Evaluating regression and probabilistic methods for ECG-based electrolyte prediction.

Imbalances in electrolyte concentrations can have severe consequences, but accurate and accessible measurements could improve patient outcomes. The current measurement method based on blood tests is accurate but invasive and time-consuming and is often unavailable for example in remote locations or an ambulance setting. In this paper, we explore the use of deep neural networks (DNNs) for regression tasks to accurately predict continuous electrolyte concentrations from electrocardiograms (ECGs), a quick and widely adopted tool. We analyze our DNN models on a novel dataset of over 290,000 ECGs across four major electrolytes and compare their performance with traditional machine learning models. For improved understanding, we also study the full spectrum from continuous predictions to a binary classification of extreme concentration levels. Finally, we investigate probabilistic regression approaches and explore uncertainty estimates for enhanced clinical usefulness. Our results show that DNNs outperform traditional models but model performance varies significantly across different electrolytes. While discretization leads to good classification performance, it does not address the original problem of continuous concentration level prediction. Probabilistic regression has practical potential, but our uncertainty estimates are not perfectly calibrated. Our study is therefore a first step towards developing an accurate and reliable ECG-based method for electrolyte concentration level prediction-a method with high potential impact within multiple clinical scenarios.

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.

Generalization challenges in electrocardiogram deep learning: insights from dataset characteristics and attention mechanism.

Aim: Deep learning's widespread use prompts heightened scrutiny, particularly in the biomedical fields, with a specific focus on model generalizability. This study delves into the influence of training data characteristics on the generalization performance of models, specifically in cardiac abnormality detection. Materials & methods: Leveraging diverse electrocardiogram datasets, models are trained on subsets with varying characteristics and subsequently compared for performance. Additionally, the introduction of the attention mechanism aims to improve generalizability. Results: Experiments reveal that using a balanced dataset, just 1% of a large dataset, leads to equal performance in generalization tasks, notably in detecting cardiology abnormalities. Conclusion: This balanced training data notably enhances model generalizability, while the integration of the attention mechanism further refines the model's ability to generalize effectively.

This study tackles a common problem for deep learning models: they often struggle when faced with new, unfamiliar data that they have not been trained on. This phenomenon is also known as performance drop in out-of-distribution generalization. This reduced performance on out-of-distribution generalization is a key focus of the research, aiming to improve the models' ability to handle diverse data sets beyond their training data.The study examines how the characteristics of the dataset used to train deep learning models affect their ability to detect abnormal heart activities when applied to new, unseen data. Researchers trained these models using various sets of electrocardiogram (ECG) data and then evaluated their performance in identifying abnormalities. They also introduced an attention mechanism to enhance the models' learning capabilities. The attention mechanism in deep learning is like a spotlight that helps the model focus on important information while ignoring less relevant details.The findings were particularly noteworthy. Despite being trained on a small, well-balanced subset of a larger dataset, the models excelled in detecting heart abnormalities in new, unfamiliar data. This training method significantly improved the models' generalization and performance with unseen data. Furthermore, integrating the attention mechanism substantially enhanced the models' ability to generalize effectively on new information.

Association between positive serology for COVID-19 and Chagas cardiomyopathy progression: The SaMi-Trop Project.

BACKGROUND: Chagas Disease (CD) can cause Chagas cardiomyopathy. The new coronavirus disease (COVID-19) also affects the cardiovascular system and may worsen Chagas cardiomyopathy. However, the cardiac evolution of patients with CD infected by COVID-19 is not known. Thus, the objective of this study is to assess, within one year, whether there was cardiac progression after COVID-19 in CD. METHODS: Longitudinal study with CD patients. The outcome was cardiac progression, defined as the appearance of new major changes in the current ECG compared to the previous ECG considered from the comparison of electrocardiograms (ECGs) performed with an interval of one year. Positive Anti-SARS-CoV2 Serology was the independent variable of interest. For each analysis, a final multiple model was constructed, adjusted for sociodemographic, clinical, and pandemic-related characteristics. RESULTS: Of the 404 individuals included, 22.8 % had positive serology for COVID-19 and 10.9 % had cardiac progression. In the final model, positive serology for COVID-19 was the only factor associated with cardiac progression in the group as a whole (OR=2.65; 95 % CI= 1.27-5.53) and for new-onset cardiomyopathy in the group with normal previous ECG (OR=3.50; 95 % CI=1.21-10.13). CONCLUSION: Our study shows an association between COVID-19 and progression of Chagas cardiomyopathy, evaluated by repeated ECGs, suggesting that COVID-19 accelerated the natural history of CD.

Epidemiology of cardiometabolic health in Latin America and strategies to address disparities.

In Latin America and the Caribbean (LAC), sociodemographic context, socioeconomic disparities and the high level of urbanization provide a unique entry point to reflect on the burden of cardiometabolic disease in the region. Cardiovascular diseases are the main cause of death in LAC, precipitated by population growth and ageing together with a rapid increase in the prevalence of cardiometabolic risk factors, predominantly obesity and diabetes mellitus, over the past four decades. Strategies to address this growing cardiometabolic burden include both population-wide and individual-based initiatives tailored to the specific challenges faced by different LAC countries, which are heterogeneous. The implementation of public policies to reduce smoking and health system approaches to control hypertension are examples of scalable strategies. The challenges faced by LAC are also opportunities to foster innovative approaches to combat the high burden of cardiometabolic diseases such as implementing digital health interventions and team-based initiatives. This Review provides a summary of trends in the epidemiology of cardiometabolic diseases and their risk factors in LAC as well as context-specific disease determinants and potential solutions to improve cardiometabolic health in the region.

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.

Effect of a Structured Multilevel Telehealth Service on Hospital Admissions and Mortality During COVID-19 in a Resource-Limited Region in Brazil: Retrospective Cohort Study.

BACKGROUND: The COVID-19 pandemic represented a great stimulus for the adoption of telehealth and many initiatives in this field have emerged worldwide. However, despite this massive growth, data addressing the effectiveness of telehealth with respect to clinical outcomes remain scarce. OBJECTIVE: The aim of this study was to evaluate the impact of the adoption of a structured multilevel telehealth service on hospital admissions during the acute illness course and the mortality of adult patients with flu syndrome in the context of the COVID-19 pandemic. METHODS: A retrospective cohort study was performed in two Brazilian cities where a public COVID-19 telehealth service (TeleCOVID-MG) was deployed. TeleCOVID-MG was a structured multilevel telehealth service, including (1) first response and risk stratification through a chatbot software or phone call center, (2) teleconsultations with nurses and medical doctors, and (3) a telemonitoring system. For this analysis, we included data of adult patients registered in the Flu Syndrome notification databases who were diagnosed with flu syndrome between June 1, 2020, and May 31, 2021. The exposed group comprised patients with flu syndrome who used TeleCOVID-MG at least once during the illness course and the control group comprised patients who did not use this telehealth service during the respiratory illness course. Sociodemographic characteristics, comorbidities, and clinical outcomes data were extracted from the Brazilian official databases for flu syndrome, Severe Acute Respiratory Syndrome (due to any respiratory virus), and mortality. Models for the clinical outcomes were estimated by logistic regression. RESULTS: The final study population comprised 82,182 adult patients with a valid registry in the Flu Syndrome notification system. When compared to patients who did not use the service (n=67,689, 82.4%), patients supported by TeleCOVID-MG (n=14,493, 17.6%) had a lower chance of hospitalization during the acute respiratory illness course, even after adjusting for sociodemographic characteristics and underlying medical conditions (odds ratio [OR] 0.82, 95% CI 0.71-0.94; P=.005). No difference in mortality was observed between groups (OR 0.99, 95% CI 0.86-1.12; P=.83). CONCLUSIONS: A telehealth service applied on a large scale in a limited-resource region to tackle COVID-19 was related to reduced hospitalizations without increasing the mortality rate. Quality health care using inexpensive and readily available telehealth and digital health tools may be delivered in areas with limited resources and should be considered as a potential and valuable health care strategy. The success of a telehealth initiative relies on a partnership between the involved stakeholders to define the roles and responsibilities; set an alignment between the different modalities and levels of health care; and address the usual drawbacks related to the implementation process, such as infrastructure and accessibility issues.

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.

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.

Association between Hemodynamic Profile at Hospital Admission and Mortality in Acute Heart Failure Patients Included in the Best Practice in Cardiology Program in Brazil. / Associação entre o Perfil Hemodinâmico da Insuficiência Cardíaca à Admissão Hospitalar e Mortalidade - Programa Boas Práticas Clínicas em Cardiologia.

BACKGROUND: Heart failure (HF) contributes to a high burden of hospitalization, and its form of presentation is associated with disease prognosis. OBJECTIVES: To describe the association of hemodynamic profile of acute HF patients at hospital admission, based on congestion (wet/dry) and perfusion (cold/warm), with mortality, hospital length of stay and risk of readmission. METHODS: Cohort study, with patients participating in the "Best Practice in Cardiology" program, admitted for acute HF in Brazilian public hospitals between March 2016 and December 2019, with a six-month follow-up. Characteristics of the population and hemodynamic profile at admission were analyzed, in addition to survival analysis using Cox proportional hazard model for associations between hemodynamic profile at admission and mortality, and logistic regression for the risk of rehospitalization, using a statistical significance level of 5%. RESULTS: A total of 1,978 patients were assessed, with mean age of 60.2 (±14.8) years and mean left ventricular ejection fraction of 39.8% (±17.3%). A high six-month mortality rate (22%) was observed, with an association of cold hemodynamic profiles with in-hospital mortality (HR=1.72, 95%CI 1.27-2.31; p < 0.001) and six-month mortality (HR= 1.61, 95%CI 1.29-2.02). Six-month rehospitalization rate was 22%, and higher among patients with wet profiles (OR 2.30; 95%CI 1.45-3.65; p < 0.001). CONCLUSIONS: Acute HF is associated with high mortality and rehospitalization rates. Patient hemodynamic profile at admission is a good prognostic marker of this condition.

FUNDAMENTO: A insuficiência cardíaca (IC) é responsável por alta carga de internações hospitalares. A sua forma de apresentação está relacionada ao prognóstico da doença. OBJETIVOS: Descrever a associação entre o perfil hemodinâmico de admissão hospitalar na IC aguda, baseado em congestão (úmido ou seco) e perfusão (frio ou quente), e desfechos de mortalidade, tempo de internação e chance de reinternação. MÉTODOS: Estudo de coorte, envolvendo pacientes do projeto "Boas Práticas Clínicas em Cardiologia", internados por IC aguda em hospitais públicos brasileiros, entre março de 2016 a dezembro de 2019, com seguimento de seis meses. Foram realizadas análises das características populacionais e do perfil hemodinâmico de admissão, além de análises de sobrevivência pelos modelos de Cox para associação entre o perfil de admissão e mortalidade, e regressão logística para chance de reinternação, considerando nível de significância estatística de 5%. RESULTADOS: Foram avaliados 1978 pacientes, com idade média foi 60,2 (±14,8) anos e fração de ejeção média do ventrículo esquerdo de 39,8% (±17,3%). Houve altas taxas de mortalidade no seguimento de seis meses (22%), com associação entre os perfis hemodinâmicos frios e a mortalidade hospitalar (HR=1,72; IC95% 1,27-2,31; p < 0,001) e em 6 meses (HR= 1,61, IC 95% 1,29-2,02). A taxa de reinternação em 6 meses foi de 22%, sendo maior para os pacientes admitidos em perfis úmidos (OR 2,30; IC95% 1,45-3,65; p < 0,001). CONCLUSÕES: A IC aguda no Brasil apresenta altas taxas de mortalidade e reinternações e os perfis hemodinâmicos de admissão hospitalar são bons marcadores prognósticos dessa evolução.

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.

Evaluation of Silybin Nanoparticles against Liver Damage in Murine Schistosomiasis mansoni Infection.

Silybin (SIB) is a hepatoprotective drug known for its poor oral bioavailability, attributed to its classification as a class IV drug with significant metabolism during the first-pass effect. This study explored the potential of solid lipid nanoparticles with (SLN-SIB-U) or without (SLN-SIB) ursodeoxycholic acid and polymeric nanoparticles (PN-SIB) as delivery systems for SIB. The efficacy of these nanosystems was assessed through in vitro studies using the GRX and Caco-2 cell lines for permeability and proliferation assays, respectively, as well as in vivo experiments employing a murine model of Schistosomiasis mansoni infection in BALB/c mice. The mean diameter and encapsulation efficiency of the nanosystems were as follows: SLN-SIB (252.8 ± 4.4 nm, 90.28 ± 2.2%), SLN-SIB-U (252.9 ± 14.4 nm, 77.05 ± 2.8%), and PN-SIB (241.8 ± 4.1 nm, 98.0 ± 0.2%). In the proliferation assay with the GRX cell line, SLN-SIB and SLN-SIB-U exhibited inhibitory effects of 43.09 ± 5.74% and 38.78 ± 3.78%, respectively, compared to PN-SIB, which showed no inhibitory effect. Moreover, SLN-SIB-U demonstrated a greater apparent permeability coefficient (25.82 ± 2.2) than PN-SIB (20.76 ± 0.1), which was twice as high as that of SLN-SIB (11.32 ± 4.6) and pure SIB (11.28 ± 0.2). These findings suggest that solid lipid nanosystems hold promise for further in vivo investigations. In the murine model of acute-phase Schistosomiasis mansoni infection, both SLN-SIB and SLN-SIB-U displayed hepatoprotective effects, as evidenced by lower alanine amino transferase values (22.89 ± 1.6 and 23.93 ± 2.4 U/L, respectively) than those in control groups I (29.55 ± 0.7 U/L) and I+SIB (34.29 ± 0.3 U/L). Among the prepared nanosystems, SLN-SIB-U emerges as a promising candidate for enhancing the pharmacokinetic properties of SIB.

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.

Influence of prenatal corticosteroid therapy on neonatal vitality and utility as a labor-inducing agent in Santa Inês ewes.

Since the 1970s, maternal corticosteroid therapy has been used successfully to induce labor. This allows for better monitoring of parturients and provision of first aid to neonates, improving neonatal viability, as this treatment induces maturation in a variety of fetal tissues, thereby reducing morbidity and mortality. Although the effects of corticosteroids are well known, few studies have investigated the influence of this therapy in Santa Inês sheep. This study aimed to evaluate the efficacy of dexamethasone at two doses (8 and 16 mg) to induce lambing in Santa Inês ewes at 145 days of gestation and assess its effects on neonatal vitality. For this study, 58 ewes raised in an extensive system were investigated. Pregnancy was confirmed after artificial insemination at a set time or after controlled mounting. Ewes were separated into three groups: an untreated control group (G1: 0 mg) and groups treated with two doses of dexamethasone (G2: 8 mg and G3: 16 mg). In total, 79 lambs were born. Their vitality was assessed based on their Apgar score, weight, temperature, and postnatal behavior. SAS v9.1.3 (SAS Institute, Cary, NC) was used to analyze data, considering a 5% significance level for all analyses. The births in the induced groups occurred 48.4 ± 22.1 h after induction, while the ewes that underwent non-induced labor gave birth 131.96 ± 41.9 h after placebo application (p < 0.05), confirming the efficacy of dexamethasone to induce and synchronize labor. The induced and non-induced neonates had similar Apgar scores, temperatures, weights, and postnatal behavioral parameters (p > 0.05). This study showed that inducing labor in Santa Inês ewes at 145 days of gestation with a full (16 mg) or half dose (8 mg) of dexamethasone is an effective technique and does not compromise neonate vitality.

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.

Toward a Universal Definition of Etiologies in Heart Failure: Categorizing Causes and Advancing Registry Science.

Heart failure (HF) is a well-described final common pathway for a broad range of diseases however substantial confusion exists regarding how to describe, study, and track these underlying etiologic conditions. We describe (1) the overlap in HF etiologies, comorbidities, and case definitions as currently used in HF registries led or managed by members of the global HF roundtable; (2) strategies to improve the quality of evidence on etiologies and modifiable risk factors of HF in registries; and (3) opportunities to use clinical HF registries as a platform for public health surveillance, implementation research, and randomized registry trials to reduce the global burden of noncommunicable diseases. Investment and collaboration among countries to improve the quality of evidence in global HF registries could contribute to achieving global health targets to reduce noncommunicable diseases and overall improvements in population health.

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.

Prevalence of clinical forms of Chagas disease: a systematic review and meta-analysis - data from the RAISE study.

Background: There is a lack of up-to-date estimates about the prevalence of Chagas disease (ChD) clinical presentations and, therefore, we aimed to assess the prevalence of clinical forms of ChD among seropositive adults, pooling available data. Methods: A systematic review was conducted in Medline, Embase, Biblioteca Virtual em Saúde and Cochrane databases looking for studies published from 1990 to August 2023, which investigated the prevalence of ChD clinical forms among seropositive adults, including: (i) indeterminate phase, (ii) chronic Chagas cardiomyopathy (CCM), (iii) digestive and (iv) mixed (CCM + digestive) forms. Pooled estimates and 95% confidence intervals (CI) were calculated using random-effects models. Studies quality and risk of bias was assessed with the Leboeuf-Yde and Lauritsen tool. Heterogeneity was assessed with the I2 statistic. The study was registered in the PROSPERO database (CRD42022354237). Findings: 1246 articles were selected for screening and 73 studies were included in the final analysis (17,132 patients, 44% men). Most studies were conducted with outpatients (n = 50), followed by population-based studies (n = 15). The pooled prevalence of the ChD clinical forms was: indeterminate 42.6% (95% CI: 36.9-48.6), CCM 42.7% (95% CI: 37.3-48.3), digestive 17.7% (95% CI: 14.9-20.9), and mixed 10.2% (95% CI: 7.9-13.2). In population-based studies, prevalence was lower for CCM (31.2%, 95% CI: 24.4-38.9) and higher for indeterminate (47.2%, 95% CI: 39.0-55.5) form. In meta-regression, age was inversely associated with the prevalence of indeterminate (ß = -0.05, P < 0.001) form, and directly associated with CCM (ß = 0.06, P < 0.001) and digestive (ß = 0.02, P < 0.001) forms. Heterogeneity was overall high. Interpretation: Compared to previous publications, our pooled estimates show a higher prevalence of CCM among ChD seropositive patients, but similar rates of the digestive form. Funding: This study was funded by the World Heart Federation, through a research collaboration with Novartis Pharma AG.