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Development of a Predictive Algorithm for the Risk of Rehospitalization of Patients With Heart Failure

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT03905226
Recruitment Status : Active, not recruiting
First Posted : April 5, 2019
Last Update Posted : January 19, 2022
Sponsor:
Information provided by (Responsible Party):
Groupe Hospitalier Paris Saint Joseph

Study Description
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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.


Condition or disease
Heart Failure

Study Design
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Study Type : Observational
Estimated Enrollment : 1600 participants
Observational Model: Cohort
Time Perspective: Retrospective
Official Title: Development of a Predictive Algorithm for the Risk of Rehospitalization of Patients With Heart Failure
Actual Study Start Date : January 12, 2019
Actual Primary Completion Date : December 31, 2019
Estimated Study Completion Date : March 31, 2022

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Heart Failure

Groups and Cohorts
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Group/Cohort
Heart Failure
Patients whom initiated a hospital pathway for heart failure management within the Paris Saint Joseph Hospital Group (GHPSJ) between January 1, 2015 and December 31, 2018.


Outcome Measures
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Primary Outcome Measures :
  1. Number of readmissions [ Time Frame: month 1 ]
    Comparison of the number of readmissions predicted to the number actually observed with calculation of the sensitivity and specificity of the model at the validation phase.

  2. Number of readmissions [ Time Frame: Month 6 ]
    Comparison of the number of readmissions predicted to the number actually observed with calculation of the sensitivity and specificity of the model at the validation phase.

  3. Number of readmissions [ Time Frame: Year 1 ]
    Comparison of the number of readmissions predicted to the number actually observed with calculation of the sensitivity and specificity of the model at the validation phase.


Eligibility Criteria
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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Patients with heart failure hospitalized in the cardiology department ath GHPSJ between january 2015 to december 2018
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
Contacts and Locations
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Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03905226


Locations
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France
Groupe Hospitalier Paris Saint-Joseph
Paris, France, 75014
Sponsors and Collaborators
Groupe Hospitalier Paris Saint Joseph
Investigators
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Principal Investigator: Philippe ABASSADE, MD Groupe Hospitalier Paris Saint Joseph
More Information
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Responsible Party: Groupe Hospitalier Paris Saint Joseph
ClinicalTrials.gov Identifier: NCT03905226    
Other Study ID Numbers: PREDIC
First Posted: April 5, 2019    Key Record Dates
Last Update Posted: January 19, 2022
Last Verified: January 2022

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Additional relevant MeSH terms:
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Heart Failure
Heart Diseases
Cardiovascular Diseases