Development and multinational validation of an ensemble deep learning algorithm for detecting and predicting structural heart disease using noisy single-lead electrocardiograms.

Aims: Artificial intelligence (AI)-enhanced 12-lead electrocardiogram (ECG) can detect a range of structural heart diseases (SHDs); however, it has a limited role in community-based screening. We developed and externally validated a noise-resilient single-lead AI-ECG algorithm that can detect SHDs and predict the risk of their development using wearable/portable devices.

Methods And Results: Using 266 740 ECGs from 99 205 patients with paired echocardiographic data at Yale New Haven Hospital, we developed AI Deep learning for Adapting Portable Technology in HEART disease detection (ADAPT-HEART), a noise-resilient, deep learning algorithm, to detect SHDs using lead I ECG.

Access to digital health technologies: personalized framework and global perspectives.

The emergence and rapid adoption of digital health technologies (DHT) present unprecedented opportunities to democratize and reduce disparities in health care by monitoring health and disease at the point of care in all patients. However, limited access to DHT is becoming a major obstacle to realizing these goals. Access to DHT is influenced not only by well-recognized social determinants of health, but also by digital determinants of health, such as digital literacy and the need for broad access to digital infrastructure, as well as commercial and economic factors.

Digital health intervention to imise eart ailure management after hospital discharge in Brazil (OPT-HF): a randomised clinical trial protocol.

Introduction: Guideline-directed medical therapy (GDMT) for heart failure (HF) reduces adverse events, but is underused. Global barriers to GDMT optimisation include low frequency of visits, clinician inertia and poor patient knowledge, which may be mitigated by digital health interventions (DHI). In Brazil, low digital literacy and reduced access to technology may compromise these potential DHI's beneficial effects.

Artificial Intelligence-Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms.

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) may enable large-scale community-based risk assessment.

Objective: To evaluate whether an artificial intelligence (AI) algorithm can predict HF risk from noisy single-lead ECGs.

Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images: PRESENT SHD.

Background: Identifying structural heart diseases (SHDs) early can change the course of the disease, but their diagnosis requires cardiac imaging, which is limited in accessibility.

Objectives: The purpose of this study was to leverage images of 12-lead electrocardiograms (ECGs) for automated detection and prediction of multiple SHDs using an ensemble deep learning approach.

Methods: We developed a series of convolutional neural network models for detecting a range of individual SHDs from images of ECGs with SHDs defined by transthoracic echocardiograms performed within 30 days of the ECG at the Yale New Haven Hospital (YNHH).

Effect of Telemedicine Interventions on Heart Failure Hospitalizations: A Randomized Trial.

Background: Telemedicine interventions (TMIs) for heart failure (HF) can reduce hospitalizations and deaths. It is unclear if low literacy and limited access to technology in low- and middle-income countries affect these benefits. We evaluated whether TMIs added to usual care could reduce HF-related rehospitalizations in patients discharged from hospitals in Brazil.

Characteristics Associated with Prevalent Atrial Fibrillation and Risk Profile for Incident Atrial Fibrillation an Elderly Population from ELSA-Brasil.

Background: Atrial fibrillation (AF) is an arrhythmia causing significant symptoms and raising the risk of complications.

Objectives: To evaluate the association of clinical, electrocardiographic, and echocardiographic parameters with prevalent atrial fibrillation or flutter (AFF) and assess the risk profile for incident AFF using the AF prediction scores CHARGE-AF and EHR in an elderly population from a developing country.

Methods: We included all participants in ELSA-Brasil aged 60 and over whose diagnosis of AFF could be defined through self-report or electrocardiogram and who had echocardiography performed at the study's baseline.

Racial and gender inequities in the control of arterial hypertension in ELSA-Brasil: An intersectional approach.

This study investigated the association of the intersectional categories of gender-race/color with inadequate blood pressure (BP) control in Brazilian adults using antihypertensive drugs to treat hypertension. This is a cross-sectional analysis conducted with 4448 participants living with hypertension from visit 2 (2012-2014) of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) undergoing pharmacological treatment. The association of the intersectional categories - White woman, Brown woman, Black woman, White man, Brown man, Black man - with inadequate BP control (systolic BP levels ≥140 mmHg and/or diastolic BP levels ≥90mmH) was estimated by the prevalence ratio (PR) and 95% confidence interval (95% CI) obtained by generalized linear models with Poisson distribution, adjusted covariates.

Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study.

Background And Aims: Current heart failure (HF) risk stratification strategies require comprehensive clinical evaluation. In this study, artificial intelligence (AI) applied to electrocardiogram (ECG) images was examined as a strategy to predict HF risk.

Methods: Across multinational cohorts in the Yale New Haven Health System (YNHHS), UK Biobank (UKB), and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), individuals without baseline HF were followed for the first HF hospitalization.

Burden of disease attributable to risk factors: Estimates of the Global Burden of Disease from 1990 to 2021.

Objectives: Analyze the burden of diseases attributable to risk factors (RF) in Brazil according to age, sex, and Brazilian states between 1990 and 2021.

Methods: This study used data from the Global Burden of Disease study 1990 to 2021. The metrics used in this analysis included: mortality rates, disability-adjusted life years (DALYs) and Summary Exposure Value (SEV).

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.

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.

Advancing Cardiovascular Health Equity Globally Through Digital Technologies.

Cardiovascular diseases (CVDs) remain the leading cause of death and disability worldwide. Digital health technologies are important public health interventions for addressing the burden of cardiovascular disease. In this article, we discuss the importance of translating digital innovations in research-funded projects to low-resource settings globally to advance global cardiovascular health equity.

Lifting all boats: strategies to promote equitable bidirectional research training opportunities to enhance global health reciprocal innovation.

Inequities in global health research are well documented. For example, training opportunities for US investigators to conduct research in low-income and middle-income countries (LMIC) have exceeded opportunities for LMIC investigators to train and conduct research in high-income countries. Reciprocal innovation addresses these inequities through collaborative research across diverse global settings.

Use of Antihypertensive Drugs and Arterial Stiffness in the Longitudinal Study of Adult Health (ELSA-Brasil).

Objective: Evaluate the longitudinal association between BP control and the use of antihypertensive classes with arterial stiffness (AS) in Brazilian adults.

Methods: This study included 1830 participants with arterial hypertension (1092 participants with controlled BP and 738 participants with uncontrolled BP) from the Longitudinal Study of Adult Health (ELSA-Brasil). AS was assessed by pulse wave velocity (PWV) and pulse pressure (PP) at baseline and repeated after approximately 9 years.

Prevalence and factors associated with possible cases of familial hypercholesterolemia in Brazilian adults: a cross-sectional study.

This study aimed to estimate the prevalence of possible cases of FH and analyze associated factors in the adult Brazilian population. Cross-sectional study with laboratory data from the Brazilian National Health Survey, with 8521 participants. Possible cases of FH were defined according to the Dutch Lipid Clinic Network criteria.

Use of Human-Centered Design Methodology to Develop a Digital Toolkit to Optimize Heart Failure Guideline-Directed Medical Therapy.

Background: Guideline-directed medical therapies (GDMTs) improve quality of life and health outcomes for patients with heart failure (HF). However, GDMT utilization is suboptimal among patients with HF.

Objective: The aims of this study were to engage key stakeholders in semistructured, virtual human-centered design sessions to identify challenges in GDMT optimization posthospitalization and inform the development of a digital toolkit aimed at optimizing HF GDMTs.

Consumption of ultra-processed foods and eight-year risk of death from all causes and noncommunicable diseases in the ELSA-Brasil cohort.

Increased consumption of ultra-processed foods (UPF) is associated with higher incidences of many noncommunicable diseases (NCDs) and death from all causes. However, the association between UPF and cardiovascular disease (CVD) mortality remains controversial. Our study investigated whether UPF consumption is associated with a higher risk of death from all causes, NCDs, and CVD.

Aspirin for Secondary Prevention of Cardiovascular Disease in 51 Low-, Middle-, and High-Income Countries.

Importance: Aspirin is an effective and low-cost option for reducing atherosclerotic cardiovascular disease (CVD) events and improving mortality rates among individuals with established CVD. To guide efforts to mitigate the global CVD burden, there is a need to understand current levels of aspirin use for secondary prevention of CVD.

Objective: To report and evaluate aspirin use for secondary prevention of CVD across low-, middle-, and high-income countries.