Distinct ECG phenotypes identified in hypertrophic cardiomyopathy using machine learning associate with arrhythmic risk markers
Lyon, A. ; Ariga, R. ; Mincholé, A. ; Mahmod, M. ; Ormondroyd, E. ; Laguna, P. (Universidad de Zaragoza) ; de Freitas, N. ; Neubauer, S. ; Watkins, H. ; Rodriguez, B.
Resumen: Aims: Ventricular arrhythmia triggers sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM), yet electrophysiological biomarkers are not used for risk stratification. Our aim was to identify distinct HCM phenotypes based on ECG computational analysis, and characterize differences in clinical risk factors and anatomical differences using cardiac magnetic resonance (CMR) imaging.
Methods: High-fidelity 12-lead Holter ECGs from 85 HCM patients and 38 healthy volunteers were analyzed using mathematical modeling and computational clustering to identify phenotypic subgroups. Clinical features and the extent and distribution of hypertrophy assessed by CMR were evaluated in the subgroups.
Results: QRS morphology alone was crucial to identify three HCM phenotypes with very distinct QRS patterns. Group 1 (n = 44) showed normal QRS morphology, Group 2 (n = 19) showed short R and deep S waves in V4, and Group 3 (n = 22) exhibited short R and long S waves in V4-6, and left QRS axis deviation. However, no differences in arrhythmic risk or distribution of hypertrophy were observed between these groups. Including T wave biomarkers in the clustering, four HCM phenotypes were identified: Group 1A (n = 20), with primary repolarization abnormalities showing normal QRS yet inverted T waves, Group 1B (n = 24), with normal QRS morphology and upright T waves, and Group 2 and Group 3 remaining as before, with upright T waves. Group 1A patients, with normal QRS and inverted T wave, showed increased HCM Risk-SCD scores (1A: 4.0%, 1B: 1.8%, 2: 2.1%, 3: 2.5%, p = 0.0001), and a predominance of coexisting septal and apical hypertrophy (p < 0.0001). HCM patients in Groups 2 and 3 exhibited predominantly septal hypertrophy (85 and 90%, respectively).
Conclusion: HCM patients were classified in four subgroups with distinct ECG features. Patients with primary T wave inversion not secondary to QRS abnormalities had increased HCM Risk-SCD scores and coexisting septal and apical hypertrophy, suggesting that primary T wave inversion may increase SCD risk in HCM, rather than T wave inversion secondary to depolarization abnormalities. Computational ECG phenotyping provides insight into the underlying processes captured by the ECG and has the potential to be a novel and independent factor for risk stratification.
Idioma: Inglés
DOI: 10.3389/fphys.2018.00213
Año: 2018
Publicado en: FRONTIERS IN PHYSIOLOGY 9, MAR (2018), 213 [13 pp]
ISSN: 1664-042X
Factor impacto JCR: 3.201 (2018)
Categ. JCR: PHYSIOLOGY rank: 25 / 81 = 0.309 (2018) - Q2 - T1
Factor impacto SCIMAGO: 1.153 - Physiology (medical) (Q2) - Physiology (Q2)
Financiación: info:eu-repo/grantAgreement/ES/DGA/Grupo Consolidado BSICoS
Financiación: info:eu-repo/grantAgreement/EC/H2020/675451/EU/A Centre of Excellence in Computational Biomedicine/CompBioMed
Financiación: info:eu-repo/grantAgreement/ES/MINECO/TEC2013-44666-R
Financiación: info:eu-repo/grantAgreement/ES/MINECO/TIN2014-5356-R
Tipo y forma: Artículo (Versión definitiva)
Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)
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Methods: High-fidelity 12-lead Holter ECGs from 85 HCM patients and 38 healthy volunteers were analyzed using mathematical modeling and computational clustering to identify phenotypic subgroups. Clinical features and the extent and distribution of hypertrophy assessed by CMR were evaluated in the subgroups.
Results: QRS morphology alone was crucial to identify three HCM phenotypes with very distinct QRS patterns. Group 1 (n = 44) showed normal QRS morphology, Group 2 (n = 19) showed short R and deep S waves in V4, and Group 3 (n = 22) exhibited short R and long S waves in V4-6, and left QRS axis deviation. However, no differences in arrhythmic risk or distribution of hypertrophy were observed between these groups. Including T wave biomarkers in the clustering, four HCM phenotypes were identified: Group 1A (n = 20), with primary repolarization abnormalities showing normal QRS yet inverted T waves, Group 1B (n = 24), with normal QRS morphology and upright T waves, and Group 2 and Group 3 remaining as before, with upright T waves. Group 1A patients, with normal QRS and inverted T wave, showed increased HCM Risk-SCD scores (1A: 4.0%, 1B: 1.8%, 2: 2.1%, 3: 2.5%, p = 0.0001), and a predominance of coexisting septal and apical hypertrophy (p < 0.0001). HCM patients in Groups 2 and 3 exhibited predominantly septal hypertrophy (85 and 90%, respectively).
Conclusion: HCM patients were classified in four subgroups with distinct ECG features. Patients with primary T wave inversion not secondary to QRS abnormalities had increased HCM Risk-SCD scores and coexisting septal and apical hypertrophy, suggesting that primary T wave inversion may increase SCD risk in HCM, rather than T wave inversion secondary to depolarization abnormalities. Computational ECG phenotyping provides insight into the underlying processes captured by the ECG and has the potential to be a novel and independent factor for risk stratification.
Idioma: Inglés
DOI: 10.3389/fphys.2018.00213
Año: 2018
Publicado en: FRONTIERS IN PHYSIOLOGY 9, MAR (2018), 213 [13 pp]
ISSN: 1664-042X
Factor impacto JCR: 3.201 (2018)
Categ. JCR: PHYSIOLOGY rank: 25 / 81 = 0.309 (2018) - Q2 - T1
Factor impacto SCIMAGO: 1.153 - Physiology (medical) (Q2) - Physiology (Q2)
Financiación: info:eu-repo/grantAgreement/ES/DGA/Grupo Consolidado BSICoS
Financiación: info:eu-repo/grantAgreement/EC/H2020/675451/EU/A Centre of Excellence in Computational Biomedicine/CompBioMed
Financiación: info:eu-repo/grantAgreement/ES/MINECO/TEC2013-44666-R
Financiación: info:eu-repo/grantAgreement/ES/MINECO/TIN2014-5356-R
Tipo y forma: Artículo (Versión definitiva)
Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)
Debe reconocer adecuadamente la autoría, proporcionar un enlace a la licencia e indicar si se han realizado cambios. Puede hacerlo de cualquier manera razonable, pero no de una manera que sugiera que tiene el apoyo del licenciador o lo recibe por el uso que hace. Exportado de SIDERAL (2021-02-24-12:57:07)
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Registro creado el 2018-04-20, última modificación el 2021-02-24