Clinical trial NCT03905226: Development of a Predictive Algorithm for the Risk of Rehospitalization of Patients With Heart Failure
Official title: Development of a Predictive Algorithm for the Risk of Rehospitalization of Patients With Heart Failure
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Heart Failure
Lead sponsor: Fondation Hôpital Saint-Joseph
Sponsor class: OTHER
First posted: 2019-04-05
Enrollment: 1486
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with heart failure hospitalized in the cardiology department ath GHPSJ between january 2015 to december 2018

Brief summary: Heart failure is a chronic disease whose prevalence, due to the aging of the population, is increasing. In France, the prevalence of this pathology is 2.3% (it reaches 10% in the over 75 years) and affects nearly a million patients.

The rehospitalization of patients with heart failure affects 25% of patients within 1-3 months of hospital discharge, and 66% at 1 year while 75% of hospitalizations are preventable. These readmissions result in decreased quality of life and increased mortality; from an economic point of view, hospitalization accounts for 70% of expenses related to the management of heart failure. Avoiding rehospitalization is therefore a major public health issue. The current predictive scores remain perfectible, even though risk factors for readmission have already been the subject of numerous studies. The identification of patients at risk of rehospitalization is still an issue, especially for patients with preserved left ventricular ejection fraction. Targeting patients requiring appropriate care remains an issue.

The rise of innovative statistical techniques around Big Data in health opens new perspectives for the scientific exploitation of data available in electronic medical records, for example in the field of prediction. This study aims to explore the risk of rehospitalization in heart failure patients by analyzing routine data collected in medical records and by mobilizing artificial intelligence algorithms. A review of the literature confirms the innovative nature of such an approach: the majority of the studies identified implemented a prospective collection of data; only 20% of the studies mobilized the medical file; no French study used the new machine learning algorithms.


Eligibility criteria: Inclusion Criteria:

* Patients older than 18 years
* Patients with heart failure hospitalized in the cardiology department ath GHPSJ between january 2015 to december 2018

Exclusion Criteria:

* Patient opposing the use of his data for this research
* Patient under tutorship or curatorship
* Patient deprived of liberty

Primary outcomes:
measure: Number of readmissions
timeFrame: month 1
measure: Number of readmissions
timeFrame: Month 6
measure: Number of readmissions
timeFrame: Year 1
Secondary outcomes:
Other outcomes:
Locations:
city: Paris
country: France
facility: Groupe Hospitalier Paris Saint-Joseph
Study officials:
affiliation: Fondation Hôpital Saint-Joseph
name: Philippe ABASSADE, MD
role: PRINCIPAL_INVESTIGATOR
References: