Construction and validation of a risk prediction model for 3- and 5-year new-onset atrial fibrillation in HFpEF patients

TL;DR

This study creates a new model to predict new-onset atrial fibrillation in patients with heart failure and preserved ejection fraction.

Contribution

A novel nomogram combining clinical and imaging factors is developed and validated for predicting atrial fibrillation in HFpEF patients.

Findings

The nomogram includes age, COPD, hyperthyroidism, renal dysfunction, LAD, and PASP as significant predictors.

The model showed strong discrimination with a C-index of 0.819 and AUCs of 0.827 and 0.825 for 3- and 5-year predictions.

The nomogram outperformed the mC2HEST score in predicting new-onset atrial fibrillation.

Abstract

Patients with heart failure (HF) with preserved ejection fraction (HFpEF) are more prone to atrial fibrillation (AF) compared to those with heart failure with reduced ejection fraction (HFrEF). Nevertheless, a risk prediction model for new-onset atrial fibrillation (NOAF) in HFpEF patients remains a notable gap, especially with respect to imaging indicators.

We retrospectively analyzed 402 HFpEF subjects reviewed at the Affiliated Hospital of Qingdao University from 2017 to 2023. Cox regression analysis was performed to screen predictors of NOAF. A nomogram was constructed based on these factors and internally validated through the bootstrap resampling method. A performance comparison between the nomogram and the mC2HEST score was performed.

Out of the 402 participants, 62 (15%) developed atrial fibrillation. The risk factors for NOAF were finally screened out to include age, chronic obstructive pulmonary disease (COPD), hyperthyroidism, renal dysfunction, left atrial anterior–posterior diameter (LAD), and pulmonary artery systolic pressure (PASP), all of which were identified to create the nomogram. We calculated the bootstrap-corrected C-index (0.819, 95% CI: 0.762–0.870) and drew receiver operator characteristic (ROC) curves [3-year areas under curves (AUC) = 0.827, 5-year AUC = 0.825], calibration curves, and clinical decision curves to evaluate the discrimination, calibration, and clinical adaptability of the six-factor nomogram. Based on two cutoff values calculated by X-tile software, the moderate- and high-risk groups had more NOAF cases than the low-risk group (P < 0.0001). Our nomogram showed better 3- and 5-year NOAF predictive performance than the mC2HEST score estimated by the Integrated Discriminant Improvement Index (IDI) and the Net Reclassification Index (NRI) (P < 0.05).

The nomogram combining clinical features with echocardiographic indices helps predict NOAF among HFpEF patients.

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