000070056 001__ 70056
000070056 005__ 20190709135702.0
000070056 0247_ $$2doi$$a10.3389/fphys.2018.00213
000070056 0248_ $$2sideral$$a105460
000070056 037__ $$aART-2018-105460
000070056 041__ $$aeng
000070056 100__ $$aLyon, A.
000070056 245__ $$aDistinct ECG phenotypes identified in hypertrophic cardiomyopathy using machine learning associate with arrhythmic risk markers
000070056 260__ $$c2018
000070056 5060_ $$aAccess copy available to the general public$$fUnrestricted
000070056 5203_ $$aAims: 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.
000070056 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/TIN2014-5356-R$$9info:eu-repo/grantAgreement/ES/MINECO/TEC2013-44666-R$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 675451-CompBioMed$$9info:eu-repo/grantAgreement/EC/H2020/675451/EU/A Centre of Excellence in Computational Biomedicine/CompBioMed$$9info:eu-repo/grantAgreement/ES/DGA/Grupo Consolidado BSICoS
000070056 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000070056 590__ $$a3.201$$b2018
000070056 591__ $$aPHYSIOLOGY$$b25 / 81 = 0.309$$c2018$$dQ2$$eT1
000070056 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000070056 700__ $$aAriga, R.
000070056 700__ $$aMincholé, A.
000070056 700__ $$aMahmod, M.
000070056 700__ $$aOrmondroyd, E.
000070056 700__ $$0(orcid)0000-0003-3434-9254$$aLaguna, P.$$uUniversidad de Zaragoza
000070056 700__ $$ade Freitas, N.
000070056 700__ $$aNeubauer, S.
000070056 700__ $$aWatkins, H.
000070056 700__ $$aRodriguez, B.
000070056 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000070056 773__ $$g9, MAR (2018), 213 [13 pp]$$pFront. physiol.$$tFrontiers in physiology$$x1664-042X
000070056 8564_ $$s319277$$uhttp://zaguan.unizar.es/record/70056/files/texto_completo.pdf$$yVersión publicada
000070056 8564_ $$s11435$$uhttp://zaguan.unizar.es/record/70056/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000070056 909CO $$ooai:zaguan.unizar.es:70056$$particulos$$pdriver
000070056 951__ $$a2019-07-09-12:50:06
000070056 980__ $$aARTICLE