5th International Conference on Heart and Cardiovascular Diseases

Katihurca Almonte Montes de Oca is a cardiologist from the Dominican Republic, affiliated with the Asociación Instituto Dominicano de Cardiología in Santo Domingo. She is an Associate Professor with expertise in cardiology and echocardiography. Her professional interests include cardiovascular imaging, cardiac amyloidosis, vascular medicine, and the evaluation of complex cardiovascular disorders. She is actively involved in advancing cardiovascular research and improving diagnostic approaches to heart disease. Her research, titled “Amyloid Cardiomyopathy–Atrial Myopathy: Ventriculo–Atrial Mechanical Uncoupling and Multimodal Risk Stratification of Atrial Fibrillation in Transthyretin Cardiac Amyloidosis,” focuses on the relationship between ventricular and atrial dysfunction in transthyretin cardiac amyloidosis. Her work explores the potential of advanced cardiac imaging and multimodal risk assessment to improve the identification of atrial fibrillation risk and support more precise clinical evaluation. Through her research, she contributes to the understanding of cardiac amyloidosis and the development of improved diagnostic and risk-stratification strategies.

Background: Atrial fibrillation (AF) is highly prevalent in transthyretin cardiac amyloidosis (ATTR-CM), yet its underlying pathophysiological mechanisms remain incompletely understood. Emerging evidence suggests that the left atrium is not merely a passive marker of diastolic dysfunction but an early infiltrated target organ. We hypothesised that AF in ATTR-CM reflects a measurable ventriculo–atrial mechanical continuum that can be quantified using advanced myocardial deformation imaging.

Purpose

1. To evaluate mechanical coupling between left ventricular global longitudinal strain (LV-GLS) and left atrial strain (LAS).

2. To develop a multimodal model for arrhythmic risk stratification in ATTR-CM.

Methods: We conducted a cross-sectional observational study including 29 patients with ATTR-CM confirmed by technetium-99m pyrophosphate (99mTc-PYP) scintigraphy. Advanced echocardiography with two-dimensional speckle-tracking analysis was performed to assess LV global longitudinal strain (GLS) and left atrial strain (LAS).

Additional parameters included left atrial volume, E/e′ ratio as an estimate of left ventricular filling pressures, relative apical sparing index (RELAPS), and NT-proBNP levels.

Two integrated risk models were developed:

• ATTR-FA score (0–8 points)

• ATTR-FA PLUS score (0–12 points)

Results: Left atrial strain demonstrated a significant correlation with LV-GLS (r = −0.55, p = 0.0018), supporting the presence of ventricular–atrial mechanical uncoupling. No correlation was observed between LAS and RELAPS (p = 0.97), suggesting independent pathophysiological domains. LAS alone showed moderate discrimination for AF (AUC = 0.75). In contrast: ATTR-FA score: AUC = 0.88 ATTR-FA PLUS score: AUC = 0.86

Each one-point increase in ATTR-FA significantly increased AF probability (OR = 3.0), remaining independently associated after adjustment for LAS (adjusted OR = 4.06; p = 0.015).

Clinical stratification demonstrated a marked AF gradient:

• Low risk: 0%

• Intermediate risk: 11.1%

• High risk: 76.5%

• High prevalence of atrial fibrillation (52%), consistent with contemporary ATTR-CM series.

• Severely reduced global longitudinal strain despite relatively preserved left ventricular ejection fraction, reflecting a classic infiltrative pattern.

• Markedly impaired left atrial strain supporting the concept of amyloid atrial myopathy.

• Elevated E/e′ ratio and NT-proBNP levels indicating advanced hemodynamics burden

Conclusion: ATTR-CM should be interpreted as an integrated amyloid cardiomyopathy–atrial myopathy characterized by progressive ventricular–atrial mechanical uncoupling. Multimodal integration of LV-GLS, left atrial strain, and biomarkers substantially improves arrhythmic risk stratification compared with isolated parameters.

This approach provides a mechanistic and clinically applicable framework for early identification of patients at high risk of AF and may inform optimization of monitoring and therapeutic strategies.