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Related Experiment Video

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A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
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The Moli-sani project: computerized ECG database in a population-based cohort study.

Licia Iacoviello1, Livia Rago, Simona Costanzo

  • 1Laboratory of Genetic and Environmental Epidemiology, Research Laboratories, Fondazione di Ricerca e Cura Giovanni Paolo II, Università Cattolica, Campobasso, Italy. licia.iacoviello@moli-sani.org

Journal of Electrocardiology
|October 2, 2012
PubMed
Summary

The Moli-sani project created a large digital electrocardiogram (ECG) database to identify prognostic factors for cardiovascular and metabolic diseases. This resource aids in analyzing ECG data for large cohort studies and understanding disease risk.

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In Silico Clinical Trials for Cardiovascular Disease
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Last Updated: May 18, 2026

A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
18:11

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Published on: December 28, 2012

In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Area of Science:

  • Cardiology
  • Epidemiology
  • Medical Informatics

Background:

  • Large cohort studies require efficient data analysis for cardiovascular disease research.
  • Electrocardiogram (ECG) data holds potential as prognostic markers.
  • The Moli-sani project is a population-based study investigating chronic-degenerative diseases.

Purpose of the Study:

  • To establish a comprehensive digital ECG database for the Moli-sani project.
  • To utilize the ECG database for assessing associations between physiological variables and disease states.
  • To identify novel prognostic factors for cardiovascular and metabolic diseases using ECG data.

Main Methods:

  • Recruited 24,325 individuals aged 35+ in the Molise region, Italy (2005-2010).
  • Collected extensive data including medical history, lifestyle, anthropometrics, spirometry, and standard 12-lead resting ECG.
  • Stored digital ECG tracings for subsequent analysis and created a computerized ECG database.

Main Results:

  • A unique, large-scale digital ECG database has been established.
  • The database is currently being analyzed to link ECG parameters with physiological variables and disease conditions.
  • Ongoing follow-up and re-examinations are planned to enhance the cohort data.

Conclusions:

  • The Moli-sani computerized ECG database is a valuable resource for cardiovascular and metabolic disease research.
  • This database offers a unique opportunity to identify and validate prognostic factors.
  • Further analysis will advance understanding of ECG's role in predicting disease risk.