Exploring phenotypes in hypertrophic cardiomyopathy with machine learning data integration. A multicentric, multimodality pilot study. (14th October 2021)

• Record Type: Journal Article
• Authors: Loncaric, F; Marti Castellote, P M; Gonzalez Lopez, E; Sanchis, L; Romero, T; Prat, S; Gonzalez Mirelis, J F; Doltra, A; Ramos Jovani, M; Aguado, A; Piella, G; Garcia Pavia, P M; Garcia-Alvarez, A; Sitges, M; Bijnens, B
• Is Part Of: European heart journal. Volume 42(2021)Supplement 1
• Publication Date: 2021-10-14
• Subjects: Artificial Intelligence (Machine Learning, Deep Learning); Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/eurheartj/ehab724.3072
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: Background: Interpreting patient phenotypes is a challenge when screening for hypertrophic cardiomyopathy (HCM). Machine learning (ML) can potentially help with advanced data integration - combining information contained in whole-cardiac cycle echo deformation and velocity profiles with standard clinical variables. The aim is to apply an ML approach to integrate whole cardiac cycle echo data with clinical variables to explore HCM phenotypes. Methods: The cohort consisted of 138 participants from two centres: HCM patients (n=91) and relatives (n=47). Echocardiography was performed, whereas magnetic resonance and genetic testing in 48% and 82%, respectively. Whole cardiac cycle echo data (mitral and aortic velocity profiles, and six regional left ventricular (LV) deformation curves) were combined with clinical variables (age, sex, heart rate, e' medial and e' lateral) and used as the ML input. An unsupervised ML algorithm created a representative space where participants were positioned based on integrated data, blinded to disease status. Clustering was used to determine phenogroups and estimate the average characteristics. Data on family history (FHx), genotype, arrhythmias or syncope, implantable cardioverter-defibrillators (ICD), and late gadolinium enhancement (LGE) were used to interpret the phenogroups. As the LA diameter was not available in the dataset, the HCM risk for sudden cardiac death (SCD) was not calculated, however, the Table shows relevant variables to infer clinical risk. Results: Clustering divided the participants into 6 phenogroups (P1–6) (Figure). Average echo profiles are shown in the Figure, while the clinical data in the Table. P1/2 was defined by symptomatic patients with a high prevalence of positive genotypes, a positive FHx of SCD, and a burden of comorbidities. Echo findings showed pronounced structural/functional remodeling, and P1 was associated with severe septal hypertrophy and outflow tract obstruction. The high prevalence of ICD devices defined P1/2 as high risk groups. In comparison, patients in P3/4 were younger, with milder LV hypertrophy, but still considerable functional impairment. P3 had a higher burden of FHX and a higher prevalence of pathogenic mutations, whereas P4 a higher incidence of hypertension, high heart rate, mitral inflow fusion and findings of LGE. Finally, P5/6 consisted of younger individuals, predominantly HCM relatives, with a mild phenotype and, thus, low inferred risk. As expected, the majority of patients with the genetic variants of undetermined significance were located in P5. Conclusion: ML can help derive clinically interpretable phenotypes in HCM based on the automated integration of whole cardiac cycle deformation and velocity data with conventional clinical parameters. The derived phenogroups correspond with established risk profiles in HCM. An expanded dataset is needed to enable further exploration of the phenotype-genotype relations and to define prognostic value. FUNDunding Acknowledgement: Type of funding sources: Public grant(s) – EU funding. Main funding source(s): This work was supported by the Horizon 2020 European Commission Project H2020-MSCA-ITN