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From Principle to Practice: Bridging the Gap in Patient Profiling
The standard clinical coagulation assays, activated partial thromboplastin time (aPTT) and prothrombin time (PT) cannot predict thrombotic or bleeding risk. Since thrombin generation is central to haemorrhage control and when unregulated, is the likely cause of thrombosis, thrombin generation assays...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3556038/ https://www.ncbi.nlm.nih.gov/pubmed/23372761 http://dx.doi.org/10.1371/journal.pone.0054728 |
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author | Foley, Jonathan H. Orfeo, Thomas Undas, Anetta McLean, Kelley C. Bernstein, Ira M. Rivard, Georges-Etienne Mann, Kenneth G. Everse, Stephen J. Brummel-Ziedins, Kathleen E. |
author_facet | Foley, Jonathan H. Orfeo, Thomas Undas, Anetta McLean, Kelley C. Bernstein, Ira M. Rivard, Georges-Etienne Mann, Kenneth G. Everse, Stephen J. Brummel-Ziedins, Kathleen E. |
author_sort | Foley, Jonathan H. |
collection | PubMed |
description | The standard clinical coagulation assays, activated partial thromboplastin time (aPTT) and prothrombin time (PT) cannot predict thrombotic or bleeding risk. Since thrombin generation is central to haemorrhage control and when unregulated, is the likely cause of thrombosis, thrombin generation assays (TGA) have gained acceptance as “global assays” of haemostasis. These assays generate an enormous amount of data including four key thrombin parameters (lag time, maximum rate, peak and total thrombin) that may change to varying degrees over time in longitudinal studies. Currently, each thrombin parameter is averaged and presented individually in a table, bar graph or box plot; no method exists to visualize comprehensive thrombin generation data over time. To address this need, we have created a method that visualizes all four thrombin parameters simultaneously and can be animated to evaluate how thrombin generation changes over time. This method uses all thrombin parameters to intrinsically rank individuals based on their haemostatic status. The thrombin generation parameters can be derived empirically using TGA or simulated using computational models (CM). To establish the utility and diverse applicability of our method we demonstrate how warfarin therapy (CM), factor VIII prophylaxis for haemophilia A (CM), and pregnancy (TGA) affects thrombin generation over time. The method is especially suited to evaluate an individual's thrombotic and bleeding risk during “normal” processes (e.g pregnancy or aging) or during therapeutic challenges to the haemostatic system. Ultimately, our method is designed to visualize individualized patient profiles which are becoming evermore important as personalized medicine strategies become routine clinical practice. |
format | Online Article Text |
id | pubmed-3556038 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-35560382013-01-31 From Principle to Practice: Bridging the Gap in Patient Profiling Foley, Jonathan H. Orfeo, Thomas Undas, Anetta McLean, Kelley C. Bernstein, Ira M. Rivard, Georges-Etienne Mann, Kenneth G. Everse, Stephen J. Brummel-Ziedins, Kathleen E. PLoS One Research Article The standard clinical coagulation assays, activated partial thromboplastin time (aPTT) and prothrombin time (PT) cannot predict thrombotic or bleeding risk. Since thrombin generation is central to haemorrhage control and when unregulated, is the likely cause of thrombosis, thrombin generation assays (TGA) have gained acceptance as “global assays” of haemostasis. These assays generate an enormous amount of data including four key thrombin parameters (lag time, maximum rate, peak and total thrombin) that may change to varying degrees over time in longitudinal studies. Currently, each thrombin parameter is averaged and presented individually in a table, bar graph or box plot; no method exists to visualize comprehensive thrombin generation data over time. To address this need, we have created a method that visualizes all four thrombin parameters simultaneously and can be animated to evaluate how thrombin generation changes over time. This method uses all thrombin parameters to intrinsically rank individuals based on their haemostatic status. The thrombin generation parameters can be derived empirically using TGA or simulated using computational models (CM). To establish the utility and diverse applicability of our method we demonstrate how warfarin therapy (CM), factor VIII prophylaxis for haemophilia A (CM), and pregnancy (TGA) affects thrombin generation over time. The method is especially suited to evaluate an individual's thrombotic and bleeding risk during “normal” processes (e.g pregnancy or aging) or during therapeutic challenges to the haemostatic system. Ultimately, our method is designed to visualize individualized patient profiles which are becoming evermore important as personalized medicine strategies become routine clinical practice. Public Library of Science 2013-01-25 /pmc/articles/PMC3556038/ /pubmed/23372761 http://dx.doi.org/10.1371/journal.pone.0054728 Text en © 2013 Foley et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Foley, Jonathan H. Orfeo, Thomas Undas, Anetta McLean, Kelley C. Bernstein, Ira M. Rivard, Georges-Etienne Mann, Kenneth G. Everse, Stephen J. Brummel-Ziedins, Kathleen E. From Principle to Practice: Bridging the Gap in Patient Profiling |
title | From Principle to Practice: Bridging the Gap in Patient Profiling |
title_full | From Principle to Practice: Bridging the Gap in Patient Profiling |
title_fullStr | From Principle to Practice: Bridging the Gap in Patient Profiling |
title_full_unstemmed | From Principle to Practice: Bridging the Gap in Patient Profiling |
title_short | From Principle to Practice: Bridging the Gap in Patient Profiling |
title_sort | from principle to practice: bridging the gap in patient profiling |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3556038/ https://www.ncbi.nlm.nih.gov/pubmed/23372761 http://dx.doi.org/10.1371/journal.pone.0054728 |
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