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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2019
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.
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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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