Adedinsewo, Demilade

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 (<18). Linear regression assessed EF–Montreal Cognitive Assessment correlation and logistic regression the association between HF subtypes and cognitive dysfunction, adjusting for age, sex, race, and vascular risk factors. Results A total of 1592 patients met inclusion criteria (mean age, 75.7±13.8 years; 48.5% women). Lower EF was associated with worse cognitive performance (postadjustment β=0.033, 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 <0.001). The prevalence of moderate to severe cognitive impairment was highest in HF with reduced EF (43.3%), followed by HF with preserved EF (34.4%), no heart failure (28.6%), and HF with mildly reduced EF (23.4%) (adjusted trend 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.

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.

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.

Abstract Background Ischemic heart disease is the leading cause of death among women. Non-obstructive coronary disease (NOCAD), often identified by a positive stress test and negative coronary angiogram result, is linked to adverse outcomes, particularly among women. Purpose This study evaluated the impact of discordant Single Photon Emission Computed Tomography (SPECT) Myocardial Perfusion Imaging (MPI) stress test and coronary angiogram results on repeat invasive angiography and survival, while assessing sex-related differences. Methods Data were extracted from a single health system for patients who underwent a SPECT MPI stress test, and invasive coronary angiography performed within 3 months from September 2002 to July 2018. Discordance was defined as: Profile A – a positive stress test with negative angiography (epicardial coronary vessel stenosis &lt;50%); Profile B – a negative stress test with positive angiography (at least one epicardial coronary vessel stenosis &gt;=50%). To assess the association between discordance and all-cause mortality, Cox-proportional hazards regression was used to analyze the most recent stress/angiogram pair (adjusting for race, age, and number of prior angiograms). For repeat invasive angiography, where mortality was considered a competing event, the Fine-Gray proportional hazards model for the sub-distribution of a competing risk was used to analyze the first stress/angiogram pair (adjusting for race and age). Hazard ratios (HRs), sub-distribution hazard ratios (SHRs), and corresponding 95% confidence intervals (CIs) were reported. Results 5,179 were included in the final analysis, median age was 67.9 years and 32.7% were female. Approximately 15% and 24% were classified as Profile A and B respectively. Patients with a negative stress/negative angiogram were designated as the reference group. Profile A was not significantly associated with mortality or repeat invasive testing in both sexes. Profile B was associated with the highest risk of death (female aHR: 1.58, P&lt;.001; male aHR: 1.54, P&lt;.001) followed by those with a positive stress/positive angiogram (female aHR: 1.39, P=.009; male aHR: 1.25, P=.05) compared to the reference group. Profile B was significantly associated with repeat invasive testing and a stronger association was seen in females (female aSHR: 9.84, P&lt; .001; male aSHR: 3.42, P&lt;.001). A similar pattern was seen for those with a positive stress/positive angiogram. Conclusion Discordant Profile A showed no significant association with mortality or repeat invasive testing. Discordant Profile B was associated with the highest mortality risk in both sexes and showed a stronger association with repeat invasive testing among females. These findings underscore the importance of careful patient selection for stress testing, the need for improved stress testing options (especially in females), and prompt coronary evaluation in patients with a negative stress test and persistent symptoms or high-risk features.

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.

Background: Cardiomyopathy occurring during pregnancy or in the postpartum period is a leading case of maternal death in the US. The most commonly described form of pregnancy related cardiomyopathy (PRCM) associated with left ventricular systolic dysfunction is peripartum cardiomyopathy, defined as left ventricular ejection fraction (LVEF) <45% on echocardiography. PRCM symptoms often overlap with those of normal pregnancy and the lack of validated screening tools limits effective recognition, contributing to diagnostic delays. Recently, several artificial intelligence enabled electrocardiogram (AI-ECG) models have been developed and evaluated for PRCM detection. Aim&Hypothesis: We sought to investigate the pooled diagnostic performance of AI-ECG models for detecting PRCM. We hypothesized that AI-ECG models would be effective for detecting PRCM. Methods: A systematic search of PubMed, Scopus, and Cochrane was performed by combining search terms “artificial intelligence”, “machine learning”, “deep learning”, “algorithm”, “ECG”, “EKG”, “electrocardiogram”, “peripartum cardiomyopathy”, “pregnancy”, “pregnancy related cardiomyopathy” , to identify studies investigating the use of AI-ECG models for PRCM detection. Studies included those utilizing standard 12-lead ECGs as well as single lead ECGs from various devices. For inclusion in the meta-analysis, studies had to provide complete confusion matrix data for the AI-ECG model. Inverse variance random effects model meta-analysis was performed. Pooled performance estimates with corresponding 95% CIs are presented in forest plots and a summary receiver operating characteristics (sROC) curve. Results: Our search identified a total of 201 studies (PubMed: 64, Embase: 117, Cochrane: 20) from which 6 studies involving 11 AI-ECG models and 3,006 patients were included in the meta-analysis (Fig. 1) . Following random effect bivariate meta-analysis, the AI-ECG models yielded a pooled sensitivity of 0.721 (95% CI 0.676–0.762); specificity of 0.931 (95% CI 0.932–0.938); diagnostic odds ratio (DOR) of 44.658 (95% CI 23.843–83.644); and AUC of 0.896 (95% CI 0.87–0.91), for PRCM detection (Fig. 2A-C, 3) . Conclusion: AI-ECG models appear to be effective tools for detecting PRCM and may represent a viable solution to reduce diagnostic delays consequently improving maternal outcomes. Additional studies are needed to evaluate its impact on clinical outcomes in pragmatic settings.

INTRODUCTION: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS/IMPLICATIONS: 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.

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.

Abstract Advancements in machine learning and computing methods have given new life and great excitement to one of the most essential diagnostic tools to date—the electrocardiogram (ECG). The application of artificial intelligence‐enabled ECG (AI‐ECG) has resulted in the ability to identify electrocardiographic signatures of conventional and unique variables and pathologies, giving way to tremendous clinical potential. However, what these AI‐ECG models are detecting that the human eye is missing remains unclear. In this article, we highlight some of the recent developments in the field and their potential clinical implications, while also attempting to shed light on the physiologic and pathophysiologic features that enable these models to have such high diagnostic yield. © 2022 American Physiological Society. Compr Physiol 12:3417‐3424, 2022.

The growing adoption of advanced imaging modalities, such as transesophageal echocardiography (TEE), has led to more frequent detection of cardiac structural abnormalities. While intracardiac cardiac tumors and shunts are infrequent and typically asymptomatic, their existence can precipitate severe outcomes, including stroke, myocardial infarction and sudden death. Case Description: A 69-year-old female presented with left sided facial droop, slurred speech and left arm weakness. A computed tomography angiography revealed focal occlusion of the distal right middle cerebral artery. She was initiated on thrombolytic therapy and experienced resolution of symptoms. Transthoracic echocardiogram revealed right to left shunting consistent with a patent foramen ovale (PFO). Further workup with TEE confirmed the presence of a PFO but also detected a mobile echogenic mass on the aortic valve leaflet, indicative of papillary fibroelastoma (PFE). Cerebral infarction was attributed to an embolic event, with the PFO and PFE being likely culprits. Discussion: Current guidelines give a conditional recommendation, low certainty of evidence, for PFO closure in adults older than 60 years, although some observational studies have shown some risk reduction in older patients. The key trials showing clinical benefit of PFO closure were in adults younger than the age of 60. For left sided cardiac tumors, such as PFE, guidelines give a 2A recommendation for resection. Addressing the risk for recurrent stroke in this 69-year-old patient was an essential part of stroke care, as such, a multi-disciplinary team of experts including cardiology, neurology, and cardiothoracic surgery was conveyed. The decision was to proceed with surgical intervention in addition to antiplatelet therapy to achieve optimal risk reduction. The patient subsequently underwent successful surgical removal of the PFE with native valve preservation and PFO closure. Conclusion: Cardiac PFEs and PFOs can be associated with increased stroke risk. Employing appropriate imaging techniques and timely interventions are of utmost importance in stroke management.