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

NCT03905226 | Completed

• 1 other identifier: PREDIC
• Study Type: observational
• Target: 1,486
• Locations: 1 country
• Sites: 1

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.

Conditions

Heart Failure

Outcome Measures

Primary Outcomes (3)

• Number of readmissions

  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.

  month 1

• Number of readmissions

  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.

  Month 6

• Number of readmissions

  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.

  Year 1

Study Arms (1)

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.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with heart failure hospitalized in the cardiology department ath GHPSJ between january 2015 to december 2018

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Fondation Hôpital Saint-Joseph: lead

Study Sites (1)

Groupe Hospitalier Paris Saint-Joseph

Paris, 75014, France

Location

MeSH Terms

Conditions

Heart Failure

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases

Study Officials

• Philippe ABASSADE, MD

  Fondation Hôpital Saint-Joseph

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 4, 2019
• First Posted: April 5, 2019
• Study Start: January 12, 2019
• Primary Completion: December 31, 2019
• Study Completion: December 31, 2019
• Last Updated: April 27, 2023

Record last verified: 2023-04

Locations

FR (1)