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Machine-Learning Clustering Uncovers Divergent Response Pathways to Mavacamten in Obstructive HCM: A Portuguese Multicentre Perspective
Session:
Sessão de Comunicações Orais 09 – Inteligência Artificial e tomada de decisão no risco cardiovascular e nos sistemas de saúde
Speaker:
Inês Pereira De Miranda
Congress:
CPC 2026
Topic:
N. E-Cardiology / Digital Health, Public Health, Health Economics, Research Methodology
Theme:
17. Myocardial Disease
Subtheme:
17.4 Myocardial Disease – Treatment
Session Type:
Comunicações Orais
FP Number:
---
Authors:
Inês Pereira de Miranda; Filipa Gerardo; Rodrigo Brandão; Julien Lopes; Débora Correia; Sérgio Maltês; José Viegas; Tânia Laranjeira; Sílvia Aguiar Rocha; Bruno Rocha; João Bicho Augusto
Abstract
<p style="text-align:justify"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif"><strong><span style="font-family:"Calibri",sans-serif">Background: </span></strong><span style="font-family:"Calibri",sans-serif">Response to mavacamten in obstructive hypertrophic cardiomyopathy (oHCM) is heterogeneous due to differences in clinical features, biomarkers, and cardiac remodeling. Traditional subgroup analyses may miss multidimensional phenotypes influencing treatment effects. Unsupervised clustering offers a data-driven approach to identify latent response profiles, potentially improving patient stratification and supporting personalized therapy</span>.</span></span></p> <p style="text-align:justify"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif"><strong><span style="font-family:"Calibri",sans-serif">Objective:</span></strong><span style="font-family:"Calibri",sans-serif"> To identify distinct mavacamten response phenotypes using unsupervised clustering of clinical, biomarker, and imaging parameters in oHCM patients, based on real-world data from three Portuguese centers.</span></span></span></p> <p style="text-align:justify"><span style="font-size:12pt"><span style="font-family:Aptos,sans-serif"><strong><span style="font-family:"Calibri",sans-serif">Methods:</span></strong><span style="font-family:"Calibri",sans-serif"> Consecutive mavacamten-treated oHCM patients from three Portuguese centers were analyzed using K-means unsupervised clustering. Input variables captured multidimensional changes from baseline to optimal dose, including LVOT gradients, maximal wall thickness, indexed LV mass, left atrial diameter (LAd), E/e′ ratio, and NYHA class, along with baseline genotype category and CYP2C19 metabolizer status. Missing values were imputed with medians and all features were standardized </span><span style="font-family:"Calibri",sans-serif">(z-scores) before model training</span><span style="font-family:"Calibri",sans-serif">. Silhouette analysis across k=2 to k=6 supported a two-cluster </span><span style="font-family:"Calibri",sans-serif">model</span><span style="font-family:"Calibri",sans-serif">, and clinical and echocardiographic profiles were examined to </span><span style="font-family:"Calibri",sans-serif">derive clinically </span><span style="font-family:"Calibri",sans-serif">interpretable response phenotypes.</span></span></span></p> <p style="text-align:justify"> </p> <p style="text-align:justify"><span style="font-size:12pt"><span style="font-family:Aptos,sans-serif"><strong><span style="font-family:"Calibri",sans-serif">Results</span></strong><span style="font-family:"Calibri",sans-serif">: A total of 83 patients were included (mean age 65±12 years; 68.7% female). Two response phenotypes emerged as the optimal cluster solution.</span></span></span></p> <p style="text-align:justify"><span style="font-size:12pt"><span style="font-family:Aptos,sans-serif"><span style="font-family:"Calibri",sans-serif">- Cluster 0 (“hyper-responders”, n=20) included younger patients (60.4±14.3 vs 66.3±10.6 years, p=0.096) with markedly higher baseline obstruction (rest LVOT gradient 101±37 vs 46±29mmHg, p<0.001; provoked 143±45 vs 85±36mmHg, p<0.001), slightly larger LAd (46±6 vs 43±6mm, p=0.06), and higher E/e′ ratio (17.7±6.0 vs 15.2±5.2, p=0.08). This group had pronounced haemodynamic and diastolic improvement, with reductions in LVOT gradients (rest -98±34 vs -32±28mmHg, p<0.001; provoked -135±45 vs -66±35mmHg, p<0.001) and E/e′ ratio (-6.7±3.5 vs -2.8±2.7, p=0.01). </span></span></span></p> <p style="text-align:justify"><span style="font-size:12pt"><span style="font-family:Aptos,sans-serif"><span style="font-family:"Calibri",sans-serif">- Cluster 1 (“typical responders”, n=63) consisted of older patients with less severe baseline gradients and exhibited a moderate but consistent response across all remodeling domains. </span></span></span></p> <p style="text-align:justify"><span style="font-size:12pt"><span style="font-family:Aptos,sans-serif"><span style="font-family:"Calibri",sans-serif">Reductions in LAd (-4.1±4.5 vs -3.3±4.1mm, p=0.42) and indexed LV mass (-32.7±41 vs -17.5±39 g/m², p=0.20) were observed in both clusters. Genotype distribution and CYP2C19 metabolizer class did not significantly differ.</span></span></span></p> <p style="text-align:justify"><span style="font-size:12pt"><span style="font-family:Aptos,sans-serif"><strong><span style="font-family:"Calibri",sans-serif">Conclusions</span></strong><span style="font-family:"Calibri",sans-serif">: Data-driven phenogrouping identified two mavacamten response pathways: a younger, highly obstructive hyper-responder group with striking reverse remodeling, and a milder obstructive group with moderate improvement. Hemodynamic load, but not genotype, drives treatment benefit. These phenogroups may support more personalized mavacamten use in real-world oHCM.</span></span></span></p>
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