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

Study Overview
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Brief Summary
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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.

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

Conditions
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Heart Failure

Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Heart Failure<br>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. | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 |