, Emergency Department of the Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330000, China (e-mail: ); Yanhua Tang, Department of Cardiovascular Surgery, the Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330000, China (e-mail: )
| | - Yanhua Tang
- Department of Cardiovascular Surgery, the Second Affiliated Hospital of Nanchang University, Nanchang, China
- * Correspondence: Lidong Wu, Emergency Department of the Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330000, China (e-mail: ); Yanhua Tang, Department of Cardiovascular Surgery, the Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330000, China (e-mail: )
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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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The Identification Nanoparticle Sensor Using Back Propagation Neural Network Optimized by Genetic Algorithm. JOURNAL OF SENSORS 2021. [DOI: 10.1155/2021/7548329] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
This study draws attention towards the application of identification nanoparticle (NPs) sensor based on back propagation (BP) neural network optimized by genetic algorithm (GA) in the early diagnosis of cancer cells. In this study, the traditional and optimized BP neural networks are compared in terms of error between the actual value and the predictive value, and they are further applied to the NP sensor for early diagnosis of cancer cells. The results show that the root mean square (RMS) and mean absolute error (MAE) of the optimized BP neural network are comparatively much smaller than the traditional ones. The particle size of silicon-coated fluorescent NPs is about 105 nm, and the relative fluorescence intensity of silicon-coated fluorescent NPs decreases slightly, maintaining the accuracy value above 80%. In the fluorescence imaging, it is found that there is obvious green fluorescence on the surface of the cancer cells, and the cancer cells still emit bright green fluorescence under the dark-field conditions. In this study, a phenolic resin polymer CMK-2 with a large surface area is successfully combined with Au. NPs with good dielectric property and bioaffinity are selectively bonded to the modified electrode through a sulfur-gold bond to prepare NP sensor. The sensor shows good stability, selectivity, and anti-interference property, providing a new method for the detection of early cancer cells.
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Development of a system to support warfarin dose decisions using deep neural networks. Sci Rep 2021; 11:14745. [PMID: 34285309 PMCID: PMC8292496 DOI: 10.1038/s41598-021-94305-2] [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: 05/04/2021] [Accepted: 07/09/2021] [Indexed: 11/09/2022] Open
Abstract
The first aim of this study was to develop a prothrombin time international normalized ratio (PT INR) prediction model. The second aim was to develop a warfarin maintenance dose decision support system as a precise warfarin dosing platform. Data of 19,719 inpatients from three institutions was analyzed. The PT INR prediction algorithm included dense and recurrent neural networks, and was designed to predict the 5th-day PT INR from data of days 1-4. Data from patients in one hospital (n = 22,314) was used to train the algorithm which was tested with the datasets from the other two hospitals (n = 12,673). The performance of 5th-day PT INR prediction was compared with 2000 predictions made by 10 expert physicians. A generator of individualized warfarin dose-PT INR tables which simulated the repeated administration of varying doses of warfarin was developed based on the prediction model. The algorithm outperformed humans with accuracy terms of within ± 0.3 of the actual value (machine learning algorithm: 10,650/12,673 cases (84.0%), expert physicians: 1647/2000 cases (81.9%), P = 0.014). In the individualized warfarin dose-PT INR tables generated by the algorithm, the 8th-day PT INR predictions were within 0.3 of actual value in 450/842 cases (53.4%). An artificial intelligence-based warfarin dosing algorithm using a recurrent neural network outperformed expert physicians in predicting future PT INRs. An individualized warfarin dose-PT INR table generator which was constructed based on this algorithm was acceptable.
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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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Nguyen VL, Nguyen HD, Cho YS, Kim HS, Han IY, Kim DK, Ahn S, Shin JG. Comparison of multivariate linear regression and a machine learning algorithm developed for prediction of precision warfarin dosing in a Korean population. J Thromb Haemost 2021; 19:1676-1686. [PMID: 33774911 DOI: 10.1111/jth.15318] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 03/19/2021] [Accepted: 03/22/2021] [Indexed: 01/10/2023]
Abstract
BACKGROUND Personalized warfarin dosing is influenced by various factors including genetic and non-genetic factors. Multiple linear regression (LR) is known as a conventional method to develop predictive models. Recently, machine learning approaches have been extensively implemented for warfarin dosing due to the hypothesis of non-linear association between covariates and stable warfarin dose. OBJECTIVE To extend the multiple linear regression algorithm for personalized warfarin dosing in a Korean population and compare with a machine learning--based algorithm. METHOD From this cohort study, we collected information on 650 patients taking warfarin who achieved steady state including demographic information, indications, comorbidities, comedications, habits, and genetic factors. The dataset was randomly split into training set (90%) and test set (10%). The LR and machine learning (gradient boosting machine [GBM]) models were developed on the training set and were evaluated on the test set. RESULT LR and GBM models were comparable in terms of accuracy of ideal dose (75.38% and 73.85%), correlation (0.77 and 0.73), mean absolute error (0.58 mg/day and 0.64 mg/day), and root mean square error (0.82 mg/day and 0.9 mg/day), respectively. VKORC1 genotype, CYP2C9 genotype, age, and weight were the highest contributors and could obtain 80% of maximum performance in both models. CONCLUSION This study shows that our LR and GMB models are satisfactory to predict warfarin dose in our dataset. Both models showed similar performance and feature contribution characteristics. LR may be the appropriate model due to its simplicity and interpretability.
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Affiliation(s)
- Van Lam Nguyen
- Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan, Korea
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Korea
| | - Hoang Dat Nguyen
- Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan, Korea
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Korea
| | - Yong-Soon Cho
- Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan, Korea
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Korea
| | - Ho-Sook Kim
- Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan, Korea
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Korea
| | - Il-Yong Han
- Department of Thoracic and Cardiovascular Surgery, Inje University Busan Paik Hospital, Busan, Korea
| | - Dae-Kyeong Kim
- Division of Cardiology, Department of Internal Medicine, Inje University Busan Paik Hospital, Busan, Korea
| | - Sangzin Ahn
- Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan, Korea
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Korea
| | - Jae-Gook Shin
- Department of Pharmacology and Pharmacogenomics Research Center, Inje University College of Medicine, Busan, Korea
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Korea
- Department of Clinical Pharmacology, Inje University Bsuan Paik Hospital, Busan, Korea
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10
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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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11
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Colin PJ, Eleveld DJ, Thomson AH. Genetic Algorithms as a Tool for Dosing Guideline Optimization: Application to Intermittent Infusion Dosing for Vancomycin in Adults. CPT Pharmacometrics Syst Pharmacol 2020; 9:294-302. [PMID: 32383808 PMCID: PMC7239335 DOI: 10.1002/psp4.12512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 04/05/2020] [Indexed: 11/10/2022] Open
Abstract
This paper demonstrates the use of a genetic algorithm (GA) for the optimization of a dosing guideline. GAs are well‐suited to derive combinations of doses and dosing intervals that go into a dosing guideline when the number of possible combinations rule out the calculation of all possible outcomes. GAs also allow for different constraints to be imposed on the optimization process to safeguard the clinical feasibility of the dosing guideline. In this work, we demonstrate the use of a GA for the optimization of intermittent vancomycin administration in adult patients. Constraints were placed on the dose strengths, the length of the dosing intervals, and the maximum infusion rate. In addition, flexibility with respect to the timing of the first maintenance dose was included in the optimization process. The GA‐based optimal solution is compared with the Scottish Antimicrobial Prescribing Group vancomycin guideline.
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Affiliation(s)
- Pieter J. Colin
- Department of Anesthesiology University Medical Center Groningen University of Groningen Groningen The Netherlands
| | - Douglas J. Eleveld
- Department of Anesthesiology University Medical Center Groningen University of Groningen Groningen The Netherlands
| | - Alison H. Thomson
- Strathclyde Institute of Pharmacy and Biomedical Sciences University of Strathclyde Glasgow UK
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12
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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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