» Articles » PMID: 30765403

SALMANTICOR Study. Rationale and Design of a Population-based Study to Identify Structural Heart Disease Abnormalities: a Spatial and Machine Learning Analysis

Abstract

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.

Citing Articles

Epidemiological Situation of High-Prevalence Non-Communicable Diseases in Spain: A Systematic Review.

Aparicio-Rodriguez Y, Alonso-Morillejo E, Garcia-Torrecillas J J Clin Med. 2023; 12(22).

PMID: 38002721 PMC: 10672730. DOI: 10.3390/jcm12227109.


[Pericardial and myocardial involvement after SARS-CoV-2 infection: a cross-sectional descriptive study in healthcare workers].

Eiros R, Barreiro-Perez M, Martin-Garcia A, Almeida J, Villacorta E, Perez-Pons A Rev Esp Cardiol. 2022; 75(9):735-747.

PMID: 35039707 PMC: 8755423. DOI: 10.1016/j.recesp.2021.10.021.


Pericardial and myocardial involvement after SARS-CoV-2 infection: a cross-sectional descriptive study in healthcare workers.

Eiros R, Barreiro-Perez M, Martin-Garcia A, Almeida J, Villacorta E, Perez-Pons A Rev Esp Cardiol (Engl Ed). 2021; 75(9):734-746.

PMID: 34866030 PMC: 8570413. DOI: 10.1016/j.rec.2021.11.001.

References
1.
Deo R . Machine Learning in Medicine. Circulation. 2015; 132(20):1920-30. PMC: 5831252. DOI: 10.1161/CIRCULATIONAHA.115.001593. View

2.
Lang R, Badano L, Mor-Avi V, Afilalo J, Armstrong A, Ernande L . Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. Eur Heart J Cardiovasc Imaging. 2015; 16(3):233-70. DOI: 10.1093/ehjci/jev014. View

3.
Auchincloss A, Gebreab S, Mair C, Diez Roux A . A review of spatial methods in epidemiology, 2000-2010. Annu Rev Public Health. 2012; 33:107-22. PMC: 3638991. DOI: 10.1146/annurev-publhealth-031811-124655. View

4.
Abellan J, Richardson S, Best N . Use of space-time models to investigate the stability of patterns of disease. Environ Health Perspect. 2008; 116(8):1111-9. PMC: 2516563. DOI: 10.1289/ehp.10814. View

5.
Tan J, Hagiwara Y, Pang W, Lim I, Oh S, Adam M . Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals. Comput Biol Med. 2018; 94:19-26. DOI: 10.1016/j.compbiomed.2017.12.023. View