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Clinical Network for Big Data and Personalized Health: Study Protocol and Preliminary Results

Author

Listed:
  • Simona Esposito

    (Department of Epidemiology and Prevention, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Neuromed, 86077 Pozzilli, Italy)

  • Sabatino Orlandi

    (Department of Epidemiology and Prevention, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Neuromed, 86077 Pozzilli, Italy)

  • Sara Magnacca

    (Mediterranea Cardiocentro, 80122 Napoli, Italy)

  • Amalia De Curtis

    (Department of Epidemiology and Prevention, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Neuromed, 86077 Pozzilli, Italy)

  • Alessandro Gialluisi

    (Department of Epidemiology and Prevention, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Neuromed, 86077 Pozzilli, Italy
    Department of Medicine and Surgery, Research Center in Epidemiology and Preventive Medicine (EPIMED), University of Insubria, 21100 Varese, Italy)

  • Licia Iacoviello

    (Department of Epidemiology and Prevention, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Neuromed, 86077 Pozzilli, Italy
    Department of Medicine and Surgery, Research Center in Epidemiology and Preventive Medicine (EPIMED), University of Insubria, 21100 Varese, Italy)

  • on behalf of The Neuromed Clinical Network Big Data and Personalised Health Investigators

    (Membership of Neuromed Clinical Network Big Data and Personalised Health are listed in the Appendix A.)

Abstract

The use of secondary hospital-based clinical data and electronical health records (EHR) represent a cost-efficient alternative to investigate chronic conditions. We present the Clinical Network Big Data and Personalised Health project, which collects EHRs for patients accessing hospitals in Central-Southern Italy, through an integrated digital platform to create a digital hub for the collection, management and analysis of personal, clinical and environmental information for patients, associated with a biobank to perform multi-omic analyses. A total of 12,864 participants (61.7% women, mean age 52.6 ± 17.6 years) signed a written informed consent to allow access to their EHRs. The majority of hospital access was in obstetrics and gynaecology (36.3%), while the main reason for hospitalization was represented by diseases of the circulatory system (21.2%). Participants had a secondary education (63.5%), were mostly retired (25.45%), reported low levels of physical activity (59.6%), had low adherence to the Mediterranean diet and were smokers (30.2%). A large percentage (35.8%) were overweight and the prevalence of hypertension, diabetes and hyperlipidemia was 36.4%, 11.1% and 19.6%, respectively. Blood samples were retrieved for 8686 patients (67.5%). This project is aimed at creating a digital hub for the collection, management and analysis of personal, clinical, diagnostic and environmental information for patients, and is associated with a biobank to perform multi-omic analyses.

Suggested Citation

  • Simona Esposito & Sabatino Orlandi & Sara Magnacca & Amalia De Curtis & Alessandro Gialluisi & Licia Iacoviello & on behalf of The Neuromed Clinical Network Big Data and Personalised Health Investigat, 2022. "Clinical Network for Big Data and Personalized Health: Study Protocol and Preliminary Results," IJERPH, MDPI, vol. 19(11), pages 1-12, May.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:11:p:6365-:d:822572
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    References listed on IDEAS

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    1. Andrea Pisesky & Eric I Benchimol & Coralie A Wong & Charles Hui & Megan Crowe & Marc-Andre Belair & Supichaya Pojsupap & Tim Karnauchow & Katie O'Hearn & Abdool S Yasseen III & James D McNally, 2016. "Incidence of Hospitalization for Respiratory Syncytial Virus Infection amongst Children in Ontario, Canada: A Population-Based Study Using Validated Health Administrative Data," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-13, March.
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