Predictive Models of Readmission in Heart Failure

Overview

This is a prospective nested case-control study. Our goals are to create predictive models to readmissions for heart failure and also to define profiles for patients who are readmitted during the first month after the index episode. Patients who will be admitted in five hospitals in spain by the reason of a heart failure will be recruited during 18 months. Those who will be readmitted in a hospital up to a month after the index episode will be considered cases. Controls will be selected from the initial cohort of hospitalized patients.

Objective: To create models for predicting the risk of readmission short term (30-90 days) and medium term (one year).To identify differences between patients readmitted and not readmitted, and profiles of frequently preventable readmissions in our setting. Methodology: Observational prospective cohort of patients who are discharged with a diagnosis of heart failure in 5 participating centers. further, a nested case-control will be developed in the previous cohort, being cases those readmitted and controls will be chosen according to age, sex, etiology of heart failure, comorbidities and functional status. Sociodemographic, clinical and health related quality of life, empowerment and baseline self-efficacy as predictors of readmission were collected. Regarding the cases and controls, the quality of life in post-discharge transitional period, empowerment and self-efficacy, caregiver burden, social support, discharge care model, use of health services and adequation, conciliation and adherence to treatment will be assessed . Cox proportional hazards models will be created, and conditional logistic regression models to identify differences between cases and controls. Expected Results: National Health System will be provided with tools for predicting the risk of readmission useful to clinicians and managers to offer discharge individualized care. The most common characteristics of readmissions and preventable readmissions will be identified, which will be helpful to create specific actions in the future.

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Eligibility

Locations (4)

Outcomes