Coronary Artery Calcium for Risk Stratification Among Persons With Very High HDL Cholesterol.

Background: Compared to normal high-density lipoprotein (HDL) cholesterol values, very high HDL cholesterol is associated with a higher incidence of mortality and atherosclerotic cardiovascular disease (ASCVD). As such, clinical risk stratification among persons with very high HDL cholesterol is challenging. Objectives: Among persons with very high HDL cholesterol, the purpose was to determine the prevalence of coronary artery calcium (CAC) and compare the association between traditional risk factors vs CAC for all-cause mortality and ASCVD. Methods: The primary analysis was completed among 446 participants from the Cedars-Sinai Medical Center of the CAC Consortium with very high HDL cholesterol (≥77 mg/dL in men, ≥97 mg/dL in women). Cox proportional hazards regression assessed the association of CAC and traditional risk factors with all-cause mortality during a median follow-up of 10.7 years. Replication and validation analyses were performed for all-cause mortality among 119 participants from the Multi-Ethnic Study of Atherosclerosis (MESA) with very high HDL cholesterol, who also had information on incident ASCVD. Results: The mean age was 57.9 years old, 49% were women, and the median HDL cholesterol was 98 mg/dL. One-half of participants (50%) had prevalent CAC, in whom the median CAC score was 118. Prevalent CAC conferred a 3.6-fold higher risk of all-cause mortality (HR: 3.64; 95% CI: 1.21-11.01), which appeared to be a more robust predictor than individual traditional risk factors beyond age. In the validation sample, prevalent CAC but not individual traditional risk factors were associated with all-cause mortality (HR: 2.39; 95% CI: 1.07-5.34) and a 4.0-fold higher risk of ASCVD (HR: 4.06; 95% CI: 1.11-14.84). Conclusions: Measurement of CAC may facilitate clinical risk assessment among individuals with very high HDL cholesterol.

AI for Multistructure Incidental Findings and Mortality Prediction at Chest CT in Lung Cancer Screening.

Background Incidental extrapulmonary findings are commonly detected on chest CT scans and can be clinically important. Purpose To integrate artificial intelligence (AI)-based segmentation for multiple structures, coronary artery calcium (CAC), and epicardial adipose tissue with automated feature extraction methods and machine learning to detect extrapulmonary abnormalities and predict all-cause mortality (ACM) in a large multicenter cohort. Materials and Methods In this post hoc analysis, baseline chest CT scans in patients enrolled in the National Lung Screening Trial (NLST) from August 2002 to September 2007 were included from 33 participating sites. Per scan, 32 structures were segmented with a multistructure model. For each structure, 15 clinically interpretable radiomic features were quantified. Four general codes describing abnormalities reported by NLST radiologists were applied to identify extrapulmonary significant incidental findings on the CT scans. Death at 2-year and 10-year follow-up and the presence of extrapulmonary significant incidental findings were predicted with ensemble AI models, and individualized structure risk scores were evaluated. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the performance of the models for prediction of ACM and extrapulmonary significant incidental findings. The Pearson χ2 test and Kruskal-Wallis rank sum test were used for statistical analyses. Results A total of 24 401 participants (median age, 61 years [IQR, 57-65 years]; 14 468 male) were included. In 3880 of 24 401 participants (16%), 4283 extrapulmonary significant incidental findings were reported. During the 10-year follow-up, 3389 of 24 401 participants (14%) died. CAC had the highest feature importance for predicting the three study end points. The 10-year ACM model demonstrated the best AUC performance (0.72; per-year mortality of 2.6% above and 0.8% below the risk threshold), followed by 2-year ACM (0.71; per-year mortality of 1.13% above and 0.3% below the risk threshold) and prediction of extrapulmonary significant incidental findings (0.70; probability of occurrence of 25.4% above and 9.6% below the threshold). Conclusion A fully automated AI model indicated extrapulmonary structures at risk on chest CT scans and predicted ACM with explanations. ClinicalTrials.gov Identifier: NCT00047385 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Yanagawa and Hata in this issue.

Coronary Artery Calcium Density and Risk of Cardiovascular Events: A Systematic Review and Meta-Analysis.

BACKGROUND: There is increasing evidence that coronary artery calcium (CAC) density is inversely associated with plaque vulnerability and atherosclerotic cardiovascular disease risk. OBJECTIVES: A systematic review and meta-analysis were performed to examine the predictive value of CAC density for future cardiovascular events in asymptomatic individuals undergoing noncontrast CAC scoring computed tomography. METHODS: Electronic databases were searched for studies reporting CAC density and subsequent cardiovascular disease (CVD) or coronary heart disease (CHD) events. Two independent reviewers performed data extraction. Random-effects models were used to estimate pooled HRs and 95% CIs. Subgroup analyses were performed with studies stratified by CVD vs CHD events and by statin use. RESULTS: Of 5,029 citations, 5 studies with 6 cohorts met inclusion criteria. In total, 1,309 (6.1%) cardiovascular events occurred in 21,346 participants with median follow-up ranging from 5.2 to 16.7 years. Higher CAC density was inversely associated with risk of cardiovascular events following adjustment for clinical risk factors and CAC volume (HR: 0.80 per SD of density [95% CI: 0.72-0.89]; P < 0.01; I2 = 0%). There was no significant difference in the pooled HRs for CVD vs CHD events (HR: 0.80 per SD [95% CI: 0.71-0.90] vs 0.74 per SD [95% CI: 0.59-0.94] respectively; P = 0.59). The protective association between CAC density and event risk persisted among statin-naive patients (HR: 0.79 per SD [95% CI: 0.70-0.89]; P < 0.01) but not statin-treated patients (HR: 0.97 per SD [95% CI: 0.77-1.22]; P = 0.78); the test for interaction indicated no significant between-group differences (P = 0.12). CONCLUSIONS: Higher CAC density is associated with a lower risk of cardiovascular events when adjusted for risk factors and CAC volume. Future work may expand the contribution of CAC density in CAC scoring, and enhance its role in CVD risk assessment, treatment, and prevention.

AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans enhances mortality prediction: multicenter study.

Background: Computed tomography attenuation correction (CTAC) scans are routinely obtained during cardiac perfusion imaging, but currently only utilized for attenuation correction and visual calcium estimation. We aimed to develop a novel artificial intelligence (AI)-based approach to obtain volumetric measurements of chest body composition from CTAC scans and evaluate these measures for all-cause mortality (ACM) risk stratification. Methods: We applied AI-based segmentation and image-processing techniques on CTAC scans from a large international image-based registry (four sites), to define chest rib cage and multiple tissues. Volumetric measures of bone, skeletal muscle (SM), subcutaneous, intramuscular (IMAT), visceral (VAT), and epicardial (EAT) adipose tissues were quantified between automatically-identified T5 and T11 vertebrae. The independent prognostic value of volumetric attenuation, and indexed volumes were evaluated for predicting ACM, adjusting for established risk factors and 18 other body compositions measures via Cox regression models and Kaplan-Meier curves. Findings: End-to-end processing time was <2 minutes/scan with no user interaction. Of 9918 patients studied, 5451(55%) were male. During median 2.5 years follow-up, 610 (6.2%) patients died. High VAT, EAT and IMAT attenuation were associated with increased ACM risk (adjusted hazard ratio (HR) [95% confidence interval] for VAT: 2.39 [1.92, 2.96], p<0.0001; EAT: 1.55 [1.26, 1.90], p<0.0001; IMAT: 1.30 [1.06, 1.60], p=0.0124). Patients with high bone attenuation were at lower risk of death as compared to subjects with lower bone attenuation (adjusted HR 0.77 [0.62, 0.95], p=0.0159). Likewise, high SM volume index was associated with a lower risk of death (adjusted HR 0.56 [0.44, 0.71], p<0.0001). Interpretations: CTAC scans obtained routinely during cardiac perfusion imaging contain important volumetric body composition biomarkers which can be automatically measured and offer important additional prognostic value.

Relationship between epicardial adipose tissue and coronary atherosclerosis by CCTA in young adults (18-45).

Objective: Epicardial adipose tissue (EAT) is implicated in the pathogenesis and progression of coronary artery disease (CAD). Limited data exists on the interplay between EAT and atherosclerosis in young individuals. Our study aims to explore the relationship between EAT and CAD in a young cohort. Methods: All young (18-45 years) patients without prior CAD, referred for coronary computed tomography angiography (CCTA) from 2016 to 2022 were included. EAT volume and coronary artery calcium (CAC) were calculated from dedicated non-contrast scans. Coronary plaque presence, extent, and volume were quantified from CCTA. Multivariable logistic regression models for the presence of CAD, defined as any coronary atherosclerosis, were performed. Results: Overall, 712 patients (39±4.8 years, 54 % female) with 45 % Hispanic, and 21 % non-Hispanic Black were included. Patients with CAD had higher EAT volume than those without (80.80 mL ± 36.00 vs 55.16 mL ± 27.92; P < 0.001). In those with CAC=0, higher EAT was associated with the presence of CAD compared to lower EAT volume (P < 0.001). An EAT volume >76 mL was associated with higher CAC (P < 0.001), segment involvement score (P < 0.001), and quantitative total, non-calcified, and low-attenuation plaque volumes (P < 0.002). At multivariable analysis, EAT volume (per 10 mL, OR: 1.21; 95 %CI: 1.12-1.30; P < 0.0001) was independently associated with the presence of CAD. Conclusion: In a diverse cohort of young adults without history of CAD and undergoing a clinically indicated CCTA, EAT volume was independently associated with the presence of CAD. Our findings highlight EAT potential as a novel marker for CAD risk-assessment and a potential therapeutic target in young patients.

Rapidly progressive coronary atherosclerosis in a young male: a retrospective advanced CCT phenotype analysis.

We present a real-life case of a very young man with multiple risk factors who progressed rapidly from minimally obstructive non-calcified plaque on computed tomography angiography (CCTA) to severe three-vessel coronary disease presenting with STEMI. It questions the reliability of zero coronary calcium in high-risk subgroups like familial hypercholesterolemia, high Lp(a), and the young. While CCTA can accurately visualize non-calcified plaque, its interpretation requires expertise and clinical judgment should consider both imaging and clinical risk factors for management. Advanced plaque quantification, peri-coronary (PCAT), and epicardial (EAT) adipose tissue could help better-stratified patients but the evidence-based clinical application remains unknown.

A Novel Pathway of Platelet Activation in ACS Mediated by LL-37 Immunoglobulin G Autoantibody Immune Complexes.

The cathelicidin antimicrobial peptide LL-37 is a self-antigen in neutrophil extracellular traps that provokes autoantibody responses in autoimmune/autoinflammatory conditions. LL-37 immunoglobulin (Ig) G autoantibody levels were measured in subjects with and without atherosclerotic cardiovascular disease assessed using the coronary artery calcium score, in patients who had a future myocardial infarction and in a cohort of acute coronary syndrome (ACS) patients. LL-37 IgG levels were not associated with coronary artery calcium score, but future myocardial infarction patients had significantly higher LL-37 IgG at baseline. Reduced LL-37 IgG in ACS was associated with increased LL-37 IgG-immune complex. ACS plasma increased activated CD62P+ platelets from healthy donors mediated in part by LL-37 IgG-immune complexes and platelet Fc gamma receptor 2a.

Deep Learning Phenotyping of Tricuspid Regurgitation for Automated High Throughput Assessment of Transthoracic Echocardiography.

Background and Aims: Diagnosis of tricuspid regurgitation (TR) requires careful expert evaluation. This study developed an automated deep learning pipeline for assessing TR from transthoracic echocardiography. Methods: 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. The pipeline was tested on a temporally distinct test set of 2,462 studies (108,138 videos) obtained in 2022 at CSMC and a geographically distinct cohort of 5,549 studies (278,377 videos) from Stanford Healthcare (SHC). Results: In the CSMC test dataset, the view classifier demonstrated an AUC of 1.000 (0.999 - 1.000) and identified at least one A4C video with colour Doppler across the tricuspid valve in 2,410 of 2,462 studies with a sensitivity of 0.975 (0.968-0.982) and a specificity of 1.000 (1.00-1.000). In the CSMC test cohort, moderate-or-severe TR was detected with an AUC of 0.928 (0.913 - 0.943) and severe TR was detected with an AUC of 0.956 (0.940 - 0.969). In the SHC cohort, the view classifier correctly identified at least one TR colour Doppler video in 5,268 of the 5,549 studies, resulting in an AUC of 0.999 (0.998 - 0.999), a sensitivity of 0.949 (0.944 - 0.955) and specificity of 0.999 (0.999 - 0.999). The AI model detected moderate-or-severe TR with an AUC of 0.951 (0.938 - 0.962) and severe TR with an AUC of 0.980 (0.966 - 0.988). Conclusions: We developed an automated pipeline to identify clinically significant TR with excellent performance. This approach carries potential for automated TR detection and stratification for surveillance and screening. Key Question: Can an automated deep learning model assess tricuspid regurgitation severity from echocardiography? Key Finding: We developed and validated an automated tricuspid regurgitation detection algorithm pipeline across two healthcare systems with high volume echocardiography labs. The algorithm correctly identifies apical-4-chamber view videos with colour Doppler across the tricuspid valve and grades clinically significant TR with strong agreement to expert clinical readers. Take Home message: A deep learning pipeline could automate TR screening, facilitating reproducible accurate assessment of TR severity, allowing rapid triage or re-review and expand access in low-resource or primary care settings.

Sex differences in coronary atherosclerotic plaque activity using 18F-sodium fluoride positron emission tomography.

INTRODUCTION: There are sex differences in the extent, severity, and outcomes of coronary artery disease. We aimed to assess the influence of sex on coronary atherosclerotic plaque activity measured using coronary 18F-sodium fluoride (18F-NaF) positron emission tomography (PET), and to determine whether 18F-NaF PET has prognostic value in both women and men. METHODS: In a post-hoc analysis of observational cohort studies of patients with coronary atherosclerosis who had undergone 18F-NaF PET CT angiography, we compared the coronary microcalcification activity (CMA) in women and men. RESULTS: Baseline 18F-NaF PET CT angiography was available in 999 participants (151 (15%) women) with 4282 patient-years of follow-up. Compared to men, women had lower coronary calcium scores (116 [interquartile range, 27-434] versus 205 [51-571] Agatston units; p = 0.002) and CMA values (0.0 [0.0-1.12] versus 0.53 [0.0-2.54], p = 0.01). Following matching for plaque burden by coronary calcium scores and clinical comorbidities, there was no sex-related difference in CMA values (0.0 [0.0-1.12] versus 0.0 [0.0-1.23], p = 0.21) and similar proportions of women and men had no 18F-NaF uptake (53.0% (n = 80) and 48.3% (n = 73); p = 0.42), or CMA values > 1.56 (21.8% (n = 33) and 21.8% (n = 33); p = 1.00). Over a median follow-up of 4.5 [4.0-6.0] years, myocardial infarction occurred in 6.6% of women (n = 10) and 7.8% of men (n = 66). Coronary microcalcification activity greater than 0 was associated with a similarly increased risk of myocardial infarction in both women (HR: 3.83; 95% CI:1.10-18.49; p = 0.04) and men (HR: 5.29; 95% CI:2.28-12.28; p < 0.001). CONCLUSION: Although men present with more coronary atherosclerotic plaque than women, increased plaque activity is a strong predictor of future myocardial infarction regardless of sex.

The lack of ethnic and racial diversity among patients undergoing coronary artery calcium scanning.

BACKGROUND: While coronary artery calcium (CAC) CAC scanning has become increasingly used as a tool for primary cardiovascular disease prevention, there has been little study regarding its comparative utilization among ethnic and racial minorities. METHODS: We contrasted the temporal trends in the ethnoracial composition for 73,856 out-patients undergoing stress/rest radionuclide myocardial perfusion imaging (MPI) between 1991 and 2020 and 32,906 undergoing CAC scanning between 1998 and 2020. Both groups were divided into those below and above 65 years. Initial medical insurance claims were used to identify which patients self-paid for SPECT-MPI and CAC studies. RESULTS: Among stress-MPI patients <65 years, the prevalence of White patients declined from 85.5% to 54.0% over the temporal span of our study while the prevalence of Blacks increased from 7.2% to 15.1% and that of Hispanics from 2.3 to 21.6%. Increasing ethnoracial diversification was also noted for SPECT-MPI patients ≥65 years. By contrast, over four-fifths of CAC studies were performed in White patients in each temporal period among both younger and older patients. Among CAC patients <65 years, over 95% of studies were self-paid by patients. For CAC patients ≥65 years, nearly two-third of studies were first submitted to Medicare, but there was no difference in the ethnoracial composition in this group versus initial self-paying patients. CONCLUSIONS: While the ethnoracial diversity of patients undergoing SPECT-MPI markedly increased at our Institution over recent decades, CAC scanning has been disproportionately and consistently utilized by self-paying White patients. These findings highlight the need to make CAC scanning more available among ethnoracial minorities.

Differentiation between descending thoracic aortic diseases using machine learning and plasma proteomic signatures.

BACKGROUND: Descending thoracic aortic aneurysms and dissections can go undetected until severe and catastrophic, and few clinical indices exist to screen for aneurysms or predict risk of dissection. METHODS: This study generated a plasma proteomic dataset from 75 patients with descending type B dissection (Type B) and 62 patients with descending thoracic aortic aneurysm (DTAA). Standard statistical approaches were compared to supervised machine learning (ML) algorithms to distinguish Type B from DTAA cases. Quantitatively similar proteins were clustered based on linkage distance from hierarchical clustering and ML models were trained with uncorrelated protein lists across various linkage distances with hyperparameter optimization using fivefold cross validation. Permutation importance (PI) was used for ranking the most important predictor proteins of ML classification between disease states and the proteins among the top 10 PI protein groups were submitted for pathway analysis. RESULTS: Of the 1,549 peptides and 198 proteins used in this study, no peptides and only one protein, hemopexin (HPX), were significantly different at an adjusted p < 0.01 between Type B and DTAA cases. The highest performing model on the training set (Support Vector Classifier) and its corresponding linkage distance (0.5) were used for evaluation of the test set, yielding a precision-recall area under the curve of 0.7 to classify between Type B from DTAA cases. The five proteins with the highest PI scores were immunoglobulin heavy variable 6-1 (IGHV6-1), lecithin-cholesterol acyltransferase (LCAT), coagulation factor 12 (F12), HPX, and immunoglobulin heavy variable 4-4 (IGHV4-4). All proteins from the top 10 most important groups generated the following significantly enriched pathways in the plasma of Type B versus DTAA patients: complement activation, humoral immune response, and blood coagulation. CONCLUSIONS: We conclude that ML may be useful in differentiating the plasma proteome of highly similar disease states that would otherwise not be distinguishable using statistics, and, in such cases, ML may enable prioritizing important proteins for model prediction.

Alterations in genes associated with cytosolic RNA sensing in whole blood are associated with coronary microvascular disease in SLE.

Systemic lupus erythematosus (SLE) patients are 90% women and over three times more likely to die of cardiovascular disease than women in the general population. Chest pain with no obstructive cardiac disease is associated with coronary microvascular disease (CMD), where narrowing of the small blood vessels can lead to ischemia, and frequently reported by SLE patients. Using whole blood RNA samples, we asked whether gene signatures discriminate SLE patients with coronary microvascular dysfunction (CMD) on cardiac MRI (n=4) from those without (n=7) and whether any signaling pathway is linked to the underlying pathobiology of SLE CMD. RNA-seq analysis revealed 143 differentially expressed (DE) genes between the SLE and healthy control (HC) groups, with virus defense and interferon (IFN) signaling being the key pathways identified as enriched in SLE as expected. We next conducted a comparative analysis of genes differentially expressed in SLE-CMD and SLE-non-CMD relative to HC samples. Our analysis highlighted differences in IFN signaling, RNA sensing and ADP-ribosylation pathways between SLE-CMD and SLE-non-CMD. This is the first study to investigate possible gene signatures associating with CMD in SLE, and our data strongly suggests that distinct molecular mechanisms underly vascular changes in CMD and non-CMD involvement in SLE.

Expanding antibiotic, vaccine, and diagnostics development and access to tackle antimicrobial resistance.

The increasing number of bacterial infections globally that do not respond to any available antibiotics indicates a need to invest in-and ensure access to-new antibiotics, vaccines, and diagnostics. The traditional model of drug development, which depends on substantial revenues to motivate investment, is no longer economically viable without push and pull incentives. Moreover, drugs developed through these mechanisms are unlikely to be affordable for all patients in need, particularly in low-income and middle-income countries. New, publicly funded models based on public-private partnerships could support investment in antibiotics and novel alternatives, and lower patients' out-of-pocket costs, making drugs more accessible. Cost reductions can be achieved with public goods, such as clinical trial networks and platform-based quality assurance, manufacturing, and product development support. Preserving antibiotic effectiveness relies on accurate and timely diagnosis; however scaling up diagnostics faces technological, economic, and behavioural challenges. New technologies appeared during the COVID-19 pandemic, but there is a need for a deeper understanding of market, physician, and consumer behaviour to improve the use of diagnostics in patient management. Ensuring sustainable access to antibiotics also requires infection prevention. Vaccines offer the potential to prevent infections from drug-resistant pathogens, but funding for vaccine development has been scarce in this context. The High-Level Meeting of the UN General Assembly in 2024 offers an opportunity to rethink how research and development can be reoriented to serve disease management, prevention, patient access, and antibiotic stewardship.