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SALMANTICOR study. Rationale and design of a population-based study to identify structural heart disease abnormalities: a spatial and machine learning analysis
INTRODUCTION: This study aims to obtain data on the prevalence and incidence of structural heart disease in a population setting and, to analyse and present those data on the application of spatial and machine learning methods that, although known to geography and statistics, need to become used for...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6398793/ https://www.ncbi.nlm.nih.gov/pubmed/30765403 http://dx.doi.org/10.1136/bmjopen-2018-024605 |
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author | Melero-Alegria, Jose Ignacio Cascon, Manuel Romero, Alfonso Vara, Pedro Pablo Barreiro-Perez, Manuel Vicente-Palacios, Victor Perez-Escanilla, Fernando Hernandez-Hernandez, Jesus Garde, Beatriz Cascon, Sara Martin-Garcia, Ana Diaz-Pelaez, Elena de Dios, Jose Maria Uribarri, Aitor Jimenez-Candil, Javier Cruz-Gonzalez, Ignacio Blazquez, Baltasara Hernandez, Jose Manuel Sanchez-Pablo, Clara Santolino, Inmaculada Ledesma, Maria Concepcion Muriel, Paz Dorado-Diaz, P Ignacio Sanchez, Pedro L |
author_facet | Melero-Alegria, Jose Ignacio Cascon, Manuel Romero, Alfonso Vara, Pedro Pablo Barreiro-Perez, Manuel Vicente-Palacios, Victor Perez-Escanilla, Fernando Hernandez-Hernandez, Jesus Garde, Beatriz Cascon, Sara Martin-Garcia, Ana Diaz-Pelaez, Elena de Dios, Jose Maria Uribarri, Aitor Jimenez-Candil, Javier Cruz-Gonzalez, Ignacio Blazquez, Baltasara Hernandez, Jose Manuel Sanchez-Pablo, Clara Santolino, Inmaculada Ledesma, Maria Concepcion Muriel, Paz Dorado-Diaz, P Ignacio Sanchez, Pedro L |
author_sort | Melero-Alegria, Jose Ignacio |
collection | PubMed |
description | INTRODUCTION: This study aims to obtain data on the prevalence and incidence of structural heart disease in a population setting and, to analyse and present those data on the application of spatial and machine learning methods that, although known to geography and statistics, need to become used for healthcare research and for political commitment to obtain resources and support effective public health programme implementation. METHODS AND ANALYSIS: We will perform a cross-sectional survey of randomly selected residents of Salamanca (Spain). 2400 individuals stratified by age and sex and by place of residence (rural and urban) will be studied. The variables to analyse will be obtained from the clinical history, different surveys including social status, Mediterranean diet, functional capacity, ECG, echocardiogram, VASERA and biochemical as well as genetic analysis. ETHICS AND DISSEMINATION: The study has been approved by the ethical committee of the healthcare community. All study participants will sign an informed consent for participation in the study. The results of this study will allow the understanding of the relationship between the different influencing factors and their relative importance weights in the development of structural heart disease. For the first time, a detailed cardiovascular map showing the spatial distribution and a predictive machine learning system of different structural heart diseases and associated risk factors will be created and will be used as a regional policy to establish effective public health programmes to fight heart disease. At least 10 publications in the first-quartile scientific journals are planned. TRIAL REGISTRATION NUMBER: NCT03429452. |
format | Online Article Text |
id | pubmed-6398793 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-63987932019-03-20 SALMANTICOR study. Rationale and design of a population-based study to identify structural heart disease abnormalities: a spatial and machine learning analysis Melero-Alegria, Jose Ignacio Cascon, Manuel Romero, Alfonso Vara, Pedro Pablo Barreiro-Perez, Manuel Vicente-Palacios, Victor Perez-Escanilla, Fernando Hernandez-Hernandez, Jesus Garde, Beatriz Cascon, Sara Martin-Garcia, Ana Diaz-Pelaez, Elena de Dios, Jose Maria Uribarri, Aitor Jimenez-Candil, Javier Cruz-Gonzalez, Ignacio Blazquez, Baltasara Hernandez, Jose Manuel Sanchez-Pablo, Clara Santolino, Inmaculada Ledesma, Maria Concepcion Muriel, Paz Dorado-Diaz, P Ignacio Sanchez, Pedro L BMJ Open Epidemiology INTRODUCTION: This study aims to obtain data on the prevalence and incidence of structural heart disease in a population setting and, to analyse and present those data on the application of spatial and machine learning methods that, although known to geography and statistics, need to become used for healthcare research and for political commitment to obtain resources and support effective public health programme implementation. METHODS AND ANALYSIS: We will perform a cross-sectional survey of randomly selected residents of Salamanca (Spain). 2400 individuals stratified by age and sex and by place of residence (rural and urban) will be studied. The variables to analyse will be obtained from the clinical history, different surveys including social status, Mediterranean diet, functional capacity, ECG, echocardiogram, VASERA and biochemical as well as genetic analysis. ETHICS AND DISSEMINATION: The study has been approved by the ethical committee of the healthcare community. All study participants will sign an informed consent for participation in the study. The results of this study will allow the understanding of the relationship between the different influencing factors and their relative importance weights in the development of structural heart disease. For the first time, a detailed cardiovascular map showing the spatial distribution and a predictive machine learning system of different structural heart diseases and associated risk factors will be created and will be used as a regional policy to establish effective public health programmes to fight heart disease. At least 10 publications in the first-quartile scientific journals are planned. TRIAL REGISTRATION NUMBER: NCT03429452. BMJ Publishing Group 2019-02-13 /pmc/articles/PMC6398793/ /pubmed/30765403 http://dx.doi.org/10.1136/bmjopen-2018-024605 Text en © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Epidemiology Melero-Alegria, Jose Ignacio Cascon, Manuel Romero, Alfonso Vara, Pedro Pablo Barreiro-Perez, Manuel Vicente-Palacios, Victor Perez-Escanilla, Fernando Hernandez-Hernandez, Jesus Garde, Beatriz Cascon, Sara Martin-Garcia, Ana Diaz-Pelaez, Elena de Dios, Jose Maria Uribarri, Aitor Jimenez-Candil, Javier Cruz-Gonzalez, Ignacio Blazquez, Baltasara Hernandez, Jose Manuel Sanchez-Pablo, Clara Santolino, Inmaculada Ledesma, Maria Concepcion Muriel, Paz Dorado-Diaz, P Ignacio Sanchez, Pedro L SALMANTICOR study. Rationale and design of a population-based study to identify structural heart disease abnormalities: a spatial and machine learning analysis |
title | SALMANTICOR study. Rationale and design of a population-based study to identify structural heart disease abnormalities: a spatial and machine learning analysis |
title_full | SALMANTICOR study. Rationale and design of a population-based study to identify structural heart disease abnormalities: a spatial and machine learning analysis |
title_fullStr | SALMANTICOR study. Rationale and design of a population-based study to identify structural heart disease abnormalities: a spatial and machine learning analysis |
title_full_unstemmed | SALMANTICOR study. Rationale and design of a population-based study to identify structural heart disease abnormalities: a spatial and machine learning analysis |
title_short | SALMANTICOR study. Rationale and design of a population-based study to identify structural heart disease abnormalities: a spatial and machine learning analysis |
title_sort | salmanticor study. rationale and design of a population-based study to identify structural heart disease abnormalities: a spatial and machine learning analysis |
topic | Epidemiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6398793/ https://www.ncbi.nlm.nih.gov/pubmed/30765403 http://dx.doi.org/10.1136/bmjopen-2018-024605 |
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