SPC365

Catheter Ablation for Atrial Fibrillation: Unveiling Key Predictors of Life Quality Enhancement

Session: Comunicações Orais - Sessão 11 - Ablação de fibrilhação auricular
Speaker: Rafael Silva Teixeira

Congress: CPC 2024
Topic: C. Arrhythmias and Device Therapy
Theme: 05. Atrial Fibrillation
Subtheme: 05.4 Atrial Fibrillation - Treatment
Session Type: Comunicações Orais
FP Number: ---
Authors: Rafael Silva Teixeira; Marta Catarina Almeida; João Almeida; Paulo Fonseca; Francisco Ramires; Madalena Plácido; Ines Rodrigues; Marco Oliveira; Helena Gonçalves; João Primo; Ricardo Fontes-Carvalho

<p style="text-align:justify"><span style="font-size:medium"><span style="font-family:&quot;Times New Roman&quot;,serif"><span style="color:#000000"><strong><span style="font-size:11pt"><span style="font-family:&quot;Avenir Book&quot;"><span style="color:black">Introduction:</span></span></span></strong><strong><span style="font-size:11pt"><span style="font-family:&quot;Avenir Book&quot;"><span style="color:black"> Traditional classification of Atrial Fibrillation (AF) based on temporal patterns doesn&#39;t fully encompass the condition&#39;s complexity. Unsupervised cluster analyses have been used recently for classifying patients into groups with similar comorbid profiles, abeit inconsistently. Bayesian profile regression, a semi-supervised machine learning technique, may improve the precision of clustering by integrating outcome data, thereby diminishing the variability observed in unsupervised methods. Since quality of life (QoL) primarily drives the indication for AF ablation, we utilized the AFEQT&nbsp;questionnaire to cluster patients.</span></span></span></strong></span></span></span></p>

<p style="text-align:justify"><span style="font-size:medium"><span style="font-family:&quot;Times New Roman&quot;,serif"><span style="color:#000000"><strong><span style="font-family:&quot;Avenir Book&quot;"><span style="color:black">Purpose: </span></span></strong><span style="font-family:&quot;Avenir Book&quot;"><span style="color:black">Our aim is to use patient-reported outcome measures to identify clinically relevant phenotypes of AF that benefit the most from catheter ablation. </span></span></span></span></span></p>

<p style="text-align:justify"><span style="font-size:medium"><span style="font-family:&quot;Times New Roman&quot;,serif"><span style="color:#000000"><strong><span style="font-family:&quot;Avenir Book&quot;"><span style="color:black">Methods:</span></span></strong> <span style="font-family:&quot;Avenir Book&quot;"><span style="color:black">We implemented a single center digital follow-up (FUP) program for patients&nbsp;referred for AF ablation since august 2020. FUP included scheduled visits and remote monitoring through a new digital health platform. AFEQT summary score reported by patients was analyzed using a non-linear mixed model and relative change in AFEQT by month 12 was used as outcome.&nbsp;Profile regression mixture modelling guided by QoL improvement was performed to create clinically relevant patient groupings. Penalized multinomial logistic regression model was used for validation. </span></span></span></span></span></p>

<p style="text-align:justify"><span style="font-size:medium"><span style="font-family:Calibri,sans-serif"><span style="color:#000000"><strong><span style="font-family:&quot;Avenir Book&quot;"><span style="color:black">Results:</span></span></strong><span style="font-family:&quot;Avenir Book&quot;"><span style="color:black">&nbsp;354 patients were enrolled until July 2023. 211 patients had FUP &gt; 12 months (age 59 &plusmn; 10 years, 31% female, 80% paroxysmal). The overall AFEQT questionnaire completeness rate was 72%. Patients were classified into 3 clusters: 1) patients with long duration AF, left heart cavities enlargement and diastolic dysfunction (n=57); 2) patients with self-reported anxiety/depression (n=50); and 3) patients with low rates of comorbidities (n=104). For clinical application, 8 variables (dyslipidemia, AF type and duration since first diagnosed, mitral E/A ratio following ablation, systolic pulmonary arterial pressure, left ventricle ejection fraction, and PROMIS T-score for anxiety and depression) were sufficient for classifying patients (85% accuracy). Patients with higher depressive and anxiety burden (cluster 2) had worse baseline AFEQT score (median: </span></span><span style="font-family:&quot;Avenir Book&quot;">46, IQR: 39-60)<span style="color:black"> and derived less benefit (median improvement: 21, IQR: 12-32), while patients with low comorbid burden (cluster 3) displayed higher baseline (median: </span>58, IQR: 48-62)<span style="color:black"> and final QoL (median improvement: 28, IQR: 21-36, p&lt;0.01).</span></span></span></span></span></p>

<p><strong><span style="font-size:12pt"><span style="font-family:&quot;Avenir Book&quot;"><span style="color:black">Conclusion: </span></span></span></strong><span style="font-size:12pt"><span style="font-family:&quot;Avenir Book&quot;"><span style="color:black">Cluster analysis identified 3 reproducible clinically relevant phenotypes of AF using widely available clinical and biological data. These clusters have distinct associations with QoL, underscoring the heterogeneity of AF and importance of comorbidities and substrates. This approach may allow for the prediction of which patients are most likely to benefit from treatment. </span></span></span></p>