Luisa Campos Caldeira Brant

A Estatística Cardiovascular – Brasil foi estabelecida para fornecer avaliação da saúde cardiovascular no Brasil de maneira periódica, abrangente e baseada em evidência, assim como para apoiar a prática clínica, a pesquisa, o planejamento em saúde pública e a elaboração de políticas.1-3 Desenvolvida sob os auspícios da Sociedade Brasileira de Cardiologia, a iniciativa reúne uma rede multidisciplinar de investigadores de instituições acadêmicas em todo o país, que são responsáveis pela revisão, análise e interpretação dos dados de múltiplas fontes complementares. [...] A Estatística Cardiovascular – Brasil foi estabelecida para fornecer avaliação da saúde cardiovascular no Brasil de maneira periódica, abrangente e baseada em evidência, assim como para apoiar a prática clínica, a pesquisa, o planejamento em saúde pública e a elaboração de políticas.1-3 Desenvolvida sob os auspícios da Sociedade Brasileira de Cardiologia, a iniciativa reúne uma rede multidisciplinar de investigadores de instituições acadêmicas em todo o país, que são responsáveis pela revisão, análise e interpretação dos dados de múltiplas fontes complementares.

BACKGROUND: It remains unclear whether social determinants contribute to major ECG abnormalities (MEA), an early marker of cardiovascular disease, which predicts adverse cardiovascular events and mortality. We investigated the incidence of MEA and its associations with race and life-course socioeconomic position, as well as gender differences. METHODS: This prospective cohort study included 11 761 participants from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil [Estudo Longitudinal de Saúde do Adulto–Brasil]) free of MEA and cardiovascular disease at baseline (2008–2010). Incident MEA cases were identified by ECG at the second (2012–2014) and third (2017–2019) cohort visits. Explanatory variables included life-course socioeconomic position (childhood, youth, and adulthood); intergenerational social mobility; and race, and racial discrimination. Parametric survival models with interval censoring and Weibull distribution stratified by gender were used. RESULTS: The mean age was 51 years (SD±8), and 55.5% of participants were women. The cumulative incidence of MEA was 15.8% in 8 years follow-up. Low childhood socioeconomic position (versus high socioeconomic position) was associated with a 48% higher incidence of MEA (95% CI, 1.17–1.87), only among women. In adulthood, low income and educational level were associated with a higher incidence of MEA in both genders, whereas occupational variables were associated only among women. Social immobility at the base of the educational and socio-occupational hierarchy was associated with an increase of 25% (95% CI, 1.04–1.49) and 31% (95% CI, 1.08–1.61) in the MEA incidence, respectively, only among women. MEA incidence was 35% higher (95% CI, 1.13–1.61) among Black women (versus White women). Furthermore, lifetime exposure to racial discrimination was associated with a higher incidence of MEA among both Black women (hazard ratio, 1.37 [95% CI, 1.04–1.82]) and Brown women (hazard ratio, 1.70 [95% CI, 1.07–2.70]). These associations were not observed among men. CONCLUSIONS: Life-course social and racial inequalities are associated with MEA incidence, particularly among women, underscoring the importance of social determinants in cardiovascular health.

BACKGROUND Heart failure (HF) is a leading cause of hospitalizations, particularly in low- and middle-income countries (LMICs). Guideline-directed medical therapy (GDMT) can reduce adverse events by 64%, yet remains underutilized. Digital health interventions (DHIs) may improve GDMT use by enhancing access to care and patient self-efficacy, but their effectiveness depends on sustained patient engagement. OBJECTIVE To describe the development of a culturally-adapted DHI to support GDMT optimization within Brazil’s public health system (OPT-HF), using human-centered design (HCD) and reciprocal innovation principles. METHODS OPT-HF was developed through structured meetings with researchers and health professionals and HCD sessions with patients and caregivers, following the iDesign framework. A three-week mixed-methods pilot study assessed usability and acceptability (primary outcomes), and engagement, self-care behavior (European Self-Care Behavior Scale), and medication optimization (secondary outcomes). An Implementation Research Logic Model (IRLM) was created to define implementation determinants, strategies, mechanisms, and expected outcomes. RESULTS The OPT-HF intervention comprises a multicomponent mobile application featuring telemonitoring of vital signs, educational content, and GDMT tracking, integrated with a dashboard and teleconsultation program. Ten patients were enrolled [mean age 57 years, 40% female] in the pilot study. The intervention demonstrated high usability and acceptability. Engagement was high for daily monitoring tasks but lower for educational videos. Self-care scores improved from 41 to 73, exceeding adequacy thresholds, and 7 of 10 participants showed significant improvement in the medication optimization score. Qualitative analyses highlighted increased perceptions of safety and support, alongside barriers related to Bluetooth connectivity and access to videos and chat functions. These findings informed iterative refinements and IRLM development. CONCLUSIONS The OPT-HF DHI showed good usability, acceptability, and preliminary effectiveness in improving HF self-care and GDMT optimization. Culturally tailored, HCD design was essential to enhance engagement. The IRLM supported the understanding of mechanisms underlying this multicomponent intervention and will inform future implementation. CLINICALTRIAL https://ensaiosclinicos.gov.br/rg/RBR-10vpf9bm INTERNATIONAL REGISTERED REPORT RR2-https://doi.org/10.1136/bmjopen-2024-091046

BACKGROUND: We aimed to refine and validate a deep neural network model from the ECG to predict atrial fibrillation (AF) risk, using samples from diverse backgrounds: the Framingham Heart Study (FHS), UK Biobank, and Estudo Longitudinal da Saúde do Adulto (ELSA-Brasil). We compared the model’s performance to the clinical Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (CHARGE-AF) risk score and evaluated the association with other cardiovascular outcomes. METHODS: The ECG-derived deep-learning prediction of AF (ECG-AF) model was refined using 60% of FHS samples free of AF. Its performance was then tested in the remaining FHS samples, UK Biobank, and ELSA-Brasil, with discrimination assessed by the area under the receiver operating characteristic curve. The association of ECG-AF with cardiovascular outcomes was assessed using Cox proportional hazards models. RESULTS: The study sample included 10 097 FHS participants (mean age 53±12 years; 54.9% women), 49 280 participants from the UK Biobank (mean age 64±8 years, 47.9% women), and 12 284 participants from ELSA-Brasil (mean age 53±8 years, 54.7% women). The ECG-AF model showed moderate discrimination for incident AF (area under the curve, 0.82 [95% CI, 0.80–0.84]) in the FHS, comparable to the CHARGE-AF score (area under the curve, 0.83 [95% CI, 0.81–0.85]), and incremental when combined (area under the curve, 0.85 [95% CI, 0.83–0.87]). In UK Biobank and ELSA-Brasil, combining ECG-AF and CHARGE also improved prediction. Higher ECG-AF scores were associated with increased risks of heart failure, myocardial infarction, stroke, and all-cause mortality in all 3 cohorts. CONCLUSIONS: In multinational cohort studies, the single-input ECG-AF deep neural network model demonstrated good performance in predicting AF and other cardiovascular outcomes, comparable to a multivariable clinical risk score, with improved performance when combined.

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. Addressing these challenges and designing unbiased systems of care are essential to enable broad access to DHT and to benefit diverse and under-represented communities. Doing so will fill gaps in the clinical evidence base and avoid perpetuating historical biases. In this Review, we propose a personalized framework to improve access to DHT, addressing determinants of access at the individual, interpersonal, community, society, government and industry levels. We frame these issues globally, highlighting how the challenges to DHT access and potential solutions might differ between continents while also emphasizing common themes. We provide perspectives from partners across the spectrum of health care, including clinicians, clinical trialists, and experts from digital health and industry.

Background: Diabetes mellitus (diabetes) is associated with the development of endothelial dysfunction. However, the directionality of this association has been debated. Our primary objective was to evaluate the association of microvascular endothelial function endothelial function with incident diabetes in Brazilian adults. We also assessed whether there was an effect modification according to obesity status. Methods: In participants from the ELSA-Brasil cohort study, free of diabetes, and with valid exams for endothelial function at baseline, we evaluated the association between endothelial function and incident diabetes after 8.8 years (SD=2.1) of follow-up. Microvascular endothelial function was assessed by peripheral arterial tonometry (PAT). The variables mean basal pulse amplitude (BPA) representing basal vascular tone, and PAT ratio, the vasodilatory capacity in response to reactive hyperemia, were analyzed in sex-specific quartiles. Higher BPA and lower PAT ratio reflect more impaired endothelial function. Cox proportional hazard models were used for statistical analyses, and multivariable models were adjusted for sex, age, educational level, physical activity level, smoking, alcohol use, and body mass index (BMI) or waist circumference (WC). Results: In 1,268 participants, mean age was 51±8 years, with 45% women, and mean BMI of 26.4±4.4 kg/m ² . After follow-up, we identified 159 new cases of diabetes. The incidence rates varied from 6.5 (95%CI 4.0-10.2) in the first quartile to 19.7 (95%CI 15.1-25.8) in the fourth quartile for BPA and from 7.2 (CI 95% 4.7-11.1) in the fourth quartile to 15.3 (CI 95% 11.4-20.6) in the first quartile of for PAT ratio. After adjustments, we found that the risk of diabetes increased significantly in those with more impaired endothelial function, for both BPA HR 2.13 (95% CI 1.24 – 3.66, p<0.05) and PAT ratio 1.54 (95% CI 0.90 – 2.60, p 0.11). There was no effect modification according to obesity status, evaluated by BMI and WC. Conclusions: In Brazilian adults, greater impairment of endothelial function at baseline predicted diabetes after 8.8 years of follow-up, independent of obesity status. These findings may have implications for understanding cardiometabolic diseases in the context of pathophysiology, prognosis and treatment.

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. Our objective is to develop and test the effectiveness of a DHI to optimise GDMT in patients recently hospitalised for HF in the Brazilian public health system ( Sistema Único de Saúde (SUS)). Methods and analysis: This is a randomised, controlled, multicentre, parallel-group, clinical trial in which 154 patients being discharged from an HF-related hospitalisation will be randomised. Inclusion criteria are ≥18 years of age, reduced ejection fraction HF (EF<50%) and medication optimisation gaps (at least one GDMT class not started or two among those with prescribed dosage≤50% of the target dose). All participants will receive a written booklet and SUS usual care. Randomisation will be stratified by site. The intervention includes a mobile application (app) to engage patients, developed through a human-centred design. The app’s main features are a check-in page for daily collection of participants’ health status, vital signs and weight; a remote educational programme; a chat function during working hours and longitudinal graphical representations of participants’ data. The participants’ data will be managed daily by a nurse, linked to a cardiologist for teleconsultations. Predefined clinical decision trees will guide actions, including alarm signs and GDMT optimisation. The primary outcome will be changes in GDMT from baseline to end of follow-up in 90 days. Secondary outcomes will include all-cause readmission, HF-related rehospitalisation, change in health status and HF knowledge, and implementation outcomes based on the RE-AIM framework. The analysis of outcomes will follow the intention-to-treat principle. Ethics and dissemination: This study was approved by the Universidade Federal de Minas Gerais. Recruitment started in November 2023, and patients involved will sign an informed consent form. Results will be presented at scientific meetings and published in scientific journals in 2025, and will be disclosed in social media and presented to public health stakeholders. Trial registration number: Universal Trial Number U1111-1295-1864 Brazilian Clinical Trials Registry ( https://ensaiosclinicos.gov.br/rg/RBR-10vpf9bm ).