What question did this study set out to answer?

To construct and validate a predictive model for atrial fibrillation risk in patients with heart failure with preserved ejection fraction.

January 10, 2026BMC Cardiovascular Disorders0 citationsOpen Access

Construction and validation of a predictive model for the risk of atrial fibrillation in patients with heart failure with preserved ejection fraction: a single-center retrospective analysis

PCPan ChenFuyang City People's Hospital XYXiaoyan YinFuyang Maternity and Child Health Care Hospital LRLei RenFuyang City People's Hospital

Key Result

A predictive model using six variables effectively predicts atrial fibrillation risk in patients with heart failure and preserved ejection fraction.

Key Points

To construct and validate a predictive model for atrial fibrillation risk in patients with heart failure with preserved ejection fraction.
Developed a predictive model using six variables: CHD, BMI, DP, FT3, GFR, and SGLT2i.
Conducted a retrospective analysis at a single center.
Evaluated the effectiveness of the model in predicting atrial fibrillation risk.
The model effectively predicts atrial fibrillation risk in heart failure with preserved ejection fraction patients.
Utilization of predictive variables significantly aids in risk stratification.

Structured PICO

Population

310 adults with heart failure with preserved ejection fraction (HFpEF), median age 70, 51.29% gender 1 (presumably male), treated at a single center in China. Key inclusion criteria: aged over 18 years, meeting 2022 AHA/ACC/HFSA guidelines for HFpEF, complete baseline clinical data, and no history of atrial fibrillation.

Outcome

Occurrence of atrial fibrillation at 1 year follow-uphard clinical

A predictive model integrating six routine clinical variables (CHD, BMI, DP, FT3, GFR, and SGLT2i use) can effectively stratify the 1-year risk of incident atrial fibrillation in patients with HFpEF.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

A predictive model constructed using six predictive variables-CHD, BMI, DP, FT3, GFR, and SGLT2i-can effectively predict the risk of atrial fibrillation in patients with HFpEF and aid in early risk stratification.

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