Demilade Adedinsewo

The field of cardio-obstetrics has grown rapidly in response to rising maternal cardiovascular morbidity and identified gaps in evidence, clinical training, and care systems. We synthesize current literature to define key priorities in research, workforce training, and health policy to improve maternal cardiovascular outcomes. Emerging registries, innovative study designs, digital health technologies, and multidisciplinary care models offer substantial opportunities to advance the field. Coordinated efforts to generate high-quality evidence, build an agile and well-trained workforce, and align health systems with evidence-informed policy are essential to transforming the future of cardio-obstetric care and improving outcomes for women across the life course.

Which architecture is best for sorting 12-lead ECGs into diagnostic superclasses still remains somewhat unsettled in the literature. In this study we compared six different models on PTB-XL (21,375 records) under one single patient-level split: a Mamba-style state space model (M1), a 1D residual CNN (M2), a BiLSTM (M3), a CRNN with attention (M4), a soft-voting ensemble of M2 to M4 (M5), and MiniRocket features with LightGBM (M6). Each record was labeled with the superclass of its highest-likelihood diagnostic code. We then scored the saved per-record predictions with paired patient-level bootstrap intervals, McNemar tests, per-class AUROC and AUPRC, sensitivity, specificity, and calibration. The ensemble reached the highest macro F1 (0.665, 95% CI 0.644–0.685), ahead of every single deep model by 0.020–0.041 (all paired CIs excluded zero) and ahead of M6 (0.645) by 0.021 (95% CI 0.002–0.040). We chose its members after seeing test results, so these margins are optimistic: ensembles built from validation-selected members, or from all four deep models, scored 0.656–0.657, about 0.009 lower (paired CIs included zero). M5 and M6 did not differ significantly in accuracy, and their macro AUROC was nearly identical (0.916 and 0.914). M1 trailed M2 and M3 by 0.021. Hypertrophy (HYP) was the hardest class (F1 0.33–0.41). Differences in HYP recall mostly reflected where each model’s threshold sat: M6 had the lowest recall but the highest AUROC (0.871). We used only a single dataset and split, with no pretraining and no external validation, and so these results do not show that any model is ready for clinical use.

Background: Studies suggest that 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. Methods: We conducted a cross‐sectional study of patients aged ≥18 years seen at the Mayo Clinic in 2022 who underwent echocardiogram and cognitive assessment within 1 year. HF was classified per the 2022 American Heart Association as no HF, HF with preserved ejection fraction (EF), HF with mildly reduced EF, and HF with reduced EF. Cognitive impairment was categorized using the Montreal Cognitive Assessment score as normal (≥26), mild (18–25), and moderate to severe (P =0.013). Patients with HF with reduced EF and patients with HF with preserved EF had significantly lower Montreal Cognitive Assessment scores than those without HF (17.6±6.2 and 18.9±5.8 versus 19.9±6.0, P P =0.032). Conclusions: HF, independent of ejection fraction alone, is associated with cognitive impairment. Further studies should confirm these findings and evaluate the impact of interventions targeting EF and HF subtypes on cognitive outcomes.

Spontaneous coronary artery dissection (SCAD) and Takotsubo syndrome (TTS) are two conditions presenting as acute coronary syndromes that predominantly affect women and are frequently triggered by emotional or physical stress. While traditionally considered distinct entities, emerging evidence suggests that these conditions may coexist more frequently than previously recognized, sharing common pathophysiological mechanisms centered on catecholamine-mediated pathways, vascular vulnerability, and hormonal influences. Their overlapping presentations create significant diagnostic challenges that may lead to misdiagnosis and suboptimal management. This narrative review examines the pathophysiological convergence between SCAD and TTS, addresses the diagnostic complexity arising when these entities coexist, and proposes a novel stepwise diagnostic algorithm integrating clinical risk stratification, coronary angiography, intracoronary imaging, and cardiac magnetic resonance imaging. The review also provides scenario-dependent management guidance and outlines priorities for future research.

Digital health technologies (DHTs) and novel artificial intelligence tools have already begun to transform healthcare in many parts of the world. Non-communicable diseases (NCDs) are on the rise in sub-Saharan Africa, with cardiovascular disease accounting for the largest NCD burden. Digital technologies can accelerate healthcare transformation and improve healthcare delivery in sub-Saharan Africa. This article explores digital technologies in sub-Saharan Africa and potential applications in the field of cardiovascular medicine.

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.