Isabella Moreira Gonzalez Fonseca

Introduction: Gain‐of‐function variants in SCN5A may cause multifocal ectopic Purkinje‐related premature contractions (MEPPC), with a high premature ventricular contraction burden that may lead to PVC‐induced cardiomyopathy. Pharmacological therapy is usually required, as catheter ablation is ineffective because of the multifocal origin of the ectopy. Methods and Results: We report two siblings with the pathogenic SCN5A p.Arg222Gln variant. In both, ranolazine markedly suppressed ventricular ectopy, improved or preserved left ventricular function, and enabled amiodarone withdrawal. Conclusion: Ranolazine may represent a useful mechanism‐guided option for SCN5A‐related MEPPC.

Background: Chagas disease affects millions worldwide and remains a leading cause of cardiomyopathy in Latin America. Early diagnosis remains challenging in endemic regions. Artificial intelligence (AI)-based electrocardiography (ECG) analysis may offer a low-cost strategy for large-scale screening in resource-limited settings. Objectives: To evaluate the performance of an AI-ECG algorithm combined with clinical data for detecting Chagas disease in a community-based screening program conducted in a highly endemic region in Northeastern Brazil. Methods: In August 2024, 1,115 adults underwent standardized 12-lead ECG acquisition during a field campaign in Feira de Santana, Bahia, Northeast Brazil. A previously trained AI model analyzed ECG tracings and incorporated three clinical variables: i) prior residence in triatomine-infested areas, ii) poor housing conditions, and iii) family history of Chagas disease. Individuals flagged as AI-positive were classified as suspected cases. Suspected cases and matched controls (2:1) underwent point-of-care serological testing. Results: The algorithm flagged 121 individuals (10.9%), corresponding to an estimated AI-based prevalence of Chagas disease of 7.8% (95%CI: 6.2-9.6). Among the 112 individuals who completed serological testing, 13 tested positive, all within the AI-positive suspected group; no seropositive cases were identified among controls. Sensitivity was 100% (95%CI: 69-100), specificity 40% (95%CI: 30-50), negative predictive value 100% (95%CI: 91-100), and positive predictive value 12% (95%CI: 11-14), with a diagnostic odds ratio of 6.6. Conclusions: An AI-ECG algorithm combined with simple clinical variables demonstrated excellent sensitivity for the detection of Chagas disease and may represent a valuable triage tool in endemic, resource-constrained settings.

Resumo Fundamento: A doença de Chagas afeta milhões de pessoas em todo o mundo e permanece como uma das principais causas de cardiomiopatia na América Latina. O diagnóstico precoce ainda representa um desafio em regiões endêmicas. A análise de eletrocardiografia (ECG) aprimorada por inteligência artificial (IA) pode oferecer uma estratégia de baixo custo para rastreamento em larga escala em contextos com recursos limitados. Objetivos: Avaliar o desempenho de um algoritmo de ECG aprimorada por IA combinado com dados clínicos para a detecção da doença de Chagas em um programa de rastreamento comunitário realizado em uma região altamente endêmica do Nordeste do Brasil. Métodos: Em agosto de 2024, 1.115 adultos foram submetidos à aquisição padronizada de ECG de 12 derivações durante uma campanha de campo em Feira de Santana, Bahia, nordeste do Brasil. Um modelo de IA previamente treinado analisou os traçados de ECG e incorporou três variáveis clínicas: i) residência prévia em áreas infestadas por triatomíneos, ii) condições habitacionais precárias e iii) histórico familiar de doença de Chagas. Indivíduos identificados como positivos pela IA foram classificados como casos suspeitos. Casos suspeitos e controles pareados (2:1) foram submetidos a testes sorológicos no ponto de cuidado. Resultados: O algoritmo identificou 121 indivíduos (10,9%), correspondendo a uma prevalência estimada de doença de Chagas baseada em IA de 7,8% (intervalo de confiança de 95% [IC95%]: 6,2-9,6). Entre os 112 indivíduos que completaram o teste sorológico, 13 apresentaram resultado positivo, todos pertencentes ao grupo suspeito positivo pela IA; nenhum caso soropositivo foi identificado entre os controles. A sensibilidade foi de 100% (IC95%: 69-100), a especificidade de 40% (IC95%: 30-50), o valor preditivo negativo de 100% (IC95%: 91-100) e o valor preditivo positivo de 12% (IC95%: 11-14), com uma razão de chances diagnóstica de 6,6. Conclusões: Um algoritmo de ECG aprimorada por IA combinado com variáveis clínicas simples demonstrou excelente sensibilidade para a detecção da doença de Chagas e pode representar uma ferramenta valiosa de triagem em contextos endêmicos com recursos limitados.

Background: Rheumatic heart disease (RHD) is the leading cause of cardiac death in children, with over 300,000 annual fatalities. Immunological, genetic, and environmental factors contribute to an increased risk of RHD. It remains unclear whether first-degree relatives have a higher RHD prevalence than the regional average. Methods: We performed a systematic review and meta-analysis of echocardiographic screening studies reporting the prevalence of RHD in family members of individuals with RHD or acute rheumatic fever. PubMed, Embase, Cochrane, and Lilacs databases were searched. Finding Forrester RHD was classified as per the 2012 World Heart Federation criteria. Random-effects models assessed definite RHD prevalence in study groups. Results: Four of the 1160 studies were included, with 776 first-degree relatives screened. Two studies were from Africa, one from South America, and one from Oceania. In the first-degree relatives of index cases, the prevalence of RHD was 7% (95% confidence interval [CI] 3.7-13). The control group, individuals screened with no known RHD cases in their family, had a lower prevalence than first-degree relatives (risk ratio [RR] 0.44, 95% CI 0.26-0.75). There was no difference in the prevalence of RHD among siblings and parents of cases. Conclusion: RHD prevalence in first-degree relatives of index cases was more significant than the There is an overall prevalence of non-relatives from the respective region, which suggests that genetic predisposition may play a role. In future studies of RHD, the systematic screening of first-degree relatives should be considered with a better control group—socioeconomic, region, age, and sex-matched.

Acute rheumatic fever (ARF) is a complex disease with several clinical manifestations. Its most significant long-term complication is valvular heart damage, commonly referred to as chronic rheumatic heart disease. The risk of ARF varies globally, with over 80% of cases occurring in low- and middle-income countries, highlighting the role of socioeconomic factors. A comprehensive understanding of the risk factors associated with ARF and its clinical, genetic, and sociodemographic mediators can help clinicians identify high-risk individuals, develop effective management strategies, enhance target screening and active case-finding initiatives, and ultimately improve patient outcomes. This review aimed to provide an overview of ARF and its global burden, focusing on the established and potential risk factors associated with its development.