Background: Increased arterial stiffness, a marker of cardiovascular disease (CVD), is prevalent among African American (AA) adults and contributes to persistent CVD disparities. The Cardio-Ankle Vascular Index (CAVI) is a validated measure of arterial stiffness and predictor of adverse CVD outcomes. However, its relationship with the novel American Heart Association’s Life’s Essential 8 (LE8) cardiovascular health (CVH) construct has not been examined among AAs. Hypothesis: We hypothesized that increased arterial stiffness would be associated with lower CVH among AA adults. Methods: We analyzed baseline data from AA adults in the Techquity by FAITH! Trial, a 2-arm cluster randomized controlled trial conducted across 18 AA churches in Rochester and Minneapolis–St. Paul, Minnesota. Eligible participants (age ≥18 years with suboptimal diet or physical activity [PA]) completed validated electronic surveys (e.g., PA patterns) and clinical assessments (e.g., blood pressure [BP], lipids). CAVI was summarized as mean and categorized as normal (<8), borderline (8 to <9), or abnormal (≥9). CVH was assessed using the LE8 score (range 0-100 points) and categorized as low (<50), moderate (50-79), or high (≥80). Associations between CAVI and LE8 were assessed using multivariable linear regression adjusted for age, sex, and income (continuous) and Fisher’s exact test (categorical). LE8 components were compared across CAVI categories using Kruskal–Wallis tests. Results: Among 140 participants (mean age 53.0 years [SD 14.6]; 68% female), the mean LE8 score was 62.7 (SD 14.0). Nicotine exposure LE8 score was the highest (84.6 [SD 26.6]) and diet was the lowest (40.7 [SD 13.7]). Mean CAVI was 7.9 (SD 1.5), with 50% of participants classified as normal, 22% borderline, and 28% abnormal. No association between continuous CAVI and LE8 scores was observed after adjustment (unadjusted R 2 = 3.5%). All participants with high LE8 had normal CAVI, while abnormal CAVI was more prevalent among those with low (40%) and moderate (30%) LE8 scores (p<0.001). LE8 scores differed across CAVI categories for BP (median 50 in normal CAVI vs 30 in borderline/abnormal CAVI; p=0.003) and glucose LE8 scores (median 100 vs 60; p=0.014). Conclusions: Greater arterial stiffness was associated with lower CVH categories, with notable component-level differences in BP and glucose. Although not independently associated with overall LE8, integrating CAVI and LE8 categories may improve CVD risk stratification among AAs.

BACKGROUND:Forecasts for the future prevalence of cardiovascular disease and stroke are crucial to guide efforts to improve health outcomes across the life course for women. METHODS:Using historical trends from the 2015 to 2020 National Health and Nutrition Examination Survey, 2015 to 2019 Medical Expenditure Panel Survey, and census estimates for population growth, we estimated trends in prevalence through 2050 for cardiovascular risk factors based on suboptimal levels of Life's Essential 8 and clinical cardiovascular disease and stroke, overall and by age and race and ethnicity. RESULTS:Among adult women overall, the prevalence of hypertension is estimated to increase from 48.6% in 2020 to 59.1% in 2050. Diabetes (14.9% to 25.3%) and obesity (43.9% to 61.2%) will increase, whereas hypercholesterolemia will decline (42.1% to 22.3%). Prevalences of suboptimal diet, inadequate physical activity, and smoking will decline over time, and inadequate sleep will increase. Prevalences of coronary disease (6.85% to 8.21%), heart failure (2.45% to 3.60%), stroke (4.14% to 6.74%), atrial fibrillation (1.58% to 2.31%), and total cardiovascular disease and stroke (10.7% to 14.4%) will rise. Similar trends are projected in girls 2 to 19 years of age, with an increase from 19.6% to 32.0% projected in obesity. Most adverse trends are projected to be more pronounced among women and girls identifying as American Indian/Alaska Native or multiracial, Black, or Hispanic. CONCLUSIONS:The prevalence of cardiovascular risk factors and disease in women and girls will increase over the next 30 years. Focused clinical and public health interventions are needed across the life course to address these adverse trends.

BACKGROUND:Cardiovascular disease (CVD) is the leading cause of death in women worldwide and a primary contributor to maternal mortality in the United States. OBJECTIVES:We compared prevalence and trends in CVD, cardio-kidney-metabolic (CKM) risk factors, and maternal mortality among women of reproductive age (15-49 years) in the United States, relative to other high-income countries. METHODS:Using Global Burden of Disease 2023 data, we evaluated CVD prevalence and disability-adjusted life years (DALYs), CKM risk factors, maternal mortality rates, and hypertensive disorders of pregnancy-associated mortality in the United States compared with other high-income countries from 2003 to 2023. RESULTS:From 2003 to 2023, the United States saw a decline in CVD prevalence (3.7% to 3.1%) and DALY rates (by 20%) among women of reproductive age. However, CVD burden remained higher than in other high-income countries. DALY rates attributable to CKM risk factors were 4-fold higher for body mass index >21 kg/m2, 3-fold higher for fasting plasma glucose >95 mg/dL, and 2-fold higher for systolic blood pressure >115 mm Hg in the United States compared to other high-income countries. In 2023, the United States and Southern Latin America had the highest maternal mortality rates (33.8 and 65.2 per 100,000 live births), and mortality rates attributed to hypertensive disorders of pregnancy (2.8 and 8.9 per 100,000 live births) compared to other high-income countries. CONCLUSIONS:The U.S. women of reproductive age face a disproportionate burden of CVD, CKM factors, and maternal mortality rates compared with other high-income countries. Integrating early CVD and CKM screening and management into U.S. health care should be an urgent national priority.

Dual antiplatelet therapy (DAPT) following percutaneous coronary intervention (PCI) is traditionally guided by rule-based scores providing static, single-time-point or fixed time-interval estimates with modest discrimination (C-index 0.63-0.73), limiting individualized DAPT duration decisions. We developed Transformer-DAPT, a transformer-based deep learning survival framework designed to estimate patient-specific ischemic and bleeding risks across clinically relevant time intervals during the first year after PCI. Using electronic health records from 29,032 patients at Mayo Clinic and externally validated in 19,173 patients from the OneFlorida+ Clinical Research Consortium, Transformer-DAPT achieved time-dependent concordance indices (Ctd) of 0.84-0.87 for ischemic events and 0.81-0.88 for bleeding events, outperforming DeepSurv and DeepHit by 2%-12%. In the external cohort, Ctd ranged from 0.74-0.84 and 0.75-0.83 for ischemic and bleeding events, respectively. Transformer-DAPT demonstrated improved discrimination and calibration performance, providing a framework for multi-interval risk prediction to support personalized DAPT management after PCI.

Introduction: Studies suggest that stroke and hypoperfusion from heart failure (HF) may contribute to cognitive decline. However, the relationship between the HF subtypes and cognition remains poorly described in real-world populations. Hypothesis: We hypothesized that HF subtypes are differentially associated with cognitive impairment and that lower left ventricular ejection fraction (LVEF) correlates with poorer cognitive performance. Methods: We conducted a cross-sectional study of patients aged ≥18 years seen at a Heath System in 2022 who underwent both echocardiogram and cognitive assessment within one year. HF was classified into four groups based on 2022 American Heart Association HF guideline: No heart failure, HFpEF (HF with preserved LVEF), HFmrEF (HF with mid-range LVEF), and HFrEF (HF with reduced LVEF). Cognitive impairment was categorized using the Montreal Cognitive Assessment (MOCA) as normal (≥26), mild (18–25), moderate (10–17), and severe (<10). Multivariable linear regression assessed the correlation between EF and MoCA, and logistic regression evaluated the association between HF subtypes and severity of cognitive loss, adjusting for age, sex, race, hypertension, diabetes, dyslipidemia, chronic kidney disease, stroke. Results: A total of 1,695 patients were included in the study (mean age, 75.3±14 years; 48.3% female). 82.0% had HFpEF; 7.1%, HFmrEF; 7.9%, HFrEF, and 3.0% no HF. LVEF was positively associated with MoCA score (β=0.028, p=0.032). Patients with LVEF ≤40% had lower MoCA scores (17.7 ± 6.2 vs. 19.1 ± 5.9, p=0.008) and higher adjusted odds of moderate/severe cognitive impairment (aOR 2.49, 95%CI 1.20-5.15, p=0.014), compared to those with LVEF >40%. Adjusted odds of moderate to severe cognitive impairment were 1.14 for HFpEF, 0.99 for HFmrEF, and 2.81 for HFrEF compared to patients with no heart failure (trend-P=0.031, Table). Conclusions: Lower LVEF was independently associated with worse cognitive performance. Patients with HFrEF had a 2.5-fold higher risk of moderate to severe cognitive impairment. This association was not observed in patients with HFpEF or HFmrEF, suggesting differential cognitive effects across HF subtypes. Further studies are warranted to confirm these findings and evaluate the impact of interventions targeting EF and HF subtypes on cognitive outcomes.

Artificial intelligence (AI) is delivering value across all aspects of clinical practice. However, bias may exacerbate healthcare disparities. This review examines the origins of bias in healthcare AI, strategies for mitigation, and responsibilities of relevant stakeholders towards achieving fair and equitable use. We highlight the importance of systematically identifying bias and engaging relevant mitigation activities throughout the AI model lifecycle, from model conception through to deployment and longitudinal surveillance.

PURPOSE:Identifying cardiovascular disease before conception and in early pregnancy can better inform obstetric cardiovascular care. Our main objective was to evaluate the diagnostic performance of artificial intelligence (AI)-enabled digital tools for detecting left ventricular systolic dysfunction (LVSD) among women of reproductive age. METHODS:In a pilot cross-sectional study, we enrolled an initial cohort of 100 consecutive women aged 18-49 years who had a primary care physician and a scheduled echocardiography at Mayo Clinic Florida (Jacksonville) (cohort 1). Twelve-lead electrocardiography (ECG) and digital stethoscope recordings (single-lead ECG + phonocardiography) were performed on the date of echocardiography. We used deep learning to generate prediction probabilities for LVSD (defined as left ventricular ejection fraction <50%) for the 12-lead ECG (AI-ECG) and stethoscope (AI-stethoscope) recordings. In a second cohort of 100 participants, we enrolled consecutive women seen in primary care to estimate the prevalence of positive AI screening results when deployed for routine use (cohort 2). RESULTS:The median age of participants was 38.6 years (quartile 1: 30.3 years, quartile 3: 45.5 years), and 71.9% identified as part of the non-Hispanic White population. Among cohort 1, 5% had LVSD. The AI-ECG had an area under the curve of 0.94, and the AI-stethoscope (maximum prediction across all chest locations) had an area under the curve of 0.98. Among cohort 2, the prevalence of a positive AI screen was 1% and 3.2% for AI-ECG and the AI-stethoscope, respectively. CONCLUSION:We found these AI tools to be effective for the detection of cardiomyopathy associated with LVSD among women of reproductive age. These tools could potentially be useful for preconception cardiovascular evaluations.

Background: In the United States, rural residents face unique challenges that affect their overall cardiovascular health and mortality including access to care for acute cardiovascular conditions. We assessed differences in mortality and mode of care among adult patients diagnosed with acute myocardial infarction (AMI) based on geographical residence. Methods: Using data from the Medicare Provider Analysis and Review files, we identified adult patients aged 45 and older who were diagnosed with AMI between 2015 and 2019. Cox-proportional hazards regression models were constructed to determine the association between patient’s geographic residence (rural vs. urban) on 30- and 90-day all-cause mortality. Results: A total of 868,955 adult patients diagnosed with AMI were included in this study. Twenty-six percent of the sample resided in rural areas. Among rural residents with AMI, 46% were managed invasively with either percutaneous coronary intervention (PCI) or coronary artery bypass graft (CABG) surgery which was similar to 45% of urban residents. After controlling for age, race, sex, and comorbid conditions, rural residents had a higher risk of death compared to urban patients (aHR 1.04, p<0.001) at 30 and 90 days. In subgroup analyses, a similar pattern was observed among those who did not undergo invasive procedures (aHR 1.06, p<0.001), however, there was no differences in mortality at 30 days (aHR 0.99, p=0.30) and 90 days (aHR: 0.99, p=0.36) between rural and urban residents who were managed by PCI or CABG. Conclusion: Rural patients with AMI had a higher mortality risk than their urban counterparts, and this difference was driven by a higher death rate among those managed conservatively. Further investigation is needed to understand the underlying factors driving rural-urban disparities in mortality, particularly with non-invasive management of AMI.

The SPEC-AI Nigeria trial (NCT05438576) was designed to use artificial intelligence (AI) to screen for pregnancy-related cardiomyopathy in the peripartum period. Using data from this study, we evaluated the utility of AI-predicted delta age (adjusted AI-predicted age − chronological age) as a surrogate for biological age. We included 1,187 pregnant and postpartum women enrolled between August 2022 and September 2023 with follow-up through May 2024. Standard 12-lead electrocardiograms (ECGs) were recorded at study entry to generate AI age predictions. Artificial intelligence-predicted age was adjusted using estimated reference ranges obtained from a community-dwelling cohort of 25,144 individuals with AI-ECG age estimated and documented chronological age. Logistic and Cox-proportional hazards regression were used to examine associations with comorbid cardiovascular conditions and maternal mortality, respectively. Adjusted AI-predicted age was significantly higher among women with any cardiovascular condition, peripartum cardiomyopathy, or who died within 18 months (7, 14, and 23 years older, respectively) compared to those without these conditions who had values similar to the normal reference ranges (adjusted AI-predicted age difference less than 1 year). Artificial intelligence-predicted delta age greater than the 75th percentile was associated with an odds ratio (OR) of 2.06 for any cardiovascular condition, OR of 4.98 for left ventricular systolic dysfunction, and a hazard ratio of 32.81 for all-cause mortality; all values of P<.001. Artificial intelligence-ECG-derived biological age appears to be a potentially useful measure of cardiovascular health status and risk among pregnant and postpartum women. However, its role in monitoring maternal health requires further exploration.

Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. This open-label, pragmatic clinical trial randomized pregnant and postpartum women to usual care or artificial intelligence (AI)-guided screening to assess its impact on the diagnosis left ventricular systolic dysfunction (LVSD) in the perinatal period. The study intervention included digital stethoscope recordings with point of-care AI predictions and a 12-lead electrocardiogram with asynchronous AI predictions for LVSD. The primary end point was identification of LVSD during the study period. In the intervention arm, the primary end point was defined as the number of identified participants with LVSD as determined by a positive AI screen, confirmed by echocardiography. In the control arm, this was the number of participants with clinical recognition and documentation of LVSD on echocardiography in keeping with current standard of care. Participants in the intervention arm had a confirmatory echocardiogram at baseline for AI model validation. A total of 1,232 (616 in each arm) participants were randomized and 1,195 participants (587 intervention arm and 608 control arm) completed the baseline visit at 6 hospitals in Nigeria between August 2022 and September 2023 with follow-up through May 2024. Using the AI-enabled digital stethoscope, the primary study end point was met with detection of 24 out of 587 (4.1%) versus 12 out of 608 (2.0%) patients with LVSD (intervention versus control odds ratio 2.12, 95% CI 1.05–4.27; P = 0.032). With the 12-lead AI-electrocardiogram model, the primary end point was detected in 20 out of 587 (3.4%) versus 12 out of 608 (2.0%) patients (odds ratio 1.75, 95% CI 0.85–3.62; P = 0.125). A similar direction of effect was observed in prespecified subgroup analysis. There were no serious adverse events related to study participation. In pregnant and postpartum women, AI-guided screening using a digital stethoscope improved the diagnosis of pregnancy-related cardiomyopathy. ClinicalTrials.gov registration: NCT05438576 In this pragmatic, randomized clinical trial involving 1,196 pregnant and postpartum women from 6 hospitals in Nigeria, AI-based electrocardiogram screening proved accurate in detecting cardiomyopathies and suggests that it could improve detection of these conditions.