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Distinct ECG Phenotypes Identified in Hypertrophic Cardiomyopathy Using Machine Learning Associate With Arrhythmic Risk Markers.

ABSTRACT: 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.

SUBMITTER: Lyon A

PROVIDER: S-EPMC5859357 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Distinct ECG Phenotypes Identified in Hypertrophic Cardiomyopathy Using Machine Learning Associate With Arrhythmic Risk Markers.

Lyon Aurore A Ariga Rina R Mincholé Ana A Mahmod Masliza M Ormondroyd Elizabeth E Laguna Pablo P de Freitas Nando N Neubauer Stefan S Watkins Hugh H Rodriguez Blanca B

Frontiers in physiology 20180313

<b>Aims:</b> 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. <b>Methods:</b> High-fidelity 12-lead Holter ECGs from 85 HCM patients and 38 healthy volunteers were ...[more]

PMID: 29593570

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Project description:Introduction: Patients with hypertrophic cardiomyopathy (HCM) are at risk for lethal ventricular arrhythmia, but the electrophysiological substrate behind this is not well-understood. We used non-invasive electrocardiographic imaging to characterize patients with HCM, including cardiac arrest survivors. Methods: HCM patients surviving ventricular fibrillation or hemodynamically unstable ventricular tachycardia (n = 17) were compared to HCM patients without a personal history of potentially lethal arrhythmia (n = 20) and a pooled control group with structurally normal hearts. Subjects underwent exercise testing by non-invasive electrocardiographic imaging to estimate epicardial electrophysiology. Results: Visual inspection of reconstructed epicardial HCM maps revealed isolated patches of late activation time (AT), prolonged activation-recovery intervals (ARIs), as well as reversal of apico-basal trends in T-wave inversion and ARI compared to controls (p < 0.005 for all). AT and ARI were compared between groups. The pooled HCM group had longer mean AT (60.1 ms vs. 52.2 ms, p < 0.001), activation dispersion (55.2 ms vs. 48.6 ms, p = 0.026), and mean ARI (227 ms vs. 217 ms, p = 0.016) than structurally normal heart controls. HCM ventricular arrhythmia survivors could be differentiated from HCM patients without a personal history of life-threatening arrhythmia by longer mean AT (63.2 ms vs. 57.4 ms, p = 0.007), steeper activation gradients (0.45 ms/mm vs. 0.36 ms/mm, p = 0.011), and longer mean ARI (234.0 ms vs. 221.4 ms, p = 0.026). A logistic regression model including whole heart mean activation time and activation recovery interval could identify ventricular arrhythmia survivors from the HCM cohort, producing a C statistic of 0.76 (95% confidence interval 0.72-0.81), with an optimal sensitivity of 78.6% and a specificity of 79.8%. Discussion: The HCM epicardial electrotype is characterized by delayed, dispersed conduction and prolonged, dispersed activation-recovery intervals. Combination of electrophysiologic measures with logistic regression can improve differentiation over single variables. Future studies could test such models prospectively for risk stratification of sudden death due to HCM.

Project description:Background: Development of advanced heart failure (HF) symptoms is the most common adverse pathway in hypertrophic cardiomyopathy (HCM) patients. Currently, there is a limited ability to identify HCM patients at risk of HF. Objectives: In this study, we present a machine learning (ML)-based model to identify individual HCM patients who are at high risk of developing advanced HF symptoms. Methods: From a consecutive cohort of HCM patients evaluated at the Tufts HCM Institute from 2001 to 2018, we extracted a set of 64 potential risk factors measured at baseline. Only patients with New York Heart Association (NYHA) functional class I/II and LV ejection fraction (LVEF) by echocardiography >35% were included. The study cohort (n = 1,427 patients) was split into three disjoint subsets: development (50%), model selection (10%), and independent validation (40%). The least absolute shrinkage and selection operator was used to select the most influential clinical variables. An ensemble of ML classifiers, including logistic regression, was used to identify patients with high risk of developing a HF outcome. Study outcomes were defined as progression to NYHA class III/IV, drop in LVEF below 35%, septal reduction procedure, and/or heart transplantation. Results: During a mean follow-up of 4.7 ± 3.7 years, advanced HF occurred in 283 (20% out of 1,427) patients. The model features included patients' sex, NYHA class (I or II), HCM type (i.e., obstructive or not), LV wall thickness, LVEF, presence of HF symptoms (e.g., dyspnea, presyncope), comorbidities (atrial fibrillation, hypertension, mitral regurgitation, and systolic anterior motion), and type of cardiac medications. The developed risk stratification model showed strong differentiation power to identify patients at advanced HF risk in the testing dataset (c-statistics = 0.81; 95% confidence interval [CI]: 0.76, 0.86). The model allowed correct identification of high-risk patients with accuracy 74% (CI: 0.70, 0.78), sensitivity 80% (CI: 0.77, 0.83), and specificity 72% (CI: 0.68, 0.76). The model performance was comparable among different sex and age groups. Conclusions: A 5-year risk prediction of progressive HF in HCM patients can be accurately estimated using ML analysis of patients' clinical and imaging parameters. A set of 17 clinical and imaging variables were identified as the most important predictors of progressive HF in HCM.

Project description:Importance:Predictors of lethal arrhythmic events (LAEs) after a pediatric diagnosis of hypertrophic cardiomyopathy (HCM) are unresolved. Existing algorithms for risk stratification are limited to patients older than 16 years because of a lack of data on younger individuals. Objective:To describe the long-term outcome of pediatric-onset HCM and identify age-specific arrhythmic risk factors. Design, Setting, and Participants:This study assessed patients with pediatric-onset hypertrophic cardiomyopathy diagnosed from 1974 to 2016 in 2 national referral centers for cardiomyopathies in Florence, Italy. Patients with metabolic and syndromic disease were excluded. Exposures:Patients were assessed at 1-year intervals, or more often, if their clinical condition required. Main Outcomes and Measures:Lethal arrhythmic events (LAEs) and death related to heart failure. Results:Of 1644 patients with HCM, 100 (6.1%) were 1 to 16 years old at diagnosis (median [interquartile range], 12.2 [7.3-14.1] years). Of these, 63 (63.0%) were boys. Forty-two of the 100 patients (42.0%) were symptomatic (defined as an New York Heart Association classification higher than 1 or a Ross score greater than 2). The yield of sarcomere gene testing was 55 of 70 patients (79%). During a median of 9.2 years during which a mean of 1229 patients were treated per year, 24 of 100 patients (24.0%) experienced cardiac events (1.9% per year), including 19 LAEs and 5 heart failure-related events (3 deaths and 2 heart transplants). Lethal arrhythmic events occurred at a mean (SD) age of 23.1 (11.5) years. Two survivors of LAEs with symptoms of heart failure experienced recurrent cardiac arrest despite an implantable cardioverter defibrillator. Risk of LAE was associated with symptoms at onset (hazard ratio [HR], 8.2; 95% CI, 1.5-68.4; P = .02) and Troponin I or Troponin T gene mutations (HR, 4.1; 95% CI, 0.9-36.5; P = .06). Adult HCM risk predictors performed poorly in this population. Data analysis occurred from December 2016 to October 2017. Conclusions and Relevance:Pediatric-onset HCM is rare and associated with adverse outcomes driven mainly by arrhythmic events. Risk extends well beyond adolescence, which calls for unchanged clinical surveillance into adulthood. In this study, predictors of adverse outcomes differ from those of adult populations with HCM. In secondary prevention, the implantable cardioverter defibrillator did not confer absolute protection in the presence of limiting symptoms of heart failure.

Dataset Information

Distinct ECG Phenotypes Identified in Hypertrophic Cardiomyopathy Using Machine Learning Associate With Arrhythmic Risk Markers.

Publications

Distinct ECG Phenotypes Identified in Hypertrophic Cardiomyopathy Using Machine Learning Associate With Arrhythmic Risk Markers.

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