Clinical Network for Big Data and Personalized Health: Study Protocol and Preliminary Results.
Simona EspositoSabatino OrlandiSara MagnaccaAmalia De CurtisAlessandro GialluisiLicia Iacoviellonull On Behalf Of The Neuromed Clinical Network Big Data And Personalised Health InvestigatorsPublished in: International journal of environmental research and public health (2022)
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.
Keyphrases
- big data
- end stage renal disease
- healthcare
- ejection fraction
- chronic kidney disease
- newly diagnosed
- physical activity
- public health
- prognostic factors
- artificial intelligence
- blood pressure
- randomized controlled trial
- study protocol
- type diabetes
- climate change
- health information
- metabolic syndrome
- deep learning
- pregnant women
- high fat diet
- high throughput
- electronic health record
- open label
- pregnancy outcomes
- data analysis
- placebo controlled