Chronic Heart Failure
Conditions
Keywords
Heart failure, Prediction, Personnalized medicine
Brief summary
Heart failure (HF) is a frequent, serious, and costly chronic disease: it leads to 150,000 hospitalizations each year in France at a cost of 525 million Euros. It is estimated that 20-40% of these hospitalizations are preventable by known interventions: home telemonitoring, care coordination, therapeutic intensification and therapeutic education. But these interventions only work if patients at high risk of rehospitalization are targeted to individualize management. In these patients, the risk of rehospitalization depends on clinical, biological, socioeconomic, care pathway, and location-related data. Existing predictive tools perform poorly due to three important limitations: non-use of unstructured clinical data, lack of integration of multimodal data, and weakness of the algorithmic approach. The objective is to design and validate a predictive algorithm for the risk of rehospitalization in heart failure patients, using multiple data sources
Interventions
None listed
Sponsors
University Hospital, Montpellier
Study design
Observational model
CASE_ONLY
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* All consecutive adults * Admitted to Montpellier University Hospital in 2017-2020 * For heart failure
Exclusion criteria
\- age \< 18 years
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 30 days all cause readmission rate | day 30 | 30 days all cause readmission rate |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| 90 days all cause readmission rate | day 90 | 90 days all cause readmission rate |
Countries
France
Contacts
Primary ContactGrégoire Mercier, PU PH
Outcome results
None listed