The Moli-sani project: computerized ECG database in a population-based cohort study

Licia Iacoviello, Livia Rago, Simona Costanzo, Augusto Di Castelnuovo, Francesco Zito, Deodato Assanelli, Fabio Badilini, Maria Benedetta Donati, Giovanni de Gaetano
2012 Journal of Electrocardiology  
Computerized electrocardiogram (ECG) acquisition and interpretation may be extremely useful in handling analysis of data from large cohort studies and exploit research on the use of ECG data as prognostic markers for cardiovascular disease. The Moli-sani project (http://www.moli-sani.org) is a population-based cohort study aiming at evaluating the risk factors linked to chronic-degenerative disease with particular regard to cardiovascular disease and cancer and intermediate metabolic phenotypes
more » ... such as hypertension, diabetes, dyslipidemia, obesity, and metabolic syndrome. Between March 2005 and April 2010, 24 325 people aged 35 years or older, living in the Molise region (Italy), were randomly recruited. A follow-up based on linkage with hospital discharge records and mortality regional registry and reexamination of the cohort is ongoing and will be repeated at prefixed times. Each subject was administered questionnaires on personal and medical history, food consumption, quality of life (FS36), and psychometry. Plasma serum, cellular pellet, and urinary spots were stored in liquid nitrogen. Subjects were measured blood pressure, weight, height, and waist and hip circumferences, and underwent spirometry to evaluate pulmonary diffusion capacity, gas diffusion, and pulmonary volumes. Standard 12-lead resting ECG was performed by a Cardiette ar2100-view electrocardiograph and tracings stored in digital standard communication protocol format for subsequent analysis. The digital ECG database of the Moli-sani project is currently being used to assess the association between physiologic variables and pathophyiosiologic conditions and parameters derived from the ECG signal. This computerized ECG database represents a unique opportunity to identify and assess prognostic factors associated with cardiovascular and metabolic diseases.
doi:10.1016/j.jelectrocard.2012.07.008 pmid:23021814 fatcat:nynzwbb3zjbyvenki766cu74n4