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The application of optimisation modelling and geospatial analysis to propose a coronary care network model for patients with ST-elevation myocardial infarction

BACKGROUND: The incidence of myocardial infarction is increasing in South Africa. Prompt treatment is indicated to reduce mortality. One way of expediting treatment is to set up regional referral recommendations that can guide prehospital providers on the best reperfusion strategy for a particular p...

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Autores principales: Stassen, Willem, Olsson, Leif, Kurland, Lisa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: African Federation for Emergency Medicine 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7723916/
https://www.ncbi.nlm.nih.gov/pubmed/33318897
http://dx.doi.org/10.1016/j.afjem.2020.04.008
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author Stassen, Willem
Olsson, Leif
Kurland, Lisa
author_facet Stassen, Willem
Olsson, Leif
Kurland, Lisa
author_sort Stassen, Willem
collection PubMed
description BACKGROUND: The incidence of myocardial infarction is increasing in South Africa. Prompt treatment is indicated to reduce mortality. One way of expediting treatment is to set up regional referral recommendations that can guide prehospital providers on the best reperfusion strategy for a particular patient. A coronary care network model for patients who present with ST-elevation myocardial infarction is proposed, using the North West province, of South Africa as a case study. METHODS: Geospatial analysis with network optimisation modelling was applied, to determine which strategy (prehospital thrombolysis, in-hospital thrombolysis or percutaneous coronary intervention) was most appropriate for patients presenting within each of the municipal wards of the North West province. RESULTS: An efficient and swift recommendation for the optimal reperfusion strategy is obtained using the current model, even in the instance of a large amount of ward data with additional constraints. For most municipal wards (204, 53%) percutaneous coronary intervention is the preferred reperfusion strategy based on proximity. For the remainder of the wards prehospital (138, 36%) or in-hospital (44, 11%) thrombolysis is recommended. CONCLUSION: A scalable and efficient method of determining the optimal reperfusion strategy for a patient presenting with ST-elevation myocardial infarction in the North West province, is presented. This approach can serve as a model which can be applied to other settings and can form the basis of regional coronary care network development priorities and resource allocations.
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spelling pubmed-77239162020-12-13 The application of optimisation modelling and geospatial analysis to propose a coronary care network model for patients with ST-elevation myocardial infarction Stassen, Willem Olsson, Leif Kurland, Lisa Afr J Emerg Med Original Article BACKGROUND: The incidence of myocardial infarction is increasing in South Africa. Prompt treatment is indicated to reduce mortality. One way of expediting treatment is to set up regional referral recommendations that can guide prehospital providers on the best reperfusion strategy for a particular patient. A coronary care network model for patients who present with ST-elevation myocardial infarction is proposed, using the North West province, of South Africa as a case study. METHODS: Geospatial analysis with network optimisation modelling was applied, to determine which strategy (prehospital thrombolysis, in-hospital thrombolysis or percutaneous coronary intervention) was most appropriate for patients presenting within each of the municipal wards of the North West province. RESULTS: An efficient and swift recommendation for the optimal reperfusion strategy is obtained using the current model, even in the instance of a large amount of ward data with additional constraints. For most municipal wards (204, 53%) percutaneous coronary intervention is the preferred reperfusion strategy based on proximity. For the remainder of the wards prehospital (138, 36%) or in-hospital (44, 11%) thrombolysis is recommended. CONCLUSION: A scalable and efficient method of determining the optimal reperfusion strategy for a patient presenting with ST-elevation myocardial infarction in the North West province, is presented. This approach can serve as a model which can be applied to other settings and can form the basis of regional coronary care network development priorities and resource allocations. African Federation for Emergency Medicine 2020 2020-05-26 /pmc/articles/PMC7723916/ /pubmed/33318897 http://dx.doi.org/10.1016/j.afjem.2020.04.008 Text en © 2020 African Federation for Emergency Medicine. Publishing services provided by Elsevier. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original Article
Stassen, Willem
Olsson, Leif
Kurland, Lisa
The application of optimisation modelling and geospatial analysis to propose a coronary care network model for patients with ST-elevation myocardial infarction
title The application of optimisation modelling and geospatial analysis to propose a coronary care network model for patients with ST-elevation myocardial infarction
title_full The application of optimisation modelling and geospatial analysis to propose a coronary care network model for patients with ST-elevation myocardial infarction
title_fullStr The application of optimisation modelling and geospatial analysis to propose a coronary care network model for patients with ST-elevation myocardial infarction
title_full_unstemmed The application of optimisation modelling and geospatial analysis to propose a coronary care network model for patients with ST-elevation myocardial infarction
title_short The application of optimisation modelling and geospatial analysis to propose a coronary care network model for patients with ST-elevation myocardial infarction
title_sort application of optimisation modelling and geospatial analysis to propose a coronary care network model for patients with st-elevation myocardial infarction
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7723916/
https://www.ncbi.nlm.nih.gov/pubmed/33318897
http://dx.doi.org/10.1016/j.afjem.2020.04.008
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