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Guo RX, Tian X, Bazoukis G, Tse G, Hong S, Chen KY, Liu T. Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia. Pacing Clin Electrophysiol 2024; 47:789-801. [PMID: 38712484 DOI: 10.1111/pace.14995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 03/29/2024] [Accepted: 04/18/2024] [Indexed: 05/08/2024]
Abstract
The rapid growth in computational power, sensor technology, and wearable devices has provided a solid foundation for all aspects of cardiac arrhythmia care. Artificial intelligence (AI) has been instrumental in bringing about significant changes in the prevention, risk assessment, diagnosis, and treatment of arrhythmia. This review examines the current state of AI in the diagnosis and treatment of atrial fibrillation, supraventricular arrhythmia, ventricular arrhythmia, hereditary channelopathies, and cardiac pacing. Furthermore, ChatGPT, which has gained attention recently, is addressed in this paper along with its potential applications in the field of arrhythmia. Additionally, the accuracy of arrhythmia diagnosis can be improved by identifying electrode misplacement or erroneous swapping of electrode position using AI. Remote monitoring has expanded greatly due to the emergence of contactless monitoring technology as wearable devices continue to develop and flourish. Parallel advances in AI computing power, ChatGPT, availability of large data sets, and more have greatly expanded applications in arrhythmia diagnosis, risk assessment, and treatment. More precise algorithms based on big data, personalized risk assessment, telemedicine and mobile health, smart hardware and wearables, and the exploration of rare or complex types of arrhythmia are the future direction.
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Affiliation(s)
- Rong-Xin Guo
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - Xu Tian
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - George Bazoukis
- Department of Cardiology, Larnaca General Hospital, Inomenon Polition Amerikis, Larnaca, Cyprus
- Department of Basic and Clinical Sciences, University of Nicosia Medical School, Nicosia, Cyprus
| | - Gary Tse
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
- Cardiovascular Analytics Group, PowerHealth Research Institute, Hong Kong, China
- School of Nursing and Health Studies, Hong Kong Metropolitan University, Hong Kong, China
| | - Shenda Hong
- National Institute of Health Data Science, Peking University, Beijing, China
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Kang-Yin Chen
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - Tong Liu
- Tianjin Key Laboratory of lonic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
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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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Danilatou V, Dimopoulos D, Kostoulas T, Douketis J. Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review. Thromb Haemost 2024. [PMID: 38574756 DOI: 10.1055/a-2299-4758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
BACKGROUND Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records. We aimed to explore ML-CPMs' applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment. METHODS Three databases were searched: PubMed, Google Scholar, and IEEE electronic library. Inclusion criteria focused on studies using structured data, excluding non-English publications, studies on non-humans, and certain data types such as natural language processing and image processing. Studies involving pregnant women, cancer patients, and children were also excluded. After excluding irrelevant studies, a total of 77 studies were included. RESULTS Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver operating area under the curve in the four clinical domains that were explored. However, the majority of the studies were retrospective, monocentric, and lacked detailed model architecture description and external validation, which are essential for quality audit. This review identified research gaps and highlighted challenges related to standardized reporting, reproducibility, and model comparison. CONCLUSION ML-CPMs show promise in improving risk assessment and individualized treatment recommendations in VTE. Apparently, there is an urgent need for standardized reporting and methodology for ML models, external validation, prospective and real-world data studies, as well as interventional studies to evaluate the impact of artificial intelligence in VTE.
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Affiliation(s)
- Vasiliki Danilatou
- School of Medicine, European University of Cyprus, Nicosia, Cyprus
- Healthcare Division, Sphynx Technology Solutions, Nicosia, Cyprus
| | - Dimitrios Dimopoulos
- School of Engineering, Department of Information and Communication Systems Engineering, University of the Aegean, North Aegean, Greece
| | - Theodoros Kostoulas
- School of Engineering, Department of Information and Communication Systems Engineering, University of the Aegean, North Aegean, Greece
| | - James Douketis
- Department of Medicine, McMaster University, Hamilton, Canada
- Department of Medicine, St. Joseph's Healthcare Hamilton, Ontario, Canada
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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. Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea. Sci Rep 2023; 13:22461. [PMID: 38105280 PMCID: PMC10725866 DOI: 10.1038/s41598-023-49831-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 12/12/2023] [Indexed: 12/19/2023] Open
Abstract
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.
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Affiliation(s)
- Heejung Choi
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43 gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Hee Jun Kang
- Division of Cardiology, Asan Medical Center, 88, Olympicro 43gil, Songpagu, Seoul, 05505, Republic of Korea
| | - Imjin Ahn
- Department of Information Medicine, Asan Medical Center, 88, Olympicro 43gil, Songpagu, Seoul, 05505, Republic of Korea
| | - Hansle Gwon
- Department of Information Medicine, Asan Medical Center, 88, Olympicro 43gil, Songpagu, Seoul, 05505, Republic of Korea
| | - Yunha Kim
- Department of Information Medicine, Asan Medical Center, 88, Olympicro 43gil, Songpagu, Seoul, 05505, Republic of Korea
| | - Hyeram Seo
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43 gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Ha Na Cho
- Department of Information Medicine, Asan Medical Center, 88, Olympicro 43gil, Songpagu, Seoul, 05505, Republic of Korea
| | - JiYe Han
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43 gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Minkyoung Kim
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43 gil, Songpa-gu, Seoul, 05505, Republic of Korea
| | - Gaeun Kee
- Department of Information Medicine, Asan Medical Center, 88, Olympicro 43gil, Songpagu, Seoul, 05505, Republic of Korea
| | - Seohyun Park
- Department of Information Medicine, Asan Medical Center, 88, Olympicro 43gil, Songpagu, Seoul, 05505, Republic of Korea
| | - Osung Kwon
- Division of Cardiology Department of Internal Medicine, Eunpyeong St Mary's Hospital, Catholic University of Korea, Seoul, Republic of Korea
| | - Jae-Hyung Roh
- Department of Internal Medicine, Chungnam National University College of Medicine, Chungnam National University Sejong Hospital, 20, Bodeum 7-ro, Sejong-si, 30099, Sejong, Republic of Korea
| | - Ah-Ram Kim
- Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Ju Hyeon Kim
- Department of Cardiology, Cardiovascular Center, Korea University Anam Hospital, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea
| | - Tae Joon Jun
- Big Data Research Center, Asan Institute for Life Sciences, Asan Medical Center, 88, Olympicro 43gil, Songpagu, Seoul, 05505, Republic of Korea
| | - Young-Hak Kim
- Division of Cardiology, Department of Information Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympicro 43gil, Songpagu, Seoul, 05505, Republic of Korea.
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Iancu A, Leb I, Prokosch HU, Rödle W. Machine learning in medication prescription: A systematic review. Int J Med Inform 2023; 180:105241. [PMID: 37939541 DOI: 10.1016/j.ijmedinf.2023.105241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 09/17/2023] [Accepted: 09/27/2023] [Indexed: 11/10/2023]
Abstract
BACKGROUND Medication prescription is a complex process that could benefit from current research and development in machine learning through decision support systems. Particularly pediatricians are forced to prescribe medications "off-label" as children are still underrepresented in clinical studies, which leads to a high risk of an incorrect dose and adverse drug effects. METHODS PubMed, IEEE Xplore and PROSPERO were searched for relevant studies that developed and evaluated well-performing machine learning algorithms following the PRISMA statement. Quality assessment was conducted in accordance with the IJMEDI checklist. Identified studies were reviewed in detail, including the required variables for predicting the correct dose, especially of pediatric medication prescription. RESULTS The search identified 656 studies, of which 64 were reviewed in detail and 36 met the inclusion criteria. According to the IJMEDI checklist, five studies were considered to be of high quality. 19 of the 36 studies dealt with the active substance warfarin. Overall, machine learning algorithms based on decision trees or regression methods performed superior regarding their predictive power than algorithms based on neural networks, support vector machines or other methods. The use of ensemble methods like bagging or boosting generally enhanced the accuracy of the dose predictions. The required input and output variables of the algorithms were considerably heterogeneous and differ strongly among the respective substance. CONCLUSIONS By using machine learning algorithms, the prescription process could be simplified and dosing correctness could be enhanced. Despite the heterogenous results among the different substances and cases and the lack of pediatric use cases, the identified approaches and required variables can serve as an excellent starting point for further development of algorithms predicting drug doses, particularly for children. Especially the combination of physiologically-based pharmacokinetic models with machine learning algorithms represents a great opportunity to enhance the predictive power and accuracy of the developed algorithms.
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Affiliation(s)
- Alexa Iancu
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Wetterkreuz 15, 91058 Erlangen, Germany
| | - Ines Leb
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Wetterkreuz 15, 91058 Erlangen, Germany
| | - Hans-Ulrich Prokosch
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Wetterkreuz 15, 91058 Erlangen, Germany
| | - Wolfgang Rödle
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Wetterkreuz 15, 91058 Erlangen, Germany.
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Nigdeli SM, Yücel M, Bekdaş G. A hybrid artificial intelligence model for design of reinforced concrete columns. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-08164-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Siemens A, Anderson SJ, Rassekh SR, Ross CJD, Carleton BC. A Systematic Review of Polygenic Models for Predicting Drug Outcomes. J Pers Med 2022; 12:jpm12091394. [PMID: 36143179 PMCID: PMC9505711 DOI: 10.3390/jpm12091394] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/21/2022] [Accepted: 08/25/2022] [Indexed: 11/16/2022] Open
Abstract
Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The search uncovered 89 papers that incorporated pharmacogenetic variants in the development of polygenic models. It was found that the most common polygenic models were constructed for drug dosing predictions in anticoagulant therapies (n = 27). While nearly all studies found a significant association with their polygenic model and the investigated drug outcome (93.3%), less than half (47.2%) compared the performance of the polygenic model against clinical predictors, and even fewer (40.4%) sought to validate model predictions in an independent cohort. Additionally, the heterogeneity of reported performance measures makes the comparison of models across studies challenging. These findings highlight key considerations for future work in developing polygenic models in pharmacogenomic research.
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Affiliation(s)
- Angela Siemens
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3N1, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC V5Z 4H4, Canada
| | - Spencer J. Anderson
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3N1, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC V5Z 4H4, Canada
| | - S. Rod Rassekh
- Division of Translational Therapeutics, Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3V4, Canada
- Division of Oncology, Hematology and Bone Marrow Transplant, University of British Columbia, Vancouver, BC V6H 3V4, Canada
| | - Colin J. D. Ross
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3N1, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC V5Z 4H4, Canada
- Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Bruce C. Carleton
- Department of Medical Genetics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3N1, Canada
- Division of Translational Therapeutics, Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, BC V6H 3V4, Canada
- Pharmaceutical Outcomes Programme, British Columbia Children’s Hospital, Vancouver, BC V5Z 4H4, Canada
- Correspondence:
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Ma Z, Wang P, Mahesh M, Elmi CP, Atashpanjeh S, Khalighi B, Cheng G, Krishnamurthy M, Khalighi K. Warfarin sensitivity is associated with increased hospital mortality in critically Ill patients. PLoS One 2022; 17:e0267966. [PMID: 35511891 PMCID: PMC9070894 DOI: 10.1371/journal.pone.0267966] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 04/19/2022] [Indexed: 11/23/2022] Open
Abstract
Background Warfarin is a widely used anticoagulant with a narrow therapeutic index and large interpatient variability in the therapeutic dose. Warfarin sensitivity has been reported to be associated with increased incidence of international normalized ratio (INR) > 5. However, whether warfarin sensitivity is a risk factor for adverse outcomes in critically ill patients remains unknown. In the present study, we aimed to evaluate the utility of different machine learning algorithms for the prediction of warfarin sensitivity and to determine the impact of warfarin sensitivity on outcomes in critically ill patients. Methods Nine different machine learning algorithms for the prediction of warfarin sensitivity were tested in the International Warfarin Pharmacogenetic Consortium cohort and Easton cohort. Furthermore, a total of 7,647 critically ill patients was analyzed for warfarin sensitivity on in-hospital mortality by multivariable regression. Covariates that potentially confound the association were further adjusted using propensity score matching or inverse probability of treatment weighting. Results We found that logistic regression (AUC = 0.879, 95% CI: 0.834–0.924) was indistinguishable from support vector machine with a linear kernel, neural network, AdaBoost and light gradient boosting trees, and significantly outperformed all the other machine learning algorithms. Furthermore, we found that warfarin sensitivity predicted by the logistic regression model was significantly associated with worse in-hospital mortality in critically ill patients with an odds ratio (OR) of 1.33 (95% CI, 1.01–1.77). Conclusions Our data suggest that the logistic regression model is the best model for the prediction of warfarin sensitivity clinically and that warfarin sensitivity is likely to be a risk factor for adverse outcomes in critically ill patients.
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Affiliation(s)
- Zhiyuan Ma
- Department of Medicine, St Luke’s University Health Network, Easton, PA, United States of America
- * E-mail: (ZM); (KK)
| | - Ping Wang
- Department of Computer Science, East Carolina University College of Engineering and Technology, Greenville, NC, United States of America
| | - Milan Mahesh
- Drexel University College of Arts and Sciences, Philadelphia, PA, United States of America
| | - Cyrus P. Elmi
- Lehigh University College of Arts and Sciences, Bethlehem, PA, United States of America
| | - Saeid Atashpanjeh
- Department of Biology, University of Hartford, West Hartford, CT, United States of America
| | - Bahar Khalighi
- School of Pharmacy, Temple University, Philadelphia, PA, United States of America
| | - Gang Cheng
- Division of Cardiology, Department of Medicine, University of Louisville School of Medicine, Louisville, KY, United States of America
| | - Mahesh Krishnamurthy
- Department of Medicine, St Luke’s University Health Network, Easton, PA, United States of America
| | - Koroush Khalighi
- Lehigh Valley Heart Institute, Easton, PA, United States of America
- * E-mail: (ZM); (KK)
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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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Maghsoudi R, Mirzarezaee M, Sadeghi M, Nadjar-Araabi B. Determining the adjusted initial treatment dose of warfarin anticoagulant medicine using kernel-based support vector regression. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 214:106589. [PMID: 34963093 DOI: 10.1016/j.cmpb.2021.106589] [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: 03/24/2021] [Revised: 09/22/2021] [Accepted: 12/14/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE A novel research field in bioinformatics is pharmacogenomics and the corresponding applications of artificial intelligence tools. Pharmacogenomics is the study of the relationship between genotype and responses to medical measures such as drug use. One of the most effective drugs is warfarin anticoagulant, but determining its initial treatment dose is challenging. Mistakes in the determination of the initial treatment dose can result directly in patient death. METHODS Some of the most successful techniques for estimating the initial treatment dose are kernel-based methods. However, all the available studies use pre-defined and constant kernels that might not necessarily address the problem's intended requirements. The present study seeks to define and present a new computational kernel extracted from a data set. This process aims to utilize all the data-related statistical features to generate a dose determination tool proportional to the data set with minimum error rate. The kernel-based version of the least square support vector regression estimator was defined. Through this method, a more appropriate approach was proposed for predicting the adjusted dose of warfarin. RESULTS AND CONCLUSION This paper benefits from the International Warfarin Pharmacogenomics Consortium (IWPC) Database. The results obtained in this study demonstrate that the support vector regression with the proposed new kernel can successfully estimate the ideal dosage of warfarin for approximately 68% of patients.
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Affiliation(s)
- Rouhollah Maghsoudi
- Department of Computer Engineering, Science and Research Branch,Islamic Azad University, Tehran, Iran
| | - Mitra Mirzarezaee
- Department of Computer Engineering, Science and Research Branch,Islamic Azad University, Tehran, Iran.
| | - Mehdi Sadeghi
- National Institute of Genetic Engineering and Biotechnology, Tehran, Iran
| | - Babak Nadjar-Araabi
- School of Electrical and Computer Eng, College of Eng, University of Tehran, Iran
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11
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Asiimwe IG, Blockman M, Cohen K, Cupido C, Hutchinson C, Jacobson B, Lamorde M, Morgan J, Mouton JP, Nakagaayi D, Okello E, Schapkaitz E, Sekaggya-Wiltshire C, Semakula JR, Waitt C, Zhang EJ, Jorgensen AL, Pirmohamed M. Stable warfarin dose prediction in sub-Saharan African patients: A machine-learning approach and external validation of a clinical dose-initiation algorithm. CPT Pharmacometrics Syst Pharmacol 2022; 11:20-29. [PMID: 34889080 PMCID: PMC8752108 DOI: 10.1002/psp4.12740] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 09/24/2021] [Accepted: 10/27/2021] [Indexed: 12/11/2022] Open
Abstract
Warfarin remains the most widely prescribed oral anticoagulant in sub‐Saharan Africa. However, because of its narrow therapeutic index, dosing can be challenging. We have therefore (a) evaluated and compared the performance of 21 machine‐learning techniques in predicting stable warfarin dose in sub‐Saharan Black‐African patients and (b) externally validated a previously developed Warfarin Anticoagulation in Patients in Sub‐Saharan Africa (War‐PATH) clinical dose–initiation algorithm. The development cohort included 364 patients recruited from eight outpatient clinics and hospital departments in Uganda and South Africa (June 2018–July 2019). Validation was conducted using an external validation cohort (270 patients recruited from August 2019 to March 2020 in 12 outpatient clinics and hospital departments). Based on the mean absolute error (MAE; mean of absolute differences between the actual and predicted doses), random forest regression (12.07 mg/week; 95% confidence interval [CI], 10.39–13.76) was the best performing machine‐learning technique in the external validation cohort, whereas the worst performing technique was model trees (17.59 mg/week; 95% CI, 15.75–19.43). By comparison, the simple, commonly used regression technique (ordinary least squares) performed similarly to more complex supervised machine‐learning techniques and achieved an MAE of 13.01 mg/week (95% CI, 11.45–14.58). In summary, we have demonstrated that simpler regression techniques perform similarly to more complex supervised machine‐learning techniques. We have also externally validated our previously developed clinical dose–initiation algorithm, which is being prospectively tested for clinical utility.
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Affiliation(s)
- Innocent G Asiimwe
- Department of Pharmacology and Therapeutics, The Wolfson Centre for Personalized Medicine, Medical Research Council Centre for Drug Safety Science, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Marc Blockman
- Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Karen Cohen
- Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Clint Cupido
- Victoria Hospital Internal Medicine Research Initiative, Victoria Hospital Wynberg, Cape Town, South Africa.,Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Claire Hutchinson
- Department of Pharmacology and Therapeutics, The Wolfson Centre for Personalized Medicine, Medical Research Council Centre for Drug Safety Science, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Barry Jacobson
- Department of Molecular Medicine and Haematology, University of the Witwatersrand, Johannesburg, South Africa
| | - Mohammed Lamorde
- Infectious Diseases Institute, Makerere University College of Health Sciences, Kampala, Uganda
| | - Jennie Morgan
- Metro District Health Services, Western Cape Department of Health, Cape Town, South Africa
| | - Johannes P Mouton
- Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa
| | | | | | - Elise Schapkaitz
- Department of Molecular Medicine and Hematology, Charlotte Maxeke Johannesburg Academic Hospital National Health Laboratory System Complex and University of Witwatersrand, Johannesburg, South Africa
| | | | - Jerome R Semakula
- Infectious Diseases Institute, Makerere University College of Health Sciences, Kampala, Uganda
| | - Catriona Waitt
- Department of Pharmacology and Therapeutics, The Wolfson Centre for Personalized Medicine, Medical Research Council Centre for Drug Safety Science, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.,Infectious Diseases Institute, Makerere University College of Health Sciences, Kampala, Uganda
| | - Eunice J Zhang
- Department of Pharmacology and Therapeutics, The Wolfson Centre for Personalized Medicine, Medical Research Council Centre for Drug Safety Science, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Andrea L Jorgensen
- Department of Health Data Science, Institute of Population Health Sciences, University of Liverpool, Liverpool, UK
| | - Munir Pirmohamed
- Department of Pharmacology and Therapeutics, The Wolfson Centre for Personalized Medicine, Medical Research Council Centre for Drug Safety Science, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
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12
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Steiner HE, Giles JB, Patterson HK, Feng J, El Rouby N, Claudio K, Marcatto LR, Tavares LC, Galvez JM, Calderon-Ospina CA, Sun X, Hutz MH, Scott SA, Cavallari LH, Fonseca-Mendoza DJ, Duconge J, Botton MR, Santos PCJL, Karnes JH. Machine Learning for Prediction of Stable Warfarin Dose in US Latinos and Latin Americans. Front Pharmacol 2021; 12:749786. [PMID: 34776967 PMCID: PMC8585774 DOI: 10.3389/fphar.2021.749786] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 09/20/2021] [Indexed: 12/14/2022] Open
Abstract
Populations used to create warfarin dose prediction algorithms largely lacked participants reporting Hispanic or Latino ethnicity. While previous research suggests nonlinear modeling improves warfarin dose prediction, this research has mainly focused on populations with primarily European ancestry. We compare the accuracy of stable warfarin dose prediction using linear and nonlinear machine learning models in a large cohort enriched for US Latinos and Latin Americans (ULLA). Each model was tested using the same variables as published by the International Warfarin Pharmacogenetics Consortium (IWPC) and using an expanded set of variables including ethnicity and warfarin indication. We utilized a multiple linear regression model and three nonlinear regression models: Bayesian Additive Regression Trees, Multivariate Adaptive Regression Splines, and Support Vector Regression. We compared each model’s ability to predict stable warfarin dose within 20% of actual stable dose, confirming trained models in a 30% testing dataset with 100 rounds of resampling. In all patients (n = 7,030), inclusion of additional predictor variables led to a small but significant improvement in prediction of dose relative to the IWPC algorithm (47.8 versus 46.7% in IWPC, p = 1.43 × 10−15). Nonlinear models using IWPC variables did not significantly improve prediction of dose over the linear IWPC algorithm. In ULLA patients alone (n = 1,734), IWPC performed similarly to all other linear and nonlinear pharmacogenetic algorithms. Our results reinforce the validity of IWPC in a large, ethnically diverse population and suggest that additional variables that capture warfarin dose variability may improve warfarin dose prediction algorithms.
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Affiliation(s)
- Heidi E Steiner
- Department of Pharmacy Practice and Science, University of Arizona College of Pharmacy, Tucson, AZ, United States
| | - Jason B Giles
- Department of Pharmacy Practice and Science, University of Arizona College of Pharmacy, Tucson, AZ, United States
| | - Hayley Knight Patterson
- Department of Pharmacy Practice and Science, University of Arizona College of Pharmacy, Tucson, AZ, United States
| | - Jianglin Feng
- Department of Pharmacy Practice and Science, University of Arizona College of Pharmacy, Tucson, AZ, United States
| | - Nihal El Rouby
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, University of Florida College of Pharmacy, Gainesville, FL, United States
| | - Karla Claudio
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, University of Florida College of Pharmacy, Gainesville, FL, United States.,Department of Pharmaceutical Sciences, University of Puerto Rico School of Pharmacy, Medical Sciences Campus, San Juan, PR, United States
| | - Leiliane Rodrigues Marcatto
- Instituto do Coracao do Hospital das Clinicas da Faculdade de Medicina, HCFMUSP, University of São Paulo, São Paulo, Brazil
| | - Leticia Camargo Tavares
- Instituto do Coracao do Hospital das Clinicas da Faculdade de Medicina, HCFMUSP, University of São Paulo, São Paulo, Brazil.,Faculty of Science, School of Biological Sciences, Monash University, Melbourne, VIC, Australia
| | - Jubby Marcela Galvez
- Center for Research in Genetics and Genomics-CIGGUR, GENIUROS Research Group, School of Medicine and Health Sciences, Universidad Del Rosario, Bogotá, Colombia
| | - Carlos-Alberto Calderon-Ospina
- Center for Research in Genetics and Genomics-CIGGUR, GENIUROS Research Group, School of Medicine and Health Sciences, Universidad Del Rosario, Bogotá, Colombia
| | - Xiaoxiao Sun
- Department of Epidemiology Biostatistics, University of Arizona College of Public Health, Tucson, AZ, United States
| | - Mara H Hutz
- Departament of Genetics, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Stuart A Scott
- Department of Pathology, Stanford University, Clinical Genomics Laboratory, Stanford Health Care, Palo Alto, CA, United States
| | - Larisa H Cavallari
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, University of Florida College of Pharmacy, Gainesville, FL, United States
| | - Dora Janeth Fonseca-Mendoza
- Center for Research in Genetics and Genomics-CIGGUR, GENIUROS Research Group, School of Medicine and Health Sciences, Universidad Del Rosario, Bogotá, Colombia
| | - Jorge Duconge
- Department of Pharmaceutical Sciences, University of Puerto Rico School of Pharmacy, Medical Sciences Campus, San Juan, PR, United States
| | - Mariana Rodrigues Botton
- Departament of Genetics, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.,Cells, Tissues and Genes Laboratory, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Paulo Caleb Junior Lima Santos
- Instituto do Coracao do Hospital das Clinicas da Faculdade de Medicina, HCFMUSP, University of São Paulo, São Paulo, Brazil.,Department of Pharmacology, Escola Paulista de Medicina, Universidade Federal de São Paulo, EPM-Unifesp, São Paulo, Brazil
| | - Jason H Karnes
- Department of Pharmacy Practice and Science, University of Arizona College of Pharmacy, Tucson, AZ, United States.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
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13
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Zhang Y, Xie C, Xue L, Tao Y, Yue G, Jiang B. A post-hoc interpretable ensemble model to feature effect analysis in warfarin dose prediction for Chinese patients. IEEE J Biomed Health Inform 2021; 26:840-851. [PMID: 34166206 DOI: 10.1109/jbhi.2021.3092170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
To interprete the importance of clinical features and genotypes for warfarin daily dose prediction, we developed a post-hoc interpretable framework based on an ensemble predictive model. This framework includes permutation importance for global interpretation and local interpretable model-agnostic explanation (LIME) and shapley additive explanations (SHAP) for local explanation. The permutation importance globally ranks the importance of features on the whole data set. This can guide us to build a predictive model with less variables and the complexity of final predictive model can be reduced. LIME and SHAP together explain how the predictive model give the predicted dosage for specific samples. This help clinicians prescribe accurate doses to patients using more effective clinical variables. Results showed that both the permutation importance and SHAP demonstrated that VKORC1, age, serum creatinine (SCr), left atrium (LA) size, CYP2C9 and weight were the most important features on the whole data set. In specific samples, both SHAP and LIME discovered that in Chinese patients, wild-type VKORC1-AA, mutant-type CYP2C9*3, age over 60, abnormal LA size, SCr within the normal range, and using amiodarone definitely required dosage reduction, whereas mutant-type VKORC1-AG/GG, small age, SCr out of normal range, normal LA size, diabetes and heavy weight required dosage enhancement.
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14
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Liu Y, Chen J, You Y, Xu A, Li P, Wang Y, Sun J, Yu Z, Gao F, Zhang J. An ensemble learning based framework to estimate warfarin maintenance dose with cross-over variables exploration on incomplete data set. Comput Biol Med 2021; 131:104242. [PMID: 33578070 DOI: 10.1016/j.compbiomed.2021.104242] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 01/20/2021] [Accepted: 01/20/2021] [Indexed: 11/16/2022]
Abstract
MOTIVATION Warfarin is a widely used oral anticoagulant, but it is challenging to select the optimal maintenance dose due to its narrow therapeutic window and complex individual factor relationships. In recent years, machine learning techniques have been widely applied for warfarin dose prediction. However, the model performance always meets the upper limit due to the ignoration of exploring the variable interactions sufficiently. More importantly, there is no efficient way to resolve missing values when predicting the optimal warfarin maintenance dose. METHODS Using an observational cohort from the Xinhua Hospital affiliated to Shanghai Jiaotong University School of Medicine, we propose a novel method for warfarin maintenance dose prediction, which is capable of assessing variable interactions and dealing with missing values naturally. Specifically, we examine single variables by univariate analysis initially, and only statistically significant variables are included. We then propose a novel feature engineering method on them to generate the cross-over variables automatically. Their impacts are evaluated by stepwise regression, and only the significant ones are selected. Lastly, we implement an ensemble learning based approach, LightGBM, to learn from incomplete data directly on the selected single and cross-over variables for dosing prediction. RESULTS 377 unique patients with eligible and time-independent 1173 warfarin order events are included in this study. Through the comprehensive experimental results in 5-fold cross-validation, our proposed method demonstrates the efficiency of exploring the variable interactions and modeling on incomplete data. The R2 can achieve 75.0% on average. Moreover, the subgroup analysis results reveal that our method performs much better than other baseline methods, especially in the medium-dose and high-dose subgroups. Lastly, the IWPC dosing prediction model is used for further comparison, and our approach outperforms it by a significant margin. CONCLUSION In summary, our proposed method is capable of exploring the variable interactions and learning from incomplete data directly for warfarin maintenance dose prediction, which has a great premise and is worthy of further research.
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Affiliation(s)
- Yan Liu
- Department of Pharmacy, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200092, China
| | - Jihui Chen
- Department of Pharmacy, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200092, China
| | - Yin You
- Department of Neurology, Changzheng Hospital, Second Military Medical University, Shanghai, 200003, China
| | - Ajing Xu
- Department of Pharmacy, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200092, China
| | - Ping Li
- Department of Pharmacy, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200092, China
| | - Yu Wang
- Beijing Medicinovo Technology Co. Ltd, Beijing, 100071, China
| | - Jiaxing Sun
- Beijing Medicinovo Technology Co. Ltd, Beijing, 100071, China
| | - Ze Yu
- Beijing Medicinovo Technology Co. Ltd, Beijing, 100071, China
| | - Fei Gao
- Beijing Medicinovo Technology Co. Ltd, Beijing, 100071, China.
| | - Jian Zhang
- Department of Pharmacy, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200092, China.
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15
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Tao Y, Jiang B, Xue L, Xie C, Zhang Y. Evolutionary synthetic oversampling technique and cocktail ensemble model for warfarin dose prediction with imbalanced data. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05568-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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16
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Kan J, Li A, Zou H, Chen L, Du J. A Machine Learning Based Dose Prediction of Lutein Supplements for Individuals With Eye Fatigue. Front Nutr 2020; 7:577923. [PMID: 33304916 PMCID: PMC7691662 DOI: 10.3389/fnut.2020.577923] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 10/27/2020] [Indexed: 01/10/2023] Open
Abstract
Purpose: Nutritional intervention was always implemented based on "one-size-fits-all" recommendation instead of personalized strategy. We aimed to develop a machine learning based model to predict the optimal dose of a botanical combination of lutein ester, zeaxanthin, extracts of black currant, chrysanthemum, and goji berry for individuals with eye fatigue. Methods: 504 features, including demographic, anthropometrics, eye-related indexes, blood biomarkers, and dietary habits, were collected at baseline from 303 subjects in a randomized controlled trial. An aggregated score of visual health (VHS) was developed from total score of eye fatigue symptoms, visuognosis persistence, macular pigment optical density, and Schirmer test to represent an overall eye fatigue level. VHS at 45 days after intervention was predicted by XGBoost algorithm using all features at baseline to show the eye fatigue improvement. Optimal dose of the combination was chosen based on the predicted VHS. Results: After feature selection and parameter optimization, a model was trained and optimized with a Pearson's correlation coefficient of 0.649, 0.638, and 0.685 in training, test and validation set, respectively. After removing the features collected by invasive blood test and costly optical coherence tomography, the model remained good performance. Among 58 subjects in test and validation sets, 39 should take the highest dose as the optimal option, 17 might take a lower dose, while 2 could not benefit from the combination. Conclusion: We applied XGBoost algorithm to develop a model which could predict optimized dose of the combination to provide personalized nutrition solution for individuals with eye fatigue.
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Affiliation(s)
- Juntao Kan
- Nutrilite Health Institute, Shanghai, China
| | - Ao Li
- Department of Bioinformatics, WuXi NextCODE Genomics, Shanghai, China
| | - Hong Zou
- Department of Bioinformatics, WuXi NextCODE Genomics, Shanghai, China
| | - Liang Chen
- Nutrilite Health Institute, Shanghai, China
| | - Jun Du
- Nutrilite Health Institute, Shanghai, China
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17
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Truda G, Marais P. Evaluating warfarin dosing models on multiple datasets with a novel software framework and evolutionary optimisation. J Biomed Inform 2020; 113:103634. [PMID: 33271340 DOI: 10.1016/j.jbi.2020.103634] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 10/26/2020] [Accepted: 11/23/2020] [Indexed: 11/19/2022]
Abstract
Warfarin is an effective preventative treatment for arterial and venous thromboembolism, but requires individualised dosing due to its narrow therapeutic range and high individual variation. Many machine learning techniques have been demonstrated in this domain. This study evaluated the accuracy of the most promising algorithms on the International Warfarin Pharmacogenetics Consortium dataset and a novel clinical dataset of South African patients. Support vectors and linear regression were amongst the top performers in both datasets and performed comparably to recent stacked ensemble approaches, whilst neural networks were one of the worst performers in both datasets. We also introduced genetic programming to automatically optimise model architectures and hyperparameters without human guidance. Remarkably, the generated models were found to match the performance of the best models hand-crafted by human experts. Finally, we present a novel software framework (Warfit-learn) for warfarin dosing research. It leverages the most successful techniques in preprocessing, imputation, and parallel evaluation, with the goal of accelerating research and making results in this domain more reproducible.
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Affiliation(s)
- Gianluca Truda
- Department of Computer Science, University of Cape Town, Rondebosch 7701, South Africa.
| | - Patrick Marais
- Department of Computer Science, University of Cape Town, Rondebosch 7701, South Africa
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18
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Asiimwe IG, Zhang EJ, Osanlou R, Jorgensen AL, Pirmohamed M. Warfarin dosing algorithms: A systematic review. Br J Clin Pharmacol 2020; 87:1717-1729. [PMID: 33080066 PMCID: PMC8056736 DOI: 10.1111/bcp.14608] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 10/04/2020] [Accepted: 10/05/2020] [Indexed: 12/11/2022] Open
Abstract
Aims Numerous algorithms have been developed to guide warfarin dosing and improve clinical outcomes. We reviewed the algorithms available for various populations and the covariates, performances and risk of bias of these algorithms. Methods We systematically searched MEDLINE up to 20 May 2020 and selected studies describing the development, external validation or clinical utility of a multivariable warfarin dosing algorithm. Two investigators conducted data extraction and quality assessment. Results Of 10 035 screened records, 266 articles were included in the review, describing the development of 433 dosing algorithms, 481 external validations and 52 clinical utility assessments. Most developed algorithms were for dose initiation (86%), developed by multiple linear regression (65%) and mostly applicable to Asians (49%) or Whites (43%). The most common demographic/clinical/environmental covariates were age (included in 401 algorithms), concomitant medications (270 algorithms) and weight (229 algorithms) while CYP2C9 (329 algorithms), VKORC1 (319 algorithms) and CYP4F2 (92 algorithms) variants were the most common genetic covariates. Only 26% and 7% algorithms were externally validated and evaluated for clinical utility, respectively, with <2% of algorithm developments and external validations being rated as having a low risk of bias. Conclusion Most warfarin dosing algorithms have been developed in Asians and Whites and may not be applicable to under‐served populations. Few algorithms have been externally validated, assessed for clinical utility, and/or have a low risk of bias which makes them unreliable for clinical use. Algorithm development and assessment should follow current methodological recommendations to improve reliability and applicability, and under‐represented populations should be prioritized.
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Affiliation(s)
- Innocent G Asiimwe
- The Wolfson Centre for Personalized Medicine, MRC Centre for Drug Safety Science, Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, United Kingdom
| | - Eunice J Zhang
- The Wolfson Centre for Personalized Medicine, MRC Centre for Drug Safety Science, Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, United Kingdom
| | - Rostam Osanlou
- The Wolfson Centre for Personalized Medicine, MRC Centre for Drug Safety Science, Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, United Kingdom
| | - Andrea L Jorgensen
- Department of Biostatistics, Institute of Population Health Sciences, University of Liverpool, United Kingdom
| | - Munir Pirmohamed
- The Wolfson Centre for Personalized Medicine, MRC Centre for Drug Safety Science, Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, United Kingdom
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19
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Siontis KC, Yao X, Pirruccello JP, Philippakis AA, Noseworthy PA. How Will Machine Learning Inform the Clinical Care of Atrial Fibrillation? Circ Res 2020; 127:155-169. [DOI: 10.1161/circresaha.120.316401] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Machine learning applications in cardiology have rapidly evolved in the past decade. With the availability of machine learning tools coupled with vast data sources, the management of atrial fibrillation (AF), a common chronic disease with significant associated morbidity and socioeconomic impact, is undergoing a knowledge and practice transformation in the increasingly complex healthcare environment. Among other advances, deep-learning machine learning methods, including convolutional neural networks, have enabled the development of AF screening pathways using the ubiquitous 12-lead ECG to detect asymptomatic paroxysmal AF in at-risk populations (such as those with cryptogenic stroke), the refinement of AF and stroke prediction schemes through comprehensive digital phenotyping using structured and unstructured data abstraction from the electronic health record or wearable monitoring technologies, and the optimization of treatment strategies, ranging from stroke prophylaxis to monitoring of antiarrhythmic drug (AAD) therapy. Although the clinical and population-wide impact of these tools continues to be elucidated, such transformative progress does not come without challenges, such as the concerns about adopting black box technologies, assessing input data quality for training such models, and the risk of perpetuating rather than alleviating health disparities. This review critically appraises the advances of machine learning related to the care of AF thus far, their potential future directions, and its potential limitations and challenges.
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Affiliation(s)
| | - Xiaoxi Yao
- Robert D and Patricia E Kern Center for the Science of Health Care Delivery (X.Y.), Mayo Clinic, Rochester, MN
- Division of Health Care Policy and Research, Department of Health Sciences Research (X.Y.), Mayo Clinic, Rochester, MN
| | - James P. Pirruccello
- Broad Institute, Cambridge, MA (J.P.P., A.A.P.)
- Division of Cardiology, Massachusetts General Hospital, Boston (J.P.P.)
| | | | - Peter A. Noseworthy
- From the Department of Cardiovascular Medicine (K.C.S., P.A.N.), Mayo Clinic, Rochester, MN
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20
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Huebner T, Steffens M, Linder R, Fracowiak J, Langner D, Garling M, Falkenberg F, Roethlein C, Gomm W, Haenisch B, Stingl J. Influence of metabolic profiles on the safety of drug therapy in routine care in Germany: protocol of the cohort study EMPAR. BMJ Open 2020; 10:e032624. [PMID: 32345696 PMCID: PMC7213853 DOI: 10.1136/bmjopen-2019-032624] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
INTRODUCTION Pre-emptive testing of pharmacogenetically relevant single-nucleotide polymorphisms can be an effective tool in the prevention of adverse drug reactions and therapy resistance. However, most of the tests are not used as standard in routine care in Germany because of lacking evidence for the clinical and economical benefit and their impact on the usage of healthcare services. We address this issue by investigating the influence of pharmacogenetic profiles on the use of healthcare services over an extended period of several years using routine care data from a statutory health insurance company. The goal is to provide clinical evidence whether pre-emptive pharmacogenetic testing of metabolic profiles in routine care in Germany is beneficial and cost-effective. METHODS AND ANALYSIS The EMPAR (Einfluss metabolischer Profile auf die Arzneimitteltherapiesicherheit in der Routineversorgung) study is a non-interventional cohort study conducted to analyse pharmacogenetic risk factors that are important for drug therapy by means of endpoints relevant for healthcare. The analysis is based on pharmacogenetic profiles and statutory health insurance data. We perform pharmacogenetic, pharmacoepidemiological and pharmacoeconomic analyses using health care utilisation scores and machine learning techniques. Therefore, we aim to include about 10 000 patients (≥18 years) insured by the health insurance provider Techniker Krankenkasse. The study focuses on patients with prescriptions of anticoagulants and prescriptions of cholesterol-lowering drugs. Also, a screening for special pharmacogenetic characteristics will be performed in patients with at least one Y57.9! diagnosis (Complication of medical and surgical care: drug or medicament, unspecified). Outcomes include the utilisation of health insurance services, the incidence of incapacity for work and costs for drugs and treatment. ETHICS AND DISSEMINATION The protocol was approved by the Ethics Committee of the Medical Faculty, University of Bonn (Lfd. Nr. 339/17). The results of this research project will be published in scientific open access journals and at conferences. TRIAL REGISTRATION NUMBER German Clinical Trials Register, DRKS00013909.
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Affiliation(s)
- Tatjana Huebner
- Research Division, Federal Institute for Drugs and Medical Devices, Bonn, North Rhine-Westphalia, Germany
| | - Michael Steffens
- Research Division, Federal Institute for Drugs and Medical Devices, Bonn, North Rhine-Westphalia, Germany
| | | | - Jochen Fracowiak
- Research Division, Federal Institute for Drugs and Medical Devices, Bonn, North Rhine-Westphalia, Germany
| | | | | | | | - Christoph Roethlein
- Population Health Sciences, German Centre for Neurodegenerative Diseases, Bonn, North Rhine-Westphalia, Germany
| | - Willy Gomm
- Population Health Sciences, German Centre for Neurodegenerative Diseases, Bonn, North Rhine-Westphalia, Germany
| | - Britta Haenisch
- Research Division, Federal Institute for Drugs and Medical Devices, Bonn, North Rhine-Westphalia, Germany
- Population Health Sciences, German Centre for Neurodegenerative Diseases, Bonn, North Rhine-Westphalia, Germany
- Centre for Translational Medicine, University of Bonn, Bonn, North Rhine-Westphalia, Germany
| | - Julia Stingl
- Institute for Clinical Pharmacology, RWTH Aachen University, Aachen, North Rhine-Westphalia, Germany
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21
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De Cosmi V, Mazzocchi A, Milani GP, Calderini E, Scaglioni S, Bettocchi S, D’Oria V, Langer T, Spolidoro GCI, Leone L, Battezzati A, Bertoli S, Leone A, De Amicis RS, Foppiani A, Agostoni C, Grossi E. Prediction of Resting Energy Expenditure in Children: May Artificial Neural Networks Improve Our Accuracy? J Clin Med 2020; 9:jcm9041026. [PMID: 32260581 PMCID: PMC7230279 DOI: 10.3390/jcm9041026] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 03/22/2020] [Accepted: 04/03/2020] [Indexed: 02/05/2023] Open
Abstract
The inaccuracy of resting energy expenditure (REE) prediction formulae to calculate energy metabolism in children may lead to either under- or overestimated real caloric needs with clinical consequences. The aim of this paper was to apply artificial neural networks algorithms (ANNs) to REE prediction. We enrolled 561 healthy children (2-17 years). Nutritional status was classified according to World Health Organization (WHO) criteria, and 113 were obese. REE was measured using indirect calorimetry and estimated with WHO, Harris-Benedict, Schofield, and Oxford formulae. The ANNs considered specific anthropometric data to model REE. The mean absolute error (mean ± SD) of the prediction was 95.8 ± 80.8 and was strongly correlated with REE values (R2 = 0.88). The performance of ANNs was higher in the subgroup of obese children (101 ± 91.8) with a lower grade of imprecision (5.4%). ANNs as a novel approach may give valuable information regarding energy requirements and weight management in children.
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Affiliation(s)
- Valentina De Cosmi
- Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Pediatric Intermediate Care Unit, 20122 Milan, Italy; (V.D.C.); (V.D.)
- Department of Clinical Sciences and Community Health, University of Milan, 20122 Milan, Italy; (A.M.); (G.P.M.); (G.C.I.S.); (L.L.)
| | - Alessandra Mazzocchi
- Department of Clinical Sciences and Community Health, University of Milan, 20122 Milan, Italy; (A.M.); (G.P.M.); (G.C.I.S.); (L.L.)
| | - Gregorio Paolo Milani
- Department of Clinical Sciences and Community Health, University of Milan, 20122 Milan, Italy; (A.M.); (G.P.M.); (G.C.I.S.); (L.L.)
- Pediatric Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Edoardo Calderini
- Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Anestesia e Terapia Intensiva Donna-Bambino, 20122 Milan, Italy; (E.C.); (T.L.)
| | - Silvia Scaglioni
- Fondazione De Marchi, Department of Pediatrics, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy;
| | - Silvia Bettocchi
- Institute of Microbiology Catholic University of the Sacred Heart, 29100 Piacenza, Italy;
| | - Veronica D’Oria
- Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Pediatric Intermediate Care Unit, 20122 Milan, Italy; (V.D.C.); (V.D.)
| | - Thomas Langer
- Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Anestesia e Terapia Intensiva Donna-Bambino, 20122 Milan, Italy; (E.C.); (T.L.)
- Department of Pathophysiology and Transplantation, University of Milan, 20100 Milan, Italy
| | - Giulia C. I. Spolidoro
- Department of Clinical Sciences and Community Health, University of Milan, 20122 Milan, Italy; (A.M.); (G.P.M.); (G.C.I.S.); (L.L.)
| | - Ludovica Leone
- Department of Clinical Sciences and Community Health, University of Milan, 20122 Milan, Italy; (A.M.); (G.P.M.); (G.C.I.S.); (L.L.)
| | - Alberto Battezzati
- International Center for the Assessment of Nutritional Status (ICANS), Department of Food Environmental and Nutritional Sciences (DeFENS), University of Milan, 20131 Milan, Italy; (A.B.); (S.B.); (A.L.); (R.S.D.A.); (A.F.)
| | - Simona Bertoli
- International Center for the Assessment of Nutritional Status (ICANS), Department of Food Environmental and Nutritional Sciences (DeFENS), University of Milan, 20131 Milan, Italy; (A.B.); (S.B.); (A.L.); (R.S.D.A.); (A.F.)
- IRCCS Istituto Auxologico Italiano, Obesity Unit and Laboratory of Nutrition and Obesity Research, Department of Endocrine and Metabolic Diseases, 20100 Milan, Italy
| | - Alessandro Leone
- International Center for the Assessment of Nutritional Status (ICANS), Department of Food Environmental and Nutritional Sciences (DeFENS), University of Milan, 20131 Milan, Italy; (A.B.); (S.B.); (A.L.); (R.S.D.A.); (A.F.)
| | - Ramona Silvana De Amicis
- International Center for the Assessment of Nutritional Status (ICANS), Department of Food Environmental and Nutritional Sciences (DeFENS), University of Milan, 20131 Milan, Italy; (A.B.); (S.B.); (A.L.); (R.S.D.A.); (A.F.)
| | - Andrea Foppiani
- International Center for the Assessment of Nutritional Status (ICANS), Department of Food Environmental and Nutritional Sciences (DeFENS), University of Milan, 20131 Milan, Italy; (A.B.); (S.B.); (A.L.); (R.S.D.A.); (A.F.)
| | - Carlo Agostoni
- Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Pediatric Intermediate Care Unit, 20122 Milan, Italy; (V.D.C.); (V.D.)
- Department of Clinical Sciences and Community Health, University of Milan, 20122 Milan, Italy; (A.M.); (G.P.M.); (G.C.I.S.); (L.L.)
- Correspondence: ; Tel.: +025-503-2452
| | - Enzo Grossi
- Villa Santa Maria Foundation, Neuropsychiatric Rehabilitation Center, Autism Unit, 22038 Tavernerio (Como), Italy;
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22
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Roche-Lima A, Roman-Santiago A, Feliu-Maldonado R, Rodriguez-Maldonado J, Nieves-Rodriguez BG, Carrasquillo-Carrion K, Ramos CM, da Luz Sant'Ana I, Massey SE, Duconge J. Machine Learning Algorithm for Predicting Warfarin Dose in Caribbean Hispanics Using Pharmacogenetic Data. Front Pharmacol 2020; 10:1550. [PMID: 32038238 PMCID: PMC6987072 DOI: 10.3389/fphar.2019.01550] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 12/02/2019] [Indexed: 12/18/2022] Open
Abstract
Despite some previous examples of successful application to the field of pharmacogenomics, the utility of machine learning (ML) techniques for warfarin dose predictions in Caribbean Hispanic patients has yet to be fully evaluated. This study compares seven ML methods to predict warfarin dosing in Caribbean Hispanics. This is a secondary analysis of genetic and non-genetic clinical data from 190 cardiovascular Hispanic patients. Seven ML algorithms were applied to the data. Data was divided into 80 and 20% to be used as training and test sets. ML algorithms were trained with the training set to obtain the models. Model performance was determined by computing the corresponding mean absolute error (MAE) and % patients whose predicted optimal dose were within ±20% of the actual stabilization dose, and then compared between groups of patients with “normal” (i.e., > 21 but <49 mg/week), low (i.e., ≤21 mg/week, “sensitive”), and high (i.e., ≥49 mg/week, “resistant”) dose requirements. Random forest regression (RFR) significantly outperform all other methods, with a MAE of 4.73 mg/week and 80.56% of cases within ±20% of the actual stabilization dose. Among those with “normal” dose requirements, RFR performance is also better than the rest of models (MAE = 2.91 mg/week). In the “sensitive” group, support vector regression (SVR) shows superiority over the others with lower MAE of 4.79 mg/week. Finally, multivariate adaptive splines (MARS) shows the best performance in the resistant group (MAE = 7.22 mg/week) and 66.7% of predictions within ±20%. Models generated by using RFR, MARS, and SVR algorithms showed significantly better predictions of weekly warfarin dosing in the studied cohorts than other algorithms. Better performance of the ML models for patients with “normal,” “sensitive,” and “resistant” to warfarin were obtained when compared to other populations and previous statistical models.
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Affiliation(s)
- Abiel Roche-Lima
- Center for Collaborative Research in Health Disparities (CCRHH), University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
| | - Adalis Roman-Santiago
- Pharmaceutical Sciences Department, School of Pharmacy, University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
| | - Roberto Feliu-Maldonado
- Center for Collaborative Research in Health Disparities (CCRHH), University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
| | - Jovaniel Rodriguez-Maldonado
- Center for Collaborative Research in Health Disparities (CCRHH), University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
| | - Brenda G Nieves-Rodriguez
- Center for Collaborative Research in Health Disparities (CCRHH), University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
| | - Kelvin Carrasquillo-Carrion
- Center for Collaborative Research in Health Disparities (CCRHH), University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
| | - Carla M Ramos
- Department of Biology, College of Natural Sciences, University of Puerto Rico Rio Piedras Campus, San Juan, Puerto Rico
| | - Istoni da Luz Sant'Ana
- Department of Biostatistics and Epidemiology, School of Public Health, University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
| | - Steven E Massey
- Department of Biology, College of Natural Sciences, University of Puerto Rico Rio Piedras Campus, San Juan, Puerto Rico
| | - Jorge Duconge
- Pharmaceutical Sciences Department, School of Pharmacy, University of Puerto Rico Medical Sciences Campus, San Juan, Puerto Rico
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23
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Tao Y, Chen YJ, Xue L, Xie C, Jiang B, Zhang Y. An Ensemble Model With Clustering Assumption for Warfarin Dose Prediction in Chinese Patients. IEEE J Biomed Health Inform 2019; 23:2642-2654. [DOI: 10.1109/jbhi.2019.2891164] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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24
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Vigna L, Silvia Tirelli A, Grossi E, Turolo S, Tomaino L, Napolitano F, Buscema M, Riboldi L. Directional Relationship Between Vitamin D Status and Prediabetes: A New Approach from Artificial Neural Network in a Cohort of Workers with Overweight-Obesity. J Am Coll Nutr 2019; 38:681-692. [PMID: 31021286 DOI: 10.1080/07315724.2019.1590249] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Objective: Despite the increasing literature on the association of diabetes with inflammation, cardiovascular risk, and vitamin D (25(OH)D) concentrations, strong evidence on the direction of causality among these factors is still lacking. This gap could be addressed by means of artificial neural networks (ANN) analysis.Methods: Retrospective observational study was carried out by means of an innovative data mining analysis-known as auto-contractive map (AutoCM)-and semantic mapping followed by Activation and Competition System on data of workers referring to an occupational-health outpatient clinic. Parameters analyzed included weight, height, waist circumference, body mass index (BMI), percentage of fat mass, glucose, insulin, glycated hemoglobin (HbA1c), creatinine, total cholesterol, low- and high-density lipoprotein cholesterol, triglycerides, uric acid, fibrinogen, homocysteine, C-reactive protein (CRP), diastolic and systolic blood pressure, and 25(OH)D.Results: The study included 309 workers. Of these, 23.6% were overweight, 40.5% were classified into the first class of obesity, 23.3% were in the second class, and 12.6% were in the third class (BMI > 40 kg/m ). All mean biochemical values were in normal range, except for total cholesterol, low- and high-density lipoprotein cholesterol, CRP, and 25(OH)D. HbA1c was between 39 and 46 mmol/mol in 51.78%. 25(OH)D levels were sufficient in only 12.6%. Highest inverse correlation for hyperglycemia onset was with BMI and waist circumference, suggesting a protective role of 25(OH)D against their increase. AutoCM processing and the semantic map evidenced direct association of 25(OH)D with high link strength (0.99) to low CRP levels and low high-density lipoprotein cholesterol levels. Low 25(OH)D led to changes in glucose, which affected metabolic syndrome biomarkers, first of which was homeostatic model assessment index and blood glucose, but not 25(OH)D.Conclusions: The use of ANN suggests a key role of 25(OH)D respect to all considered metabolic parameters in the development of diabetes and evidences a causation between low 25(OH)D and high glucose concentrations.
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Affiliation(s)
- Luisella Vigna
- Department of Preventive Medicine, Occupational Health Unit, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Amedea Silvia Tirelli
- Department of Clinical Chemistry and Microbiology Bacteriology and Virology Units, Ospedale Maggiore Policlinico, Milan, Italy
| | - Enzo Grossi
- Villa Santa Maria Foundation, Tavernerio, Italy
| | - Stefano Turolo
- Pediatric Nephrology & Dialysis, Milano Fondazione IRCCS Cà Grande Ospedale Maggiore Policlinico University of Milan, Milan, Italy
| | - Laura Tomaino
- Pediatric Intermediate Care Unit, Department of Clinical and Community Health Sciences (DISCCO), Fondazione IRCCS Ospedale CàGranda-Ospedale Maggiore Policlinico, University of Milan, Milan, Italy
| | - Filomena Napolitano
- Department of Clinical Chemistry and Microbiology Bacteriology and Virology Units, Ospedale Maggiore Policlinico, Milan, Italy
| | - Massimo Buscema
- Semeion Research Centre of Sciences of Communication, Rome, Italy
- Department of Mathematics, University of Colorado, Denver, Colorado, USA
| | - Luciano Riboldi
- Department of Preventive Medicine, Occupational Health Unit, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
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25
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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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26
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Abstract
Warfarin is a widely used anticoagulant with a narrow therapeutic index and large interpatient variability in the therapeutic dose. Complications from inappropriate warfarin dosing are one of the most common reasons for emergency room visits. Approximately one third of warfarin dose variability results from common genetic variants. Therefore, it is very necessary to recognize warfarin sensitivity in individuals caused by genetic variants. Based on combined polymorphisms in CYP2C9 and VKORC1, we established a clinical classification for warfarin sensitivity. In the International Warfarin Pharmacogenetic Consortium (IWPC) with 5542 patients, we found that 95.1% of the Black in the IWPC cohort were normal warfarin responders, while 74.8% of the Asian were warfarin sensitive (P < 0.001). Moreover, we created a clinical algorithm to predict warfarin sensitivity in individual patients using logistic regression. Compared to a fixed-dose approach, the clinical algorithm provided significantly better performance. In addition, we validated the derived clinical algorithm using the external Easton cohort with 106 chronic warfarin users. The AUC was 0.836 vs. 0.867 for the Easton cohort and the IWPC cohort, respectively. With the use of this algorithm, it is very likely to facilitate patient care regarding warfarin therapy, thereby improving clinical outcomes.
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27
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Tao Y, Chen YJ, Fu X, Jiang B, Zhang Y. Evolutionary Ensemble Learning Algorithm to Modeling of Warfarin Dose Prediction for Chinese. IEEE J Biomed Health Inform 2019; 23:395-406. [DOI: 10.1109/jbhi.2018.2812165] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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28
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Ma Z, Wang P, Gao Z, Wang R, Khalighi K. Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose. PLoS One 2018; 13:e0205872. [PMID: 30339708 PMCID: PMC6195267 DOI: 10.1371/journal.pone.0205872] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 10/02/2018] [Indexed: 11/19/2022] Open
Abstract
Warfarin dosing remains challenging due to narrow therapeutic index and highly individual variability. Incorrect warfarin dosing is associated with devastating adverse events. Remarkable efforts have been made to develop the machine learning based warfarin dosing algorithms incorporating clinical factors and genetic variants such as polymorphisms in CYP2C9 and VKORC1. The most widely validated pharmacogenetic algorithm is the IWPC algorithm based on multivariate linear regression (MLR). However, with only a single algorithm, the prediction performance may reach an upper limit even with optimal parameters. Here, we present novel algorithms using stacked generalization frameworks to estimate the warfarin dose, within which different types of machine learning algorithms function together through a meta-machine learning model to maximize the prediction accuracy. Compared to the IWPC-derived MLR algorithm, Stack 1 and 2 based on stacked generalization frameworks performed significantly better overall. Subgroup analysis revealed that the mean of the percentage of patients whose predicted dose of warfarin within 20% of the actual stable therapeutic dose (mean percentage within 20%) for Stack 1 was improved by 12.7% (from 42.47% to 47.86%) in Asians and by 13.5% (from 22.08% to 25.05%) in the low-dose group compared to that for MLR, respectively. These data suggest that our algorithms would especially benefit patients requiring low warfarin maintenance dose, as subtle changes in warfarin dose could lead to adverse clinical events (thrombosis or bleeding) in patients with low dose. Our study offers novel pharmacogenetic algorithms for clinical trials and practice.
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Affiliation(s)
- Zhiyuan Ma
- Easton Cardiovascular Associates, Easton, PA, United States of America
- Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, NC, United States of America
| | - Ping Wang
- Easton Cardiovascular Associates, Easton, PA, United States of America
| | - Zehui Gao
- Department of Mathematics and Statistics, San Diego State University, La Mesa, CA, United States of America
| | - Ruobing Wang
- Department of Chemistry and Social Science Research Institute, Duke University, Durham, NC, United States of America
- Science Center of Opera Solutions LLC, San Diego, CA, United States of America
| | - Koroush Khalighi
- Easton Cardiovascular Associates, Easton, PA, United States of America
- Division of Cardiology, Department of Medicine, Easton Hospital, Easton, PA, United States of America
- Drexel University College of Medicine, Philadelphia, PA, United States of America
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29
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Sharabiani A, Nutescu EA, Galanter WL, Darabi H. A New Approach towards Minimizing the Risk of Misdosing Warfarin Initiation Doses. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2018; 2018:5340845. [PMID: 29861781 PMCID: PMC5971298 DOI: 10.1155/2018/5340845] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Revised: 03/07/2018] [Accepted: 04/02/2018] [Indexed: 01/09/2023]
Abstract
It is a challenge to be able to prescribe the optimal initial dose of warfarin. There have been many studies focused on an efficient strategy to determine the optimal initial dose. Numerous clinical, genetic, and environmental factors affect the warfarin dose response. In practice, it is common that the initial warfarin dose is substantially different from the stable maintenance dose, which may increase the risk of bleeding or thrombosis prior to achieving the stable maintenance dose. In order to minimize the risk of misdosing, despite popular warfarin dose prediction models in the literature which create dose predictions solely based on patients' attributes, we have taken physicians' opinions towards the initial dose into consideration. The initial doses selected by clinicians, along with other standard clinical factors, are used to determine an estimate of the difference between the initial dose and estimated maintenance dose using shrinkage methods. The selected shrinkage method was LASSO (Least Absolute Shrinkage and Selection Operator). The estimated maintenance dose was more accurate than the original initial dose, the dose predicted by a linear model without involving the clinicians initial dose, and the values predicted by the most commonly used model in the literature, the Gage clinical model.
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Affiliation(s)
- Ashkan Sharabiani
- Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Edith A. Nutescu
- Department of Pharmacy Systems Outcomes and Policy and Center for Pharmacoepidemiology and Pharmacoeconomic Research, University of Illinois at Chicago, Chicago, IL, USA
| | - William L. Galanter
- Department of Pharmacy Systems Outcomes and Policy and Center for Pharmacoepidemiology and Pharmacoeconomic Research, University of Illinois at Chicago, Chicago, IL, USA
- Department of Medicine, University of Illinois at Chicago, Chicago, IL, USA
| | - Houshang Darabi
- Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL, USA
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30
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Tao H, Li Q, Zhou Q, Chen J, Fu B, Wang J, Qin W, Hou J, Chen J, Dong L. A prediction study of warfarin individual stable dose after mechanical heart valve replacement: adaptive neural-fuzzy inference system prediction. BMC Surg 2018; 18:10. [PMID: 29448930 PMCID: PMC5815201 DOI: 10.1186/s12893-018-0343-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Accepted: 02/01/2018] [Indexed: 02/05/2023] Open
Abstract
Background It’s difficult but urgent to achieve the individualized rational medication of the warfarin, we aim to predict the individualized warfarin stable dose though the artificial intelligent Adaptive neural-fuzzy inference system (ANFIS). Methods Our retrospective analysis based on a clinical database, involving 21,863 patients from 15 Chinese provinces who receive oral warfarin after the heart valve replacement. They were allocated into four groups: the external validation group (A group), the internal validation group (B group), training group (C group) and stratified training group (D group). We used a univariate analysis of general linear models(GLM-univariate) to select the input variables and construct two prediction models by the ANFIS with the training and stratified training group, and then verify models with two validation groups by the mean squared error(MSE), mean absolute error(MAE) and the ideal predicted percentage. Results A total of 13,639 eligible patients were selected, including 1639 in A group, 3000 in B group, 9000 in C group, and 3192 in D group. Nine input variables were selected out and two five-layered ANFIS models were built. ANFIS model achieved the highest total ideal predicted percentage 63.7%. In the dose subgroups, all the models performed best in the intermediate-dose group with the ideal predicted percentage 82.4~ 86.4%, and the use of the stratified training group slightly increased the prediction accuracy in low-dose group by 8.8 and 5.2%, respectively. Conclusion As a preliminary attempt, ANFIS model predicted the warfarin stable dose properly after heart valve surgery among Chinese, and also proved that Chinese need lower anticoagulation intensity INR (1.5–2.5) to warfarin by reference to the recommended INR (2.5–3.5) in the developed countries.
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Affiliation(s)
- Huan Tao
- Department of Evidence-based Medicine and clinical epidemiology, West China Hospital, Sichuan University, 37 Guo Xue Xiang, ChengDu, 610041, China
| | - Qian Li
- Department of Evidence-based Medicine and clinical epidemiology, West China Hospital, Sichuan University, 37 Guo Xue Xiang, ChengDu, 610041, China
| | - Qin Zhou
- Department of Nutrition, The Second affiliated hospital of Chongqing medical university, Chongqing, China
| | - Jie Chen
- Department of Anesthesiology, China Mianyang Central Hospital, Mianyang, China
| | - Bo Fu
- Department of Cardiovascular Surgery, Tianjin central hospital, Tianjin, China
| | - Jing Wang
- Department of Career development division, The fourth affiliated hospital of Anhui Medical University, Hefei, China
| | - Wenzhe Qin
- Department of Social Medicine and Health Management, Shandong University, Jinan, China
| | - Jianglong Hou
- Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Jin Chen
- Department of Evidence-based Medicine and clinical epidemiology, West China Hospital, Sichuan University, 37 Guo Xue Xiang, ChengDu, 610041, China.
| | - Li Dong
- Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu, China.
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31
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Podda GM, Grossi E, Palmerini T, Buscema M, Femia EA, Della Riva D, de Servi S, Calabrò P, Piscione F, Maffeo D, Toso A, Palmieri C, De Carlo M, Capodanno D, Genereux P, Cattaneo M. Prediction of high on-treatment platelet reactivity in clopidogrel-treated patients with acute coronary syndromes. Int J Cardiol 2017; 240:60-65. [PMID: 28343766 DOI: 10.1016/j.ijcard.2017.03.074] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Revised: 03/01/2017] [Accepted: 03/15/2017] [Indexed: 02/08/2023]
Abstract
BACKGROUND About 40% of clopidogrel-treated patients display high platelet reactivity (HPR). Alternative treatments of HPR patients, identified by platelet function tests, failed to improve their clinical outcomes in large randomized clinical trials. A more appealing alternative would be to identify HPR patients a priori, based on the presence/absence of demographic, clinical and genetic factors that affect PR. Due to the complexity and multiplicity of these factors, traditional statistical methods (TSMs) fail to identify a priori HPR patients accurately. The objective was to test whether Artificial Neural Networks (ANNs) or other Machine Learning Systems (MLSs), which use algorithms to extract model-like 'structure' information from a given set of data, accurately predict platelet reactivity (PR) in clopidogrel-treated patients. METHODS A complete set of fifty-nine demographic, clinical, genetic data was available of 603 patients with acute coronary syndromes enrolled in the prospective GEPRESS study, which showed that HPR after 1month of clopidogrel treatment independently predicted adverse cardiovascular events in patients with Syntax Score >14. Data were analysed by MLSs and TSMs. ANNs identified more variables associated PR at 1month, compared to TSMs. RESULTS ANNs overall accuracy in predicting PR, although superior to other MLSs was 63% (95% CI 59-66). PR phenotype changed in both directions in 35% of patients across the 3 time points tested (before PCI, at hospital discharge and at 1month). CONCLUSIONS Despite their ability to analyse very complex non-linear phenomena, ANNs or MLS were unable to predict PR accurately, likely because PR is a highly unstable phenotype.
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Affiliation(s)
- G M Podda
- Unità di Medicina III, ASST Santi Paolo e Carlo, Dipartimento di Scienze della Salute, Università degli Studi di Milano, Milano, Italy
| | - E Grossi
- Centro Diagnostico Italiano, Milano, Italy
| | - T Palmerini
- Dipartimento Cardiovascolare, Policlinico S. Orsola, Bologna, Italy
| | - M Buscema
- Semeion Research Centre, Roma, Italy
| | - E A Femia
- Unità di Medicina III, ASST Santi Paolo e Carlo, Dipartimento di Scienze della Salute, Università degli Studi di Milano, Milano, Italy
| | - D Della Riva
- Dipartimento Cardiovascolare, Policlinico S. Orsola, Bologna, Italy
| | - S de Servi
- Unità Coronarica IRCCS Policlinico San Matteo, Pavia, Italy
| | - P Calabrò
- Divisione di Cardiologia, Seconda Università di Napoli, Napoli, Italy
| | - F Piscione
- Dipartimento di Medicina e Chirurgia, Schola Medica Salernitana, Università di Salerno, Salerno, Italy
| | - D Maffeo
- Unità di Cardiologia, Servizio di Emodinamica, Istituto Ospedaliero Fondazione Poliambulanza, Brescia, Italy
| | - A Toso
- Divisione di Cardiologia, Ospedale Santo Stefano, Prato, Italy
| | - C Palmieri
- Ospedale del Cuore, Fondazione Toscana Gabriele Monasterio, Massa, Italy
| | - M De Carlo
- Dipartimento Cardiotoracico e Vascolare, Azienda Ospedaliero-Universitaria Pisana, Pisa, Italy
| | - D Capodanno
- Ospedale Ferrarotto, Università di Catania, Catania, Italy
| | - P Genereux
- The Cardiovascular Research Foundation, New York, NY, USA
| | - M Cattaneo
- Unità di Medicina III, ASST Santi Paolo e Carlo, Dipartimento di Scienze della Salute, Università degli Studi di Milano, Milano, Italy.
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Tang J, Liu R, Zhang YL, Liu MZ, Hu YF, Shao MJ, Zhu LJ, Xin HW, Feng GW, Shang WJ, Meng XG, Zhang LR, Ming YZ, Zhang W. Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients. Sci Rep 2017; 7:42192. [PMID: 28176850 PMCID: PMC5296901 DOI: 10.1038/srep42192] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Accepted: 01/06/2017] [Indexed: 01/08/2023] Open
Abstract
Tacrolimus has a narrow therapeutic window and considerable variability in clinical use. Our goal was to compare the performance of multiple linear regression (MLR) and eight machine learning techniques in pharmacogenetic algorithm-based prediction of tacrolimus stable dose (TSD) in a large Chinese cohort. A total of 1,045 renal transplant patients were recruited, 80% of which were randomly selected as the “derivation cohort” to develop dose-prediction algorithm, while the remaining 20% constituted the “validation cohort” to test the final selected algorithm. MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied and their performances were compared in this work. Among all the machine learning models, RT performed best in both derivation [0.71 (0.67–0.76)] and validation cohorts [0.73 (0.63–0.82)]. In addition, the ideal rate of RT was 4% higher than that of MLR. To our knowledge, this is the first study to use machine learning models to predict TSD, which will further facilitate personalized medicine in tacrolimus administration in the future.
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Affiliation(s)
- Jie Tang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, P. R. China.,Institute of Clinical Pharmacology, Central South University; Hunan Key Laboratory of Pharmacogenetics, Changsha, 410078, Hunan, P. R. China
| | - Rong Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, P. R. China.,Institute of Clinical Pharmacology, Central South University; Hunan Key Laboratory of Pharmacogenetics, Changsha, 410078, Hunan, P. R. China
| | - Yue-Li Zhang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, P. R. China.,Institute of Clinical Pharmacology, Central South University; Hunan Key Laboratory of Pharmacogenetics, Changsha, 410078, Hunan, P. R. China
| | - Mou-Ze Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, P. R. China.,Institute of Clinical Pharmacology, Central South University; Hunan Key Laboratory of Pharmacogenetics, Changsha, 410078, Hunan, P. R. China
| | - Yong-Fang Hu
- Peking University Third Hospital, Beijing, 100191, P. R. China
| | - Ming-Jie Shao
- Research Center of Chinese Health Ministry of Transplantation Medicine Engineering and Technology, Third Affiliated Hospital, Central South University, Changsha, 410013, Hunan, P. R. China
| | - Li-Jun Zhu
- Research Center of Chinese Health Ministry of Transplantation Medicine Engineering and Technology, Third Affiliated Hospital, Central South University, Changsha, 410013, Hunan, P. R. China
| | - Hua-Wen Xin
- Department of Clinical Pharmacology, Wuhan General Hospital of Guangzhou Command, Wuhan, 430070, Hubei, P. R. China
| | - Gui-Wen Feng
- Department of Renal Transplantation, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, P. R. China
| | - Wen-Jun Shang
- Department of Renal Transplantation, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, P. R. China
| | - Xiang-Guang Meng
- School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, 450001, Henan, P. R. China
| | - Li-Rong Zhang
- School of Basic Medical Sciences, Zhengzhou University, Zhengzhou, 450001, Henan, P. R. China
| | - Ying-Zi Ming
- Research Center of Chinese Health Ministry of Transplantation Medicine Engineering and Technology, Third Affiliated Hospital, Central South University, Changsha, 410013, Hunan, P. R. China
| | - Wei Zhang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, 410008, Hunan, P. R. China.,Institute of Clinical Pharmacology, Central South University; Hunan Key Laboratory of Pharmacogenetics, Changsha, 410078, Hunan, P. R. China
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Pavani A, Naushad SM, Kumar RM, Srinath M, Malempati AR, Kutala VK. Artificial neural network-based pharmacogenomic algorithm for warfarin dose optimization. Pharmacogenomics 2015; 17:121-31. [PMID: 26666467 DOI: 10.2217/pgs.15.161] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
AIM To develop more precise pharmacogenomic algorithm for prediction of safe and effective dose of warfarin. MATERIALS & METHODS An artificial neural network (ANN) algorithm was developed by using age, gender, BMI, plasma vitamin K levels, thyroid status and ten genetic variables as the inputs and therapeutic warfarin dose as the output. Hyperbolic tangent function was used to build an ANN architecture. RESULTS This model explained 93.5% variability in warfarin dosing and predicted warfarin dose accurately in 74.5% patients whose international normalized ratio (INR) was less than 2.0 and in 83.3% patients whose INR was more than 3.5. This algorithm reduced the out-of-range INRs (odds ratio [OR]: 0.49; 95% CI: 0.30-0.79; p = 0.003), the rate of adverse drug reactions (OR: 0.00; 95% CI: 0.00-1.21; p = 0.06) and time to reach first therapeutic INR (OR: 6.73; 95% CI: 2.17-22.31; p < 0.0001). This algorithm was found to be applicable in both euthyroid and hypothyroid status. S-warfarin/7-hydroxywarfarin ratio was found to increase in subjects with CYP2C9*2 and CYP2C9*3 justifying the warfarin sensitivity attributed to these variants. CONCLUSION An application of ANN for warfarin dosing improves predictability and provides safe and effective dosing.
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Affiliation(s)
- Addepalli Pavani
- Department of Clinical Pharmacology & Therapeutics, Nizam's Institute of Medical Sciences, Hyderabad 500082, India
| | | | | | - Murali Srinath
- School of Chemical & Biotechnology, SASTRA University, Thanjavur 613401, India
| | - Amaresh Rao Malempati
- Department of Cardiothoracic Surgery, Nizam's Institute of Medical Sciences, Hyderabad 500082, India
| | - Vijay Kumar Kutala
- Department of Clinical Pharmacology & Therapeutics, Nizam's Institute of Medical Sciences, Hyderabad 500082, India
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Kesharaju M, Nagarajah R. Feature selection for neural network based defect classification of ceramic components using high frequency ultrasound. ULTRASONICS 2015; 62:271-277. [PMID: 26081920 DOI: 10.1016/j.ultras.2015.05.027] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2015] [Accepted: 05/30/2015] [Indexed: 06/04/2023]
Abstract
The motivation for this research stems from a need for providing a non-destructive testing method capable of detecting and locating any defects and microstructural variations within armour ceramic components before issuing them to the soldiers who rely on them for their survival. The development of an automated ultrasonic inspection based classification system would make possible the checking of each ceramic component and immediately alert the operator about the presence of defects. Generally, in many classification problems a choice of features or dimensionality reduction is significant and simultaneously very difficult, as a substantial computational effort is required to evaluate possible feature subsets. In this research, a combination of artificial neural networks and genetic algorithms are used to optimize the feature subset used in classification of various defects in reaction-sintered silicon carbide ceramic components. Initially wavelet based feature extraction is implemented from the region of interest. An Artificial Neural Network classifier is employed to evaluate the performance of these features. Genetic Algorithm based feature selection is performed. Principal Component Analysis is a popular technique used for feature selection and is compared with the genetic algorithm based technique in terms of classification accuracy and selection of optimal number of features. The experimental results confirm that features identified by Principal Component Analysis lead to improved performance in terms of classification percentage with 96% than Genetic algorithm with 94%.
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Affiliation(s)
- Manasa Kesharaju
- Swinburne University of Technology, Faculty of Engineering & Industrial Sciences, Melbourne, Victoria 3122, Australia; Defence Materials Technology Centre (DMTC LTD), Melbourne, Victoria 3122, Australia.
| | - Romesh Nagarajah
- Swinburne University of Technology, Faculty of Engineering & Industrial Sciences, Melbourne, Victoria 3122, Australia; Defence Materials Technology Centre (DMTC LTD), Melbourne, Victoria 3122, Australia
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Liu R, Li X, Zhang W, Zhou HH. Comparison of Nine Statistical Model Based Warfarin Pharmacogenetic Dosing Algorithms Using the Racially Diverse International Warfarin Pharmacogenetic Consortium Cohort Database. PLoS One 2015; 10:e0135784. [PMID: 26305568 PMCID: PMC4549222 DOI: 10.1371/journal.pone.0135784] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2014] [Accepted: 07/27/2015] [Indexed: 12/03/2022] Open
Abstract
Objective Multiple linear regression (MLR) and machine learning techniques in pharmacogenetic algorithm-based warfarin dosing have been reported. However, performances of these algorithms in racially diverse group have never been objectively evaluated and compared. In this literature-based study, we compared the performances of eight machine learning techniques with those of MLR in a large, racially-diverse cohort. Methods MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied in warfarin dose algorithms in a cohort from the International Warfarin Pharmacogenetics Consortium database. Covariates obtained by stepwise regression from 80% of randomly selected patients were used to develop algorithms. To compare the performances of these algorithms, the mean percentage of patients whose predicted dose fell within 20% of the actual dose (mean percentage within 20%) and the mean absolute error (MAE) were calculated in the remaining 20% of patients. The performances of these techniques in different races, as well as the dose ranges of therapeutic warfarin were compared. Robust results were obtained after 100 rounds of resampling. Results BART, MARS and SVR were statistically indistinguishable and significantly out performed all the other approaches in the whole cohort (MAE: 8.84–8.96 mg/week, mean percentage within 20%: 45.88%–46.35%). In the White population, MARS and BART showed higher mean percentage within 20% and lower mean MAE than those of MLR (all p values < 0.05). In the Asian population, SVR, BART, MARS and LAR performed the same as MLR. MLR and LAR optimally performed among the Black population. When patients were grouped in terms of warfarin dose range, all machine learning techniques except ANN and LAR showed significantly higher mean percentage within 20%, and lower MAE (all p values < 0.05) than MLR in the low- and high- dose ranges. Conclusion Overall, machine learning-based techniques, BART, MARS and SVR performed superior than MLR in warfarin pharmacogenetic dosing. Differences of algorithms’ performances exist among the races. Moreover, machine learning-based algorithms tended to perform better in the low- and high- dose ranges than MLR.
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Affiliation(s)
- Rong Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, P. R. China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, P. R. China
| | - Xi Li
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, P. R. China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, P. R. China
- * E-mail: (XL); (HHZ)
| | - Wei Zhang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, P. R. China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, P. R. China
| | - Hong-Hao Zhou
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, P. R. China
- Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, Changsha, P. R. China
- * E-mail: (XL); (HHZ)
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Jaja C, Bowman L, Wells L, Patel N, Xu H, Lyon M, Kutlar A. Preemptive Genotyping of CYP2C8 and CYP2C9 Allelic Variants Involved in NSAIDs Metabolism for Sickle Cell Disease Pain Management. Clin Transl Sci 2015; 8:272-80. [PMID: 25640739 PMCID: PMC4522406 DOI: 10.1111/cts.12260] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Interindividual variability in analgesic effects of nonsteroidal anti-inflammatory drugs prescribed for sickle cell disease (SCD) pain is attributed to polymorphisms in the CYP2C8 and CYP2C9 enzymes. We described CYP2C8 and CYP2C9 genotype/phenotype profiles and frequency of emergency department (ED) visits for pain management in an African American SCD patient cohort. DNA from 165 unrelated patients was genotyped for seven CYP2C8 and 15 CYP2C9 alleles using the iPLEX ADME PGx multiplexed panel. CYP2C8*1 (0.806),*2 (0.164), *3 (0.018), and *4 (0.012) alleles were identified. Genotype frequencies were distributed as homozygous wild type (66.7%), heterozygous (27.8%), and homozygous variant/compound heterozygous (5.4%), respectively. CYP2C9*1 (0.824), *2 (0.027), *3 (0.012), *5 (0.009), *6 (0.009), *8 (0.042), *9 (0.061), and *11(0.015) were observed with extensive (68.5%), intermediate (18.1%) and poor predicted metabolizers (0.6%), respectively. Fifty-two and 55 subjects, respectively had at least one variant CYP2C8 or CYP2C9 allele. Although the distribution of the CYP2C9 (p = 0.0515) phenotypes was marginally significantly in high and low ED users; some CYP2C8 and CYP2C9 allelic combinations observed in 15.2% (25) of the cohort are associated with higher risks for analgesic failure. CYP2C8 and CYP2C9 preemptive genotyping could potentially enable clinicians to identify patients with impaired metabolic phenotypes.
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Affiliation(s)
- Cheedy Jaja
- College of Nursing, University of CincinnatiCincinnatiOhioUSA
| | - Latanya Bowman
- Department of Medicine, Georgia Regents UniversityAugustaGeorgiaUSA
| | - Leigh Wells
- Department of Medicine, Georgia Regents UniversityAugustaGeorgiaUSA
| | - Niren Patel
- Department of Medicine, Georgia Regents UniversityAugustaGeorgiaUSA
| | - Hongyan Xu
- Department of Biostatistics, Georgia Regents UniversityAugustaGeorgiaUSA
| | - Matt Lyon
- Department of Emergency Medicine Georgia Regents UniversityAugustaGeorgiaUSA
| | - Abdullah Kutlar
- Department of Medicine, Georgia Regents UniversityAugustaGeorgiaUSA
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Karaca S, Bozkurt NC, Cesuroglu T, Karaca M, Bozkurt M, Eskioglu E, Polimanti R. International warfarin genotype-guided dosing algorithms in the Turkish population and their preventive effects on major and life-threatening hemorrhagic events. Pharmacogenomics 2015. [PMID: 26216670 DOI: 10.2217/pgs.15.58] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
AIM To determine the accuracy of international warfarin pharmacogenetic algorithms developed on large multiethnic cohorts (comprising more than 1000 subjects) to predict therapeutic warfarin doses in Turkish patients. MATERIALS & METHODS We investigated two Turkish warfarin-treated cohorts: patients with no history of hemorrhagic or thromboembolic event and patients with major and life-threatening hemorrhagic events. RESULTS International pharmacogenetic algorithms showed good performances in predicting the therapeutic dose of patients with no history of bleedings, but they did not significantly detect the incorrect warfarin dose of patients with major and life-threatening hemorrhagic events. CONCLUSION Although genetic information can predict the therapeutic warfarin dose, the accuracy of the international pharmacogenetic algorithms is not sufficient to be used for warfarin screening in Turkish patients.
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Affiliation(s)
- Sefayet Karaca
- School of Health Science, Aksaray University, Aksaray, Turkey.,GENAR Institute for Public Health & Genomics Research, Ankara, Turkey
| | - Nujen Colak Bozkurt
- Department of Endocrinology & Metabolism, Diskapi Yildirim Beyazit Training & Research Hospital, Ankara, Turkey
| | - Tomris Cesuroglu
- GENAR Institute for Public Health & Genomics Research, Ankara, Turkey.,Department of Social Medicine, Maastricht University, Maastricht, The Netherlands
| | - Mehmet Karaca
- Department of Biology, Faculty of Science & Arts, Aksaray University, Aksaray, Turkey
| | - Mehmet Bozkurt
- Department of Cardiology, Ataturk Training & Research Hospital, Ankara, Turkey
| | - Erdal Eskioglu
- Metabolism Unit, Numune Training & Research Hospital, Ankara, Turkey
| | - Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, West Haven, Connecticut, USA
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Revisiting Warfarin Dosing Using Machine Learning Techniques. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:560108. [PMID: 26146514 PMCID: PMC4471424 DOI: 10.1155/2015/560108] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2015] [Revised: 05/11/2015] [Accepted: 05/21/2015] [Indexed: 12/23/2022]
Abstract
Determining the appropriate dosage of warfarin is an important yet challenging task. Several prediction models have been proposed to estimate a therapeutic dose for patients. The models are either clinical models which contain clinical and demographic variables or pharmacogenetic models which additionally contain the genetic variables. In this paper, a new methodology for warfarin dosing is proposed. The patients are initially classified into two classes. The first class contains patients who require doses of >30 mg/wk and the second class contains patients who require doses of ≤30 mg/wk. This phase is performed using relevance vector machines. In the second phase, the optimal dose for each patient is predicted by two clinical regression models that are customized for each class of patients. The prediction accuracy of the model was 11.6 in terms of root mean squared error (RMSE) and 8.4 in terms of mean absolute error (MAE). This was 15% and 5% lower than IWPC and Gage models (which are the most widely used models in practice), respectively, in terms of RMSE. In addition, the proposed model was compared with fixed-dose approach of 35 mg/wk, and the model proposed by Sharabiani et al. and its outperformance were proved in terms of both MAE and RMSE.
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Li X, Liu R, Luo ZY, Yan H, Huang WH, Yin JY, Mao XY, Chen XP, Liu ZQ, Zhou HH, Zhang W. Comparison of the predictive abilities of pharmacogenetics-based warfarin dosing algorithms using seven mathematical models in Chinese patients. Pharmacogenomics 2015; 16:583-90. [PMID: 25872772 DOI: 10.2217/pgs.15.26] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
AIM This study is aimed to find the best predictive model for warfarin stable dosage. MATERIALS & METHODS Seven models, namely multiple linear regression (MLR), artificial neural network, regression tree, boosted regression tree, support vector regression, multivariate adaptive regression spines and random forest regression, as well as the genetic and clinical data of two Chinese samples were employed. RESULTS The average predicted achievement ratio and mean absolute error of the algorithms were ranging from 52.31 to 58.08% and 4.25 to 4.84 mg/week in validation samples, respectively. The algorithm based on MLR showed the highest predicted achievement ratio and the lowest mean absolute error. CONCLUSION At present, MLR may be still the best model for warfarin stable dosage prediction in Chinese population. Original submitted 10 November 2014; Revision submitted 18 February 2015.
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Affiliation(s)
- Xi Li
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 110 Xiang Ya Road, Changsha 410008, PR China
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