Management of Inherited Arrhythmia Syndromes: A HiRO Consensus Handbook on Process of Care

Serial Exercise Testing in Children With Known or Suspected Congenital and Acquired Heart Disease: A Narrative Review and Survey of Current Practice

BACKGROUND

Exercise parameters can be altered in children with congenital heart disease or acquired heart disease compared with children with normal hearts. Exercise testing has proven a useful tool to predict patient outcomes and even the need for reintervention in several cardiovascular disease processes. There are established guidelines for serial exercise stress testing in adults with congenital heart disease, but corollary guidelines do not exist for the pediatric population.

METHODS AND RESULTS

A narrative literature review was completed. Evidence was ranked by a 4-point scale as outlined by the American College of Sports Medicine evidence categories. A survey was sent to experts in pediatric exercise physiology across the country regarding their current testing practices for 26 unique congenital heart disease or known or suspected acquired heart disease lesions. Survey questions were related to the frequency of testing, the age at which exercise testing is started, and if the frequency of testing is altered by a patient presenting with symptoms. Our literature search yielded 122 relevant studies pertaining to exercise stress testing in pediatric heart disease. We received 59 responses to our survey from 33 unique institutions in the United States and Canada.

CONCLUSIONS

Twenty-one summaries were provided regarding exercise stress testing in pediatric patients with heart disease. Multicentered or national stress testing registries may allow for adequate sample sizes of rare pediatric diseases to allow for development of improved guidelines regarding the type and timing of stress testing.

The Diagnostic Utility of Holter Monitoring in Catecholaminergic Polymorphic Ventricular Tachycardia

BACKGROUND

Holter monitoring may raise suspicion of an underlying catecholaminergic polymorphic ventricular tachycardia (CPVT) diagnosis. Although not a primary investigation for CPVT, Holter monitoring is ubiquitously used as a diagnostic tool in the heart rhythm clinic.

OBJECTIVES

The objective of this study was to explore Holter monitoring in CPVT diagnosis.

METHODS

This retrospective cohort study analyzed off-therapy Holter monitoring from 13 ryanodine receptor 2-positive CPVT and 34 healthy patients from the Canadian Hearts in Rhythm Organization national registry. Using the Edwards method, the ratio of ambient-maximum heart rate during Holter monitoring was correlated with exertion level to separate premature ventricular contractions (PVCs) during periods of adrenergic and nonadrenergic stress. A receiver operating characteristic curve analysis determined the optimal threshold for isolating CPVT-induced PVCs during adrenergic states.

RESULTS

PVC burden differed between groups (P = 0.001) but was within population norm, suggesting ambient PVCs are uncommon in CPVT. CPVT patients had higher PVC counts than healthy controls (P = 0.002), with a different distribution based on adrenergic state. The optimal threshold for separating PVCs into periods of adrenergic and nonadrenergic stress in CPVT patients was 76% of the maximum heart rate during the monitoring period. Compared with healthy controls, CPVT patients had a higher PVC count, limited to periods of adrenergic stress, defined by >76% maximum heart rate threshold (P = 0.002; area under the receiver operating characteristic curve: 0.84). Below this threshold, there was no significant PVC difference (P = 0.604).

CONCLUSIONS

Holter monitor PVC counts alone are inadequate for CPVT diagnosis, owing to the adrenergic nature of the disease. Quantifying PVC prevalence at a heart rate threshold >76% identified CPVT with moderate sensitivity (69%) and high specificity (94%).

Cardiac channelopathies in pediatrics: a genetic update

Cardiac channelopathies are a group of inherited syndromes that can cause malignant arrhythmias and sudden cardiac death, particularly in the pediatric population. Today, a 12-lead electrocardiogram is the most effective tool to diagnose these diseases. Incomplete penetrance and variable expressivity are hallmarks of these syndromes. Some of these malignant entities may remain hidden and only a trigger such as exercise, emotions or fever can unmask the electrical pattern to diagnose the disease. Sudden cardiac death may be the first manifestation of any of these syndromes. The use of complementary tests that allow early diagnosis is strongly recommended, among which we find: pharmacological provocations, exercise tests, and genetic analysis. Genetic testing makes it possible to unravel the origin of the disease, and also identify family members who carry the harmful genetic defect and are therefore at risk. One of the main challenges in this area is the large number of genetic variants of uncertain significance, which prevent effective translation into clinical practice. Early identification of the pediatric population at risk and adequate risk stratification are crucial to adopting personalized preventive measures that reduce the risk of lethal episodes in this population. What is Known: • In the pediatric population, malignant arrhythmias leading to sudden cardiac death are mainly caused by inherited syndromes. • A conclusive genetic diagnosis unravels the origin of the syndrome and allows cascade screening to identify relatives carrying the genetic alteration. What is New: • The use of sequencing technologies allows a broad genetic analysis, helping to unravel new genetic alterations causing inherited arrhythmogenic syndromes. • A periodic reanalysis of genetic variants that currently have an ambiguous role will help discern those that are truly pathogenic.

Applying Artificial Intelligence for Phenotyping of Inherited Arrhythmia Syndromes

Inherited arrhythmia disorders account for a significant proportion of sudden cardiac death, particularly among young individuals. Recent advances in our understanding of these syndromes have improved patient diagnosis and care, yet certain clinical gaps remain, particularly within case ascertainment, access to genetic testing, and risk stratification. Artificial intelligence (AI), specifically machine learning and its subset deep learning, present promising solutions to these challenges. The capacity of AI to process vast amounts of patient data and identify disease patterns differentiates them from traditional methods, which are time- and resource-intensive. To date, AI models have shown immense potential in condition detection (including asymptomatic/concealed disease) and genotype and phenotype identification, exceeding expert cardiologists in these tasks. Additionally, they have exhibited applicability for general population screening, improving case ascertainment in a set of conditions that are often asymptomatic such as left ventricular dysfunction. Third, models have shown the ability to improve testing protocols; through model identification of disease and genotype, specific clinical testing (eg, drug challenges or further diagnostic imaging) can be avoided, reducing health care expenses, speeding diagnosis, and possibly allowing for more incremental or targeted genetic testing approaches. These significant benefits warrant continued investigation of AI, particularly regarding the development and implementation of clinically applicable screening tools. In this review we summarize key developments in AI, including studies in long QT syndrome, Brugada syndrome, hypertrophic cardiomyopathy, and arrhythmogenic cardiomyopathies, and provide direction for effective future AI implementation in clinical practice.