Relation of 12-lead electrocardiogram patterns to implanted defibrillator-terminated ventricular tachyarrhythmias in hypertrophic cardiomyopathy

Identification of high-risk imaging features in hypertrophic cardiomyopathy using electrocardiography: A deep-learning approach

BACKGROUND

Patients with hypertrophic cardiomyopathy (HCM) are at risk of sudden death, and individuals with ≥1 major risk markers are considered for primary prevention implantable cardioverter-defibrillators. Guidelines recommend cardiac magnetic resonance (CMR) imaging to identify high-risk imaging features. However, CMR imaging is resource intensive and is not widely accessible worldwide.

OBJECTIVE

The purpose of this study was to develop electrocardiogram (ECG) deep-learning (DL) models for the identification of patients with HCM and high-risk imaging features.

METHODS

Patients with HCM evaluated at Tufts Medical Center (N = 1930; Boston, MA) were used to develop ECG-DL models for the prediction of high-risk imaging features: systolic dysfunction, massive hypertrophy (≥30 mm), apical aneurysm, and extensive late gadolinium enhancement. ECG-DL models were externally validated in a cohort of patients with HCM from the Amrita Hospital HCM Center (N = 233; Kochi, India).

RESULTS

ECG-DL models reliably identified high-risk features (systolic dysfunction, massive hypertrophy, apical aneurysm, and extensive late gadolinium enhancement) during holdout testing (c-statistic 0.72, 0.83, 0.93, and 0.76) and external validation (c-statistic 0.71, 0.76, 0.91, and 0.68). A hypothetical screening strategy using echocardiography combined with ECG-DL-guided selective CMR use demonstrated a sensitivity of 97% for identifying patients with high-risk features while reducing the number of recommended CMRs by 61%. The negative predictive value with this screening strategy for the absence of high-risk features in patients without ECG-DL recommendation for CMR was 99.5%.

CONCLUSION

In HCM, novel ECG-DL models reliably identified patients with high-risk imaging features while offering the potential to reduce CMR testing requirements in underresourced areas.

Hypertrophic cardiomyopathy in patients with a normal electrocardiogram: A view from the east side of the Atlantic Ocean

BACKGROUND

Although the 12‑lead electrocardiogram (ECG) is abnormal in most patients with hypertrophic cardiomyopathy (HCM), some present normal ECG. This study aimed to analyse the baseline characteristics, clinical presentation and outcomes of HCM patients with normal ECG and to compare them with those with abnormal ECG.

METHODS AND RESULTS

Baseline characteristics, clinical presentation, data from complementary exams and clinical outcomes of 1070 consecutive patients included in the Portuguese Registry of HCM (Pro-HCM registry) were compared between two groups of patients: normal Vs. abnormal ECG. Among this population, 98 (9.2%) patients had normal ECG at presentation; they were significantly younger and had lower frequency of hypertension, symptoms at presentation, heart failure, angina, cardiac and non-cardiac diseases. ESC and AHA risk scores for Sudden Cardiac Death (SCD) were not significantly different between the two groups. Patients with normal ECG had higher prevalence of family history of SCD and lower degree of left ventricular (LV) hypertrophy, LV systolic dysfunction, LV outflow tract obstruction and myocardial fibrosis. The combined endpoint of cardiac death, SCD, cardiac arrest, appropriate ICD shocks or evolution to systolic dysfunction, during a mean follow-up of 5 years was significantly less frequent in patients with normal ECG (2.1% Vs. 6.5%; p = 0.043).

CONCLUSIONS

A normal ECG is not a marker of an overall benign profile in HCM patients. Though a normal ECG at presentation is associated with a less severe phenotype and a lower probability of evolution to heart failure at 5-years, this finding did not show a protective effect in other clinical outcomes.

Association of QTc Interval and V4-S Wave With Appropriate ICD Therapy in Hypertrophic Cardiomyopathy

Background

Ventricular arrhythmias in patients with hypertrophic cardiomyopathy (HCM) may lead to sudden cardiac death (SCD). We aimed to investigate the relationship between electrocardiogram (ECG) indicators and the risk of appropriate implantable cardioverter-defibrillator (ICD) therapy in HCM.

Methods

The HCM patients receiving ICD implantation were enrolled consecutively. QT interval correction (QTc) was calculated using Bazett's formula. Long or deep S wave in V4 lead was defined as duration time >50 ms and/or voltage amplitude >0.6 mV. The endpoint in our study was at least one ICD appropriate therapy triggered by ventricular tachyarrhythmia (VT) or ventricular fibrillation (VF), including anti-tachyarrhythmia pacing (ATP) and electrical shock.

Results

A total of 149 patients with HCM (mean age 53 ± 14 years, male 69.8%) were studied. Appropriate ICD therapies occurred in 47 patients (31.5%) during a median follow-up of 2.9 years. Cox regression analysis showed that long or deep S wave in V4 lead [hazard ratio (HR) 1.955, 95% confidence interval (CI) 1.017–3.759, P = 0.045] and QTc interval (HR 1.014, 95% CI 1.008–1.021, P < 0.001) were independent risk factors for appropriate ICD therapy. The ROC showed that the optimal cut-off point value for the QTc interval to predict the appropriate ICD therapy was 464 ms, and the AUC was 0.658 (95% CI 0.544–0.762, P = 0.002). The AUC for S wave anomalies in V4 lead was 0.608 (95% CI 0.511–0.706, P = 0.034). We developed a new model that combined the QTc interval and S wave anomalies in V4 lead based on four patient groups. Patients with QTc ≥464 ms and long or deep V4-S wave had the highest risk of developing appropriate ICD therapy (log-rank P < 0.0001). After adding QTc interval and V4-S wave anomalies into the HCM-risk-SCD model, the prediction effect of the new model was significantly improved, and the NRI was 0.302.

Conclusions

In this HCM cohort, QTc and S wave anomalies in V4 lead were found to be significant and strong predictors of the risk of appropriate ICD therapy. Patients with QTc ≥464 ms and long or deep S wave had the highest risk. After QTc interval and V4-S wave anomalies adding to the HCM-risk-SCD model, the prediction effect is significantly improved.

Improving the clinical understanding of hypertrophic cardiomyopathy by combining patient data, machine learning and computer simulations: A case study

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In this paper, we present how, by combining electrocardiogram and imaging data, machine learning and high performance computing simulations, we identified four phenotypes in hypertrophic cardiomyopathy (HCM), with differences in arrhythmic risk, and provided two distinct possible mechanisms that may explain the heterogeneity of HCM manifestation.

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This led to a better HCM patient stratification and understanding of the underlying disease mechanisms, providing a step further towards tailored HCM patient management and treatment.

Most patients with hypertrophic cardiomyopathy (HCM), the most common genetic cardiac disease, remain asymptomatic, but others may suffer from sudden cardiac death. A better identification of those patients at risk, together with a better understanding of the mechanisms leading to arrhythmia, are crucial to target high-risk patients and provide them with appropriate treatment. However, this currently remains a challenge. In this paper, we present a successful example of implementing computational techniques for clinically-relevant applications. By combining electrocardiogram and imaging data, machine learning and high performance computing simulations, we identified four phenotypes in HCM, with differences in arrhythmic risk, and provided two distinct possible mechanisms that may explain the heterogeneity of HCM manifestation. This led to a better HCM patient stratification and understanding of the underlying disease mechanisms, providing a step further towards tailored HCM patient management and treatment.

The electrocardiogram in the diagnosis and management of patients with hypertrophic cardiomyopathy

In an era of rapid technological development and evolving diagnostic possibilities, the electrocardiogram (ECG) is living an authentic "renaissance" in myocardial diseases. To date, the ECG remains an irreplaceable first step when evaluating patients with hypertrophic cardiomyopathy (HCM) and an abnormal ECG may be the only manifestation of disease at an early stage. In some instances, specific electrical anomalies may differentiate HCM from phenocopies such as cardiac amyloidosis and glycogen storage diseases. The exponential growth in knowledge of the complexity of HCM has led to new challenges in terms of early identification of the disease, differential diagnosis, risk stratification, and development of targeted therapies. In this scenario, the apparently "old fashioned" ECG and the array of ECG-based techniques, ranging from Holter monitoring and loop recorders to exercise testing, are as contemporary as ever. In the present review, we discuss the current role of the ECG in the diagnosis and management of HCM, focusing on various clinical settings where its appropriate use and interpretation can make a difference.

Electrocardiogram phenotypes in hypertrophic cardiomyopathy caused by distinct mechanisms: apico-basal repolarization gradients vs. Purkinje-myocardial coupling abnormalities

AIMS

To identify key structural and electrophysiological features explaining distinct electrocardiogram (ECG) phenotypes in hypertrophic cardiomyopathy (HCM).

METHODS AND RESULTS

Human heart-torso anatomical models were constructed from cardiac magnetic resonance (CMR) images of HCM patients, representative of ECG phenotypes identified previously. High performance computing simulations using bidomain models were conducted to dissect key features explaining the ECG phenotypes with increased HCM Risk-SCD scores, namely Group 1A, characterized by normal QRS but inverted T waves laterally and coexistence of apical and septal hypertrophy; and Group 3 with marked QRS abnormalities (deep and wide S waves laterally) and septal hypertrophy. Hypertrophic cardiomyopathy abnormalities characterized from CMR, such as hypertrophy, tissue microstructure alterations, abnormal conduction system, and ionic remodelling, were selectively included to assess their influence on ECG morphology. Electrocardiogram abnormalities could not be explained by increased wall thickness nor by local conduction abnormalities associated with fibre disarray or fibrosis. Inverted T wave with normal QRS (Group 1A) was obtained with increased apico-basal repolarization gradient caused by ionic remodelling in septum and apex. Lateral QRS abnormalities (Group 3) were only recovered with abnormal Purkinje-myocardium coupling.

CONCLUSION

Two ECG-based HCM phenotypes are explained by distinct mechanisms: ionic remodelling and action potential prolongation in hypertrophied apical and septal areas lead to T wave inversion with normal QRS complexes, whereas abnormal Purkinje-myocardial coupling causes abnormal QRS morphology in V4-V6. These findings have potential implications for patients' management as they point towards different arrhythmia mechanisms in different phenotypes.

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

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.

QT spatial dispersion and sudden cardiac death in hypertrophic cardiomyopathy: Time for reappraisal

BACKGROUND

The 12-lead surface electrocardiographic (ECG) analysis is able to provide independent predictors of prognosis in several cardiovascular settings, including hypertrophic cardiomyopathy (HCM). The present single-center study investigated the possible ability of several ECG-derived variables in stratifying sudden cardiac death (SCD) risk and, possibly, in improving the accuracy of the 2014 European Society of Cardiology guidelines.

METHODS

A total of 221 consecutive HCM outpatients were recruited and prospectively followed. All of them underwent a full clinical and instrumental examination, including a 12-lead surface ECG to calculate the dispersion for the following intervals: QRS, Q-Tend (QT), Q-Tpeak (QTp), Tpeak-Tend (TpTe), J-Tpeak (JTp), and J-Tend (JT). The study composite end-point was SCD, aborted SCD, and appropriate implantable cardioverter defibrillator (ICD) interventions.

RESULTS

During a median follow-up of 4.4 years (25th-75th interquartile range: 2.4-9.4 years), 23 patients reached the end-point at 5-years (3 SCD, 3 aborted SCD, 17 appropriate ICD interventions). At multivariate analysis, the spatial QT dispersion corrected according to Bazett's formula (QTcd) remains independently associated to the study endpoint over the HCM Risk-SCD score (C-index 0.737). A QTcd cut-off value of 93ms showed the best accuracy in predicting the SCD endpoint within the entire HCM study cohort (sensitivity 56%, specificity 75%, positive predictive value 22%, negative predictive value 97%).

CONCLUSION

Our data suggest that the QTcd might be helpful in SCD risk stratification, particularly in those HCM categories classified at low-intermediate SCD risk according to the contemporary guidelines.

Spatial QT Dispersion Predicts Nonsustained Ventricular Tachycardia and Correlates with Confined Systodiastolic Dysfunction in Hypertrophic Cardiomyopathy

Objectives: An increased dispersion of myocardial repolarization represents one of the mechanisms underlying the arrhythmic risk in hypertrophic cardiomyopathy (HCM). We investigated spatial myocardial repolarization dispersion indices in HCM patients with nonsustained ventricular tachycardia (NSVT) and, contextually, their main clinical determinants. Methods: Fifty-two well-matched HCM outpatients were categorized into two groups according to the presence or the absence of NSVT at 24-hour Holter electrocardiogram (ECG) monitoring. Each patient underwent a clinical examination, including Doppler echocardiogram integrated with tissue Doppler imaging, cardiac magnetic resonance, and 12-lead surface ECG to calculate the dispersion for the following intervals: QRS, Q-Tend (QTe), Q-Tpeak, Tpeak-Tend (TpTe), J-Tpeak, and J-Tend. Results: The NSVT group showed only QTe dispersion and TpTe dispersion values to be significantly higher than their counterparts. NSVT occurrence was independently predicted by late gadolinium enhancement presence (p = 0.021) and QTe Bazett dispersion (p = 0.030), the latter strongly associated with the myocardial performance index (MPI) obtained at the basal segment of the interventricular septum (p = 0.0004). Conclusion: Our data support QTe dispersion as an easy and noninvasive tool for identifying HCM patients with NSVT propensity. The strong relationship between QTe dispersion and MPI allows us to hypothesize an intriguing link between electrical instability and confined myocardial areas of systodiastolic dysfunction.

QRS Fragmentation and QTc Duration Relate to Malignant Ventricular Tachyarrhythmias and Sudden Cardiac Death in Patients with Hypertrophic Cardiomyopathy

BACKGROUND

QRS fragmentation (fQRS) and prolonged QTc interval on surface ECG are prognostic in various cardiomyopathies other than hypertrophic cardiomyopathy (HCM). The association between fQRS and prolonged QTc duration with occurrence of ventricular tachyarrhythmias or sudden cardiac death (VTA/SCD) in patients with HCM was explored.

METHODS AND RESULTS

One hundred and ninety-five clinical HCM patients were studied. QTc duration was derived applying Bazett's formula; fQRS was defined as presence of various RSR' patterns, R or S notching and/or >1 additional R wave in any non-aVR lead in patients without pacing or (in)complete bundle branch block. The endpoints comprised SCD, ECG documented sustained VTA (tachycardia or fibrillation) or appropriate implantable cardioverter defibrillator (ICD) therapies (antitachycardia pacing [ATP] or shock) for VTA in ICD recipients (n = 58 [30%]). QT prolonging drugs recipients were excluded. After a median follow-up of 5.7 years (IQR 2.7-9.1), 26 (13%) patients experienced VTA or SCD. Patients with fQRS in ≥3 territories (inferior, lateral, septal, and/or anterior) (p = 0.004) or QTc ≥460 ms (p = 0.009) had worse cumulative survival free of VTA/SCD than patients with fQRS in <3 territories or QTc <460 ms. fQRS in ≥3 territories (ß 4.5, p = 0.020, 95%CI 1.41-14.1) and QTc ≥460 ms (ß 2.7, p = 0.037, 95%CI 1.12-6.33) were independently associated with VTA/SCD. Likelihood ratio test indicated assessment of fQRS and QTc on top of conventional SCD risk factors provides incremental predictive value for VTA/SCD (p = 0.035).

CONCLUSIONS

Both fQRS in ≥3 territories and QTc duration are associated with VTA/SCD in HCM patients, independently of and incremental to conventional SCD risk factors.

Hypertrophic cardiomyopathy: Can the noninvasive diagnostic testing identify high risk patients?

Hypertrophic cardiomyopathy (HCM) is the most common cause of sudden cardiac death (SCD) in the young, particularly among athletes. Identifying high risk individuals is very important for SCD prevention. The purpose of this review is to stress that noninvasive diagnostic testing is important for risk assessment. Extreme left ventricular hypertrophy and documented ventricular tachycardia and fibrillation increase the risk of SCD. Fragmented QRS and T wave inversion in multiple leads are more common in high risk patients. Cardiac magnetic resonance imaging provides complete visualization of the left ventricular chamber, allowing precise localization of the distribution of hypertrophy and measurement of wall thickness and cardiac mass. Moreover, with late gadolinium enhancement, patchy myocardial fibrosis within the area of hypertrophy can be detected, which is also helpful in risk stratification. Genetic testing is encouraged in all cases, especially in those with a family history of HCM and SCD.

Greater insulin resistance indicates decreased diurnal variation in the QT interval in patients with type 2 diabetes

Circadian variations in the QT interval (QT) and QT dispersion are decreased in patients with type 2 diabetes because of cardioneuropathy. Insulin resistance has been recently identified as an independent determinant of QT prolongation in normoglycemic women. However, the relationship between QT prolongation and the degree of insulin resistance as well as circadian variation remains unclear in diabetic patients. This study was designed to assess the relationship between insulin resistance and the circadian variation in QT measurements in patients with type 2 diabetes. In 14 patients with diabetes, QT, corrected QT (QTc), QT dispersion, QTc dispersion, and RR interval (RR) were analyzed using 12-lead Holter monitoring and commercial software. The degree of diurnal variation in each measurement was defined as the amplitude between the maximum and mean values on curves fitted using the mean cosinor method (A_QT, A_QTc, A_QT dispersion, A_QTc dispersion, and A_RR). The cosine curve was fitted to all measured values in each QT measurement and RR for 24 h. Insulin resistance (glucose infusion rate (GIR)) was measured using the euglycemic hyperinsulinemic glucose clamp method. The maximum QT, QTc, QT dispersion, and QTc dispersion were >450 ms. GIR was significantly correlated with A_QT only (r = 0.59, P < 0.05). GIR was not correlated with other variables, and was dependent only on the circadian variation in QT.

Surface fragmented QRS in a patient with hypertrophic cardiomyopathy and malignant arrhythmias: Is there an association?

An 18- year old woman with hypertrophic cardiomyopathy, aborted sudden cardiac death and implanted with an implantable cardioverter defibrillator (ICD), developed progressive fragmentation of her surface 12-lead electrocardiogram (ECG). During the follow-up, she presented with multiple appropriate ICD discharges. Here, we discuss the possible association between surface fragmented ECG and the risk of ventricular arrhythmias in patients with hypertrophic cardiomyopathy.

Ventricular arrhythmias complicating hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy is the most common genetic cardiovascular disorder and the leading cause of sudden cardiac death in the young. This article reviews the ventricular arrhythmias associated with hypertrophic cardiomyopathy, the difficulties in risk stratification, and current and future therapeutic strategies.