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Updated: Jan 9, 2026

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
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Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

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Development, Validation, and Subtype Analysis of a Predictive Model for Atrial Fibrillation in Patients With

Ailian Shen1, Jing Xu2, Qiucang Xue3

  • 1Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Jiangsu University, 210008 Nanjing, Jiangsu, China.

Reviews in Cardiovascular Medicine
|December 8, 2025
PubMed

Insights

Atrial fibrillation (AF) risk in hypertrophic cardiomyopathy (HCM) can be predicted using a multiparametric cardiac magnetic resonance (CMR) model. This model incorporates specific metrics and subtype analyses for personalized patient monitoring and early intervention.

Area of Science:

  • Cardiology
  • Medical Imaging
  • Biostatistics

Background:

  • Atrial fibrillation (AF) is a significant complication of hypertrophic cardiomyopathy (HCM), impacting patient prognosis.
  • Existing risk prediction models for AF in HCM often lack comprehensive cardiac magnetic resonance (CMR) imaging metrics and subtype-specific details.

Purpose of the Study:

  • To develop and validate a multiparametric CMR model for predicting AF risk in HCM patients.
  • To identify independent predictors of AF in the overall HCM cohort and within specific HCM subtypes (obstructive and non-obstructive).

Main Methods:

  • Retrospective analysis of 405 HCM patients (2019-2024), with 86 diagnosed with AF.
  • Utilized Least Absolute Shrinkage and Selection Operator (LASSO) and multivariable logistic regression to identify AF predictors after excluding highly correlated variables.
  • Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis, with subgroup analyses for obstructive (HOCM) and non-obstructive (HNCM) HCM.

Main Results:

  • Key predictors of AF in the overall HCM cohort included right atrial diameter (anteroposterior), left ventricular end-systolic volume, septal mitral annular plane systolic excursion (MAPSE septal), tricuspid annular plane systolic excursion (TAPSE), and maximum left atrial volume (MaxLAV).
  • The developed CMR model demonstrated strong predictive performance with an area under the curve (AUC) of 0.850 in the training set and 0.861 in the validation set.
  • Specific predictors varied by HCM subtype: septal MAPSE and left atrial ejection fraction (LAEF) for HOCM, and septal MAPSE, MaxLAV, and right atrial ejection fraction (RAEF) for HNCM.

Conclusions:

  • A validated multiparametric CMR model accurately predicts AF risk in HCM patients.
  • Subtype-specific predictors identified allow for tailored monitoring strategies.
  • The findings support personalized risk assessment and early intervention for AF in HCM.
Abstract

Keywords:
atrial fibrillationcardiac magnetic resonance imaginghypertrophic cardiomyopathyobstructive and non-obstructive subtypesrisk prediction model

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