Ignite Creation Date:
2025-12-26 @ 11:10 AM
Ignite Modification Date:
2026-01-13 @ 9:55 AM
Study NCT ID:
NCT04984928
Status:
UNKNOWN
Last Update Posted:
2022-04-12
First Post:
2021-07-19
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Readmission Risk of Patients With Heart Failure.
Sponsor:
University Hospital, Montpellier
Organization:
Study Overview
Official Title:
Predicting the Risk of Readmission in Patients With Chronic Heart Failure
Status:
UNKNOWN
Status Verified Date:
2022-04
Last Known Status:
RECRUITING
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
PREDIC
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
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
Detailed Description:
None
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: