Noncommunicable disease burden in Brazil and its states from 1990 to 2021, with projections for 2030.

OBJECTIVES: The aim of this study was to analyse the burden of disease due to noncommunicable diseases (NCDs) between 1990 and 2021 in Brazil. In addition, this study compared mortality from NCDs with mortality from all causes and COVID-19, analysed NCD mortality trends and projections for 2030, and analysed NCD mortality rates and risk factors attributed to these deaths among the 27 states of Brazil. STUDY DESIGN: Ecological studies. METHODS: This study used the Global Burden of Disease study (GBD) database from 1990 to 2021. Premature deaths from four NCDs (neoplasms, cardiovascular disease, chronic respiratory diseases and diabetes mellitus) were analysed. The following metrics were used to analyse the burden of NCDs in Brazil: absolute number of deaths, proportional mortality, mortality rate, years of life lost due to premature death (YLL), years lived with disabilities (YLD) and disability-adjusted years of life lost due to premature death (DALY). For comparison between the years studied and states, age-standardised rates were used. RESULTS: Finding from this study showed that there was increase in the proportion of premature deaths due to NCDs between 1990 and 2019 (29.4 % in 1990, 30.8 % in 2019), and a reduction in 2021 (24.7 %). The mortality rates, DALY and YLL from NCDs declined between 1990 and 2019 (-37.7 %, -34.5 % and -38.3 %, respectively); however, a stability in mortality rates, DALY, YLD, YLL was observed between 2019 and 2021 (-0.1 %, 0.7 %, -0.1 % and 0.8 %, respectively). Between 1990 and 2021, there was a decline in mortality rates, DALY and YLL for most states and an increase in YLD rates. However, results suggest that the Sustainable Development Goal (SDG) for the reduction in mortality from NCDs by one-third by 2030 will not be achieved. The main risk factors associated with premature death from NCDs in 2021 were high blood pressure, tobacco use, dietary risks, high body mass index (BMI) and high blood glucose levels. The correlation between sociodemographic index and percentage change in mortality rates was significant for the following total NCDs, cardiovascular disease, chronic respiratory disease, diabetes and neoplasms. CONCLUSIONS: The current study highlights the importance of deaths from NCDs in Brazil and the worsening of mortality rates since 2016, as a result of austerity measures and the COVID-19 pandemic, which compromises the achievement of the SDG reduced mortality targets for NCDs. There was a reduction in risk factors for NCDs, mainly behavioural, although metabolic risk factors are of great concern and require new strategies to promote health, prevention and comprehensive care.

Prevalence of Chagas disease among Latin American immigrants in non-endemic countries: an updated systematic review and meta-analysis.

Background: Chagas disease (CD), endemic in 21 Latin American countries, has gradually spread beyond its traditional borders due to migratory movements and emerging as a global health concern. We conducted a systematic review and meta-analysis of available data to establish updated prevalence estimates of CD in Latin American migrants residing in non-endemic countries. Methods: A systematic search was conducted in MEDLINE/PubMed, Embase, Cochrane Library, Scopus, Web of Science, and LILACS via Virtual Health Library (Biblioteca Virtual em Saúde - BVS), including references published until November 1st, 2023. Pooled prevalence estimates and 95% confidence intervals (CI) were calculated using random effect models. Heterogeneity was assessed by the chi-square test and the I2 statistic. Subgroup analyses were performed to explore potential sources of heterogeneity among studies. The study was registered in the PROSPERO database (CRD42022354237). Findings: From a total of 1474 articles screened, 51 studies were included. Studies were conducted in eight non-endemic countries (most in Spain), between 2006 and 2023, and involving 82,369 screened individuals. The estimated pooled prevalence of CD in Latin American migrants living in non-endemic countries was 3.5% (95% CI: 2.5-4.7; I2: 97.7%), considering studies in which screening was indicated simply because the person was Latin American. Per subgroups, the pooled CD prevalence was 11.0% (95% CI: 7.7-15.5) in non-targeted screening (unselected population in reference centers) (27 studies); in blood donors (4 studies), the pooled prevalence was 0.8% (95% CI: 0.2-3.4); among people living with HIV Latin American immigrants (4 studies) 2.4% (95% CI: 1.4-4.3) and for Latin American pregnant and postpartum women (14 studies) 3.7% (95 CI: 2.4-5.6). The pooled proportion of congenital transmission was 4.4% (95% CI: 3.3-5.8). Regarding the participants' country of origin, 7964 were from Bolivia, of which 1715 (21,5%) were diagnosed with CD, and 21,304 were from other Latin American countries of which 154 (0,72%) were affected. Interpretation: CD poses a significant burden of disease in Latin American immigrants in non-endemic countries, suggesting that CD is no longer a problem limited to the American continent and must be considered as a global health challenge. Funding: This study was funded by the World Heart Federation, through a research collaboration with Novartis Pharma AG.

Prevalence analysis of Chagas disease by age group in an endemic region of Brazil: possible scenario of active vectorial transmission.

Objectives: Chagas disease (CD) is an infectious disease that predominantly affects poor and vulnerable populations. The last estimate conducted by the World Health Organization in Latin America regarding the prevalence of CD occurred more than 10 years ago. However, there is a scarcity of data assessing the magnitude of CD in populations residing in considered high-risk regions. Therefore, this study aimed to assess the seroprevalence of CD in an endemic region in Northern Minas Gerais through serologic screening. Methods: This is a prevalence study conducted in the municipalities of Catuti, Mato Verde, Mirabela, Montes Azul, and São Francisco, Minas Gerais, Brazil. Data collection occurred between December 2021 and December 2022, involving a questionnaire with closed-ended questions. The variables analyzed included serologic test results, stratified age groups, health indicators, and housing conditions. Results: Of the 2978 participants, 272 individuals (9.1%) tested positive for CD serology. In the age group of 4 to 14 years, 15 to 49 years, and 50 years or older, the prevalence of positive serology was 0.8% (95% confidence interval [CI] 0.16-1.43), 5.5% (95% CI 4.20-6.83), and 18.8% (95% CI 16.48-21.11), respectively. Among the participating municipalities, Mato Verde had the highest prevalence of positive serology for CD (17%). For participants aged 4 to 14 years with positive serology for CD, first-degree relatives were invited to undergo serologic testing. It was possible to collect samples from relatives of all participants in this age group. However, none of the relatives tested positive. Conclusion: This study identified a 9.1% prevalence of individuals affected by CD who were unaware of their condition. In addition, having infected children in the 4 to 14 age group with mothers with negative serology would rule out congenital transmission of the disease.

Identification of Chagas disease biomarkers using untargeted metabolomics.

Untargeted metabolomic analysis is a powerful tool used for the discovery of novel biomarkers. Chagas disease (CD), caused by Trypanosoma cruzi, is a neglected tropical disease that affects 6-7 million people with approximately 30% developing cardiac manifestations. The most significant clinical challenge lies in its long latency period after acute infection, and the lack of surrogate markers to predict disease progression or cure. In this cross-sectional study, we analyzed sera from 120 individuals divided into four groups: 31 indeterminate CD, 41 chronic chagasic cardiomyopathy (CCC), 18 Latin Americans with other cardiomyopathies and 30 healthy volunteers. Using a high-throughput panel of 986 metabolites, we identified three distinct profiles among individuals with cardiomyopathy, indeterminate CD and healthy volunteers. After a more stringent analysis, we identified some potential biomarkers. Among peptides, phenylacetylglutamine and fibrinopeptide B (1-13) exhibited an increasing trend from controls to ICD and CCC. Conversely, reduced levels of bilirubin and biliverdin alongside elevated urobilin correlated with disease progression. Finally, elevated levels of cystathionine, phenol glucuronide and vanillactate among amino acids distinguished CCC individuals from ICD and controls. Our novel exploratory study using metabolomics identified potential biomarker candidates, either alone or in combination that if confirmed, can be translated into clinical practice.

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.

Understanding the Personal Barriers of Elderly Patients for Carrying out Teleconsultations During COVID-19 Pandemic: An Observational Study.

Introduction: The expansion of telehealth during the COVID-19 pandemic may widen digital divides. It is essential to better understand the use of telehealth by the elderly population for the development of equitable telehealth tools. Objectives: This study aimed to describe the socioeconomic, clinical, and functional characteristics of elderly patients who were supported by a COVID-19 telehealth program. It also investigated the characteristics associated with the need for support for teleconsultations, hospitalization, and mortality. Methods: >Elderly patients supported by the TeleCOVID-MG program, between June 2020 and December 2021, in two Brazilian municipalities (Divinópolis and Teófilo Otoni) were included. Data were collected from electronic records and through phone call interviews. Descriptive and multivariable analyses were performed. Results: Among the 237 patients,121 were women (51.1%), mean age was 70.8 years (±8.5), 121 (51.1%) had less than 4 years of formal education, 123 patients (51.9%) had two or more comorbidities, and 68 (29%) reported functional decline in activities of daily life. Age greater than 80 years (odds ratio [OR]:4.68, 95% confidence interval [CI] 1.93-11.37, p = 0.001), lower educational level (OR:3.85, 95% CI 1.8-8.21, p < 0.001), hearing (OR:5.46, 95% CI: 1.24-11.27, p = 0.019), and visual (OR:15.10, 95% CI: 3.21-71.04, p = 0.001) impairments were characteristics associated with the need for support for teleconsultations. The need for support was associated with hospitalization and mortality (OR:5.08, 95% CI: 2.35-10.98, p < 0.001). Conclusion: Older age, lower educational level, and sensory impairments may compromise the effectiveness and the safety of the telehealth assistance to the elderly population. Functional evaluation and frailty screening should be considered part of the telehealth assessment of elderly patients.

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.

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.

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.

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.

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.

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.

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.

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.

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.