Prognostic Models for Mortality and Morbidity in Heart Failure With Preserved Ejection Fraction.

Area of Science:

• Cardiology
• Clinical Prediction Models
• Heart Failure Research

Background:

• Accurate risk prediction is crucial for managing heart failure with preserved ejection fraction (HFpEF).
• Current models may not fully leverage routinely collected clinical variables for optimal risk stratification.

Purpose of the Study:

• To develop and validate a novel prediction model for clinical outcomes in HFpEF using routinely collected variables.
• To compare the performance of this novel model against existing biomarker-driven approaches.

Main Methods:

• The prediction model was derived using data from the DELIVER trial (n=6263).
• Validation was performed using data from the PARAGON-HF (n=4796) and I-PRESERVE (n=4128) trials.
• The model incorporated 11 variables, including NT-proBNP, HF hospitalization history, creatinine, and diabetes, to predict composite outcomes of HF hospitalization or cardiovascular death, cardiovascular death, and all-cause death.

Main Results:

• The developed model demonstrated good discrimination and calibration in validation cohorts (1-year C-statistic 0.71-0.75).
• It showed significantly better discrimination than the MAGGIC risk score and NT-proBNP level alone.
• An online calculator was developed for accessible risk assessment.

Conclusions:

• A robust, validated prediction model for HFpEF clinical outcomes was developed using routinely collected variables.
• This model offers improved predictive performance compared to NT-proBNP alone.
• The model can assist clinicians in identifying high-risk HFpEF patients and informing treatment strategies.