Next-generation, Integrative, and Personalized Risk Assessment to Prevent Recurrent Heart Failure Events: the ORACLE Study
Trial Snapshot
- Phase
- Not Applicable
- Status
- Recruiting
- Sponsor
- Enrollment
- 1,134
- Locations
- 1
- Primary Endpoint
- To compare the model performance of the comprehensive risk assessment algorithm with a traditional risk model to predict HF related hospitalizations or all-cause death
Study Overview
Brief Summary
The aim of this study is to develop and validate an improved, comprehensive risk assessment algorithm integrating blood RNA-based biomarkers, clinical, and patient-centered data and to assess the incremental predictive value (discrimination and reclassification) compared to a traditional risk model (change in the c-statistics for prediction of the primary endpoint).
Detailed Description
The ORACLE study is a multicenter, observational, prospective, cross-sectional and longitudinal study integrated by 3 different cohorts for 1) RNA biomarker discovery (60 nested case-control sample), 2) model derivation (516 nested case-control sample) and 3) external validation (new prospective cohort of 558 consecutive patients recruited in 4 hospital centers) according to a TRIPOD Statement type 3 analysis. In total 1134 consecutive patients with a HF hospitalization or urgent HF visit < 30 days before inclusion and followed for 6 months will be studied.
This study include the discovery of novel RNA-based biomarkers using next-generation sequencing technology to define and validate a new biomarker set and clinical and patient-centered risk determinants definition. A new model will be constructed; training and internal validation in the derivation cohort using machine learning methods, and finally an external validation of the new next generation integrative risk assessment model will be performed.
Study Design
- Study Type
- Observational
- Observational Model
- Other
- Time Perspective
- Cross Sectional
Eligibility Criteria
- Ages
- 18 Years to — (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •Age ≥ 18 years old.
- •Patients with a recent (<30 days) acute decompensation of HF requiring intravenous diuretic therapy (either hospitalized or in ambulatory care) or intensification of oral diuretics (ambulatory care).
- •HF diagnosis according to European Society of Cardiology (ESC) criteria.
- •Written informed consent
- •Patients receiving oral standard medication for chronic HF.
Exclusion Criteria
- •Age<18 years old.
- •Death before hospital discharge.
- •The patient is unable or unwilling to give the informed consent to participate.
- •Unstable patients with signs of fluid overload or low cardiac output at the moment of enrollment.
Outcomes
Primary Outcomes
To compare the model performance of the comprehensive risk assessment algorithm with a traditional risk model to predict HF related hospitalizations or all-cause death
Time Frame: 6 months
To compare in an external validation cohort of patients a recent acute HF event, the performance (discrimination, additive predictive value, and reclassification ability) of a comprehensive risk assessment algorithm with the performance of a traditional risk model to predict the occurrence of the primary composite clinical end-point of HF related hospitalizations (readmissions) or all-cause death at 180 days after hospital discharge or after an urgent HF visit (acute HF event requiring intravenous administration of diuretics without admission).
Secondary Outcomes
- To compare the model performance of the comprehensive risk assessment algorithm with a traditional risk model to predict the occurrence of HF-related hospitalizations (readmissions) or all-cause death at 30, and 90 days after hospital discharge.(6 months)
- To compare the model performance of the comprehensive risk assessment algorithm with a traditional risk model to predict the occurrence of HF-related hospitalizations (readmissions) at 30, 90, and 180 days after hospital discharge.(6 months)
- To compare the model performance of the comprehensive risk assessment algorithm with a traditional risk model to predict the occurrence of all-cause death at 30, 90, and 180 days, after hospital discharge.(6 months)
Investigators
Josep Comín
Prof. Josep Comín-Colet, MD, PhD
Hospital Universitari de Bellvitge