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

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.