Cardiac involvement in Chagas disease and African trypanosomiasis.

Trypanosomiases are diseases caused by various species of protozoan parasite in the genus Trypanosoma, each presenting with distinct clinical manifestations and prognoses. Infections can affect multiple organs, with Trypanosoma cruzi predominantly affecting the heart and digestive system, leading to American trypanosomiasis or Chagas disease, and Trypanosoma brucei primarily causing a disease of the central nervous system known as human African trypanosomiasis or sleeping sickness. In this Review, we discuss the effects of these infections on the heart, with particular emphasis on Chagas disease, which continues to be a leading cause of cardiomyopathy in Latin America. The epidemiology of Chagas disease has changed substantially since 1990 owing to the emigration of over 30 million Latin American citizens, primarily to Europe and the USA. This movement of people has led to the global dissemination of individuals infected with T. cruzi. Therefore, cardiologists worldwide must familiarize themselves with Chagas disease and the severe, chronic manifestation - Chagas cardiomyopathy - because of the expanded prevalence of this disease beyond traditional endemic regions.

Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease.

The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI-ECG model to predict body mass index (BMI) from the ECG alone. Developed from 512,950 12-lead ECGs from the Beth Israel Deaconess Medical Center (BIDMC), a secondary care cohort, and validated on UK Biobank (UKB) (n = 42,386), the model achieved a Pearson correlation coefficient (r) of 0.65 and 0.62, and an R2 of 0.43 and 0.39 in the BIDMC cohort and UK Biobank, respectively for AI-ECG BMI vs. measured BMI. We found delta-BMI, the difference between measured BMI and AI-ECG-predicted BMI (AI-ECG-BMI), to be a biomarker of cardiometabolic health. The top tertile of delta-BMI showed increased risk of future cardiometabolic disease (BIDMC: HR 1.15, p < 0.001; UKB: HR 1.58, p < 0.001) and diabetes mellitus (BIDMC: HR 1.25, p < 0.001; UKB: HR 2.28, p < 0.001) after adjusting for covariates including measured BMI. Significant enhancements in model fit, reclassification and improvements in discriminatory power were observed with the inclusion of delta-BMI in both cohorts. Phenotypic profiling highlighted associations between delta-BMI and cardiometabolic diseases, anthropometric measures of truncal obesity, and pericardial fat mass. Metabolic and proteomic profiling associates delta-BMI positively with valine, lipids in small HDL, syntaxin-3, and carnosine dipeptidase 1, and inversely with glutamine, glycine, colipase, and adiponectin. A genome-wide association study revealed associations with regulators of cardiovascular/metabolic traits, including SCN10A, SCN5A, EXOG and RXRG. In summary, our AI-ECG-BMI model accurately predicts BMI and introduces delta-BMI as a non-invasive biomarker for cardiometabolic risk stratification.

Using Artificial Intelligence to Predict Heart Failure Risk from Single-lead Electrocardiographic Signals: A Multinational Assessment.

Importance: Despite the availability of disease-modifying therapies, scalable strategies for heart failure (HF) risk stratification remain elusive. Portable devices capable of recording single-lead electrocardiograms (ECGs) can enable large-scale community-based risk assessment. Objective: To evaluate an artificial intelligence (AI) algorithm to predict HF risk from noisy single-lead ECGs. Design: Multicohort study. Setting: Retrospective cohort of individuals with outpatient ECGs in the integrated Yale New Haven Health System (YNHHS) and prospective population-based cohorts of UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Participants: Individuals without HF at baseline. Exposures: AI-ECG-defined risk of left ventricular systolic dysfunction (LVSD). Main Outcomes and Measures: Among individuals with ECGs, we isolated lead I ECGs and deployed a noise-adapted AI-ECG model trained to identify LVSD. We evaluated the association of the model probability with new-onset HF, defined as the first HF hospitalization. We compared the discrimination of AI-ECG against the pooled cohort equations to prevent HF (PCP-HF) score for new-onset HF using Harrel's C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI). Results: There were 194,340 YNHHS patients (age 56 years [IQR, 41-69], 112,082 women [58%]), 42,741 UKB participants (65 years [59-71], 21,795 women [52%]), and 13,454 ELSA-Brasil participants (56 years [41-69], 7,348 women [55%]) with baseline ECGs. A total of 3,929 developed HF in YNHHS over 4.5 years (2.6-6.6), 46 in UKB over 3.1 years (2.1-4.5), and 31 in ELSA-Brasil over 4.2 years (3.7-4.5). A positive AI-ECG screen was associated with a 3- to 7-fold higher risk for HF, and each 0.1 increment in the model probability portended a 27-65% higher hazard across cohorts, independent of age, sex, comorbidities, and competing risk of death. AI-ECG's discrimination for new-onset HF was 0.725 in YNHHS, 0.792 in UKB, and 0.833 in ELSA-Brasil. Across cohorts, incorporating AI-ECG predictions in addition to PCP-HF resulted in improved Harrel's C-statistic (Δ=0.112-0.114), with an IDI of 0.078-0.238 and an NRI of 20.1%-48.8% for AI-ECG vs. PCP-HF. Conclusions and Relevance: Across multinational cohorts, a noise-adapted AI model with lead I ECGs as the sole input defined HF risk, representing a scalable portable and wearable device-based HF risk-stratification strategy.

Performance of contemporary cardiovascular risk stratification scores in Brazil: an evaluation in the ELSA-Brasil study.

AIMS: Despite notable population differences in high-income and low- and middle-income countries (LMICs), national guidelines in LMICs often recommend using US-based cardiovascular disease (CVD) risk scores for treatment decisions. We examined the performance of widely used international CVD risk scores within the largest Brazilian community-based cohort study (Brazilian Longitudinal Study of Adult Health, ELSA-Brasil). METHODS: All adults 40-75 years from ELSA-Brasil (2008-2013) without prior CVD who were followed for incident, adjudicated CVD events (fatal and non-fatal MI, stroke, or coronary heart disease death). We evaluated 5 scores-Framingham General Risk (FGR), Pooled Cohort Equations (PCEs), WHO CVD score, Globorisk-LAC and the Systematic Coronary Risk Evaluation 2 score (SCORE-2). We assessed their discrimination using the area under the receiver operating characteristic curve (AUC) and calibration with predicted-to-observed risk (P/O) ratios-overall and by sex/race groups. RESULTS: There were 12 155 individuals (53.0±8.2 years, 55.3% female) who suffered 149 incident CVD events. All scores had a model AUC>0.7 overall and for most age/sex groups, except for white women, where AUC was <0.6 for all scores, with higher overestimation in this subgroup. All risk scores overestimated CVD risk with 32%-170% overestimation across scores. PCE and FGR had the highest overestimation (P/O ratio: 2.74 (95% CI 2.42 to 3.06)) and 2.61 (95% CI 1.79 to 3.43)) and the recalibrated WHO score had the best calibration (P/O ratio: 1.32 (95% CI 1.12 to 1.48)). CONCLUSION: In a large prospective cohort from Brazil, we found that widely accepted CVD risk scores overestimate risk by over twofold, and have poor risk discrimination particularly among Brazilian women. Our work highlights the value of risk stratification strategies tailored to the unique populations and risks of LMICs.

Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification.

Aims: Electrocardiogram (ECG) is widely considered the primary test for evaluating cardiovascular diseases. However, the use of artificial intelligence (AI) to advance these medical practices and learn new clinical insights from ECGs remains largely unexplored. We hypothesize that AI models with a specific design can provide fine-grained interpretation of ECGs to advance cardiovascular diagnosis, stratify mortality risks, and identify new clinically useful information. Methods and results: Utilizing a data set of 2 322 513 ECGs collected from 1 558 772 patients with 7 years follow-up, we developed a deep-learning model with state-of-the-art granularity for the interpretable diagnosis of cardiac abnormalities, gender identification, and hypertension screening solely from ECGs, which are then used to stratify the risk of mortality. The model achieved the area under the receiver operating characteristic curve (AUC) scores of 0.998 (95% confidence interval (CI), 0.995-0.999), 0.964 (95% CI, 0.963-0.965), and 0.839 (95% CI, 0.837-0.841) for the three diagnostic tasks separately. Using ECG-predicted results, we find high risks of mortality for subjects with sinus tachycardia (adjusted hazard ratio (HR) of 2.24, 1.96-2.57), and atrial fibrillation (adjusted HR of 2.22, 1.99-2.48). We further use salient morphologies produced by the deep-learning model to identify key ECG leads that achieved similar performance for the three diagnoses, and we find that the V1 ECG lead is important for hypertension screening and mortality risk stratification of hypertensive cohorts, with an AUC of 0.816 (0.814-0.818) and a univariate HR of 1.70 (1.61-1.79) for the two tasks separately. Conclusion: Using ECGs alone, our developed model showed cardiologist-level accuracy in interpretable cardiac diagnosis and the advancement in mortality risk stratification. In addition, it demonstrated the potential to facilitate clinical knowledge discovery for gender and hypertension detection which are not readily available.

Combination of Tele-Cardiology Tools for Cardiovascular Risk Stratification in Primary Care: Data from the PROVAR+ Study. / Combinação de Ferramentas de Telecardiologia para Estratificação de Risco Cardiovascular na Atenção Primária: Dados do Estudo PROVAR.

BACKGROUND: Tele-cardiology tools are valuable strategies to improve risk stratification. OBJECTIVE: We aimed to evaluate the accuracy of tele-electrocardiography (ECG) to predict abnormalities in screening echocardiography (echo) in primary care (PC). METHODS: In 17 months, 6 health providers at 16 PC units were trained on simplified handheld echo protocols. Tele-ECGs were recorded for final diagnosis by a cardiologist. Consented patients with major ECG abnormalities by the Minnesota code, and a 1:5 sample of normal individuals underwent clinical questionnaire and screening echo interpreted remotely. Major heart disease was defined as moderate/severe valve disease, ventricular dysfunction/hypertrophy, pericardial effusion, or wall-motion abnormalities. Association between major ECG and echo abnormalities was assessed by logistic regression as follows: 1) unadjusted model; 2) model 1 adjusted for age/sex; 3) model 2 plus risk factors (hypertension/diabetes); 4) model 3 plus history of cardiovascular disease (Chagas/rheumatic heart disease/ischemic heart disease/stroke/heart failure). P-values < 0.05 were considered significant. RESULTS: A total 1,411 patients underwent echo; 1,149 (81%) had major ECG abnormalities. Median age was 67 (IQR 60 to 74) years, and 51.4% were male. Major ECG abnormalities were associated with a 2.4-fold chance of major heart disease on echo in bivariate analysis (OR = 2.42 [95% CI 1.76 to 3.39]), and remained significant after adjustments in models (p < 0.001) 2 (OR = 2.57 [95% CI 1.84 to 3.65]), model 3 (OR = 2.52 [95% CI 1.80 to3.58]), and model 4 (OR = 2.23 [95%CI 1.59 to 3.19]). Age, male sex, heart failure, and ischemic heart disease were also independent predictors of major heart disease on echo. CONCLUSIONS: Tele-ECG abnormalities increased the likelihood of major heart disease on screening echo, even after adjustments for demographic and clinical variables.

FUNDAMENTO: As ferramentas de telecardiologia são estratégias valiosas para melhorar a estratificação de risco. OBJETIVO: Objetivamos avaliar a acurácia da tele-eletrocardiografia (ECG) para predizer anormalidades no ecocardiograma de rastreamento na atenção primária. MÉTODOS: Em 17 meses, 6 profissionais de saúde em 16 unidades de atenção primária foram treinados em protocolos simplificados de ecocardiografia portátil. Tele-ECGs foram registrados para diagnóstico final por um cardiologista. Pacientes consentidos com anormalidades maiores no ECG pelo código de Minnesota e uma amostra 1:5 de indivíduos normais foram submetidos a um questionário clínico e ecocardiograma de rastreamento interpretado remotamente. A doença cardíaca grave foi definida como doença valvular moderada/grave, disfunção/hipertrofia ventricular, derrame pericárdico ou anormalidade da motilidade. A associação entre alterações maiores do ECG e anormalidades ecocardiográficas foi avaliada por regressão logística da seguinte forma: 1) modelo não ajustado; 2) modelo 1 ajustado por idade/sexo; 3) modelo 2 mais fatores de risco (hipertensão/diabetes); 4) modelo 3 mais história de doença cardiovascular (Chagas/cardiopatia reumática/cardiopatia isquêmica/AVC/insuficiência cardíaca). Foram considerados significativos valores de p < 0,05. RESULTADOS: No total, 1.411 pacientes realizaram ecocardiograma, sendo 1.149 (81%) com anormalidades maiores no ECG. A idade mediana foi de 67 anos (intervalo interquartil de 60 a 74) e 51,4% eram do sexo masculino. As anormalidades maiores no ECG se associaram a uma chance 2,4 vezes maior de doença cardíaca grave no ecocardiograma de rastreamento na análise bivariada (OR = 2,42 [IC 95% 1,76 a 3,39]) e permaneceram significativas (p < 0,001) após ajustes no modelo 2 (OR = 2,57 [IC 95% 1,84 a 3,65]), modelo 3 (OR = 2,52 [IC 95% 1,80 a 3,58]) e modelo 4 (OR = 2,23 [IC 95% 1,59 a 3,19]). Idade, sexo masculino, insuficiência cardíaca e doença cardíaca isquêmica também foram preditores independentes de doença cardíaca grave no ecocardiograma. CONCLUSÕES: As anormalidades do tele-ECG aumentaram a probabilidade de doença cardíaca grave no ecocardiograma de rastreamento, mesmo após ajustes para variáveis demográficas e clínicas.

Scalable Risk Stratification for Heart Failure Using Artificial Intelligence applied to 12-lead Electrocardiographic Images: A Multinational Study.

Background: Current risk stratification strategies for heart failure (HF) risk require either specific blood-based biomarkers or comprehensive clinical evaluation. In this study, we evaluated the use of artificial intelligence (AI) applied to images of electrocardiograms (ECGs) to predict HF risk. Methods: Across multinational longitudinal cohorts in the integrated Yale New Haven Health System (YNHHS) and in population-based UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), we identified individuals without HF at baseline. Incident HF was defined based on the first occurrence of an HF hospitalization. We evaluated an AI-ECG model that defines the cross-sectional probability of left ventricular dysfunction from a single image of a 12-lead ECG and its association with incident HF. We accounted for the competing risk of death using the Fine-Gray subdistribution model and evaluated the discrimination using Harrel's c-statistic. The pooled cohort equations to prevent HF (PCP-HF) were used as a comparator for estimating incident HF risk. Results: Among 231,285 individuals at YNHHS, 4472 had a primary HF hospitalization over 4.5 years (IQR 2.5-6.6) of follow-up. In UKB and ELSA-Brasil, among 42,741 and 13,454 people, 46 and 31 developed HF over a follow-up of 3.1 (2.1-4.5) and 4.2 (3.7-4.5) years, respectively. A positive AI-ECG screen portended a 4-fold higher risk of incident HF among YNHHS patients (age-, sex-adjusted HR [aHR] 3.88 [95% CI, 3.63-4.14]). In UKB and ELSA-Brasil, a positive-screen ECG portended 13- and 24-fold higher hazard of incident HF, respectively (aHR: UKBB, 12.85 [6.87-24.02]; ELSA-Brasil, 23.50 [11.09-49.81]). The association was consistent after accounting for comorbidities and the competing risk of death. Higher model output probabilities were progressively associated with a higher risk for HF. The model's discrimination for incident HF was 0.718 in YNHHS, 0.769 in UKB, and 0.810 in ELSA-Brasil. Across cohorts, incorporating model probability with PCP-HF yielded a significant improvement in discrimination over PCP-HF alone. Conclusions: An AI model applied to images of 12-lead ECGs can identify those at elevated risk of HF across multinational cohorts. As a digital biomarker of HF risk that requires just an ECG image, this AI-ECG approach can enable scalable and efficient screening for HF risk.

Agreement between Handheld and Standard Echocardiography for Diagnosis of Latent Rheumatic Heart Disease in Brazilian Schoolchildren from High-Prevalence Settings (Agreement between Screening and Standard Echo for RHD).

Introduction: Handheld echocardiography (echo) is the tool of choice for rheumatic heart disease (RHD) screening. We aimed to assess the agreement between screening and standard echo for latent RHD diagnosis in schoolchildren from an endemic setting. Methods: Over 14 months, 3 nonphysicians used handheld machines and the 2012 WHF Criteria to determine RHD prevalence in consented schoolchildren from Brazilian low-income public schools. Studies were interpreted by telemedicine by 3 experts (Brazil, US). RHD-positive children (borderline/definite) and those with congenital heart disease (CHD) were referred for standard echo, acquired and interpreted by a cardiologist. Agreement between screening and standard echo, by WHF subgroups, was assessed. Results: 1390 students were screened in 6 schools, with 110 (7.9%, 95% CI 6.5-9.5) being screen positive (14 ± 2 years, 72% women). Among 16 cases initially diagnosed as definite RHD, 11 (69%) were confirmed, 4 (25%) reclassified to borderline, and 1 to normal. Among 79 cases flagged as borderline RHD, 19 (24%) were confirmed, 50 (63%) reclassified to normal, 8 (10%) reclassified as definite RHD, and 2 had mild CHD. Considering the 4 diagnostic categories, kappa was 0.18. In patients with borderline RHD reclassified to non-RHD, the most frequent WHF criterion was B (isolated mitral regurgitation, 64%), followed by A (2 mitral valve morphological features, 31%). In 1 patient with definite RHD reclassified to normal, the WHF criterion was D (borderline RHD in aortic and mitral valves). After standard echo, RHD prevalence was 3.2% (95% CI 2.3-4.2). Conclusions: Although practical, RHD screening with handheld devices tends to overestimate prevalence.

Evaluation of patients with implantable cardioverter-defibrillator in a Latin American tertiary center.

INTRODUCTION: Despite advancements in implantable cardioverter-defibrillator (ICD) technology, sudden cardiac death (SCD) remains a persistent public health concern. Chagas disease (ChD), prevalent in Brazil, is associated with increased ventricular tachycardia (VT) and ventricular fibrillation (VF) events and SCD compared to other cardiomyopathies. METHODS: This retrospective observational study included patients who received ICDs between October 2007 and December 2018. The study aims to assess whether mortality and VT/VF events decreased in patients who received ICDs during different time periods (2007-2010, 2011-2014, and 2015-2018). Additionally, it seeks to compare the prognosis of ChD patients with non-ChD patients. Time periods were chosen based on the establishment of the Arrhythmia Service in 2011. The primary outcome was overall mortality, assessed across the entire sample and the three periods. Secondary outcomes included VT/VF events and the combined outcome of death or VT/VF. RESULTS: Of the 885 patients included, 31% had ChD. Among them, 28% died, 14% had VT/VF events, and 37% experienced death and/or VT/VF. Analysis revealed that period 3 (2015-2018) was associated with better death-free survival (p = .007). ChD was the only variable associated with a higher rate of VT/VF events (p < .001) and the combined outcome (p = .009). CONCLUSION: Mortality and combined outcome rates decreased gradually for ICD patients during the periods 2011-2014 and 2015-2018 compared to the initial period (2007-2010). ChD was associated with higher VT/VF events in ICD patients, only in the first two periods.

Efficacy of three benznidazole dosing strategies for adults living with chronic Chagas disease (MULTIBENZ): an international, randomised, double-blind, phase 2b trial.

BACKGROUND: Treatment with benznidazole for chronic Chagas disease is associated with low cure rates and substantial toxicity. We aimed to compare the parasitological efficacy and safety of 3 different benznidazole regimens in adult patients with chronic Chagas disease. METHODS: The MULTIBENZ trial was an international, randomised, double-blind, phase 2b trial performed in Argentina, Brazil, Colombia, and Spain. We included participants aged 18 years and older diagnosed with Chagas disease with two different serological tests and detectable T cruzi DNA by qPCR in blood. Previously treated people, pregnant women, and people with severe cardiac forms were excluded. Participants were randomly assigned 1:1:1, using a balanced block randomisation scheme stratified by country, to receive benznidazole at three different doses: 300 mg/day for 60 days (control group), 150 mg/day for 60 days (low dose group), or 400 mg/day for 15 days (short treatment group). The primary outcome was the proportion of patients with a sustained parasitological negativity by qPCR during a follow-up period of 12 months. The primary safety outcome was the proportion of people who permanently discontinued the treatment. Both primary efficacy analysis and primary safety analysis were done in the intention-to-treat population. The trial is registered with EudraCT, 2016-003789-21, and ClinicalTrials.gov, NCT03191162, and is completed. FINDINGS: From April 20, 2017, to Sept 20, 2020, 245 people were enrolled, and 234 were randomly assigned: 78 to the control group, 77 to the low dose group, and 79 to the short treatment group. Sustained parasitological negativity was observed in 42 (54%) of 78 participants in the control group, 47 (61%) of 77 in the low dose group, and 46 (58%) of 79 in the short treatment group. Odds ratios were 1·41 (95% CI 0·69-2·88; p=0·34) when comparing the low dose and control groups and 1·23 (0·61-2·50; p=0·55) when comparing short treatment and control groups. 177 participants (76%) had an adverse event: 62 (79%) in the control group, 56 (73%) in the low dose group, and 59 (77%) in the short treatment group. However, discontinuations were less frequent in the short treatment group compared with the control group (2 [2%] vs 11 [14%]; OR 0·20, 95% CI 0·04-0·95; p=0·044). INTERPRETATION: Participants had a similar parasitological responses. However, reducing the usual treatment from 8 weeks to 2 weeks might maintain the same response while facilitating adherence and increasing treatment coverage. These findings should be confirmed in a phase 3 clinical trial. FUNDING: European Community's 7th Framework Programme.

Gastrointestinal Manifestations of Chagas Disease: A Systematic Review with Meta-Analysis.

The aims of this study were to estimate the prevalence of gastrointestinal manifestations among individuals with positive serology for Chagas disease (ChD) and to describe the clinical gastrointestinal manifestations of the disease. A systematic review with meta-analysis was conducted based on the criteria and recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. The PubMed, Scopus, Virtual Health Library, Web of Science, and Embase databases were used to search for evidence. Two reviewers independently selected eligible articles and extracted data. RStudio® software was used for the meta-analysis. For subgroup analysis, the studies were divided according to the origin of the individuals included: 1) individuals from health units were included in the health care service prevalence analysis, and 2) individuals from the general population were included in the population prevalence analysis. A total of 2,570 articles were identified, but after removal of duplicates and application of inclusion criteria, 24 articles were included and 21 were part of the meta-analysis. Most of the studies were conducted in Brazil. Radiological diagnosis was the most frequent method used to identify the gastrointestinal clinical form. The combined effect of meta-analysis studies showed a prevalence of gastrointestinal manifestations in individuals with ChD of 12% (95% CI, 8.0-17.0%). In subgroup analysis, the prevalence for studies involving health care services was 16% (95% CI, 11.0-23.0%), while the prevalence for population-based studies was 9% (95% CI, 5.0-15.0%). Megaesophagus and megacolon were the main forms of ChD presentation in the gastrointestinal form. The prevalence of gastrointestinal manifestations of ChD was 12%. Knowing the prevalence of ChD in its gastrointestinal form is an important step in planning health actions for these patients.

Adherence to the Dietary Approaches to Stop Hypertension (DASH) and Serum Urate Concentrations: A Longitudinal Analysis from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil).

BACKGROUND: Increased serum urate (SU) and hyperuricemia (HU) are associated with chronic noncommunicable diseases and mortality. SU concentrations are affected by several factors, including diet, and are expected to rise with age. We investigated whether the Dietary Approaches to Stop Hypertension (DASH) diet alter this trend. OBJECTIVE: The objective was to assess whether adherence to the DASH diet predicts a longitudinal change in SU concentrations and risk of HU in 8 y of follow-up. METHODS: Longitudinal analyses using baseline (2008-2010, aged 35-74 y), second (2012-2014), and third (2016-2018) visits data from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). The inclusion criteria were having complete food frequency questionnaire (FFQ) and urinary sodium measurement, in addition to having SU measurement at the 1st visit and at least 1 of the 2 follow-up visits. For the HU incidence analyses, participants had also to be free from HU at baseline. The final samples included 12575 individuals for the SU change analyses and 10549 for the HU incidence analyses. Adherence to DASH diet was assessed as continuous value. HU was defined as SU>6.8 mg/dL and/or urate-lowering therapy use. Mixed-effect linear and Poisson regressions (incidence rate ratio [IRR] and 95% confidence interval [CI]) were used in the analyses, adjusted for confounders. RESULTS: The mean age was 51.4 (8.7) y, and 55.4% were females. SU means (standard deviation) were 5.4 (1.4) at 1st visit, 5.2 (1.4) at 2nd visit, and 5.1(1.3) mg/dL at 3rd visit. The HU incidence rate was 8.87 per 1000 person-y. Each additional point in adherence to the DASH diet accelerated SU decline (P< 0.01) and lowered the incidence of HU by 4.3% (IRR: 0.957; 95% CI: 0.938,0.977) in adjusted model. CONCLUSION: The present study findings reinforce the importance of encouraging the DASH diet as a healthy dietary pattern to control and reduce the SU concentrations and risk of HU.

Vagal dysautonomia in patients with Chagas disease and mortality: 14-year results of a population cohort of the elderly.

INTRODUCTION: Great part of Chagas disease (ChD) mortality occurs due to ventricular arrhythmias, and autonomic function (AF) may predict unfavorable outcomes. We aimed to evaluate the predictive value of AF indexes in ChD patients. METHODS: The Bambuí Study of Aging is a prospective cohort of residents ≥60 years at study onset (1997), in the southeastern Brazilian city of Bambuí (15,000 inhabitants). Consented participants underwent annual follow-up visits, and death certificates were tracked. AF was assessed by the maximum expiration on minimum inspiration (E:I) ratio during ECG acquisition and by heart rate variability indices: SDRR (standard deviation of adjacent RR intervals) and RMSSD (square root of the mean of the sum of squares of the differences between adjacent RR intervals)), calculated using a computer algorithm. Cox proportional hazards regression was performed to access the prognostic value of AF indexes, expressed as terciles, for all-cause mortality, after adjustment for demographic, clinical and ECG variables. RESULTS: From 1742 qualifying residents, 1000 had valid AF tests, being 321 with ChD. Among these, median age was 68 (64-74) years, and 32.5% were men. In Cox survival analyses, only SDRR was associated with all-cause mortality in non-adjusted models: SDRR (hazard ratio (HR): 1.26 (95% CI 1.08-1.47), p < 0.001), E:I ratio (HR: 1.13 (95% CI 0,98-1.31), p = 0.10) and RMSSD (HR: 0.99 (0.86-1.16), p = 0.95). After adjustment for sex and age, none of the indexes remained as independent predictors. CONCLUSION: Among elderly patients with ChD, AF indexes available in this cohort were not independent predictors of 14-year mortality.

Combinação de Ferramentas de Telecardiologia para Estratificação de Risco Cardiovascular na Atenção Primária: Dados do Estudo PROVAR+ / Combination of Tele-Cardiology Tools for Cardiovascular Risk Stratification in Primary Care: Data from the PROVAR+ Study

Resumo Fundamento: As ferramentas de telecardiologia são estratégias valiosas para melhorar a estratificação de risco. Objetivo: Objetivamos avaliar a acurácia da tele-eletrocardiografia (ECG) para predizer anormalidades no ecocardiograma de rastreamento na atenção primária. Métodos: Em 17 meses, 6 profissionais de saúde em 16 unidades de atenção primária foram treinados em protocolos simplificados de ecocardiografia portátil. Tele-ECGs foram registrados para diagnóstico final por um cardiologista. Pacientes consentidos com anormalidades maiores no ECG pelo código de Minnesota e uma amostra 1:5 de indivíduos normais foram submetidos a um questionário clínico e ecocardiograma de rastreamento interpretado remotamente. A doença cardíaca grave foi definida como doença valvular moderada/grave, disfunção/hipertrofia ventricular, derrame pericárdico ou anormalidade da motilidade. A associação entre alterações maiores do ECG e anormalidades ecocardiográficas foi avaliada por regressão logística da seguinte forma: 1) modelo não ajustado; 2) modelo 1 ajustado por idade/sexo; 3) modelo 2 mais fatores de risco (hipertensão/diabetes); 4) modelo 3 mais história de doença cardiovascular (Chagas/cardiopatia reumática/cardiopatia isquêmica/AVC/insuficiência cardíaca). Foram considerados significativos valores de p < 0,05. Resultados: No total, 1.411 pacientes realizaram ecocardiograma, sendo 1.149 (81%) com anormalidades maiores no ECG. A idade mediana foi de 67 anos (intervalo interquartil de 60 a 74) e 51,4% eram do sexo masculino. As anormalidades maiores no ECG se associaram a uma chance 2,4 vezes maior de doença cardíaca grave no ecocardiograma de rastreamento na análise bivariada (OR = 2,42 [IC 95% 1,76 a 3,39]) e permaneceram significativas (p < 0,001) após ajustes no modelo 2 (OR = 2,57 [IC 95% 1,84 a 3,65]), modelo 3 (OR = 2,52 [IC 95% 1,80 a 3,58]) e modelo 4 (OR = 2,23 [IC 95% 1,59 a 3,19]). Idade, sexo masculino, insuficiência cardíaca e doença cardíaca isquêmica também foram preditores independentes de doença cardíaca grave no ecocardiograma. Conclusões: As anormalidades do tele-ECG aumentaram a probabilidade de doença cardíaca grave no ecocardiograma de rastreamento, mesmo após ajustes para variáveis demográficas e clínicas.

Abstract Background: Tele-cardiology tools are valuable strategies to improve risk stratification. Objective: We aimed to evaluate the accuracy of tele-electrocardiography (ECG) to predict abnormalities in screening echocardiography (echo) in primary care (PC). Methods: In 17 months, 6 health providers at 16 PC units were trained on simplified handheld echo protocols. Tele-ECGs were recorded for final diagnosis by a cardiologist. Consented patients with major ECG abnormalities by the Minnesota code, and a 1:5 sample of normal individuals underwent clinical questionnaire and screening echo interpreted remotely. Major heart disease was defined as moderate/severe valve disease, ventricular dysfunction/hypertrophy, pericardial effusion, or wall-motion abnormalities. Association between major ECG and echo abnormalities was assessed by logistic regression as follows: 1) unadjusted model; 2) model 1 adjusted for age/sex; 3) model 2 plus risk factors (hypertension/diabetes); 4) model 3 plus history of cardiovascular disease (Chagas/rheumatic heart disease/ischemic heart disease/stroke/heart failure). P-values < 0.05 were considered significant. Results: A total 1,411 patients underwent echo; 1,149 (81%) had major ECG abnormalities. Median age was 67 (IQR 60 to 74) years, and 51.4% were male. Major ECG abnormalities were associated with a 2.4-fold chance of major heart disease on echo in bivariate analysis (OR = 2.42 [95% CI 1.76 to 3.39]), and remained significant after adjustments in models (p < 0.001) 2 (OR = 2.57 [95% CI 1.84 to 3.65]), model 3 (OR = 2.52 [95% CI 1.80 to3.58]), and model 4 (OR = 2.23 [95%CI 1.59 to 3.19]). Age, male sex, heart failure, and ischemic heart disease were also independent predictors of major heart disease on echo. Conclusions: Tele-ECG abnormalities increased the likelihood of major heart disease on screening echo, even after adjustments for demographic and clinical variables.

Outcomes of latent rheumatic heart disease: External validation of a simplified score in patients with and without secondary prophylaxis.

BACKGROUND: Secondary antibiotic prophylaxis reduces progression of latent rheumatic heart disease (RHD) but not all children benefit. Improved risk stratification could refine recommendations following positive screening. We aimed to evaluate the performance of a previously developed echocardiographic risk score to predict mid-term outcomes among children with latent RHD. METHODS: We included children who completed the GOAL, a randomized trial of secondary antibiotic prophylaxis among children with latent RHD in Uganda. Outcomes were determined by a 4-member adjudication panel. We applied the point-based score, consisting of 5 variables (mitral valve (MV) anterior leaflet thickening (3 points), MV excessive leaflet tip motion (3 points), MV regurgitation jet length ≥ 2 cm (6 points), aortic valve focal thickening (4 points) and any aortic regurgitation (5 points)), to panel results. Unfavorable outcome was defined as progression of diagnostic category (borderline to definite, mild definite to moderate/severe definite), worsening valve involvement or remaining with mild definite RHD. RESULTS: 799 patients (625 borderline and 174 definite RHD) were included, with median follow-up of 24 months. At total 116 patients (14.5%) had unfavorable outcome per study criteria, 57.8% not under prophylaxis. The score was strongly associated with unfavorable outcome (HR = 1.26, 95% CI 1.16-1.37, p < 0.001). Unfavorable outcome rates in low (≤6 points), intermediate (7-9 points) and high-risk (≥10 points) children at follow-up were 11.8%, 30.4%, and 42.2%, (p < 0.001) respectively (C-statistic = 0.64 (95% CI 0.59-0.69)). CONCLUSIONS: The simple risk score provided an accurate prediction of RHD status at 2-years, showing a good performance in a population with milder RHD phenotypes.

Percentiles of predicted 10-year cardiovascular disease risk by sex and age in Brazil and their association with estimated risk of long-term atherosclerotic events.

OBJECTIVE: Expressing the cardiovascular disease (CVD) risk in relation to peers may complement the estimation of absolute CVD risk. We aimed to determine 10-year CVD risk percentiles by sex and age in the Brazilian population and evaluate their association with estimated long-term atherosclerotic CVD (ASCVD) risk. METHODS: A cross-sectional analysis of baseline data from the ELSA-Brasil study was conducted in individuals aged 40-74 years without prior ASCVD. Ten-year CVD risk and long-term ASCVD risk were estimated by the WHO risk score and the Multinational Cardiovascular Risk Consortium tool, respectively. Ten-year risk percentiles were determined by ranking the calculated risks within each sex and age group. RESULTS: Ten-year CVD risk versus percentile plots were constructed for each sex and age group using data from 13,364 participants (55% females; median age, 52 [IQR, 46-59] years). Long-term ASCVD risk was calculated in 12,973 (97.1%) participants. Compared to individuals at the <25th risk percentile, those at the ≥75th percentile had a greater risk of being in the highest quartile of long-term risk (ORs [95% CIs] 6.57 [5.18-8.30] in females and 11.59 [8.42-15.96] in males) in regression models adjusted for age, race, education, and 10-year CVD risk. In both sexes, the association between risk percentile and long-term risk weakened after age 50. A tool for calculating 10-year CVD risk and the corresponding percentile is available at https://bit.ly/3CzPUi6. CONCLUSIONS: We established percentiles of predicted 10-year CVD risk by sex and age in the Brazilian population, which independently reflect the estimated long-term ASCVD risk in younger individuals.

Heart age gap estimated by explainable advanced electrocardiography is associated with cardiovascular risk factors and survival.

Aims: Deep neural network artificial intelligence (DNN-AI)-based Heart Age estimations have been presented and used to show that the difference between an electrocardiogram (ECG)-estimated Heart Age and chronological age is associated with prognosis. An accurate ECG Heart Age, without DNNs, has been developed using explainable advanced ECG (A-ECG) methods. We aimed to evaluate the prognostic value of the explainable A-ECG Heart Age and compare its performance to a DNN-AI Heart Age. Methods and results: Both A-ECG and DNN-AI Heart Age were applied to patients who had undergone clinical cardiovascular magnetic resonance imaging. The association between A-ECG or DNN-AI Heart Age Gap and cardiovascular risk factors was evaluated using logistic regression. The association between Heart Age Gaps and death or heart failure (HF) hospitalization was evaluated using Cox regression adjusted for clinical covariates/comorbidities. Among patients [n = 731, 103 (14.1%) deaths, 52 (7.1%) HF hospitalizations, median (interquartile range) follow-up 5.7 (4.7-6.7) years], A-ECG Heart Age Gap was associated with risk factors and outcomes [unadjusted hazard ratio (HR) (95% confidence interval) (5 year increments): 1.23 (1.13-1.34) and adjusted HR 1.11 (1.01-1.22)]. DNN-AI Heart Age Gap was associated with risk factors and outcomes after adjustments [HR (5 year increments): 1.11 (1.01-1.21)], but not in unadjusted analyses [HR 1.00 (0.93-1.08)], making it less easily applicable in clinical practice. Conclusion: A-ECG Heart Age Gap is associated with cardiovascular risk factors and HF hospitalization or death. Explainable A-ECG Heart Age Gap has the potential for improving clinical adoption and prognostic performance compared with existing DNN-AI-type methods.

End-to-end risk prediction of atrial fibrillation from the 12-Lead ECG by deep neural networks.

BACKGROUND: Atrial fibrillation (AF) is one of the most common cardiac arrhythmias that affects millions of people each year worldwide and it is closely linked to increased risk of cardiovas- cular diseases such as stroke and heart failure. Machine learning methods have shown promising results in evaluating the risk of developing atrial fibrillation from the electrocardiogram. We aim to develop and evaluate one such algorithm on a large CODE dataset collected in Brazil. METHODS: We used the CODE cohort to develop and test a model for AF risk prediction for individual patients from the raw ECG recordings without the use of additional digital biomarkers. The cohort is a collection of ECG recordings and annotations by the Telehealth Network of Minas Gerais, in Brazil. A convolutional neural network based on a residual network architecture was implemented to produce class probabilities for the classification of AF. The probabilities were used to develop a Cox proportional hazards model and a Kaplan-Meier model to carry out survival analysis. Hence, our model is able to perform risk prediction for the development of AF in patients without the condition. RESULTS: The deep neural network model identified patients without indication of AF in the presented ECG but who will develop AF in the future with an AUC score of 0.845. From our survival model, we obtain that patients in the high-risk group (i.e. with the probability of a future AF case being >0.7) are 50% more likely to develop AF within 40 weeks, while patients belonging to the minimal-risk group (i.e. with the probability of a future AF case being less than or equal to 0.1) have >85% chance of remaining AF free up until after seven years. CONCLUSION: We developed and validated a model for AF risk prediction. If applied in clinical practice, the model possesses the potential of providing valuable and useful information in decision- making and patient management processes.

ISE/ISHNE Expert Consensus Statement on ECG Diagnosis of Left Ventricular Hypertrophy: The Change of the Paradigm. The joint paper of the International Society of Electrocardiology and the International Society for Holter Monitoring and Noninvasive Electrocardiology.

The ECG diagnosis of LVH is predominantly based on the QRS voltage criteria, i.e. the increased QRS complex amplitude in defined leads. The classical ECG diagnostic paradigm postulates that the increased left ventricular mass generates a stronger electrical field, increasing the leftward and posterior QRS forces. These increased forces are reflected in the augmented QRS amplitude in the corresponding leads. However, the clinical observations document increased QRS amplitude only in the minority of patients with LVH. The low sensitivity of voltage criteria has been repeatedly documented. We discuss possible reasons for this shortcoming and proposal of a new paradigm.

Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images.

BACKGROUND: Left ventricular (LV) systolic dysfunction is associated with a >8-fold increased risk of heart failure and a 2-fold risk of premature death. The use of ECG signals in screening for LV systolic dysfunction is limited by their availability to clinicians. We developed a novel deep learning-based approach that can use ECG images for the screening of LV systolic dysfunction. METHODS: Using 12-lead ECGs plotted in multiple different formats, and corresponding echocardiographic data recorded within 15 days from the Yale New Haven Hospital between 2015 and 2021, we developed a convolutional neural network algorithm to detect an LV ejection fraction <40%. The model was validated within clinical settings at Yale New Haven Hospital and externally on ECG images from Cedars Sinai Medical Center in Los Angeles, CA; Lake Regional Hospital in Osage Beach, MO; Memorial Hermann Southeast Hospital in Houston, TX; and Methodist Cardiology Clinic of San Antonio, TX. In addition, it was validated in the prospective Brazilian Longitudinal Study of Adult Health. Gradient-weighted class activation mapping was used to localize class-discriminating signals on ECG images. RESULTS: Overall, 385 601 ECGs with paired echocardiograms were used for model development. The model demonstrated high discrimination across various ECG image formats and calibrations in internal validation (area under receiving operation characteristics [AUROCs], 0.91; area under precision-recall curve [AUPRC], 0.55); and external sets of ECG images from Cedars Sinai (AUROC, 0.90 and AUPRC, 0.53), outpatient Yale New Haven Hospital clinics (AUROC, 0.94 and AUPRC, 0.77), Lake Regional Hospital (AUROC, 0.90 and AUPRC, 0.88), Memorial Hermann Southeast Hospital (AUROC, 0.91 and AUPRC 0.88), Methodist Cardiology Clinic (AUROC, 0.90 and AUPRC, 0.74), and Brazilian Longitudinal Study of Adult Health cohort (AUROC, 0.95 and AUPRC, 0.45). An ECG suggestive of LV systolic dysfunction portended >27-fold higher odds of LV systolic dysfunction on transthoracic echocardiogram (odds ratio, 27.5 [95% CI, 22.3-33.9] in the held-out set). Class-discriminative patterns localized to the anterior and anteroseptal leads (V2 and V3), corresponding to the left ventricle regardless of the ECG layout. A positive ECG screen in individuals with an LV ejection fraction ≥40% at the time of initial assessment was associated with a 3.9-fold increased risk of developing incident LV systolic dysfunction in the future (hazard ratio, 3.9 [95% CI, 3.3-4.7]; median follow-up, 3.2 years). CONCLUSIONS: We developed and externally validated a deep learning model that identifies LV systolic dysfunction from ECG images. This approach represents an automated and accessible screening strategy for LV systolic dysfunction, particularly in low-resource settings.