AIMS:Premature advanced subclinical coronary atherosclerosis among young adults is an under-recognized and unique disease phenotype that has not been well characterized. METHODS AND RESULTS:We used data from 44 047 participants with no prior CVD history (59.8% male) from the Coronary Artery Calcium (CAC) Consortium. We defined advanced disease as CAC ≥ 90th percentile for age, sex, and race and compared the risk factor profile of persons with advanced disease to those without CAC and those with CAC < 90th percentile. Using multivariable-adjusted Cox proportional hazard and competing risks regression, we assessed the association of premature advanced disease with all-cause, cardiovascular, and coronary heart disease (CHD) mortality. Of 44 047 participants, 18 561 (42.2%) had CAC. Among those with CAC, 6680 (36.0%) had CAC ≥ 90th percentile. Notably, 76.4% of those with CAC ≥ 90th percentile had multivessel CAC compared with 40.6% of those with CAC < 90th percentile. After a mean follow-up of 12.5 ± 3.6 years, the incidence per 1000 person-years of all-cause (2.93 vs. 1.85 vs. 1.11), cardiovascular (1.11 vs. 0.39 vs. 0.21), and CHD mortality (0.65 vs. 0.19 vs. 0.08) was highest in the advanced disease group compared with CAC < 90th percentile and the no CAC group. Persons with CAC ≥ 90th percentile had a higher multivariable-adjusted risk of all-cause [HR: 2.17 (1.83-2.57)], cardiovascular [sub-distribution hazard ratios (SHR): 3.89 (2.78-5.44)], and CHD mortality [SHR: 5.45 (3.38-8.78)], compared with those without CAC. In the subgroup analysis, there was no difference in mortality between men and women with advanced CAC. CONCLUSION:Premature advanced atherosclerosis is a distinct clinical phenotype that strongly predicts all-cause and cause-specific mortality. Among persons with CAC at young age, those with scores ≥90th percentile have the highest risk of early death and should be identified in future guidelines as a focus for aggressive clinical prevention.

Myocardial bridging is the most common congenital coronary anomaly, characterized by an intramyocardial course of a segment of an epicardial coronary artery, with considerable variability in its depth and length. In most cases, MB is a benign anatomical variant without clinical significance, often asymptomatic and not requiring treatment. However, with the increasing use of coronary computed tomography angiography for coronary assessment, myocardial bridging is now more frequently identified during non-invasive imaging. It can also be diagnosed via invasive coronary angiography, where it appears as a dynamic systolic compression of the tunnelled artery segment, and further anatomical characterization can be enhanced by intravascular ultrasound or optical coherence tomography. Growing recognition of myocardial bridging's role in ischaemia with non-obstructive coronary arteries has heightened clinical interest in its prevalence, diagnostic strategies, and management approaches. As such, this narrative review provides clinicians with an updated, evidence-based guide to the diagnosis and therapeutic management of myocardial bridging, while also addressing ongoing controversies and areas of uncertainty.

Relentless mechanical work of the heart is powered by continuous oxygen consumption. How the heart uses oxygen is a defining feature of its health. Invasive studies have established that impaired oxygen consumption by the myocardium predicts contractile dysfunction and adverse outcomes. Despite its importance, noninvasive quantification of myocardial oxygen use remains limited. Magnetic resonance imaging (MRI) signal is known to be sensitive to blood oxygenation and has the potential to quantify myocardial oxygen consumption noninvasively, without exogenous contrast agents and free of ionizing radiation. However, its clinical translation has been impeded by the need for complex biophysical calibration, vulnerability to imaging artifacts and consistent vital motions, and the requirement of lengthy acquisition times. Here, we introduce a rapid, self-calibrated cardiac MRI framework that overcomes these barriers through high-resolution, motion-resolved coronary sinus oximetry, which can quantify myocardial oxygen extraction of the whole heart within 3 minutes. We optimized the imaging parameters via numerical simulations and validated them against invasive coronary sinus catheterization in a porcine model. We combined the method with clinical MRI sequences and demonstrated the feasibility of quantifying myocardial oxygen consumption and myocardial oxygen efficiency in patients with and without heart failure secondary to myocardial infarction in a single institution. This needle-free approach establishes a practical framework for noninvasive characterization of myocardial oxygen metabolism. It holds the potential to facilitate early disease detection, inform personalized therapeutic strategies, and guide the development of cardiometabolic therapies aimed at addressing the ongoing heart failure epidemic.

BACKGROUND:Noninvasive assessment of coronary artery disease (CAD) in obese patients is challenging due to suboptimal image quality. We performed a prespecified secondary analysis of obese patients from the second Phase III trial of flurpiridaz-18F positron emission tomography (PET). METHODS:In total, 604 patients with suspected CAD underwent flurpiridaz-18F PET and 99mTc-single-photon emission computerized tomography (SPECT) myocardial perfusion imaging (MPI) before invasive coronary angiography (ICA) across 48 sites. MPI images were interpreted by three blinded experts. The primary endpoint was sensitivity and specificity for diagnosing CAD (≥50% stenosis on quantitative coronary angiography), requiring the lower 95% confidence interval to exceed 60% by 2 same blinded expert core lab readers, assessed using one-sided z-test (α = 0.025). Sensitivity and specificity were compared between PET and SPECT and between obese (body mass index [BMI] ≥30 kg/m2) and nonobese patients. RESULTS:Of 578 evaluable patients, 298 (51.6%) were obese (mean age: 62.0 years, 64.4% male, mean BMI: 35.6 kg/m2) and 117 (39.3%) had CAD by ICA. In obese patients, flurpiridaz-18F PET MPI met the primary endpoint, with lower 95% confidence limits greater than 60% and one-sided P < 0.025 by the same 2 readers (Readers 1 and 2). Sensitivity across 3 readers ranged from 70.1% to 88.0% and specificity ranged from 53.6% to 74.0%. In obese patients, flurpiridaz-18F PET MPI showed significantly higher sensitivity compared to 99ᵐTc-SPECT MPI by Readers 1 and 3 (Reader 1: 72.6% vs 60.7%, P = 0.01; Reader 3: 88.0% vs 74.4%, P = 0.002), with non-inferior specificity by the same two readers (Reader 1: 68.0% vs 61.3%; Reader 3: 53.6% vs 50.8%; P < 0.01 for noninferiority, for both), noting that the primary endpoint was met by a partially overlapping reader pair (Readers 1 and 2). Diagnostic performance of flurpiridaz-18F PET was higher than SPECT across BMI categories. No statistically significant difference in diagnostic performance was observed between obese and nonobese patients (Sensitivity P = 0.26, specificity P = 0.25 and accuracy P = 1.0). CONCLUSION:Flurpiridaz-18F PET MPI demonstrates high diagnostic efficacy for detecting CAD across BMI categories in obese patients compared to ICA and 99mTc-SPECT, with no loss of diagnostic performance in obese relative to nonobese patients. CLINICAL TRIAL REGISTRATION:NCT03354273.

BACKGROUND:Over the past two decades, the range of noninvasive imaging modalities for evaluating suspected or established coronary artery disease (CAD) has expanded substantially. Yet how clinicians apply these anatomic and physiologic tests in real-world practice-across different age groups and CAD strata-remains poorly characterized. METHODS:We analyzed 71,987 patients who underwent noninvasive diagnostic and/or prognostic cardiac imaging between 2008 and 2024, including 28,671 undergoing coronary CT angiography (CCTA), 34,913 undergoing SPECT myocardial perfusion imaging (MPI), and 13,830 undergoing PET-MPI imaging. Patients were classified by age and presence or absence of known CAD, and temporal trends in test utilization were evaluated across modalities. RESULTS:Over the study period, cardiac imaging utilization shifted profoundly, characterized by a steady increase in the use of CCTA and a corresponding progressive decline in SPECT-MPI. Among patients without known CAD, CCTA use rose across all age groups; by 2024, CCTA was the primary diagnostic test in 84% of patients <50 years, 81% of those 50-59 years, 73% of those 60-69 years, and 63% of those ≥70 years. In patients with known CAD, CCTA use also increased, though physiologic stress testing remained the predominant strategy in approximately three-fifths of known CAD patients. CONCLUSIONS:Over 17 years, imaging practice at a tertiary center shifted markedly toward an anatomic-first approach in patients without known CAD, while physiologic testing remained common among those with established CAD. These real-world patterns both anticipated and align with contemporary guideline recommendations, reflecting evolving, age- and indication-specific imaging strategies in clinical care.

Positron emission tomography (PET)/computed tomography (CT) for myocardial perfusion imaging (MPI) provides multiple imaging biomarkers, often evaluated separately. We developed an artificial intelligence (AI) model integrating key clinical PET MPI parameters to improve the diagnosis of obstructive coronary artery disease (CAD). From 17,348 patients undergoing cardiac PET/CT across four sites, 1664 with invasive coronary angiography and no prior CAD were retrospectively analyzed. Coronary artery calcium (CAC) scores were derived from CT attenuation correction maps, and XGBoost model was trained on one site using 10 image-derived parameters: CAC, stress/rest left ventricular ejection fraction, stress myocardial blood flow (MBF), myocardial flow reserve (MFR), ischemic and stress total perfusion deficit (TPD), transient ischemic dilation ratio, rate pressure product, and sex. External validation was performed across three independent sites. In the testing cohort (n = 1278; CAD prevalence 53

Importance The extent and severity of coronary artery disease (CAD) on coronary CT angiography (CCTA) is predictive of mortality, cardiovascular death, and non-fatal myocardial infarction in both men and women; however, associations of various measures of coronary plaque burden with prognosis in younger women and men has had little study. Objective To compare prognostic significance of CAD plaque burden and distribution on CCTA in women and men <60. Design 3813 consecutive patients (age: 49.4±8.1 years, 36% women) without CAD history who underwent CCTA (2007-2019) with clinical follow-up. Setting Single academic medical center Interventions None Main Outcomes CCTA plaque burden was assessed by stenosis severity, segment involvement score (SIS), segment stenosis score (SSS), and coronary artery calcium (CAC) scores. Primary endpoint: all-cause mortality (ACM); secondary outcome: ACM or non-fatal MI (NFMI). Results 180 (4.7%) ACM occurred over a median of 7.4 years, Severity of stenosis, SIS, SSS and extent of CAC were higher in men compared to women (all p<0.001). In multivariable Cox models, there were significant interactions between sex and all CCTA measures, with women having higher risk than men of ACM with increase of each plaque burden measure compared to men: coronary stenosis (CAD≥50% HR 6.52 vs 1.86, interaction p=0.021), SIS (SIS>4 HR 6.94 vs 1.68, interaction p=0.006), SSS (SSS>6 HR 7.91 vs 1.79, interaction p=0.006) and CAC (CAC>300 HR 9.62 vs 1.82, interaction p=0.004). All plaque burden measures discriminated events in women better than men, with C-statistics for multivariable models ranging from 0.729-0.760 in women, compared to 0.674-0.690 in men. Findings were similar for ACM/NFMI. Conclusions Prognostic significance of multiple measures of plaque burden identified by CCTA had greater prognostic impact in younger women, compared with younger men. Trial Registration NA

BACKGROUND:Over 50% of women evaluated for suspected ischemia have no obstructive coronary artery disease (INOCA). Statins, angiotensin converting enzyme inhibitors (ACEI) or angiotensin receptor blockers (ARB) are effective in intermediate outcome trials; however, impact on coronary plaque has not been well characterized. OBJECTIVES:The Women's IschemiA TRial to Reduce Events In Non-ObstRuctive CAD (WARRIOR NCT03417388) trial testing intensive medical therapy (IMT) (high intensity statin, ACEI or ARB and low dose aspirin) vs usual care (UC) in women with suspected INOCA offers the opportunity to evaluate the impact of IMT vs UC on plaque composition, and chest pain symptoms by coronary CT angiography (CCTA). We hypothesize that IMT provides beneficial data on plaque composition impacting flow reserve and trial outcomes. METHODS:This WARRIOR ancillary study will consecutively enroll 200 eligible participants randomized to IMT vs UC by baseline and exit CCTA. Changes in plaque and peri‑coronary artery adipose tissue attenuation (PCAT) characteristics will be quantified. RESULTS:Results will address: (1) Changes in coronary plaque characteristics and their hemodynamic significance using AI-enabled quantification of CCTA; (2) Changes in plaque inflammatory characteristics through pericoronary adipose tissue (PCAT) density analysis; (3) Plaque burden, composition and PCAT density changes related to angina score (Seattle Angina Questionnaire [SAQ]), (4) Derive a quantitative machine learning risk score (MLRS) using CCTA-derived variables for prediction of change in angina. CONCLUSIONS:The ancillary study will be the first to quantify the impact of IMT vs UC on plaque composition, and outcomes in women with suspected INOCA. TRIAL REGISTRATION:WARRIOR Ancillary Study for CCTA Analysis, NCT05035056.

Computed tomography (CT)-derived Epicardial Adipose Tissue (EAT) is linked to cardiovascular disease outcomes. However, its role in patients undergoing Transcatheter Aortic Valve Replacement (TAVR) and the interplay with aortic stenosis (AS) cardiac damage (CD) remains unexplored. We aim to investigate the relationship between EAT characteristics, AS CD, and all-cause mortality. We retrospectively included consecutive patients who underwent CT-TAVR followed by TAVR. EAT volume and density were estimated using a deep-learning platform and CD was assessed using echocardiography. Patients were classified according to low/high EAT volume and density. All-cause mortality at 4 years was compared using Kaplan-Meier and Cox regression analyses. A total of 666 patients (median age 81 [74–86] years; 54

Introduction: Coronary inflammation may be a factor in developing heart failure with preserved ejection fraction (HFpEF). Epicardial fat volume (EFV) measured using non-contrast computed tomography (CT) and pericoronary adipose tissue (PCAT) attenuation on coronary CT angiography (CCTA) are considered to be inflammatory mediators, which play an important role in the development of coronary plaque. Hypothesis: We hypothesize that there is an interrelationship between these measures in HFpEF and no obstructive coronary artery disease (CAD). Methods: 28 subjects with HFpEF (European Society of Cardiology criteria), with coronary CCTA-documented absence of obstructive CAD. Non-calcified plaque (NCP), low-density non-calcified plaque (LDNCP), PCAT attenuation were quantified using semi-automated software and EFV was quantified using QFAT software. PCAT attenuation in Hounsfield Units (HU) was measured in a standardized 40 mm segment around the proximal right coronary artery. Statistical analysis was performed using Spearman correlation and multivariable regression models adjusted for age, BMI, EFV, and PCAT tested relations to NCP burden. Results: Overall, the mean age of 64.8±12 years, 74% women, BMI 29.3±4, total cholesterol 162±41mg/dl, LDL 85±29 mg/dl, HDL 55±14 mg/dl, systolic BP 132±22mmHg, diastolic BP 77±12mmHg, EFV 139±73 cm 3 and, PCAT attenuation -78±8 HU. Mean total NCP burden 34±13%, total LDNCP burden 3±1.8 %, and coronary calcium score (CCS) 414±920. EFV and PCAT attenuation correlated with the coronary risk markers including CCS and NCP burden variables (Table). In multivariable analysis, age and PCAT (β±SE=0.43±0.2, p=0.04, 0.64±0.3, p=0.03 per 1%-change in NCP burden respectively) were related to NCP burden. Conclusions: EFV and PCAT attenuation are associated with non-calcified plaque burden and may play an important role in the pathophysiological process of HFpEF. Further studies are needed to further understand these findings.

Importance:Accurate assessment of tricuspid regurgitation (TR) is necessary for identification and risk stratification. Objective:To design a deep learning computer vision workflow for identifying color Doppler echocardiogram videos and characterizing TR severity. Design, Setting, and Participants:An automated deep learning workflow was developed using 47 312 studies (2 079 898 videos) from Cedars-Sinai Medical Center (CSMC) between 2011 and 2021. Data analysis was performed in 2024. The pipeline was tested on a temporally distinct test set of 2462 studies (108 138 videos) obtained in 2022 at CSMC and a geographically distinct cohort of 5549 studies (278 377 videos) from Stanford Healthcare (SHC). Training and validation cohorts contained data from 31 708 patients at CSMC receiving care between 2011 and 2021. Patients were chosen for parity across TR severity classes, with no exclusion criteria based on other clinical or demographic characteristics. The 2022 CSMC test cohort and SHC test cohorts contained studies from 2170 patients and 5014 patients, respectively. Exposure:Deep learning computer vision model. Main Outcomes and Measures:The main outcomes were area under the receiver operating characteristic curve (AUC), sensitivity, and specificity in identifying apical 4-chamber (A4C) videos with color Doppler across the tricuspid valve and AUC in identifying studies with moderate to severe or severe TR. Results:In the CSMC test dataset, the view classifier demonstrated an AUC of 1.000 (95% CI, 0.999-1.000) and identified at least 1 A4C video with color Doppler across the tricuspid valve in 2410 of 2462 studies with a sensitivity of 0.975 (95% CI, 0.968-0.982) and a specificity of 1.000 (95% CI, 1.000-1.000). In the CSMC test cohort, moderate or severe TR was detected with an AUC of 0.928 (95% CI, 0.913-0.943), and severe TR was detected with an AUC of 0.956 (95% CI, 0.940-0.969). In the SHC cohort, the view classifier correctly identified at least 1 TR color Doppler video in 5268 of the 5549 studies, resulting in an AUC of 0.999 (95% CI, 0.998-0.999), a sensitivity of 0.949 (95% CI, 0.944-0.955), and a specificity of 0.999 (95% CI, 0.999-0.999). The artificial intelligence model detected moderate or severe TR with an AUC of 0.951 (95% CI, 0.938-0.962) and severe TR with an AUC of 0.980 (95% CI, 0.966-0.988). Conclusions and Relevance:In this study, an automated pipeline was developed to identify clinically significant TR with excellent performance. With open-source code and weights, this project can serve as the foundation for future prospective evaluation of artificial intelligence-assisted workflows in echocardiography.

BACKGROUND:Limited contemporary evidence exists on risk prediction by stress imaging and exercise electrocardiography (ECG) among patients with chronic coronary syndromes (CCS). Objectives From the ISCHEMIA (International Study of Comparative Health Effectiveness with Medical and Invasive Approaches) study, prognosis was examined by core laboratory-defined stress imaging and exercise ECG findings in CCS patients. METHODS:A total of 5,179 patients (qualifying by stress nuclear imaging [n = 2,567], echocardiography [n = 1,085], cardiac magnetic resonance [CMR] [n = 257], and ECG [n = 1,270]) were randomized. Cox models assessed associations between trial endpoints and the number of scarred and ischemic segments, rest/stress left ventricular ejection fraction (LVEF), and ST-segment depression. HRs and 95% CIs were calculated per millimeter, segment, or 5% of LVEF. We examined prognostic models for the following trial endpoints: 1) the trial's primary endpoint of cardiovascular (CV) death, myocardial infarction (MI), resuscitated cardiac arrest, or hospitalization for unstable angina or heart failure; 2) CV death; 3) spontaneous MI; 4) procedural MI; and 5) type 2 MI. RESULTS:The number of scarred segments (HR: 1.07 [95% CI: 1.02-1.13]; P = 0.0209), rest LVEF (HR: 0.88 [95% CI: 0.83-0.93]; P < 0.001), and stress LVEF (HR: 0.87 [95% CI: 0.83-0.91]; P < 0.001) predicted the trial's primary endpoint of CV death, MI, resuscitated cardiac arrest, or hospitalization for unstable angina or heart failure. The extent of scar and rest/stress LVEF on echocardiography and nuclear imaging predicted several trial endpoints. The number of ischemic segments predicted spontaneous (HR: 1.08 [95% CI: 1.03-1.14]; P = 0.0104) and procedural MI (HR: 1.14 [95% CI: 1.03-1.25]; P = 0.0015) but was of borderline significance for the trial's primary endpoint (P = 0.0746). Ischemia extent by CMR predicted the trial's primary endpoint (P = 0.0068) and spontaneous MI (P = 0.0042). CONCLUSIONS:ISCHEMIA trial findings from 320 worldwide centers revealed that stress imaging and exercise ECG measures exhibited a variable association with key trial endpoints delineating risk patterns for ischemia and infarction. Stress CMR ischemia predicted several trial endpoints, supporting an expanded role in the evaluation of patients with CCS (ISCHEMIA [International Study of Comparative Health Effectiveness With Medical and Invasive Approaches]; NCT01471522).

BACKGROUND:Echocardiography is the most common modality for assessing cardiac structure and function. Although cardiac magnetic resonance (CMR) imaging is less accessible, it can provide unique tissue characterization, including late gadolinium enhancement (LGE), T1 and T2 mapping, and extracellular volume (ECV), which are associated with tissue fibrosis, infiltration, and inflammation. Deep learning has been shown to uncover findings not recognized by clinicians, but it is unknown whether CMR-based tissue characteristics can be derived from echocardiographic videos using deep learning. The aim of this study was to assess the performance of a deep learning model applied to echocardiography to detect CMR-specific parameters, including LGE presence and abnormal T1, T2, or ECV. METHODS:In a retrospective single-center study, adult patients with CMR and echocardiographic studies within 30 days were included. A video-based convolutional neural network was trained on echocardiographic videos to predict CMR-derived labels, including LGE presence and abnormal T1, T2, or ECV across echocardiographic views. The model was also trained to predict the presence or absence of wall motion abnormality (WMA) as a positive control for model function. The model performance was evaluated in a held-out test data set not used for training. RESULTS:The study population included 1,453 adult patients (mean age, 56 ± 18 years; 42% women) with 2,556 paired echocardiographic studies occurring at a median of 2 days after CMR (interquartile range, 2 days before to 6 days after). The model had high predictive capability for the presence of WMA (area under the curve [AUC] = 0.873; 95% CI, 0.816-0.922), which was used for positive control. However, the model was unable to reliably detect the presence of LGE (AUC = 0.699; 95% CI, 0.613-0.780) and abnormal native T1 (AUC = 0.614; 95% CI, 0.500-0.715), T2 (AUC = 0.553; 95% CI, 0.420-0.692), or ECV (AUC = 0.564; 95% CI, 0.455-0.691). CONCLUSIONS:Deep learning applied to echocardiography accurately identified CMR-based WMA but was unable to predict tissue characteristics, suggesting that signal for these tissue characteristics may not be present within ultrasound videos and that the use of CMR for tissue characterization remains essential within cardiology.