Observational studyBMJ open2019
SALMANTICOR study. Rationale and design of a population-based study to identify structural heart disease abnormalities: a spatial and machine learning analysis.
Observational study in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03429452 (Population-based Study to Identify Structural Heart Disease Abnormalities in a Spanish Community), which is not on this map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Population-based Study to Identify Structural Heart Disease Abnormalities in a Spanish Community: Salamanca. The SALMANTICOR Study
Who cites it
7 citing papers in PubMed.
- Associations Between Chocolate Consumption and Cardiometabolic Risk Markers Across Metabolic Profiles: Insights from a Population-Based Study.Nutrients · 2026Article
- Growth Differentiation Factor 15 as a Biomarker of Cardiovascular Burden and Mortality in a Population-Based Cohort.International journal of molecular sciences · 2026Article
- Cohort profile: the CORDELIA study (Collaborative cOhorts Reassembled Data to study mEchanisms and Longterm Incidence of chronic diseAses).European journal of epidemiology · 2025Article
- Epidemiological Situation of High-Prevalence Non-Communicable Diseases in Spain: A Systematic Review.Journal of clinical medicine · 2023Review
- Pericardial and myocardial involvement after SARS-CoV-2 infection: a cross-sectional descriptive study in healthcare workers.Revista espanola de cardiologia (English ed.) · 2022Article
- [Pericardial and myocardial involvement after SARS-CoV-2 infection: a cross-sectional descriptive study in healthcare workers].Revista espanola de cardiologia · 2022Article
- Deep learning and the electrocardiogram: review of the current state-of-the-art.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
24 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThis 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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Registered trials
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