Publications

Noninvasive cardiac imaging plays a critical role in the diagnosis and risk stratification of cardiovascular disease in older adults, a population marked by clinical heterogeneity, multimorbidity, and age-related physiologic changes. This review outlines the strengths and limitations of commonly used imaging modalities including echocardiography, transesophageal echocardiography, cardiac computed tomography, nuclear imaging tests, and cardiac magnetic resonance imaging in the context of aging. We highlight diagnostic challenges such as limited exercise capacity, image quality artifacts, reduced specificity in the setting of multivessel, or microvascular disease and intolerance to longer scan protocols. Advances in imaging technology, including artificial intelligence and hybrid protocols, offer opportunities to improve accuracy, access, and individualized decision-making. The review emphasizes the importance of tailoring test selection to patient comorbidities and goals of care. Addressing current evidence gaps through trials inclusive of older adults and geriatric-focused imaging guidelines is essential to delivering equitable, high-value cardiovascular care to older adults.

The international Contract Ultrasound Society (ICUS) held a round table discussion on the safety of ultrasound contrast agents for cardiology, radiology, and pediatrics on September 4, 2024. The panel included international experts on ultrasound contrast. The panel reviewed the literature on the safety of ultrasound contrast agents and discussed their experiences. The panelists gave recommendations for maintaining safety in administering these agents.

Hypertensive heart disease (HHD) is a major contributor to cardiovascular (CV) morbidity and mortality. Once primarily seen in older adults, recent data suggest a rising burden among younger populations. National Center for Health Statistics (NCHS) mortality data for United States adults aged 15 to 44 from 1999 to 2024 were analyzed. Age-adjusted mortality rates were calculated overall and by demographic subgroup, including sex, race, ethnicity, age group, rural and urban residence, state, and Census region. The proportion of HHD mortality relative to other cardiovascular disease (CVD) deaths were examined. Joinpoint regression identified annual percent changes and inflection points. From 1999 to 2024, there were 119,264 HHD-related deaths among young adults. HHD mortality rose from 1.3 (95% CI, 1.23-1.36) to 6.3 (95% CI, 6.12-6.40), with the sharpest increase from 2018 to 2021. Males experienced greater HHD mortality over the study period (increasing from 1.76 to 9.13 deaths per 100,000 person-years) than females (0.76 to 3.31 deaths per 100,000 person-years). Differences were also noted by race and ethnicity, with Non-Hispanic Black individuals experiencing greater HHD mortality that Non-Hispanic White and Hispanic individuals. Age-related, and geographic differences were also observed. The proportionate HHD mortality increased from 3.8% in 1999 to 16.8% in 2024. Sustained increases in HHD mortality were observed after the COVID-19 pandemic relative to pre-pandemic levels. HHD-related mortality among young adults in the United States has risen significantly, with differences noted by sex, race and ethnicity, age, rural and urban residence, state, and Census region. The growing share of HHD deaths among CVD deaths in young adults signals its increasing role in premature CVD mortality. In conclusion, these trends underscore the urgent need for early prevention, equitable care, and targeted strategies to reduce HHD in young adults.

The American Society of Echocardiography (ASE) plays a vital role in establishing practice standards and guidelines within the echocardiography field. Its influence is comprehensive, covering training, image acquisition, nomenclature, measurements, diagnosis, and quality improvement. This report focuses on the final phases of the diagnostic imaging process, specifically reporting and communicating exam results. It provides updates to previously published guidelines on the required components of a comprehensive echocardiography report. Standardization within echocardiography reports is essential to uphold quality, consistency, and interoperability across various echocardiography (echo) labs, institutions, and healthcare systems, as well as over different time points. Additionally, standardized reporting is crucial for facilitating big data analysis, aligning with the current emphasis on machine learning and artificial intelligence. This document delineates core measurements and statements applicable to transthoracic, transesophageal, and stress echocardiography. It also elucidates abbreviations, acronyms, terminology, and definitions to enhance communication. The path from preliminary report to final submission is clarified, alongside examples of critical, urgent, and significant findings. Recommendations include comparison of serial echocardiograms and, when clinically relevant, comparisons with other imaging modalities. The document addresses the integration of simple congenital heart disease (CHD) findings appropriate for an adult echo lab. Standardization facilitates clinical and research endeavors by ensuring clear and consistent data reporting, thereby enabling seamless data sharing and reusability.

The PRIME 2.0 checklist is an updated, domain-specific framework designed to standardize the development, evaluation, and reporting of artificial intelligence (AI) applications in cardiovascular imaging. This update specifically responds to rapid advances from traditional machine learning to deep learning, large language models, and multimodal generative AI. The updated checklist was developed through a modified Delphi process by an international panel of clinical and technical experts. In contrast to general AI reporting guidelines, it delivers detailed, practical recommendations on all critical aspects of AI research, and builds upon the original seven-domain framework by incorporating cardiovascular imaging-specific complexities such as cardiac motion, imaging artifacts, and inter-observer variability. By promoting transparency and rigor, PRIME 2.0 can serve as a vital resource for researchers, clinicians, peer reviewers, and journal editors working at the forefront of AI in cardiovascular imaging.

BACKGROUND AND AIMS: Accurate differentiation of cardiac amyloidosis (CA) from phenotypic mimics remains challenging using current clinical and echocardiographic techniques. The accuracy of a novel artificial intelligence (AI) screening algorithm for echocardiography-based CA detection was assessed.

METHODS: Utilizing a multisite, multiethnic dataset (n = 2612, 52% CA), a convolutional neural network was trained to differentiate CA from phenotypic controls using transthoracic apical four-chamber video clips. External validation was conducted globally across 18 sites including 597 CA cases and 2122 controls. Classification accuracy was assessed on the entire external validation dataset, and subgroup analyses were performed both on technetium pyrophosphate scintigraphy referrals, and individuals matched for age, sex, and wall thickness. Model accuracy was also compared with the transthyretin CA score and the increased wall thickness score within a subset of older heart failure with preserved ejection fraction patients with increased wall thickness.

RESULTS: Cardiac amyloidosis patients and controls displayed similar age, sex, race, and comorbidities. After the removal of uncertain AI predictions (13%), model discrimination and classification were excellent for the entire external validation dataset [area under the receiver operating characteristic curve (AUROC) 0.93, sensitivity 85%, specificity 93%], irrespective of CA subtype (sensitivity: light-chain = 84%, wild-type transthyretin = 85%, and hereditary transthyretin = 86%). Performance was maintained in subgroup analysis in patients clinically referred for technetium pyrophosphate scintigraphy imaging (AUROC 0.86, sensitivity 77%, specificity 86%) and matched patients (AUROC 0.92, sensitivity 84%, specificity 91%). The AI model (AUROC 0.93) also outperformed transthyretin CA score (AUROC 0.73) and increased wall thickness (AUROC 0.80) scores.

CONCLUSIONS: This AI screening model-using only an apical four-chamber view-effectively differentiated CA from other causes of increased left ventricular wall thickness.

BACKGROUND: Patients with cardiovascular (CV) diseases are increasingly frail but rarely represented in trials. Understanding effect modification by frailty on CV trials is critical as it could help define treatment strategies in frail patients.

OBJECTIVES: This meta-analysis aims to assess the implications of frailty on CV outcomes in clinical trials.

METHODS: Randomized controlled trials examining the effects of frailty in the context of CV trials were included (CRD42024528279). Outcomes included a composite of major adverse cardiac events (MACE), all-cause mortality, CV mortality, hospitalizations, and frailty-specific outcomes (physical, quality of life, and frailty scores). HRs and 95% CIs were pooled for clinical endpoints, and standardized mean differences (SMDs) were calculated for frailty-specific outcomes.

RESULTS: Thirty unique randomized controlled trials were included with a pooled total of 87,711 participants. Frail patients had a significantly increased risk of MACE (HR: 2.33 [95% CI: 1.87-2.91], P < 0.001, I2 = 83%), all-cause mortality (HR: 2.34 [95% CI: 1.80-3.05], P < 0.01, I2 = 75%), CV mortality (HR: 1.76 [95% CI: 1.60-1.93], P < 0.001, I2 = 0%), and hospitalizations (HR: 2.38 [95% CI: 1.65-3.43], P < 0.001, I2 = 92%) compared to nonfrail patients. In the frailest group, trial interventions decreased MACE (HR: 0.81 [95% CI: 0.74-0.88], P < 0.001, I2 = 0%) and hospitalization (HR: 0.81 [95% CI: 0.72-0.90], P < 0.001, I2 = 0%) risks with no significant difference in mortality risk (P > 0.05) compared with the control group. Trial interventions significantly improved physical (SMD: 0.15, 0.04-0.26) and quality of life (SMD: 0.15, 0.09-0.21) but not frailty scores (P > 0.05).

CONCLUSIONS: While frailty prognosticated a higher risk of CV events and mortality, frailty did not reduce treatment efficacy. CV trial interventions appear beneficial even in the frailest group.