Comparing the performance of published risk scores in Brugada syndrome: a multi-center cohort study

Electrophysiological patterns and structural substrates of Brugada syndrome: Critical appraisal and computational analyses

Brugada syndrome (BrS) is a cardiac electrophysiological disease with unknown etiology, associated with sudden cardiac death. Symptomatic patients are treated with implanted cardiac defibrillator, but no risk stratification strategy is effective in patients that are at low to medium arrhythmic risk. Cardiac computational modeling is an emerging tool that can be used to verify the hypotheses of pathogenesis and inspire new risk stratification strategies. However, to obtain reliable results computational models must be validated with consistent experimental data. We reviewed the main electrophysiological and structural variables from BrS clinical studies to assess which data could be used to validate a computational approach. Activation delay in the epicardial right ventricular outflow tract is a consistent finding, as well as increased fibrosis and subclinical alterations of right ventricular functional and morphological parameters. The comparison between other electrophysiological variables is hindered by methodological differences between studies, which we commented. We conclude by presenting a recent theory unifying electrophysiological and structural substrate in BrS and illustrate how computational modeling could help translation to risk stratification.

Association of Late Potentials With Fatal Arrhythmic Events in Patients With Brugada Syndrome-A Meta-analysis

Risk stratification of patients with Brugada syndrome (BrS) remains challenging. Signal-averaged electrocardiogram (SAECG) is a noninvasive tool that can be used to identify the electrophysiologic substrate potentially underlying fatal ventricular arrhythmias. The aim of this meta-analysis is to summarize the existing evidence about the role of late potentials (LP) as a predictor for arrhythmic events in patients with BrS. A systematic search in the MedLine database through to June 2022 without any limitations was performed. Ten studies were included in the quantitative synthesis (1431 patients with BrS, mean age 47.4 years, males 86%). Of these, 1220 patients underwent SAECG evaluation (53.2% had positive LP, and 20.6% had a fatal arrhythmic event). There was a nonsignificant association between positive LPs and fatal arrhythmic events [RR: 2.06 (0.98-4.36), P = 0.06, I 2 = 82%]. By including only studies with patients without a history of fatal arrhythmia, the association between LP with arrhythmic events remained nonsignificant [RR: 1.29 (0.67-2.48), P = 0.44, I 2 = 54%]. In conclusion, there is a possible association between LP and fatal arrhythmic events in patients with BrS, but the literature remains inconclusive. Large cohort studies using a multiparametric approach for risk stratification purposes are needed to improve the risk stratification of BrS and to optimize the selection of BrS patients that should be referred for implantable cardioverter-defibrillator.

Application of artificial intelligence in the diagnosis and treatment of cardiac arrhythmia

The rapid growth in computational power, sensor technology, and wearable devices has provided a solid foundation for all aspects of cardiac arrhythmia care. Artificial intelligence (AI) has been instrumental in bringing about significant changes in the prevention, risk assessment, diagnosis, and treatment of arrhythmia. This review examines the current state of AI in the diagnosis and treatment of atrial fibrillation, supraventricular arrhythmia, ventricular arrhythmia, hereditary channelopathies, and cardiac pacing. Furthermore, ChatGPT, which has gained attention recently, is addressed in this paper along with its potential applications in the field of arrhythmia. Additionally, the accuracy of arrhythmia diagnosis can be improved by identifying electrode misplacement or erroneous swapping of electrode position using AI. Remote monitoring has expanded greatly due to the emergence of contactless monitoring technology as wearable devices continue to develop and flourish. Parallel advances in AI computing power, ChatGPT, availability of large data sets, and more have greatly expanded applications in arrhythmia diagnosis, risk assessment, and treatment. More precise algorithms based on big data, personalized risk assessment, telemedicine and mobile health, smart hardware and wearables, and the exploration of rare or complex types of arrhythmia are the future direction.

Healthcare Big Data in Hong Kong: Development and Implementation of Artificial Intelligence-Enhanced Predictive Models for Risk Stratification

Routinely collected electronic health records (EHRs) data contain a vast amount of valuable information for conducting epidemiological studies. With the right tools, we can gain insights into disease processes and development, identify the best treatment and develop accurate models for predicting outcomes. Our recent systematic review has found that the number of big data studies from Hong Kong has rapidly increased since 2015, with an increasingly common application of artificial intelligence (AI). The advantages of big data are that i) the models developed are highly generalisable to the population, ii) multiple outcomes can be determined simultaneously, iii) ease of cross-validation by for model training, development and calibration, iv) huge numbers of useful variables can be analyzed, v) static and dynamic variables can be analyzed, vi) non-linear and latent interactions between variables can be captured, vii) artificial intelligence approaches can enhance the performance of prediction models. In this paper, we will provide several examples (cardiovascular disease, diabetes mellitus, Brugada syndrome, long QT syndrome) to illustrate efforts from a multi-disciplinary team to identify data from different modalities to develop models using territory-wide datasets, with the possibility of real-time risk updates by using new data captured from patients. The benefit is that only routinely collected data are required for developing highly accurate and high-performance models. AI-driven models outperform traditional models in terms of sensitivity, specificity, accuracy, area under the receiver operating characteristic and precision-recall curve, and F1 score. Web and/or mobile versions of the risk models allow clinicians to risk stratify patients quickly in clinical settings, thereby enabling clinical decision-making. Efforts are required to identify the best ways of implementing AI algorithms on the web and mobile apps.

Secular trends of health care resource utilization and costs between Brugada syndrome and congenital long QT syndrome: A territory-wide study

BACKGROUND

Health care resource utilization (HCRU) and costs are important metrics of health care burden, but they have rarely been explored in the setting of cardiac ion channelopathies.

HYPOTHESIS

This study tested the hypothesis that attendance-related HCRUs and costs differed between patients with Brugada syndrome (BrS) and congenital long QT syndrome (LQTS).

METHODS

This was a retrospective cohort study of consecutive BrS and LQTS patients at public hospitals or clinics in Hong Kong, China. HCRUs and costs (in USD) for Accident and Emergency (A&E), inpatient, general outpatient and specialist outpatient attendances were analyzed between 2001 and 2019 at the cohort level. Comparisons were made using incidence rate ratios (IRRs [95% confidence intervals]).

RESULTS

Over the 19-year period, 516 BrS (median age of initial presentation: 51 [interquartile range: 38-61] years, 92% male) and 134 LQTS (median age of initial presentation: 21 [9-44] years, 32% male) patients were included. Compared to LQTS patients, BrS patients had lower total costs (2 008 126 [2 007 622-2 008 629] vs. 2 343 864 [2 342 828-2 344 900]; IRR: 0.857 [0.855-0.858]), higher costs for A&E attendances (83 113 [83 048-83 177] vs. 70 604 [70 487-70 721]; IRR: 1.177 [1.165-1.189]) and general outpatient services (2,176 [2,166-2,187] vs. 921 [908-935]; IRR: 2.363 [2.187-2.552]), but lower costs for inpatient stay (1 391 624 [1 391 359-1 391 889] vs. 1 713 742 [1 713 166-1 714 319]; IRR: 0.812 [0.810-0.814]) and lower costs for specialist outpatient services (531 213 [531 049-531 376] vs. 558 597 [558268-558926]; IRR: 0.951 [0.947-0.9550]).

CONCLUSIONS

Overall, BrS patients consume 14% less health care resources compared to LQTS patients in terms of attendance costs. BrS patients require more A&E and general outpatient services, but less inpatient and specialist outpatient services than LQTS patients.

The Role of Electrophysiological Study in the Risk Stratification of Brugada Syndrome

Brugada syndrome (BrS) is a complex arrhythmogenic disease associated with an increased risk of sudden cardiac death (SCD). The role of electrophysiological study (EPS) for risk stratification purposes of asymptomatic BrS patients remains still controversial. This study aims to summarize the existing data about the role of electrophysiological study for arrhythmic risk stratification of BrS patients without a prior history of aborted SCD or fatal arrhythmic event. Two independent investigators (G.B. and G.T.) performed a systematic search in the MedLine database and Cochrane library from their inception until April 2022 without any limitations. The reference lists of the relevant research studies as well as the relevant review studies and meta-analyses were manually searched. Nineteen studies were included in the final analysis. The included studies enrolled 6218 BrS patients (mean age: 46.9 years old, males: 76%) while 4265 (68.6%) patients underwent an EPS. The quantitative synthesis showed that a positive EPS study was significantly associated with arrhythmic events in BrS patients (RR, 1.74 [1.23-2.45]; P = 0.002; I2 = 63%]. By including the studies that provided data on the association of EPS with arrhythmic events during follow-up in patients without a prior history of aborted SCD or fatal arrhythmic event, the association between positive EPS study and future arrhythmic events remained significant (RR, 1.60 [1.08-2.36]; P = 0.02; I2 = 19%). In conclusion, EPS is a useful invasive tool for the risk stratification of BrS patients and can be used to identify the population of BrS patients who may be candidates for primary prevention of SCD with implantable cardioverter-defibrillator (ICD) implantation.

Risk stratification of sudden cardiac death in asymptomatic female Brugada syndrome patients: A literature review

BACKGROUND AND OBJECTIVES

Risk stratification in Brugada syndrome remains a difficult problem. Given the male predominance of this disease and their elevated risks of arrhythmic events, affected females have received less attention. It is widely known that symptomatic patients are at increased risk of sudden cardiac death (SCD) than asymptomatic patients, while this might be true in the male population; recent studies have shown that this association might not be significant in females. Over the past few decades, numerous markers involving clinical symptoms, electrocardiographic (ECG) indices, and genetic tests have been explored, with several risk-scoring models developed so far. The objective of this study is to review the current evidence of clinical and ECG markers as well as risk scores on asymptomatic females with Brugada syndrome.

FINDINGS

Gender differences in ECG markers, the yield of genetic findings, and the applicability of risk scores are highlighted.

CONCLUSIONS

Various clinical, electrocardiographic, and genetic risk factors are available for assessing SCD risk amongst asymptomatic female BrS patients. However, due to the significant gender discrepancy in BrS, the SCD risk amongst females is often underestimated, and there is a lack of research on female-specific risk factors and multiparametric risk scores. Therefore, multinational studies pooling female BrS patients are needed for the development of a gender-specific risk stratification approach amongst asymptomatic BrS patients.

Attendance-related Healthcare Resource Utilisation and Costs in Patients With Brugada Syndrome in Hong Kong: A Retrospective Cohort Study

Understanding health care resource utilisation and its associated costs are important for identifying areas of improvement regarding resource allocations. However, there is limited research exploring this issue in the setting of Brugada syndrome (BrS).This was a retrospective territory-wide study of BrS patients from Hong Kong. Healthcare resource utilisation for accident and emergency (A&E), inpatient and specialist outpatient attendances were analyzed over a 19-year period, with their associated costs presented in US dollars. A total of 507 BrS patients with a mean presentation age of 49.9 ± 16.3 years old were included. Of these, 384 patients displayed spontaneous type 1 electrocardiographic (ECG) Brugada pattern and 77 patients had presented with ventricular tachycardia/ventricular fibrillation (VT/VF). At the individual patient level, the median annualized costs were $110 (52-224) at the (A&E) setting, $6812 (1982-32414) at the inpatient setting and $557 (326-1001) for specialist outpatient attendances. Patients with initial VT/VF presentation had overall greater costs in inpatient ($20161 [9147-189215] vs $5290 [1613-24937],P < 0.0001) and specialist outpatient setting ($776 [438-1076] vs $542 [293-972],P = 0.015) compared to those who did not present VT. In addition, patients without Type 1 ECG pattern had greater median costs in the specialist outpatient setting ($7036 [3136-14378] vs $4895 [2409-10554],p=0.019). There is a greater health care demand in the inpatient and specialist outpatient settings for BrS patients. The most expensive attendance type was inpatient setting stay at $6812 per year. The total median annualized cost of BrS patients without VT/VF presentation was 78% lower compared to patients with VT/VF presentation.