Outcome of Patients With Hypertrophic Cardiomyopathy and a Normal Electrocardiogram

Hypertrophic cardiomyopathy detection with artificial intelligence electrocardiography in international cohorts: an external validation study

Aims

Recently, deep learning artificial intelligence (AI) models have been trained to detect cardiovascular conditions, including hypertrophic cardiomyopathy (HCM), from the 12-lead electrocardiogram (ECG). In this external validation study, we sought to assess the performance of an AI-ECG algorithm for detecting HCM in diverse international cohorts.

Methods and results

A convolutional neural network-based AI-ECG algorithm was developed previously in a single-centre North American HCM cohort (Mayo Clinic). This algorithm was applied to the raw 12-lead ECG data of patients with HCM and non-HCM controls from three external cohorts (Bern, Switzerland; Oxford, UK; and Seoul, South Korea). The algorithm's ability to distinguish HCM vs. non-HCM status from the ECG alone was examined. A total of 773 patients with HCM and 3867 non-HCM controls were included across three sites in the merged external validation cohort. The HCM study sample comprised 54.6% East Asian, 43.2% White, and 2.2% Black patients. Median AI-ECG probabilities of HCM were 85% for patients with HCM and 0.3% for controls (P < 0.001). Overall, the AI-ECG algorithm had an area under the receiver operating characteristic curve (AUC) of 0.922 [95% confidence interval (CI) 0.910-0.934], with diagnostic accuracy 86.9%, sensitivity 82.8%, and specificity 87.7% for HCM detection. In age- and sex-matched analysis (case-control ratio 1:2), the AUC was 0.921 (95% CI 0.909-0.934) with accuracy 88.5%, sensitivity 82.8%, and specificity 90.4%.

Conclusion

The AI-ECG algorithm determined HCM status from the 12-lead ECG with high accuracy in diverse international cohorts, providing evidence for external validity. The value of this algorithm in improving HCM detection in clinical practice and screening settings requires prospective evaluation.

Hypertrophic cardiomyopathy-related left ventricular pseudoaneurysm: A case report

Background

Myocardial infarction-related left ventricular pseudoaneurysm (LVP), covered by the adjacent pericardial or scar tissue, is a fatal sequela of left ventricular rupture. Whereas hypertrophic cardiomyopathy (HCM) may cause left ventricular true aneurysm. Differentiating LVP from left ventricular true aneurysm is crucial because their natural histories and treatment strategies are distinct. However, the incidence and management of HCM-related LVP remain unknown.

Case presentation

An 88-year-old man was admitted to our hospital with sudden-onset chest pain. Upon initial examination, vital signs were stable, and a grade 4/6 systolic murmur was noted. An electrocardiogram revealed atrial fibrillation and poor R-wave progression without ST-T changes or negative T-waves. An echocardiography showed mild left ventricular hypertrophy, mid-ventricular obstruction with a significant intraventricular pressure gradient, left ventricular outflow tract obstruction, and a small left ventricular apical outpouching. Cardiac computed tomography angiography (CCTA) assisted in the diagnosis of LVP, and an accompanying pericardial effusion suggested impending cardiac rupture. Because the patient initially refused our proposed urgent surgery, medication was initiated with continuous hemodynamic monitoring in the intensive care unit; however, the patient's condition did not improve. During a semi-urgent surgical repair of the aneurysmal wall, LVP was observed and confirmed by pathology. Myocardial tissue adjacent to the pseudoaneurysm was consistent with that of HCM. Subsequently, a final diagnosis of HCM-related LVP was made. The postoperative course was notable for transient profound hypotension. Thereafter, the patient died of non-occlusive mesenteric ischemia on day 6.

Conclusions

To our knowledge, this is the first reported case of HCM-related LVP mimicking impending cardiac rupture. Our case highlights the importance of considering HCM-related LVP in patients with left ventricular outpouching and CCTA in the LVP diagnosis. In further research, data on the appropriate management of HCM-related LVP should be accumulated.

Role of Artificial Intelligence in Improving Syncope Management

Syncope is common in the general population and a common presenting symptom in acute care settings. Substantial costs are attributed to the care of patients with syncope. Current challenges include differentiating syncope from its mimickers, identifying serious underlying conditions that caused the syncope, and wide variations in current management. Although validated risk tools exist, especially for short-term prognosis, there is inconsistent application, and the current approach does not meet patient needs and expectations. Artificial intelligence (AI) techniques, such as machine learning methods including natural language processing, can potentially address the current challenges in syncope management. Preliminary evidence from published studies indicates that it is possible to accurately differentiate syncope from its mimickers and predict short-term prognosis and hospitalisation. More recently, AI analysis of electrocardiograms has shown promise in detection of serious structural and functional cardiac abnormalities, which has the potential to improve syncope care. Future AI studies have the potential to address current issues in syncope management. AI can automatically prognosticate risk in real time by accessing traditional and nontraditional data. However, steps to mitigate known problems such as generalisability, patient privacy, data protection, and liability will be needed. In the past AI has had limited impact due to underdeveloped analytical methods, lack of computing power, poor access to powerful computing systems, and availability of reliable high-quality data. All impediments except data have been solved. AI will live up to its promise to transform syncope care if the health care system can satisfy AI requirement of large scale, robust, accurate, and reliable data.

Management of hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy is an important cause of heart failure and arrhythmias, including sudden death, with a major impact on the healthcare system. Genetic causes and different phenotypes are now increasingly being identified for this condition. In addition, specific medications, such as myosin inhibitors, have been recently shown as potentially able to modify its symptoms, hemodynamic abnormalities and clinical course. Our article aims to provide a comprehensive outline of the epidemiology, diagnosis and treatment of hypertrophic cardiomyopathy in the current era.

Evaluating convolutional neural network-enhanced electrocardiography for hypertrophic cardiomyopathy detection in a specialized cardiovascular setting

The efficacy of convolutional neural network (CNN)-enhanced electrocardiography (ECG) in detecting hypertrophic cardiomyopathy (HCM) and dilated HCM (dHCM) remains uncertain in real-world applications. This retrospective study analyzed data from 19,170 patients (including 140 HCM or dHCM) in the Shinken Database (2010-2017). We evaluated the sensitivity, positive predictive rate (PPR), and F1 score of CNN-enhanced ECG in a ''basic diagnosis'' model (total disease label) and a ''comprehensive diagnosis'' model (including disease subtypes). Using all-lead ECG in the "basic diagnosis" model, we observed a sensitivity of 76%, PPR of 2.9%, and F1 score of 0.056. These metrics improved in cases with a diagnostic probability of ≥ 0.9 and left ventricular hypertrophy (LVH) on ECG: 100% sensitivity, 8.6% PPR, and 0.158 F1 score. The ''comprehensive diagnosis'' model further enhanced these figures to 100%, 13.0%, and 0.230, respectively. Performance was broadly consistent across CNN models using different lead configurations, particularly when including leads viewing the lateral walls. While the precision of CNN models in detecting HCM or dHCM in real-world settings is initially low, it improves by targeting specific patient groups and integrating disease subtype models. The use of ECGs with fewer leads, especially those involving the lateral walls, appears comparably effective.

The Majority of Participants With Suspected Hypertrophic Cardiomyopathy Documented During Screening Echocardiography Have a Normal Electrocardiogram

BACKGROUND

Patients with hypertrophic cardiomyopathy (HCM) usually have abnormal electrocardiograms consistent with left ventricular hypertrophy (LVH). The goal of this study was to evaluate the prevalence of abnormal ECG findings (LVH, T wave inversion, left bundle branch block, and left atrial enlargement) in participants with suspected HCM detected during screening echocardiography.

METHOD

The Anthony Bates Foundation has been performing screening echocardiography across the United States for the prevention of sudden death since 2001. A total of 682 subjects between the ages of 8 and 71 underwent echocardiographic screening together with ECG documentation. We evaluated the prevalence of abnormal ECG in participants with suspected HCM defined as any left ventricular wall thickness ≥15 mm.

RESULTS

The prevalence of LVH and T wave inversion were higher in HCM subjects as expected [HCM occurred in 23.5% (4/17) vs. 5.6% (37/665), P = 0.002, T wave inversion occurred in 17.6% (3/17) vs. 4.1% (27/664), P = 0.007]. However, despite adding these 2 common ECG abnormalities in this population, the presence of detected abnormal ECG remained less than 25% (23.5% of HCM subjects had LVH or T wave inversion on ECG vs. 8.7% of control, P = 0.036). Left bundle branch block or abnormal left atrium on ECG were not found in any participants with suspected HCM.

CONCLUSIONS

The prevalence of abnormal ECG in the participants with suspected HCM detected during screening echocardiography is less than 25%. This suggests that ECG alone is not a sensitive marker for the detection of HCM.

Advances in Multi-Modality Imaging in Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is characterized by abnormal growth of the myocardium with myofilament disarray and myocardial hyper-contractility, leading to left ventricular hypertrophy and fibrosis. Where culprit genes are identified, they typically relate to cardiomyocyte sarcomere structure and function. Multi-modality imaging plays a crucial role in the diagnosis, monitoring, and risk stratification of HCM, as well as in screening those at risk. Following the recent publication of the first European Society of Cardiology (ESC) cardiomyopathy guidelines, we build on previous reviews and explore the roles of electrocardiography, echocardiography, cardiac magnetic resonance (CMR), cardiac computed tomography (CT), and nuclear imaging. We examine each modality's strengths along with their limitations in turn, and discuss how they can be used in isolation, or in combination, to facilitate a personalized approach to patient care, as well as providing key information and robust safety and efficacy evidence within new areas of research.

Identification of Hypertrophic Cardiomyopathy on Electrocardiographic Images with Deep Learning

Background

Hypertrophic cardiomyopathy (HCM) affects 1 in every 200 individuals and is the leading cause of sudden cardiac death in young adults. HCM can be identified using an electrocardiogram (ECG) raw voltage data and deep learning approaches, but their point-of-care application is limited by the inaccessibility of these signal data. We developed a deep learning-based approach that overcomes this limitation and detects HCM from images of 12-lead ECGs across layouts.

Methods

We identified ECGs from patients with HCM features present on cardiac magnetic resonance imaging (CMR) or those within 30 days of an echocardiogram documenting thickened interventricular septum (end-diastolic interventricular septum thickness > 15mm). Patients with CMR-confirmed HCM were considered as cases during the final model evaluation. The model was validated within clinical settings at YNHH and externally on ECG images from the prospective, population-based UK Biobank cohort. We localized class-discriminating signals in ECG images using gradient-weighted class activation mapping.

Results

Overall, 124,553 ECGs from 66,987 individuals (HCM cases and controls) were used for model development. The model demonstrated high discrimination for HCM across various ECG image formats and calibrations in internal validation (area under receiving operation characteristics [AUROC] 0.96) and external sets of ECG images from UK Biobank (AUROC 0.94). A positive screen for HCM was associated with a 100-fold higher odds of CMR-confirmed HCM (OR 102.4, 95% Confidence Interval, 57.4 - 182.6) in the held-out set. Class-discriminative patterns localized to the anterior and lateral leads (V4-V5).

Conclusions

We developed and externally validated a deep learning model that identifies HCM from ECG images with excellent discrimination. This approach represents an automated, efficient, and accessible screening strategy for HCM.

Diagnostic significance of paradoxical left ventricular hypertrophy in detecting cardiac amyloidosis

Background

Cardiac amyloidosis (CA) progresses rapidly with a poor prognosis. Therefore, methods for early diagnosis that are easily accessible in any hospital, are required. We hypothesized that based on the pathology of CA, morphological left ventricular hypertrophy (LVH) without electrical augmentation, namely paradoxical LVH, could be used to diagnose CA. This study aimed to investigate whether paradoxical LVH has diagnostic significance in identifying CA in patients with LVH.

Methods

Patients who presented with left ventricular (LV) wall thickness ≥ 12 mm on cardiac magnetic resonance (CMR) were enrolled from a multicentre CMR registry. Paradoxical LVH was defined as a LV wall thickness ≥ 12 mm on CMR, SV1 + RV5 < 3.5 mV, and a lack of secondary ST-T abnormalities. The diagnostic significance of paradoxical LVH in identifying CA was assessed.

Results

Of the 110 patients enrolled, 30 (27 %) were diagnosed with CA and 80 (73 %) with a non-CA aetiology. The CA group demonstrated paradoxical LVH more frequently than the non-CA group (80 % vs. 16 %, P < 0.001). It was an independent predictor for detecting CA in patients with LVH (odds ratio: 33.44, 95 % confidence interval: 8.325-134.3, P < 0.001). The sensitivity, specificity, positive predict value, negative predict value and accuracy of paradoxical LVH for CA detection were 80 %, 84 %, 65 %, 92 % and 83 %, respectively.

Conclusions

Paradoxical LVH can be used for identifying CA in patients with LVH. Our findings could contribute to the early diagnosis of CA, even in non-specialized hospitals.

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.

Saliency maps provide insights into artificial intelligence-based electrocardiography models for detecting hypertrophic cardiomyopathy

INTRODUCTION

A 12‑lead electrocardiography (ECG)-based convolutional neural network (CNN) model can detect hypertrophic cardiomyopathy (HCM). However, since these models do not rely on discrete measurements as inputs, it is not apparent what drives their performance. We hypothesized that saliency maps could be used to visually identify ECG segments that contribute to a CNN's robust classification of HCM.

METHODS

We derived a new one‑lead (lead I) CNN model based on median beats using the same methodology and cohort used for the original 12‑lead CNN model (3047 patients with HCM, and 63,926 sex- and age-matched non-HCM controls). One‑lead, median-beat saliency maps were generated and visually evaluated in an independent cohort of 100 patients with a diagnosis of HCM and a high artificial intelligence (AI)-ECG-HCM probability score to determine which ECG segments contributed to the model's detection of HCM.

RESULTS

The one‑lead, median-beat CNN had an AUC of 0.90 (95% CI 0.89-0.92) for HCM detection, similar to the original 12‑lead ECG model. In the independent HCM cohort (n = 100), saliency maps highlighted the ST-T segment in 92 ECGs, the atrial depolarization segment in 12 ECGs, and the QRS complex in 5 ECGs.

CONCLUSIONS

Saliency maps of a one‑lead, median-beat-based CNN model identified perturbations in ventricular repolarization as the main region of interest in detecting HCM.

Patient-Level Artificial Intelligence-Enhanced Electrocardiography in Hypertrophic Cardiomyopathy: Longitudinal Treatment and Clinical Biomarker Correlations

BACKGROUND

Artificial intelligence (AI) applied to 12-lead electrocardiographs (ECGs) can detect hypertrophic cardiomyopathy (HCM).

OBJECTIVES

The purpose of this study was to determine if AI-enhanced ECG (AI-ECG) can track longitudinal therapeutic response and changes in cardiac structure, function, or hemodynamics in obstructive HCM during mavacamten treatment.

METHODS

We applied 2 independently developed AI-ECG algorithms (University of California-San Francisco and Mayo Clinic) to serial ECGs (n = 216) from the phase 2 PIONEER-OLE trial of mavacamten for symptomatic obstructive HCM (n = 13 patients, mean age 57.8 years, 69.2% male). Control ECGs from 2,600 age- and sex-matched individuals without HCM were obtained. AI-ECG output was correlated longitudinally to echocardiographic and laboratory metrics of mavacamten treatment response.

RESULTS

In the validation cohorts, both algorithms exhibited similar performance for HCM diagnosis, and exhibited mean HCM score decreases during mavacamten treatment: patient-level score reduction ranged from approximately 0.80 to 0.45 for Mayo and 0.70 to 0.35 for USCF algorithms; 11 of 13 patients demonstrated absolute score reduction from start to end of follow-up for both algorithms. HCM scores were significantly associated with other HCM-relevant parameters, including left ventricular outflow tract gradient at rest, postexercise, and with Valsalva, and NT-proBNP level, independent of age and sex (all P < 0.01). For both algorithms, the strongest longitudinal correlation was between AI-ECG HCM score and left ventricular outflow tract gradient postexercise (slope estimate: University of California-San Francisco 0.70 [95% CI: 0.45-0.96], P < 0.0001; Mayo 0.40 [95% CI: 0.11-0.68], P = 0.007).

CONCLUSIONS

AI-ECG analysis longitudinally correlated with changes in echocardiographic and laboratory markers during mavacamten treatment in obstructive HCM. These results provide early evidence for a potential paradigm for monitoring HCM therapeutic response.

Patterns of Electrocardiographic Abnormalities in Children with Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM), a common cardiomyopathy in children, is an important cause of morbidity and mortality. Early recognition and appropriate management are important. An electrocardiogram (ECG) is often used as a screening tool in children to detect heart disease. The ECG patterns in children with HCM are not well described.ECGs collected from an international cohort of children, and adolescents (≤ 21 years) with HCM were reviewed. 482 ECGs met inclusion criteria. Age ranged from 1 day to 21 years, median 13 years. Of the 482 ECGs, 57 (12%) were normal. The most common abnormalities noted were left ventricular hypertrophy (LVH) in 108/482 (22%) and biventricular hypertrophy (BVH) in 116/482 (24%) Of the patients with LVH/BVH (n = 224), 135 (60%) also had a strain pattern (LVH in 83, BVH in 52). Isolated strain pattern (in the absence of criteria for hypertrophy) was seen in 43/482 (9%). Isolated pathologic Q waves were seen in 71/482 (15%). Pediatric HCM, 88% have an abnormal ECG. The most common ECG abnormalities were LVH or BVH with or without strain. Strain pattern without hypertrophy and a pathologic Q wave were present in a significant proportion (24%) of patients. Thus, a significant number of children with HCM have ECG abnormalities that are not typical for "hypertrophy". The presence of the ECG abnormalities described above in a child should prompt further examination with an echocardiogram to rule out HCM.

Sudden cardiac arrest response preparedness in Durham County schools

Sudden cardiac arrest is an uncommon event with high morbidity and mortality. There are improved outcomes with early access to an automated external defibrillator and cardiopulmonary resuscitation. We assessed the availability of automated external defibrillators and emergency cardiac arrest plans in schools. A cross-sectional electronic survey was conducted to determine the status of emergency cardiac arrest plans and automated external defibrillator presence. Most schools (88%) had access to an automated external defibrillator; however, trained staff and maintenance plans were highly variable. Automated external defibrillator availability did not differ by racial/ethnic or socio-economic composition; however, there was a relationship between number of automated external defibrillators and student population (p = 0.0030). The majority of schools either did not have (28%) or did not know if they had (36%) an emergency cardiac arrest plan. Even without state legislation, automated external defibrillators were largely available in schools. However, there remains a paucity of emergency cardiac arrest plans and automated external defibrillator maintenance plans.

Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives

Artificial intelligence (AI), the technique of extracting information from complex database using sophisticated computer algorithms, has incorporated itself in medical field. AI techniques have shown the potential to accelerate the progression of diagnosis and treatment of cardiovascular diseases (CVDs), including heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease and so on. In clinical scenario, AI have been proved to apply well in CVD diagnosis, enhance effectiveness of auxiliary tools, disease stratification and typing, and outcome prediction. Deeply developed to capture subtle connections from massive amounts of healthcare data, recent AI algorithms are expected to handle even more complex tasks than traditional methods. The aim of this review is to introduce current applications of AI in CVDs, which may allow clinicians who have limited expertise of computer science to better understand the frontier of the subject and put AI algorithms into clinical practice.

Comparison of four electrocardiographic criteria for the detection of cardiac abnormalities in Singapore athletes

Introduction

Sudden cardiac death in athletes is a rare occurrence, the most common cause being hypertrophic cardiomyopathy, which increases the risk of sustained ventricular tachycardia or ventricular fibrillation. Most of these young athletes are asymptomatic prior to the cardiac arrest. Several electrocardiogram criteria such as the European Society of Cardiology group 2 Criteria changes, Seattle Criteria, Refined Criteria, and most recently the 2017 International Criteria, have sought to improve the accuracy of identifying these at-risk athletes during pre-participation screening while minimising unnecessary investigations for the majority of athletes at low risk.We aimed to compare the above four criteria in our Singapore athlete population to identify which criterion performed the best in detecting cardiac abnormalities on echocardiography.

Method

Out of 1,515 athletes included in Changi General Hospital, Singapore registry between June 2007 and June 2014, the electrocardiograms of 270 athletes with further cardiac investigations were analysed. We compared the above four electrocardiographic criteria to evaluate which performed best for detecting cardiac abnormalities on echocardiography in our Southeast Asian athlete population.

Results

The European Society of Cardiology, Seattle, Refined and 2017 International Criteria had a sensitivity of 20%, 0%, 20% and 5%, respectively; a specificity of 64%, 93%, 84% and 97%, respectively; a positive predictive value of 4%, 0%, 9% and 11%, respectively; and a negative predictive value of 91%, 92%, 93% and 93%, respectively for detecting abnormalities on echocardiography.

Conclusion

The latest 2017 International Criteria performed the best as it had the highest specificity and positive predictive value, joint highest negative predictive value, and lowest false positive rate.

Diagnostic and prognostic electrocardiographic features in patients with hypertrophic cardiomyopathy

The standard 12-lead electrocardiogram (ECG) represents a cornerstone for the diagnosis and evaluation of hypertrophic cardiomyopathy (HCM), the most common genetically determined heart muscle disease, due to its cost-effectiveness and wide availability. The ECG may surprisingly look normal in 4-6% of adult patients, and in less than 3% of paediatric patients, but it is abnormal in the vast majority of the remaining patients. 'Specific' features comprise pathological Q-waves, deep S-waves in V1-V3, or high R-waves in V4-V6 due to left ventricular hypertrophy with T-wave (TW) depression or negative TWs. Negative giant TWs are often found in apical HCM. However, in many patients, the ECG may only show non-specific ST-T changes with diphasic or flat TWs. An isolated inverted TW in lateral leads (usually aVL) may be the only marker for HCM in some patients. Electrocardiogram helps to diagnose sarcomeric HCM and distinguish it from different phenocopies, such as cardiac amyloidosis, glycogen storage, or Fabry disease. Electrocardiogram may also have a prognostic role, identifying high-risk features that could impact the clinical outcome.

Indications and management of implantable cardioverter-defibrillator therapy in childhood hypertrophic cardiomyopathy

Sudden cardiac death is the most common mode of death during childhood and adolescence in hypertrophic cardiomyopathy, and identifying those individuals at highest risk is a major aspect of clinical care. The mainstay of preventative therapy is the implantable cardioverter-defibrillator, which has been shown to be effective at terminating malignant ventricular arrhythmias in children with hypertrophic cardiomyopathy but can be associated with substantial morbidity. Accurate identification of those children at highest risk who would benefit most from implantable cardioverter-defibrillator implantation while minimising the risk of complications is, therefore, essential. This position statement, on behalf of the Association for European Paediatric and Congenital Cardiology (AEPC), reviews the currently available data on established and proposed risk factors for sudden cardiac death in childhood-onset hypertrophic cardiomyopathy and current approaches for risk stratification in this population. It also provides guidance on identification of individuals at risk of sudden cardiac death and optimal management of implantable cardioverter-defibrillators in children and adolescents with hypertrophic cardiomyopathy.

From the phenotype to precision medicine: an update on the cardiomyopathies diagnostic workflow

Cardiomyopathies are disease of the cardiac muscle largely due to genetic alterations of proteins with 'structural' or 'functional' roles within the cardiomyocyte, going from the regulation of contraction-relaxation, metabolic and energetic processes to ionic fluxes. Modifications occurring to these proteins are responsible, in the vast majority of cases, for the phenotypic manifestations of the disease, including hypertrophic, dilated, arrhythmogenic and restrictive cardiomyopathies. Secondary nonhereditary causes to be excluded include infections, toxicity from drugs or alcohol or medications, hormonal imbalance and so on. Obtaining a phenotypic definition and an etiological diagnosis is becoming increasingly relevant and feasible, thanks to the availability of new tailored treatments and the diagnostic advancements made particularly in the field of genetics. This is, for example, the case for transthyretin cardiac amyloidosis, Fabry disease or dilated cardiomyopathies due to laminopathies. For these diseases, specific medications have been developed, and a more tailored arrhythmic risk stratification guides the implantation of a defibrillator. In addition, new medications directly targeting the altered protein responsible for the phenotype are becoming available (including the myosin inhibitors mavacantem and aficamten, monoclonal antibodies against Ras-MAPK, genetic therapies for sarcoglycanopathies), thus making a precision medicine approach less unrealistic even in the field of cardiomyopathies. For these reasons, a contemporary approach to cardiomyopathies must consider diagnostic algorithms founded on the clinical suspicion of the disease and developed towards a more precise phenotypic definition and etiological diagnosis, based on a multidisciplinary methodology putting together specialists from different disciplines, facilities for advanced imaging testing and genetic and anatomopathological competencies.

Sex-Related Differences among Adults with Hypertrophic Obstructive Cardiomyopathy Undergoing Transcoronary Ablation of Septal Hypertrophy

(1) Background: The transcoronary ablation of septal hypertrophy (TASH) is an established therapy for hypertrophic obstructive cardiomyopathy (HOCM). Previous studies on this topic are characterised by a consistent male predominance and show a worse prognosis in females. (2) Methods: This study is a retrospective analysis of all TASH procedures conducted between 2006 and 2021 at a tertiary academic centre. A solution of 75 µm microspheres (Embozene^®, Boston Scientific, Marlborough, MA, USA) was used as an embolising agent. The outcomes of interest were left ventricular outflow tract (LVOT) gradient reduction and symptom improvement among males vs. that among females. Secondarily, we analysed the sex-related differences in procedural safety outcomes and mortality. (3) Results: The study population consisted of 76 patients, with a median age of 61 years. Females comprised 57% of the cohort. We observed no sex-related differences in the baseline LVOT gradients at rest or under provocation (p = 0.560 and p = 0.208, respectively). Females were significantly older at the time of the procedure (p < 0.001), had lower tricuspid annular systolic excursion (TAPSE) (p = 0.009), presented a worse clinical status according to the NYHA functional classification (for NYHA ≥ 3, p < 0.001), and were more often on diuretics (p < 0.001). We did not observe sex-related differences in absolute gradient reduction at rest (p = 0.147) and under provocation (p = 0.709). There was a reduction in the NYHA class by a median value of 1 (p = 0.636) at follow-up for both sexes. Postprocedural access site complications were documented in four cases (two of which concerned females), and complete atrioventricular block was noted in five patients (three of which concerned females). The 10-year survival rates were comparable between the sexes (85% in females and 88% in males). The female sex was not associated with enhanced mortality according to multivariate analysis after adjusting for the confounding variables (HR 0.94; 95% CI 0.376-2.350; p = 0.895), but we observed age-related differences in long-term mortality (HR 1.035; 95% CI 1.007-1.063; p = 0.015). (4) Conclusions: TASH is safe and effective in both sexes, irrespective of their clinical differences. Women present at an advanced age and with more severe symptoms. An advanced age at the time of the intervention is an independent predictor of mortality.

Sudden cardiac death in childhood hypertrophic cardiomyopathy

The most prevalent cause of mortality in children with hypertrophic cardiomyopathy (HCM) is sudden cardiac death (SCD), which happens more frequently than in adult patients. Risk stratification tactics have generally been drawn from adult practice, however emerging data has revealed significant disparities between children and adult cohorts, implying the need for pediatric-specific risk stratification methodologies. We conducted an all-language literature search on Medline, Cochrane, Embase, and Google Scholar until October 2021. The following search strings and Medical Subject Heading (MeSH) terms were used: "HCM," "SCD," "Sudden Cardiac Death," and "Childhood Onset HCM." We explored the literature on the risk of SCD in HCM for its epidemiology, pathophysiology, the role of various genes and their influence, associated complications leading to SCD and preventive and treatment modalities. Childhood-onset HCM is linked to significant life-long morbidity and mortality, including a higher SCD rate in children than in adults. The present focus is on symptom relief and avoiding illness-related consequences, but the prospect of future disease-modifying medicines offers an intriguing opportunity to alter disease expression and outcomes in these young individuals. Current preventive recommendations promote implantable cardioverter defibrillator placement based on cumulative risk factor thresholds, although they have been demonstrated to have weak discriminating capacity. This article addresses questions and discusses the etiology, risk factors, and method to risk stratification for SCD in children with HCM.

Tandem deep learning and logistic regression models to optimize hypertrophic cardiomyopathy detection in routine clinical practice

Background

An electrocardiogram (ECG)-based artificial intelligence (AI) algorithm has shown good performance in detecting hypertrophic cardiomyopathy (HCM). However, its application in routine clinical practice may be challenging owing to the low disease prevalence and potentially high false-positive rates.

Objective

Identify clinical characteristics associated with true- and false-positive HCM AI-ECG results to improve its clinical application.

Methods

We reviewed the records of the 200 patients with highest HCM AI-ECG scores in January 2021 at our institution. Logistic regression was used to create a clinical variable-based "Candidacy for HCM Detection (HCM-DETECT)" score, differentiating true-positive from false-positive AI-ECG results. We validated the HCM-DETECT score in an independent cohort of 200 patients with the highest AI-ECG scores from January 2022.

Results

In the 2021 cohort (median age 71 [interquartile range 58-80] years, 48% female), the rates of true-positive, false-positive, and indeterminate AI-ECG results for HCM detection were 36%, 48%, and 16%, respectively. In the 2022 cohort, the rates were 26%, 47%, and 27%, respectively. The HCM-DETECT score included age, coronary artery disease, prior pacemaker, and prior cardiac valve surgery, and had an area under the receiver operating characteristic curve of 0.81 (95% confidence interval 0.73-0.87) for differentiating true- vs false-positive AI results. When the 2022 cohort was limited to HCM detection candidates identified with the HCM-DETECT score, the false-positive AI-ECG rate was reduced from 47% to 13.5%.

Conclusion

Application of a clinical score (HCM-DETECT) in tandem with an AI-ECG model improved HCM detection yield, reducing the false-positive rate of AI-ECG more than 3-fold.

Phenotype variation of hypertrophic cardiomyopathy in carriers of the p.Arg870His pathogenic variant in the MYH7 gene

The review analyzes variability of clinical manifestations of p.Arg870His in the MYH7 gene, which is repeatedly registered in patients with hypertrophic cardiomyopathy (HCM). The analysis involves the data from scientific publications obtained as a search result in the PubMed, СlinVar, and eLibrary.ru databases, as well as authors’ own results. A wide range of phenotypic manifestations have been revealed in carriers of p.Arg870His, from the asymptomatic to severe course, rapid progression, and early death. The review considers possible factors that modify the effect of the pathogenic variant (i.e. dosage of the pathogenic variant, the presence of other unfavorable genetic variants, etc.). The importance of accumulating information on the clinical features of HCM in the carriers of specific gene variants is emphasized in order to clarify their pathogenicity and to identify factors modifying the clinical outcome, which is important for the choice of the treatment strategy for HCM.

Hypertrophic cardiomyopathy with heart failure and ST-segment elevation of the lateral wall

Hypertrophic cardiomyopathy (HCM) is the most common cardiovascular disease that is inherited from a single gene. Its clinical manifestations range from asymptomatic mutant gene carriers to patients with severe left ventricular effluent tract obstruction and end-stage HCM with motor restriction. In this case, we present a patient with the main presentation of heart failure and ST-segment elevation of the lateral wall, as determined by electrocardiogram. The patient was finally diagnosed with HCM because of genetic testing and the presentation of extensive myocardial fibrosis with reduced systolic function on cardiac magnetic resonance imaging. The patient's clinical findings, electrocardiogram, and cardiac magnetic resonance imaging were different from those of typical patients with HCM.

Toward ECG-based analysis of hypertrophic cardiomyopathy: a novel ECG segmentation method for handling abnormalities

OBJECTIVE

Abnormalities in impulse propagation and cardiac repolarization are frequent in hypertrophic cardiomyopathy (HCM), leading to abnormalities in 12-lead electrocardiograms (ECGs). Computational ECG analysis can identify electrophysiological and structural remodeling and predict arrhythmias. This requires accurate ECG segmentation. It is unknown whether current segmentation methods developed using datasets containing annotations for mostly normal heartbeats perform well in HCM. Here, we present a segmentation method to effectively identify ECG waves across 12-lead HCM ECGs.

METHODS

We develop (1) a web-based tool that permits manual annotations of P, P', QRS, R', S', T, T', U, J, epsilon waves, QRS complex slurring, and atrial fibrillation by 3 experts and (2) an easy-to-implement segmentation method that effectively identifies ECG waves in normal and abnormal heartbeats. Our method was tested on 131 12-lead HCM ECGs and 2 public ECG sets to evaluate its performance in non-HCM ECGs.

RESULTS

Over the HCM dataset, our method obtained a sensitivity of 99.2% and 98.1% and a positive predictive value of 92% and 95.3% when detecting QRS complex and T-offset, respectively, significantly outperforming a state-of-the-art segmentation method previously employed for HCM analysis. Over public ECG sets, it significantly outperformed 3 state-of-the-art methods when detecting P-onset and peak, T-offset, and QRS-onset and peak regarding the positive predictive value and segmentation error. It performed at a level similar to other methods in other tasks.

CONCLUSION

Our method accurately identified ECG waves in the HCM dataset, outperforming a state-of-the-art method, and demonstrated similar good performance as other methods in normal/non-HCM ECG sets.

Accuracy of the "International Criteria" for ECG screening in athletes in comparison with previous published criteria: rationale and design of a diagnostic meta-analysis

INTRODUCTION

The impact of the International Criteria for ECG interpretation in athletes has further improved the diagnostic accuracy of the 12-lead ECG use for preparticipation screening (PPS); moreover, these criteria have been evaluated in different populations of athletes and settings proving good results.

EVIDENCE ACQUISITION

We aimed to perform a comprehensive review of the use of the International Criteria for ECG interpretation in athletes, stemming from a systematic review to diagnostic meta-analysis, limiting our inclusion only to observational studies to determine the diagnostic accuracy of ECG for detecting cardiac anomalies related to sudden cardiac death in athletes.

EVIDENCE SYNTHESIS

This meta-analysis is expected to include several important studies related to PPS on different populations of athletes comparing different ECG criteria and detail important data on the diagnostic accuracy of ECG in PPS. Furthermore, we intend to highlight the advantage of using ECG in PPS.

CONCLUSIONS

The present diagnostic meta-analysis results will aid sports medicine physicians and cardiologist in adhering to the most accurate criteria for ECG evaluation in athletes and it may help to solve controversies aroused regarding the excess cost of ECG in PPS related to the amount of false positive cases.

Cardiomyopathies in the Clinical Practice - an Overview

Cardiomyopathies are myocardial disorders with a structurally and functionally abnormal heart muscle. In this review, we describe pathophysiological aspects, clinical presentation, diagnosis, risk stratification and therapeutical concepts of the three most common forms of cardiomyopathy: hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), and arrhythmogenic cardiomyopathy (ACM).

Artificial Intelligence-Enabled ECG: Physiologic and Pathophysiologic Insights and Implications

Advancements in machine learning and computing methods have given new life and great excitement to one of the most essential diagnostic tools to date-the electrocardiogram (ECG). The application of artificial intelligence-enabled ECG (AI-ECG) has resulted in the ability to identify electrocardiographic signatures of conventional and unique variables and pathologies, giving way to tremendous clinical potential. However, what these AI-ECG models are detecting that the human eye is missing remains unclear. In this article, we highlight some of the recent developments in the field and their potential clinical implications, while also attempting to shed light on the physiologic and pathophysiologic features that enable these models to have such high diagnostic yield. © 2022 American Physiological Society. Compr Physiol 12:3417-3424, 2022.

Derivation and Validation of a Screening Model for Hypertrophic Cardiomyopathy Based on Electrocardiogram Features

Background

Hypertrophic cardiomyopathy (HCM) is a widely distributed, but clinically heterogeneous genetic heart disease, affects approximately 20 million people worldwide. Nowadays, HCM is treatable with the advancement of medical interventions. However, due to occult clinical presentations and a lack of easy, inexpensive, and widely popularized screening approaches in the general population, 80-90% HCM patients are not clinically identifiable, which brings certain safety hazards could have been prevented. The majority HCM patients showed abnormal and diverse electrocardiogram (ECG) presentations, it is unclear which ECG parameters are the most efficient for HCM screening.

Objective

We aimed to develop a pragmatic prediction model based on the most common ECG features to screen for HCM.

Methods

Between April 1st and September 30th, 2020, 423 consecutive subjects from the International Cooperation Center for Hypertrophic Cardiomyopathy of Xijing Hospital [172 HCM patients, 251 participants without left ventricular hypertrophy (non-HCM)] were prospectively included in the training cohort. Between January 4th and February 30th, 2021, 163 participants from the same center were included in the temporal internal validation cohort (62 HCM patients, 101 non-HCM participants). External validation was performed using retrospectively collected ECG data from Xijing Hospital (3,232 HCM ECG samples from January 1st, 2000, to March 31st, 2020; 95,184 non-HCM ECG samples from January 1st to December 31st, 2020). The C-statistic was used to measure the discriminative ability of the model.

Results

Among 30 ECG features examined, all except abnormal Q wave significantly differed between the HCM patients and non-HCM comparators. After several independent feature selection approaches and model evaluation, we included only two ECG features, T wave inversion (TWI) and the amplitude of S wave in lead V1 (SV1), in the HCM prediction model. The model showed a clearly useful discriminative performance (C-statistic > 0.75) in the training [C-statistic 0.857 (0.818-0.896)], and temporal validation cohorts [C-statistic 0.871 (0.812-0.930)]. In the external validation cohort, the C-statistic of the model was 0.833 [0.825-0.841]. A browser-based calculator was generated accordingly.

Conclusion

The pragmatic model established using only TWI and SV1 may be helpful for predicting the probability of HCM and shows promise for use in population-based HCM screening.

What Aspects of Phenotype Determine Risk for Sudden Cardiac Death in Pediatric Hypertrophic Cardiomyopathy?

Sudden cardiac death due to hypertrophic cardiomyopathy (HCM), is the most common autopsy-proven cause of unexpected medical death in children after infancy. This mode of death is preventable by implantation of an internal cardiac defibrillator (ICD), a procedure that has considerable morbidity in childhood patients, and even mortality. Since HCM is an inheritable disease (usually autosomal dominant, occasionally recessive), family screening may identify subjects at risk. This review summarizes published studies carried out to identify which phenotypic markers are important risk factors in childhood patients with HCM and reviews the performance of existing risk-stratification algorithms (HCM Risk-Kids, PRIMaCY) against those of single phenotypic markers. A significant proportion of HCM-patients diagnosed in childhood are associated with RASopathies such as Noonan syndrome, but a knowledge gap exists over risk stratification in this patient group. In conclusion, pediatric risk-stratification algorithms for sudden cardiac death perform better in children than adult HCM risk-stratification strategies. However, current multivariable algorithms overestimate risk substantially without having high sensitivity, and remain ‘a work in progress’. To include additional phenotypic parameters that can be reproducibly measured such as ECG-markers, e.g., ECG risk score (which has high sensitivity and negative predictive value), tissue Doppler diastolic function measurements, and quantification of myocardial scarring on cardiac magnetic resonance imaging, has the potential to improve risk-stratification algorithms. Until that work has been achieved, these are three factors that the clinician can combine with the current algorithm-calculated per cent risk, in order better to assess risk.

Differences in Atrial Remodeling in Hypertrophic Cardiomyopathy Compared to Hypertensive Heart Disease and Athletes' Hearts

Background: Hypertrophic cardiomyopathy (HCM), hypertensive heart disease (HHD) and athletes’ heart share an increased prevalence of atrial fibrillation. Atrial cardiomyopathy in these patients may have different characteristics and help to distinguish these conditions. Methods: In this single-center study, we prospectively collected and analyzed electrocardiographic (12-lead ECG, signal-averaged ECG (SAECG), 24 h Holter ECG) and echocardiographic data in patients with HCM and HHD and in endurance athletes. Patients with atrial fibrillation were excluded. Results: We compared data of 27 patients with HCM (70% males, mean age 50 ± 14 years), 324 patients with HHD (52% males, mean age 75 ± 5.5 years), and 215 endurance athletes (72% males, mean age 42 ± 7.5 years). HCM patients had significantly longer filtered P-wave duration (153 ± 26 ms) and PR interval (191 ± 48 ms) compared to HHD patients (144 ± 16 ms, p = 0.012 and 178 ± 31, p = 0.034, respectively) and athletes (134 ± 14 ms, p = 0.001 and 165 ± 26 ms, both p < 0.001, respectively). HCM patients had a mean of 4.9 ± 16 premature atrial complexes per hour. Premature atrial complexes per hour were significantly more frequent in HHD patients (27 ± 86, p < 0.001), but not in athletes (2.7 ± 23, p = 0.639). Left atrial volume index (LAVI) was 43 ± 14 mL/m2 in HCM patients and significantly larger than age- and sex-corrected LAVI in HHD patients 30 ± 10 mL/m2; p < 0.001) and athletes (31 ± 9.5 mL/m2; p < 0.001). A borderline interventricular septum thickness ≥13 mm and ≤15 mm was found in 114 (35%) HHD patients, 12 (6%) athletes and 3 (11%) HCM patients. Conclusions: Structural and electrical atrial remodeling is more advanced in HCM patients compared to HHD patients and athletes.

A complex unit for a complex disease: the HCM-Family Unit

Hypertrophic cardiomyopathy (HCM) is a group of heterogeneous disorders that are most commonly passed on in a heritable manner. It is a relatively rare disease around the globe, but due to increased rates of consanguinity within the Kingdom of Saudi Arabia, we speculate a high incidence of undiagnosed cases. The aim of this paper is to elucidate a systematic approach in dealing with HCM patients and since HCM has variable presentation, we have summarized differentials for diagnosis and how different subtypes and genes can have an impact on the clinical picture, management and prognosis. Moreover, we propose a referral multi-disciplinary team HCM-Family Unit in Saudi Arabia and an integrated role in a network between King Faisal Hospital and Inherited and Rare Cardiovascular Disease Unit-Monaldi Hospital, Italy (among the 24 excellence centers of the European Reference Network (ERN) GUARD-Heart).   Graphical Abstract.

Sudden cardiac death in childhood hypertrophic cardiomyopathy is best predicted by a combination of electrocardiogram risk-score and HCMRisk-Kids score

AIM

To compare risk algorithms (HCMRisk-Kids, ECG Risk-score) in hypertrophic cardiomyopathy (HCM) without syndrome association (ns-HCM) and with Noonan-like syndromes (RAS-HCM).

METHODS

A national paediatric HCM cohort (n = 151), presenting <19 years of age, mean follow-up 13.3 years, from all Swedish centres of Paediatric Cardiology (presenting 1972-2015), with 41 RAS-HCM patients (61% males), and 110 ns-HCM patients (68% familial; 65% males). The end-point was a composite of sudden cardiac death and resuscitated cardiac arrest (SCD/CA). Risk-factors were studied with Cox-hazard regression, and receiver operating characteristic curve analysis (C-statistic).

RESULTS

There were 33 SCD/CA, 27/110 in ns-HCM and 6/41 in RAS-HCM (p = 0.27). In ns-HCM HCMRisk-Kids ≥6% at diagnosis had C-statistic of 0.69 for predicting SCD/CA during first 5 years of follow-up and positive predictive value (PPV) of 22%. After 7 years of age (HCMRisk-Kids7plus), C-statistic was 0.76. ECG Risk-score ≥6 at diagnosis had C-statistic 0.87 and PPV of 31%. Independent risk factors for SCD/CA were HCMRisk-Kids7plus score (p = 0.005) and ECG risk-score (p < 0.001), whereas early beta-blocker dose (p = 0.001) and myectomy (p = 0.004) reduced risk. The sum of HCMRisk-Kids7yplus and ECG Risk-score7yplus ≥14 best predicted SCD/CA within 5 years in ns-HCM with C-statistic of 0.90 [0.83-0.96], sensitivity 100% and PPV 38%.

CONCLUSION

Combining the ECG Risk-score with HCMRisk-Kids improves risk stratification in ns-HCM and shows promise in RAS-HCM.

Detection of hypertrophic cardiomyopathy by an artificial intelligence electrocardiogram in children and adolescents

BACKGROUND

There is no established screening approach for hypertrophic cardiomyopathy (HCM). We recently developed an artificial intelligence (AI) model for the detection of HCM based on the 12‑lead electrocardiogram (AI-ECG) in adults. Here, we aimed to validate this approach of ECG-based HCM detection in pediatric patients (age ≤ 18 years).

METHODS

We identified a cohort of 300 children and adolescents with HCM (mean age 12.5 ± 4.6 years, male 68%) who had an ECG and echocardiogram at our institution. Patients were age- and sex-matched to 18,439 non-HCM controls. Diagnostic performance of the AI-ECG model for the detection of HCM was estimated using the previously identified optimal diagnostic threshold of 11% (the probability output derived by the model above which an ECG is considered to belong to an HCM patient).

RESULTS

Mean AI-ECG probabilities of HCM were 92% and 5% in the case and control groups, respectively. The area under the receiver operating characteristic curve (AUC) of the AI-ECG model for HCM detection was 0.98 (95% CI 0.98-0.99) with corresponding sensitivity 92% and specificity 95%. The positive and negative predictive values were 22% and 99%, respectively. The model performed similarly in males and females and in genotype-positive and genotype-negative HCM patients. Performance tended to be superior with increasing age. In the age subgroup <5 years, the test's AUC was 0.93. In comparison, the AUC was 0.99 in the age subgroup 15-18 years.

CONCLUSIONS

A deep-learning, AI model can detect pediatric HCM with high accuracy from the standard 12‑lead ECG.

Automatic Electrocardiogram Detection of Suspected Hypertrophic Cardiomyopathy: Application to Wearable Heart Monitors

In this letter, an automatic detection algorithm for hypertrophic cardiomyopathy (HCM) is presented. Of particular interest is the algorithm's ability to differentiate HCM subjects and healthy volunteers from a single lead ECG dataset. Suspected HCM subjects are identified by the primary clinical abnormality associated with HCM: left ventricular hypertrophy (LVH). In total, n = 43 human subjects ECG datasets are investigated: n = 21 healthy volunteers and n = 22 LVH patients. Significant differences of p-value 0.01 and 0.04 were found for the respective ECG parameters, i.e., S-wave amplitude and ST-segment, when differentiating between the LVH patients and healthy human volunteers.

Deciphering hypertrophic cardiomyopathy with electrocardiography

The comprehensive assessment of patients with hypertrophic cardiomyopathy is a complex process, with each step concurrently focusing on confirmation of the diagnosis, differentiation between sarcomeric and non-sarcomeric disease (phenocopy), and prognostication. Novel modalities such as genetic testing and advanced imaging have allowed for substantial advancements in the understanding of this condition and facilitate patient management. However, their availability is at present not universal, and interpretation requires a high level of expertise. In this setting, electrocardiography, a fast and widely available method, still retains a significant role in everyday clinical assessment of this population. In our review, we follow a stepwise approach for the interpretation of each electrocardiographic segment, discussing clinical implications of electrocardiographic patterns in sarcomeric disease, their value in the differential diagnosis from phenocopies, and impact on patient management. Outlining the substantial amount of information to be obtained from a simple tracing, we exhibit how electrocardiography is likely to remain an integral diagnostic tool in the future as well.

Cardiomyopathies: An Overview

Background: Cardiomyopathies are a heterogeneous group of pathologies characterized by structural and functional alterations of the heart. Aims: The purpose of this narrative review is to focus on the most important cardiomyopathies and their epidemiology, diagnosis, and management. Methods: Clinical trials were identified by Pubmed until 30 March 2021. The search keywords were “cardiomyopathies, sudden cardiac arrest, dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), restrictive cardiomyopathy, arrhythmogenic cardiomyopathy (ARCV), takotsubo syndrome”. Results: Hypertrophic cardiomyopathy (HCM) is the most common primary cardiomyopathy, with a prevalence of 1:500 persons. Dilated cardiomyopathy (DCM) has a prevalence of 1:2500 and is the leading indication for heart transplantation. Restrictive cardiomyopathy (RCM) is the least common of the major cardiomyopathies, representing 2% to 5% of cases. Arrhythmogenic cardiomyopathy (ARCV) is a pathology characterized by the substitution of the myocardium by fibrofatty tissue. Takotsubo cardiomyopathy is defined as an abrupt onset of left ventricular dysfunction in response to severe emotional or physiologic stress. Conclusion: In particular, it has been reported that HCM is the most important cause of sudden death on the athletic field in the United States. It is needless to say how important it is to know which changes in the heart due to physical activity are normal, and when they are pathological.

Hypertrophic Cardiomyopathy and Primary Restrictive Cardiomyopathy: Similarities, Differences and Phenocopies

Hypertrophic cardiomyopathy (HCM) and primary restrictive cardiomyopathy (RCM) have a similar genetic background as they are both caused mainly by variants in sarcomeric genes. These “sarcomeric cardiomyopathies” also share diastolic dysfunction as the prevalent pathophysiological mechanism. Starting from the observation that patients with HCM and primary RCM may coexist in the same family, a characteristic pathophysiological profile of HCM with restrictive physiology has been recently described and supports the hypothesis that familiar forms of primary RCM may represent a part of the phenotypic spectrum of HCM rather than a different genetic cardiomyopathy. To further complicate this scenario some infiltrative (amyloidosis) and storage diseases (Fabry disease and glycogen storage diseases) may show either a hypertrophic or restrictive phenotype according to left ventricular wall thickness and filling pattern. Establishing a correct etiological diagnosis among HCM, primary RCM, and hypertrophic or restrictive phenocopies is of paramount importance for cascade family screening and therapy.

The role of the electrocardiographic phenotype in risk stratification for sudden cardiac death in childhood hypertrophic cardiomyopathy

AIMS

The 12-lead electrocardiogram (ECG) is routinely performed in children with hypertrophic cardiomyopathy (HCM). An ECG risk score has been suggested as a useful tool for risk stratification, but this has not been independently validated. This aim of this study was to describe the ECG phenotype of childhood HCM in a large, international, multi-centre cohort and investigate its role in risk prediction for arrhythmic events.

METHODS AND RESULTS

Data from 356 childhood HCM patients with a mean age of 10.1 years (±4.5) were collected from a retrospective, multi-centre international cohort. Three hundred and forty-seven (97.5%) patients had ECG abnormalities at baseline, most commonly repolarization abnormalities (n = 277, 77.8%); left ventricular hypertrophy (n = 240, 67.7%); abnormal QRS axis (n = 126, 35.4%); or QT prolongation (n = 131, 36.8%). Over a median follow-up of 3.9 years (interquartile range 2.0-7.7), 25 (7%) had an arrhythmic event, with an overall annual event rate of 1.38 (95% CI 0.93-2.04). No ECG variables were associated with 5-year arrhythmic event on univariable or multivariable analysis. The ECG risk score threshold of >5 had modest discriminatory ability [C-index 0.60 (95% CI 0.484-0.715)], with corresponding negative and positive predictive values of 96.7% and 6.7.

CONCLUSION

In a large, international, multi-centre cohort of childhood HCM, ECG abnormalities were common and varied. No ECG characteristic, either in isolation or combined in the previously described ECG risk score, was associated with 5-year sudden cardiac death risk. This suggests that the role of baseline ECG phenotype in improving risk stratification in childhood HCM is limited.

High ECG Risk-Scores Predict Late Gadolinium Enhancement on Magnetic Resonance Imaging in HCM in the Young

An ECG risk-score has been described that predicts high risk of subsequent cardiac arrest in young patients with hypertrophic cardiomyopathy (HCM). Myocardial fibrosis measured by cardiac magnetic resonance (CMR) late gadolinium enhancement (LGE) also affects prognosis. We assessed whether an ECG risk-score could be used as an indicator of myocardial fibrosis or perfusion deficit on CMR in HCM. In total 42 individuals (7-31 years); 26 HCM patients, seven genotype-positive, phenotype-negative individuals at risk of HCM (first-degree relatives) and nine healthy volunteers, underwent CMR to identify, and grade extent of, myocardial fibrosis and perfusion defect. 12-lead ECG was used for calculating the ECG risk-score (grading 0-14p). High-risk ECG (risk-score > 5p) occurred only in the HCM group (9/26), and the proportion was significantly higher vs mutation carriers combined with healthy volunteers (0/16, p = 0.008). Extent of LGE correlated to the ECG-score (R² = 0.47, p = 0.001) in sarcomeric mutations. In low-risk ECG-score patients (0-2p), median percent of myocardium showing LGE (LGE%LVM) were: 0% [interquartile range, IQR, 0-0%], in intermediate-risk (3-5p): 5.4% [IQR 0-13.5%] and in high-risk (6-14p): 10.9% [IQR 4.2-12.3%]. ECG-score > 2p had a sensitivity and specificity of 79% and 84% to detect positive LGE on CMR and 77% vs. 75% to detect perfusion defects in sarcomeric mutations carriers. In patients with myocardial fibrosis as identified by LGE, median ECG risk-score was 8p [range 3-10p]. In conclusions, ECG risk-score > 2 p could be used as a cut-off for screening of myocardial fibrosis. Thus ECG risk-score is an inexpensive complementary tool in risk stratification of HCM in the young.

Artificial intelligence-enhanced electrocardiography in cardiovascular disease management

The application of artificial intelligence (AI) to the electrocardiogram (ECG), a ubiquitous and standardized test, is an example of the ongoing transformative effect of AI on cardiovascular medicine. Although the ECG has long offered valuable insights into cardiac and non-cardiac health and disease, its interpretation requires considerable human expertise. Advanced AI methods, such as deep-learning convolutional neural networks, have enabled rapid, human-like interpretation of the ECG, while signals and patterns largely unrecognizable to human interpreters can be detected by multilayer AI networks with precision, making the ECG a powerful, non-invasive biomarker. Large sets of digital ECGs linked to rich clinical data have been used to develop AI models for the detection of left ventricular dysfunction, silent (previously undocumented and asymptomatic) atrial fibrillation and hypertrophic cardiomyopathy, as well as the determination of a person's age, sex and race, among other phenotypes. The clinical and population-level implications of AI-based ECG phenotyping continue to emerge, particularly with the rapid rise in the availability of mobile and wearable ECG technologies. In this Review, we summarize the current and future state of the AI-enhanced ECG in the detection of cardiovascular disease in at-risk populations, discuss its implications for clinical decision-making in patients with cardiovascular disease and critically appraise potential limitations and unknowns.

Hypertrophic Cardiomyopathy: A Review

A cardiomiopatia hipertrófica (CMH) é a doença cardíaca de origem genética mais comum, cuja principal característica consiste na hipertrofia ventricular esquerda que acontece na ausência de outras patologias que desencadeiam tal alteração. A CMH pode se apresentar desde formas assintomáticas até manifestações de morte cardíaca súbita e de insuficiência cardíaca refratária. Métodos de imagem contemporâneos de alta resolução e escores clínicos mais acurados vêm sendo utilizados e desenvolvidos no sentido de propiciar uma avaliação prognóstica e funcional mais adequada, bem como possibilitar a estratificação dos casos de maior gravidade. Nesta revisão, serão abordados esses aspectos, entre outros tópicos clássicos inerentes ao estudo dessa doença.

Is Exercise Helpful or Harmful in Dealing With Specific Arrhythmia

Exercise is universally known to benefit health by lowering risk for cardiovascular disease and mortality. However, in patients with pre-existing cardiac conditions, including channelopathies, cardiomyopathies and coronary artery disease, exercise can cause sudden cardiac death (SCD). In this review, we explore exercise related risks and current recommendations for specific conditions. The risk of myocardial infarction (MI) during strenuous exercise in asymptomatic individuals with coronary artery disease is decreased with habitual exercise, especially if they have a normal ejection fraction and no ischemia. Furthermore, cardiac rehabilitation has been shown to be beneficial in heart failure. On the other hand, surgery is recommended for certain anomalous coronaries prior to engaging in vigorous activity. In addition, both exercise-induced disease progression and SCD in arrhythmogenic cardiomyopathy restrict ability to engage in competitive sports, as is the case in hypertrophic cardiomyopathy. Other diseases, like myocarditis only cause temporary risk for SCD. Previously considered benign, common conditions like early repolarization do increase SCD risk. Finally, certain gear including thicker chest protectors for athletes engaging in sports with hard, small spherical objects decrease risk of commotio cordis. While significant advances have been achieved in diagnosing and treating previously unrecognized conditions that predispose to sudden cardiac death, more research is needed to further tailor recommendations to allow beneficial exercise in those with rarer conditions that are under-represented in large systemic studies.

Epidemiology of the inherited cardiomyopathies

In the absence of contemporary, population-based epidemiological studies, estimates of the incidence and prevalence of the inherited cardiomyopathies have been derived from screening studies, most often of young adult populations, to assess cardiovascular risk or to detect the presence of disease in athletes or military recruits. The global estimates for hypertrophic cardiomyopathy (1/500 individuals), dilated cardiomyopathy (1/250) and arrhythmogenic right ventricular cardiomyopathy (1/5,000) are probably conservative given that only individuals who fulfil diagnostic criteria would have been included. This caveat is highly relevant because a substantial minority or even a majority of individuals who carry disease-causing genetic variants and are at risk of disease complications have incomplete and/or late-onset disease expression. The genetic literature on cardiomyopathy, which is often focused on the identification of genetic variants, has been biased in favour of pedigrees with higher penetrance. In clinical practice, an abnormal electrocardiogram with normal or non-diagnostic imaging results is a common finding for the sarcomere variants that cause hypertrophic cardiomyopathy, the titin and sarcomere variants that cause dilated cardiomyopathy and the desmosomal variants that cause either arrhythmogenic right ventricular cardiomyopathy or dilated cardiomyopathy. Therefore, defining the genetic epidemiology is also challenging given the overlapping phenotypes, incomplete and age-related expression, and highly variable penetrance even within individual families carrying the same genetic variant.

Electrocardiographic voltage criteria in patients with hypertrophic cardiomyopathy

BACKGROUND

Several ECG voltage criteria have been proposed for the diagnosis of left ventricular hypertrophy (LVH). Notably, ECG criteria have been historically validated in concentric LVH but not in hypertrophic cardiomyopathy (HCM), wherein the hypertrophy pattern is typically asymmetric.

OBJECTIVES

The aim of our study was to evaluate the performance of ECG voltage criteria for LVH diagnosis in the HCM population.

MATERIAL AND METHODS

The electrocardiograms of 92 HCM patients and 41 sex- and age-matched controls were evaluated with the most frequently used ECG voltage criteria for LVH diagnosis. Cardiac magnetic resonance (MRI) was performed in HCM and controls in order to quantify LVH and its distribution.

RESULTS

In the HCM population, the maximal diagnostic accuracy was achieved by Amplitude total and Amplitude total product criteria (58% for both), while the Cornell Voltage best performed in septal HCM (62%), the Sokolov in aVL and Gubner criteria in apical HCM (79% for both) and the Cornell Voltage and Product in anterior HCM (86% for both). All the ECG voltage criteria showed a poor correlation with left ventricular mass and maximal thickness measured by cardiac MRI.

CONCLUSIONS

In our study, only a few ECG voltage criteria used for the detection of LVH in clinical practice showed an acceptable performance in the HCM population. Further studies are needed to clarify the role of ECG for LVH detection in HCM patients.

Current controversies in pre-participation cardiovascular screening for young competitive athletes

INTRODUCTION

Pre-participation cardiovascular screening (PPCS) in athletes is recommended by numerous medical and sporting societies. While there is consensus that young athletes should be screened prior to participation in competitive sports, there are on-going debates regarding the true incidence of sudden cardiac death (SCD), the most frequent causes of SCD, and the optimal methods for PPCS.

AREAS COVERED

This review focuses on the current evidence for the incidence of SCD, causes of SCD, and the pros and cons of a history and physical exam (H&P) and electrocardiogram (ECG) in PPCS of young competitive athletes.

EXPERT OPINION

With significant controversy surrounding PPCS in athletes, a large-randomized trial powered for mortality is needed to assess the utility of PPCS and to define the optimal screening methods to detect cardiovascular diseases that may lead to SCD in competitive athletes. Until a trial of this caliber is created, controversy will remain and heterogeneity in care will exist. Future research should also define the optimal timing and frequency of PPCS given age-related penetrance of certain diseases, create evidence-based history questionnaires, continue to optimize ECG screening criteria, and create more learning modules for ECG interpretation in athletes.

Artificial Intelligence-Enabled ECG: a Modern Lens on an Old Technology

PURPOSE OF REVIEW

To (i) review the concept of artificial intelligence (AI); (ii) summarize recent developments in artificial intelligence-enabled electrocardiogram (AI-ECG); (iii) address notable inherent limitations and challenges of AI-ECG; and (iv) discuss the future direction of the field.

RECENT FINDINGS

Advancements in machine learning and computing methods have led to application of AI-ECG and potential new applications to patient care. Further study is needed to verify previous findings in diverse populations as well as begin to confront the limitations needed for clinical implementation. Nearly one century after the Nobel Prize was awarded to Willem Einthoven for demonstrating that an electrocardiogram (ECG) could record the electrical signature of the heart, the ECG remains one of the most important diagnostic tests in modern medicine. We now stand at the edge of true ECG innovation. Simultaneous advancements in computing power, wireless technology, digitized data availability, and machine learning have led to the birth of AI-ECG algorithms with novel capabilities and real potential for clinical application. AI has the potential to improve diagnostic accuracy and efficiency by providing fully automated, unbiased, and unambiguous ECG analysis along with promising new findings that may unlock new value in the ECG. These breakthroughs may cause a paradigm shift in clinical workflow as well as patient monitoring and management.