Análise do perfil clínico e eletrocardiográfico dos pacientes chagásicos assistidos pela Rede de Teleassistência de Minas Gerais

A Doença de Chagas (DC) é uma enfermidade endêmica causada pelo agente etiológico Trypanossoma cruzi. O curso clínico dessa doença se divide em 3 fases: aguda, intermediária e crônica. Nessa última fase, as principais manifestações se relacionam com a cardiomiopatia chagásica, levando a diversas alterações eletrocardiográficas que são diagnosticadas através da realização do eletrocardiograma (ECG). O objetivo deste presente estudo é analisar o perfil clínico e eletrocardiográfico dos pacientes portadores de Doença de Chagas (DC) atendidos na Rede de Teleassistência de Minas Gerais (RTMG). Trata-se de estudo observacional retrospectivo de pacientes maiores de 18 anos que se autorrelataram portadores de DC e que realizaram ECG pela RTMG no período de 2010 a 2017. Os ECGs com problemas técnicos foram excluídos. Foram realizadas uma análise descritiva e análise univariada através do teste qui quadrado de Pearson dos dados coletados. Diante dos resultados encontrados foi traçado um perfil de alterações cardíacas do portador crônico de DC que se manifesta através de duas alterações eletrocardiográficas principalmente, presença de onda Q patológica e sinais de bloqueio de ramo direito (BRD) e bloqueio de ramo esquerdo (BRE). O conhecimento desse estudo se faz necessário uma vez que essas alterações são causas importantes de morte súbita dos pacientes, advinda de arritmias. Além disso, os aspectos apontados neste estudo, contribuem para otimizar o tratamento, acompanhamento e diagnóstico precoce dos portadores de Doença de Chagas

Electrocardiographic findings and prognostic values in patients hospitalised with COVID-19 in the World Heart Federation Global Study

BACKGROUND COVID-19 affects the cardiovascular system and ECG abnormalities may be associated with worse prognosis. We evaluated the prognostic value of ECG abnormalities in individuals with COVID-19. METHODS Multicentre cohort study with adults hospitalised with COVID-19 from 40 hospitals across 23 countries. Patients were followed-up from admission until 30 days. ECG were obtained at each participating site and coded according to the Minnesota coding criteria. The primary outcome was defined as death from any cause. Secondary outcomes were admission to the intensive care unit (ICU) and major adverse cardiovascular events (MACE). Multiple logistic regression was used to evaluate the association of ECG abnormalities with the outcomes. RESULTS Among 5313 participants, 2451 had at least one ECG and were included in this analysis. The mean age (SD) was 58.0 (16.1) years, 60.7% were male and 61.1% from lower-income to middle-income countries. The prevalence of major ECG abnormalities was 21.3% (n=521), 447 (18.2%) patients died, 196 (8.0%) had MACE and 1115 (45.5%) were admitted to an ICU. After adjustment, the presence of any major ECG abnormality was associated with a higher risk of death (OR 1.39; 95% CI 1.09 to 1.78) and cardiovascular events (OR 1.81; 95% CI 1.30 to 2.51). Sinus tachycardia (>120 bpm) with an increased risk of death (OR 3.86; 95% CI 1.97 to 7.48), MACE (OR 2.68; 95% CI 1.10 to 5.85) and ICU admission OR 1.99; 95% CI 1.03 to 4.00). Atrial fibrillation, bundle branch block, ischaemic abnormalities and prolonged QT interval did not relate to the outcomes. CONCLUSION Major ECG abnormalities and a heart rate >120 bpm were prognostic markers in adults hospitalised with COVID-19

Deep neural network-estimated electrocardiographic age as a mortality predictor

The electrocardiogram (ECG) is the most commonly used exam for the evaluation of cardiovascular diseases. Here we propose that the age predicted by artificial intelligence (AI) from the raw ECG (ECG-age) can be a measure of cardiovascular health. A deep neural network is trained to predict a patient’s age from the 12-lead ECG in the CODE study cohort (n = 1,558,415 patients). On a 15% hold-out split, patients with ECG-age more than 8 years greater than the chronological age have a higher mortality rate (hazard ratio (HR) 1.79, p < 0.001), whereas those with ECG-age more than 8 years smaller, have a lower mortality rate (HR 0.78, p < 0.001). Similar results are obtained in the external cohorts ELSA-Brasil (n = 14,236) and SaMi-Trop (n = 1,631). Moreover, even for apparent normal ECGs, the predicted ECG-age gap from the chronological age remains a statistically significant risk predictor. These results show that the AI-enabled analysis of the ECG can add prognostic information

Associação entre Bloqueio Atrioventricular e Mortalidade em Pacientes de Atenção Primária: O Estudo CODE

Resumo Fundamento O bloqueio atrioventricular (BAV) descreve um comprometimento na condução dos átrios para os ventrículos. Embora o curso clínico do BAV tenha sido avaliado, os achados são de países de alta renda e, portanto, não podem ser extrapolados para a população latina. Objetivo Avaliar a associação entre BAV e mortalidade. Métodos Foram incluídos pacientes do estudo CODE (Clinical Outcomes in Digital Electrocardiology), maiores de 16 anos que realizaram eletrocardiograma (ECG) digital de 2010 a 2017. Os ECGs foram relatados por cardiologistas e por software automatizado. Para avaliar a relação entre BAV e mortalidade, foram utilizados o modelo log-normal e as curvas de Kaplan-Meier com valores de p bicaudais < 0,05 considerados estatisticamente significativos. Resultados O estudo incluiu 1.557.901 pacientes; 40,23% eram homens e a média de idade foi de 51,7 (DP ± 17,6) anos. Durante um seguimento médio de 3,7 anos, a mortalidade foi de 3,35%. A prevalência de BAV foi de 1,38% (21.538). Os pacientes com BAV de primeiro, segundo e terceiro graus foram associados a uma taxa de sobrevida 24% (taxa de sobrevida relativa [RS] = 0,76; intervalo de confiança [IC] de 95%: 0,71 a 0,81; p < 0,001), 55% (RS = 0,45; IC de 95%: 0,27 a 0,77; p = 0,01) e 64% (RS = 0,36; IC de 95%: 0,26 a 0,49; p < 0,001) menor quando comparados ao grupo controle, respectivamente. Os pacientes com BAV 2:1 tiveram 79% (RS = 0,21; IC de 95%: 0,08 a 0,52; p = 0,005) menor taxa de sobrevida do que o grupo controle. Apenas Mobitz tipo I não foi associado a maior mortalidade (p = 0,27). Conclusão BAV foi um fator de risco independente para mortalidade geral, com exceção do BAV Mobitz tipo I

Correlation of Electrocardiographic Changes with Cardiac Magnetic Resonance Findings in Patients with Hypertrophic Cardiomyopathy

Abstract Background: Electrocardiogram is the initial test in the investigation of heart disease. Electrocardiographic changes in hypertrophic cardiomyopathy have no set pattern, and correlates poorly with echocardiographic findings. Cardiac magnetic resonance imaging has been gaining momentum for better assessment of hypertrophy, as well as the detection of myocardial fibrosis. Objectives: To correlate the electrocardiographic changes with the location of hypertrophy in hypertrophic cardiomyopathy by cardiac magnetic resonance. Methods: This descriptive cross-sectional study evaluated 68 patients with confirmed diagnosis of hypertrophic cardiomyopathy by cardiac magnetic resonance. The patients’ electrocardiogram was compared with the location of the greatest myocardial hypertrophy by cardiac magnetic resonance. Statistical significance level of 5% and 95% confidence interval were adopted. Results: Of 68 patients, 69% had septal hypertrophy, 21% concentric and 10% apical hypertrophies. Concentric hypertrophy showed the greatest myocardial fibrosis mass (p < 0.001) and the greatest R wave size in D1 (p = 0.0280). The amplitudes of R waves in V5 and V6 (p = 0.0391, p = 0.0148) were higher in apical hypertrophy, with statistical significance. Apical hypertrophy was also associated with higher T wave negativity in D1, V5 and V6 (p < 0.001). Strain pattern was found in 100% of the patients with apical hypertrophy (p < 0.001). Conclusion: The location of myocardial hypertrophy by cardiac magnetic resonance can be correlated with electrocardiographic changes, especially for apical hypertrophy