Multimodality imaging for the evaluation and management of patients with long-term (durable) left ventricular assist devices A Clinical Consensus Statement of the European Association of Cardiovascular Imaging (EACVI) of the ESC.

Left ventricular assist devices (LVADs) are gaining increasing importance as therapeutic strategy in advanced heart failure (HF), not only as bridge to recovery or to transplant, but also as destination therapy. Even though long-term LVADs are considered a precious resource to expand the treatment options and improve clinical outcome these patients, these are limited by peri-operative and post-operative complications, such as device-related infections, haemocompatibility-related events, device mispositioning and right ventricular failure. For this reason, a precise pre-operative, peri-operative and post-operative evaluation of these patients is crucial for the selection of LVADs candidates and the management LVADs recipients. The use of different imaging modalities offers important information to complete the study of patients with LVADs in each phase of their assessment, with peculiar advantages/disadvantages, ideal application and reference parameters for each modality. This clinical consensus statement sought to guide the use of multimodality imaging for the evaluation of patients with advanced HF undergoing LVADs implantation.

Distal Ventricular Pacing for Drug-Refractory Mid-Cavity Obstructive Hypertrophic Cardiomyopathy: A Randomized, Placebo-Controlled Trial of Personalized Pacing.

BACKGROUND: Patients with refractory, symptomatic left ventricular (LV) mid-cavity obstructive (LVMCO) hypertrophic cardiomyopathy have few therapeutic options. Right ventricular pacing is associated with modest hemodynamic and symptomatic improvement, and LV pacing pilot data suggest therapeutic potential. We hypothesized that site-specific pacing would reduce LVMCO gradients and improve symptoms. METHODS: Patients with symptomatic-drug-refractory LVMCO were recruited for a randomized, blinded trial of personalized prescription of pacing (PPoP). Multiple LV and apical right ventricular pacing sites were assessed during an invasive hemodynamic study of multisite pacing. Patient-specific pacing-site and atrioventricular delays, defining PPoP, were selected on the basis of LVMCO gradient reduction and acceptable pacing parameters. Patients were randomized to 6 months of active PPoP or backup pacing in a crossover design. The primary outcome examined invasive gradient change with best-site pacing. Secondary outcomes assessed quality of life and exercise following randomization to PPoP. RESULTS: A total of 17 patients were recruited; 16 of whom met primary end points. Baseline New York Heart Association was 3±0.6, despite optimal medical therapy. Hemodynamic effects were assessed during pacing at the right ventricular apex and at a mean of 8 LV sites. The gradients in all 16 patients fell with pacing, with maximum gradient reduction achieved via LV pacing in 14 (88%) patients and right ventricular apex in 2. The mean baseline gradient of 80±29 mm Hg fell to 31±21 mm Hg with best-site pacing, a 60% reduction (P<0.0001). One cardiac vein perforation occurred in 1 case, and 15 subjects entered crossover; 2 withdrawals occurred during crossover. Of the 13 completing crossover, 9 (69%) chose active pacing in PPoP configuration as preferred setting. PPoP was associated with improved 6-minute walking test performance (328.5±99.9 versus 285.8±105.5 m; P=0.018); other outcome measures also indicated benefit with PPoP. CONCLUSIONS: In a randomized placebo-controlled trial, PPoP reduces obstruction and improves exercise performance in severely symptomatic patients with LVMCO. REGISTRATION: URL: https://clinicaltrials.gov/study; Unique Identifier: NCT03450252.

The Society for Cardiovascular Magnetic Resonance Registry at 150,000.

BACKGROUND: Cardiovascular magnetic resonance (CMR) is increasingly utilized to evaluate expanding cardiovascular conditions. The Society for Cardiovascular Magnetic Resonance (SCMR) Registry is a central repository for real-world clinical data to support cardiovascular research, including those relating to outcomes, quality improvement, and machine learning. The SCMR Registry is built on a regulatory-compliant, cloud-based infrastructure that houses searchable content and Digital Imaging and Communications in Medicine images. The goal of this study is to summarize the status of the SCMR Registry at 150,000 exams. METHODS: The processes for data security, data submission, and research access are outlined. We interrogated the Registry and presented a summary of its contents. RESULTS: Data were compiled from 154,458 CMR scans across 20 United States sites, containing 299,622,066 total images (∼100 terabytes of storage). Across reported values, the human subjects had an average age of 58 years (range 1 month to >90 years old), were 44% (63,070/145,275) female, 72% (69,766/98,008) Caucasian, and had a mortality rate of 8% (9,962/132,979). The most common indication was cardiomyopathy (35,369/131,581, 27%), and most frequently used current procedural terminology code was 75561 (57,195/162,901, 35%). Macrocyclic gadolinium-based contrast agents represented 89% (83,089/93,884) of contrast utilization after 2015. Short-axis cines were performed in 99% (76,859/77,871) of tagged scans, short-axis late gadolinium enhancement (LGE) in 66% (51,591/77,871), and stress perfusion sequences in 30% (23,241/77,871). Mortality data demonstrated increased mortality in patients with left ventricular ejection fraction <35%, the presence of wall motion abnormalities, stress perfusion defects, and infarct LGE, compared to those without these markers. There were 456,678 patient-years of all-cause mortality follow-up, with a median follow-up time of 3.6 years. CONCLUSION: The vision of the SCMR Registry is to promote evidence-based utilization of CMR through a collaborative effort by providing a web mechanism for centers to securely upload de-identified data and images for research, education, and quality control. The Registry quantifies changing practice over time and supports large-scale real-world multicenter observational studies of prognostic utility.

AI-driven discovery of blood xenobiotic biomarkers in neovascular age-related macular degeneration using iterative random forests.

PURPOSE: To investigate the xenobiotic profiles of patients with neovascular age-related macular degeneration (nAMD) undergoing anti-vascular endothelial growth factor (anti-VEGF) intravitreal therapy (IVT) to identify biomarkers indicative of clinical phenotypes through advanced AI methodologies. METHODS: In this cross-sectional observational study, we analyzed 156 peripheral blood xenobiotic features in a cohort of 46 nAMD patients stratified by choroidal neovascularization (CNV) control under anti-VEGF IVT. We employed Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) for measurement and leveraged an AI-driven iterative Random Forests (iRF) approach for robust pattern recognition and feature selection, aligning molecular profiles with clinical phenotypes. RESULTS: AI-augmented iRF models effectively refined the metabolite spectrum by discarding non-predictive elements. Perfluorooctanesulfonate (PFOS) and Ethyl ß-glucopyranoside were identified as significant biomarkers through this process, associated with various clinically relevant phenotypes. Unlike single metabolite classes, drug metabolites were distinctly correlated with subretinal fluid presence. CONCLUSIONS: This study underscores the enhanced capability of AI, particularly iRF, in dissecting complex metabolomic data to elucidate the xenobiotic landscape of nAMD and environmental impact on the disease. The preliminary biomarkers discovered offer promising directions for personalized treatment strategies, although further validation in broader cohorts is essential for clinical application.

Inflammatory risk and cardiovascular events in patients without obstructive coronary artery disease: the ORFAN multicentre, longitudinal cohort study.

BACKGROUND: Coronary computed tomography angiography (CCTA) is the first line investigation for chest pain, and it is used to guide revascularisation. However, the widespread adoption of CCTA has revealed a large group of individuals without obstructive coronary artery disease (CAD), with unclear prognosis and management. Measurement of coronary inflammation from CCTA using the perivascular fat attenuation index (FAI) Score could enable cardiovascular risk prediction and guide the management of individuals without obstructive CAD. The Oxford Risk Factors And Non-invasive imaging (ORFAN) study aimed to evaluate the risk profile and event rates among patients undergoing CCTA as part of routine clinical care in the UK National Health Service (NHS); to test the hypothesis that coronary arterial inflammation drives cardiac mortality or major adverse cardiac events (MACE) in patients with or without CAD; and to externally validate the performance of the previously trained artificial intelligence (AI)-Risk prognostic algorithm and the related AI-Risk classification system in a UK population. METHODS: This multicentre, longitudinal cohort study included 40 091 consecutive patients undergoing clinically indicated CCTA in eight UK hospitals, who were followed up for MACE (ie, myocardial infarction, new onset heart failure, or cardiac death) for a median of 2·7 years (IQR 1·4-5·3). The prognostic value of FAI Score in the presence and absence of obstructive CAD was evaluated in 3393 consecutive patients from the two hospitals with the longest follow-up (7·7 years [6·4-9·1]). An AI-enhanced cardiac risk prediction algorithm, which integrates FAI Score, coronary plaque metrics, and clinical risk factors, was then evaluated in this population. FINDINGS: In the 2·7 year median follow-up period, patients without obstructive CAD (32 533 [81·1%] of 40 091) accounted for 2857 (66·3%) of the 4307 total MACE and 1118 (63·7%) of the 1754 total cardiac deaths in the whole of Cohort A. Increased FAI Score in all the three coronary arteries had an additive impact on the risk for cardiac mortality (hazard ratio [HR] 29·8 [95% CI 13·9-63·9], p<0·001) or MACE (12·6 [8·5-18·6], p<0·001) comparing three vessels with an FAI Score in the top versus bottom quartile for each artery. FAI Score in any coronary artery predicted cardiac mortality and MACE independently from cardiovascular risk factors and the presence or extent of CAD. The AI-Risk classification was positively associated with cardiac mortality (6·75 [5·17-8·82], p<0·001, for very high risk vs low or medium risk) and MACE (4·68 [3·93-5·57], p<0·001 for very high risk vs low or medium risk). Finally, the AI-Risk model was well calibrated against true events. INTERPRETATION: The FAI Score captures inflammatory risk beyond the current clinical risk stratification and CCTA interpretation, particularly among patients without obstructive CAD. The AI-Risk integrates this information in a prognostic algorithm, which could be used as an alternative to traditional risk factor-based risk calculators. FUNDING: British Heart Foundation, NHS-AI award, Innovate UK, National Institute for Health and Care Research, and the Oxford Biomedical Research Centre.

Myocardial Blood Flow by Magnetic Resonance in Patients With Suspected Coronary Stenosis: Comparison to PET and Invasive Physiology.

BACKGROUND: Despite recent guideline recommendations, quantitative perfusion (QP) estimates of myocardial blood flow from cardiac magnetic resonance (CMR) have only been sparsely validated. Furthermore, the additional diagnostic value of utilizing QP in addition to the traditional visual expert interpretation of stress-perfusion CMR remains unknown. The aim was to investigate the correlation between myocardial blood flow measurements estimated by CMR, positron emission tomography, and invasive coronary thermodilution. The second aim is to investigate the diagnostic performance of CMR-QP to identify obstructive coronary artery disease (CAD). METHODS: Prospectively enrolled symptomatic patients with >50% diameter stenosis on computed tomography angiography underwent dual-bolus CMR and positron emission tomography with rest and adenosine-stress myocardial blood flow measurements. Subsequently, an invasive coronary angiography (ICA) with fractional flow reserve and thermodilution-based coronary flow reserve was performed. Obstructive CAD was defined as both anatomically severe (>70% diameter stenosis on quantitative coronary angiography) or hemodynamically obstructive (ICA with fractional flow reserve ≤0.80). RESULTS: About 359 patients completed all investigations. Myocardial blood flow and reserve measurements correlated weakly between estimates from CMR-QP, positron emission tomography, and ICA-coronary flow reserve (r<0.40 for all comparisons). In the diagnosis of anatomically severe CAD, the interpretation of CMR-QP by an expert reader improved the sensitivity in comparison to visual analysis alone (82% versus 88% [P=0.03]) without compromising specificity (77% versus 74% [P=0.28]). In the diagnosis of hemodynamically obstructive CAD, the accuracy was only moderate for a visual expert read and remained unchanged when additional CMR-QP measurements were interpreted. CONCLUSIONS: CMR-QP correlates weakly to myocardial blood flow measurements by other modalities but improves diagnosis of anatomically severe CAD. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03481712.

Management of cardiac sarcoidosis.

Cardiac sarcoidosis (CS) is a form of inflammatory cardiomyopathy associated with significant clinical complications such as high-degree atrioventricular block, ventricular tachycardia, and heart failure as well as sudden cardiac death. It is therefore important to provide an expert consensus statement summarizing the role of different available diagnostic tools and emphasizing the importance of a multidisciplinary approach. By integrating clinical information and the results of diagnostic tests, an accurate, validated, and timely diagnosis can be made, while alternative diagnoses can be reasonably excluded. This clinical expert consensus statement reviews the evidence on the management of different CS manifestations and provides advice to practicing clinicians in the field on the role of immunosuppression and the treatment of cardiac complications based on limited published data and the experience of international CS experts. The monitoring and risk stratification of patients with CS is also covered, while controversies and future research needs are explored.

Feasibility of multiorgan risk prediction with routinely collected diagnostics: a prospective cohort study in the UK Biobank.

OBJECTIVES: Despite rising rates of multimorbidity, existing risk assessment tools are mostly limited to a single outcome of interest. This study tests the feasibility of producing multiple disease risk estimates with at least 70% discrimination (area under the receiver operating curve, AUROC) within the time and information constraints of the existing primary care health check framework. DESIGN: Observational prospective cohort study SETTING: UK Biobank. PARTICIPANTS: 228 240 adults from the UK population. INTERVENTIONS: None. MAIN OUTCOME MEASURES: Myocardial infarction, atrial fibrillation, heart failure, stroke, all-cause dementia, chronic kidney disease, fatty liver disease, alcoholic liver disease, liver cirrhosis and liver failure. RESULTS: Using a set of predictors easily gathered at the standard primary care health check (such as the National Health Service Health Check), we demonstrate that it is feasible to simultaneously produce risk estimates for multiple disease outcomes with AUROC of 70% or greater. These predictors can be entered once into a single form and produce risk scores for stroke (AUROC 0.727, 95% CI 0.713 to 0.740), all-cause dementia (0.823, 95% CI 0.810 to 0.836), myocardial infarction (0.785, 95% CI 0.775 to 0.795), atrial fibrillation (0.777, 95% CI 0.768 to 0.785), heart failure (0.828, 95% CI 0.818 to 0.838), chronic kidney disease (0.774, 95% CI 0.765 to 0.783), fatty liver disease (0.766, 95% CI 0.753 to 0.779), alcoholic liver disease (0.864, 95% CI 0.835 to 0.894), liver cirrhosis (0.763, 95% CI 0.734 to 0.793) and liver failure (0.746, 95% CI 0.695 to 0.796). CONCLUSIONS: Easily collected diagnostics can be used to assess 10-year risk across multiple disease outcomes, without the need for specialist computing or invasive biomarkers. Such an approach could increase the utility of existing data and place multiorgan risk information at the fingertips of primary care providers, thus creating opportunities for longer-term multimorbidity prevention. Additional work is needed to validate whether these findings would hold in a larger, more representative cohort outside the UK Biobank.

Bone health, cardiovascular disease, and imaging outcomes in UK Biobank: a causal analysis.

This study examined the association of estimated heel bone mineral density (eBMD, derived from quantitative ultrasound) with: (1) prevalent and incident cardiovascular diseases (CVDs: ischemic heart disease (IHD), myocardial infarction (MI), heart failure (HF), non-ischemic cardiomyopathy (NICM), arrhythmia), (2) mortality (all-cause, CVD, IHD), and (3) cardiovascular magnetic resonance (CMR) measures of left ventricular and atrial structure and function and aortic distensibility, in the UK Biobank. Clinical outcomes were ascertained using health record linkage over 12.3 yr of prospective follow-up. Two-sample Mendelian randomization (MR) was conducted to assess causal associations between BMD and CMR metrics using genetic instrumental variables identified from published genome-wide association studies. The analysis included 485 257 participants (55% women, mean age 56.5 ± 8.1 yr). Higher heel eBMD was associated with lower odds of all prevalent CVDs considered. The greatest magnitude of effect was seen in association with HF and NICM, where 1-SD increase in eBMD was associated with 15% lower odds of HF and 16% lower odds of NICM. Association between eBMD and incident IHD and MI was non-significant; the strongest relationship was with incident HF (SHR: 0.90 [95% CI, 0.89-0.92]). Higher eBMD was associated with a decreased risk in all-cause, CVD, and IHD mortality, in the fully adjusted model. Higher eBMD was associated with greater aortic distensibility; associations with other CMR metrics were null. Higher heel eBMD is linked to reduced risk of a range of prevalent and incident CVD and mortality outcomes. Although observational analyses suggest associations between higher eBMD and greater aortic compliance, MR analysis did not support a causal relationship between genetically predicted BMD and CMR phenotypes. These findings support the notion that bone-cardiovascular associations reflect shared risk factors/mechanisms rather than direct causal pathways.

Global Longitudinal Active Strain Energy Density (GLASED): A Powerful Prognostic Marker in a Community-Based Cohort.

BACKGROUND: Identifying the imaging method that best predicts all-cause mortality, cardiovascular adverse events and heart failure risk is crucial for tailoring optimal management. Potential prognostic markers include left ventricular myocardial mass, ejection fraction, myocardial strain, stroke work, contraction fraction, pressure-strain product and a new measurement called global longitudinal active strain density (GLASED). OBJECTIVES: This study sought to compare the utility of 23 potential left ventricular prognostic markers of structure and contractile function in a community-based cohort. METHODS: The impact of cardiovascular magnetic resonance image-derived markers extracted by machine learning algorithms was compared to the future risk of adverse events in a group of 44,957 UK Biobank participants. RESULTS: Most markers, including the left ventricular ejection fraction, have limited prognostic value. GLASED was significantly associated with all-cause mortality and major adverse cardiovascular events, with the largest hazard ratio, highest ranking and differentiated risk in all three tertiles (P ≤ 0.0003). CONCLUSIONS: GLASED predicted all-cause mortality and major cardiovascular adverse events better than conventional markers of risk and is recommended for assessing patient prognosis.

Evaluation of Deep Learning Estimation of Whole Heart Anatomy from Automated Cardiovascular Magnetic Resonance Short- and Long-Axis Analyses in UK Biobank.

AIMS: Standard methods of heart chamber volume estimation in cardiovascular magnetic resonance (CMR) typically utilize simple geometric formulae based on a limited number of slices. We aimed to evaluate whether an automated deep learning neural network prediction of 3D anatomy of all four chambers would show stronger associations with cardiovascular risk factors and disease than standard volume estimation methods in the UK Biobank. METHODS: A deep learning network was adapted to predict 3D segmentations of left and right ventricles (LV, RV) and atria (LA, RA) at ∼1mm isotropic resolution from CMR short and long axis 2D segmentations obtained from a fully automated machine learning pipeline in 4723 individuals with cardiovascular disease (CVD) and 5733 without in the UK Biobank. Relationships between volumes at end-diastole (ED) and end-systole (ES) and risk/disease factors were quantified using univariate, multivariate and logistic regression analyses. Strength of association between deep learning volumes and standard volumes was compared using the area under the receiving operator characteristic curve (AUC). RESULTS: Univariate and multivariate associations between deep learning volumes and most risk and disease factors were stronger than for standard volumes (higher R2 and more significant P values), particularly for sex, age, and body mass index. AUC for all logistic regressions were higher for deep learning volumes than standard volumes (p<0.001 for all four chambers at ED and ES). CONCLUSIONS: Neural network reconstructions of whole heart volumes had significantly stronger associations with cardiovascular disease and risk factors than standard volume estimation methods in an automatic processing pipeline.

A history of cardiovascular magnetic resonance imaging in clinical practice and population science.

Cardiovascular magnetic resonance (CMR) imaging has become an invaluable clinical and research tool. Starting from the discovery of nuclear magnetic resonance, this article provides a brief overview of the key developments that have led to CMR as it is today, and how it became the modality of choice for large-scale population studies.

Characterizing the Contribution of Dependent Features in XAI Methods.

Explainable Artificial Intelligence (XAI) provides tools to help understanding how AI models work and reach a particular decision or outcome. It helps to increase the interpretability of models and makes them more trustworthy and transparent. In this context, many XAI methods have been proposed to make black-box and complex models more digestible from a human perspective. However, one of the main issues that XAI methods have to face especially when dealing with a high number of features is the presence of multicollinearity, which casts shadows on the robustness of the XAI outcomes, such as the ranking of informative features. Most of the current XAI methods either do not consider the collinearity or assume the features are independent which, in general, is not necessarily true. Here, we propose a simple, yet useful, proxy that modifies the outcome of any XAI feature ranking method allowing to account for the dependency among the features, and to reveal their impact on the outcome. The proposed method was applied to SHAP, as an example of XAI method which assume that the features are independent. For this purpose, several models were exploited for a well-known classification task (males versus females) using nine cardiac phenotypes extracted from cardiac magnetic resonance imaging as features. Principal component analysis and biological plausibility were employed to validate the proposed method. Our results showed that the proposed proxy could lead to a more robust list of informative features compared to the original SHAP in presence of collinearity.

Cardiovascular Magnetic Resonance Reference Ranges From the Healthy Hearts Consortium.

BACKGROUND: The absence of population-stratified cardiovascular magnetic resonance (CMR) reference ranges from large cohorts is a major shortcoming for clinical care. OBJECTIVES: This paper provides age-, sex-, and ethnicity-specific CMR reference ranges for atrial and ventricular metrics from the Healthy Hearts Consortium, an international collaborative comprising 9,088 CMR studies from verified healthy individuals, covering the complete adult age spectrum across both sexes, and with the highest ethnic diversity reported to date. METHODS: CMR studies were analyzed using certified software with batch processing capability (cvi42, version 5.14 prototype, Circle Cardiovascular Imaging) by 2 expert readers. Three segmentation methods (smooth, papillary, anatomic) were used to contour the endocardial and epicardial borders of the ventricles and atria from long- and short-axis cine series. Clinically established ventricular and atrial metrics were extracted and stratified by age, sex, and ethnicity. Variations by segmentation method, scanner vendor, and magnet strength were examined. Reference ranges are reported as 95% prediction intervals. RESULTS: The sample included 4,452 (49.0%) men and 4,636 (51.0%) women with average age of 61.1 ± 12.9 years (range: 18-83 years). Among these, 7,424 (81.7%) were from White, 510 (5.6%) South Asian, 478 (5.3%) mixed/other, 341 (3.7%) Black, and 335 (3.7%) Chinese ethnicities. Images were acquired using 1.5-T (n = 8,779; 96.6%) and 3.0-T (n = 309; 3.4%) scanners from Siemens (n = 8,299; 91.3%), Philips (n = 498; 5.5%), and GE (n = 291, 3.2%). CONCLUSIONS: This work represents a resource with healthy CMR-derived volumetric reference ranges ready for clinical implementation.

Left Ventricular Trabeculations at Cardiac MRI: Reference Ranges and Association with Cardiovascular Risk Factors in UK Biobank.

Background The extent of left ventricular (LV) trabeculation and its relationship with cardiovascular (CV) risk factors is unclear. Purpose To apply automated segmentation to UK Biobank cardiac MRI scans to (a) assess the association between individual characteristics and CV risk factors and trabeculated LV mass (LVM) and (b) establish normal reference ranges in a selected group of healthy UK Biobank participants. Materials and Methods In this cross-sectional secondary analysis, prospectively collected data from the UK Biobank (2006 to 2010) were retrospectively analyzed. Automated segmentation of trabeculations was performed using a deep learning algorithm. After excluding individuals with known CV diseases, White adults without CV risk factors (reference group) and those with preexisting CV risk factors (hypertension, hyperlipidemia, diabetes mellitus, or smoking) (exposed group) were compared. Multivariable regression models, adjusted for potential confounders (age, sex, and height), were fitted to evaluate the associations between individual characteristics and CV risk factors and trabeculated LVM. Results Of 43 038 participants (mean age, 64 years ± 8 [SD]; 22 360 women), 28 672 individuals (mean age, 66 years ± 7; 14 918 men) were included in the exposed group, and 7384 individuals (mean age, 60 years ± 7; 4729 women) were included in the reference group. Higher body mass index (BMI) (ß = 0.66 [95% CI: 0.63, 0.68]; P < .001), hypertension (ß = 0.42 [95% CI: 0.36, 0.48]; P < .001), and higher physical activity level (ß = 0.15 [95% CI: 0.12, 0.17]; P < .001) were associated with higher trabeculated LVM. In the reference group, the median trabeculated LVM was 6.3 g (IQR, 4.7-8.5 g) for men and 4.6 g (IQR, 3.4-6.0 g) for women. Median trabeculated LVM decreased with age for men from 6.5 g (IQR, 4.8-8.7 g) at age 45-50 years to 5.9 g (IQR, 4.3-7.8 g) at age 71-80 years (P = .03). Conclusion Higher trabeculated LVM was observed with hypertension, higher BMI, and higher physical activity level. Age- and sex-specific reference ranges of trabeculated LVM in a healthy middle-aged White population were established. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Kawel-Boehm in this issue.

Social media for cardiac imagers: a review.

Cardiac imaging plays a pivotal role in the diagnosis and management of cardiovascular diseases. In the burgeoning landscape of digital technology and social media platforms, it becomes essential for cardiac imagers to know how to effectively increase the visibility and the impact of their activity. With the availability of social sites like X (formerly Twitter), Instagram and Facebook, cardiac imagers can now reach a wider audience and engage with peers, sharing their findings, insights, and discussions. The integration of persistent identifiers, such as Digital Object Identifiers (DOIs), facilitates traceability and citation of cardiac imaging publications across various digital platforms, further enhancing their discoverability. To maximize visibility, practical advice is provided, including the use of visually engaging infographics and videos, as well as the strategic implementation of relevant hashtags and keywords. These techniques can significantly improve the discoverability of cardiac imaging research through search engine optimization and social media algorithms. Tracking impact and engagement is crucial in the digital age, and this review discusses various metrics and tools to gauge the reach and influence of cardiac imaging publications. This includes traditional citation-based metrics and altmetrics. Moreover, this review underscores the importance of creating and updating professional profiles on social platforms and participating in relevant scientific communities online. The adoption of digital technology, social platforms, and a strategic approach to publication sharing can empower cardiac imaging professionals to enhance the visibility and impact of their research, ultimately advancing the field and improving patient care.

Noninvasive Techniques for Tracking Biological Aging of the Cardiovascular System: JACC Family Series.

Population aging is one of the most important demographic transformations of our time. Increasing the "health span"-the proportion of life spent in good health-is a global priority. Biological aging comprises molecular and cellular modifications over many years, which culminate in gradual physiological decline across multiple organ systems and predispose to age-related illnesses. Cardiovascular disease is a major cause of ill health and premature death in older people. The rate at which biological aging occurs varies across individuals of the same age and is influenced by a wide range of genetic and environmental exposures. The authors review the hallmarks of biological cardiovascular aging and their capture using imaging and other noninvasive techniques and examine how this information may be used to understand aging trajectories, with the aim of guiding individual- and population-level interventions to promote healthy aging.

Exploring the Impact of Saccharin on Neovascular Age-Related Macular Degeneration: A Comprehensive Study in Patients and Mice.

Purpose: We aimed to determine the impact of artificial sweeteners (AS), especially saccharin, on the progression and treatment efficacy of patients with neovascular age-related macular degeneration (nAMD) under anti-vascular endothelial growth factor (anti-VEGF-A) treatment. Methods: In a cross-sectional study involving 46 patients with nAMD undergoing intravitreal anti-VEGF therapy, 6 AS metabolites were detected in peripheral blood using liquid chromatography - tandem mass spectrometry (LC-MS/MS). Disease features were statistically tested against these metabolite levels. Additionally, a murine choroidal neovascularization (CNV) model, induced by laser, was used to evaluate the effects of orally administered saccharin, assessing both imaging outcomes and gene expression patterns. Polymerase chain reaction (PCR) methods were used to evaluate functional expression of sweet taste receptors in a retinal pigment epithelium (RPE) cell line. Results: Saccharin levels in blood were significantly higher in patients with well-controlled CNV activity (P = 0.004) and those without subretinal hyper-reflective material (P = 0.015). In the murine model, saccharin-treated mice exhibited fewer leaking laser scars, lesser occurrence of bleeding, smaller fibrotic areas (P < 0.05), and a 40% decrease in mononuclear phagocyte accumulation (P = 0.06). Gene analysis indicated downregulation of inflammatory and VEGFR-1 response genes in the treated animals. Human RPE cells expressed taste receptor type 1 member 3 (TAS1R3) mRNA and reacted to saccharin stimulation with changes in mRNA expression. Conclusions: Saccharin appears to play a protective role in patients with nAMD undergoing intravitreal anti-VEGF treatment, aiding in better pathological lesion control and scar reduction. The murine study supports this observation, proposing saccharin's potential in mitigating pathological VEGFR-1-induced immune responses potentially via the RPE sensing saccharin in the blood stream.

Identification of direct connections between the dura and the brain.

The arachnoid barrier delineates the border between the central nervous system and dura mater. Although the arachnoid barrier creates a partition, communication between the central nervous system and the dura mater is crucial for waste clearance and immune surveillance1,2. How the arachnoid barrier balances separation and communication is poorly understood. Here, using transcriptomic data, we developed transgenic mice to examine specific anatomical structures that function as routes across the arachnoid barrier. Bridging veins create discontinuities where they cross the arachnoid barrier, forming structures that we termed arachnoid cuff exit (ACE) points. The openings that ACE points create allow the exchange of fluids and molecules between the subarachnoid space and the dura, enabling the drainage of cerebrospinal fluid and limited entry of molecules from the dura to the subarachnoid space. In healthy human volunteers, magnetic resonance imaging tracers transit along bridging veins in a similar manner to access the subarachnoid space. Notably, in neuroinflammatory conditions such as experimental autoimmune encephalomyelitis, ACE points also enable cellular trafficking, representing a route for immune cells to directly enter the subarachnoid space from the dura mater. Collectively, our results indicate that ACE points are a critical part of the anatomy of neuroimmune communication in both mice and humans that link the central nervous system with the dura and its immunological diversity and waste clearance systems.

Identifying opportunities for high resolution pesticide usage data to improve the efficiency of endangered species pesticide risk assessment.

The US pesticide registration and review process requires regular re-assessment of the risk of pesticide use to species listed under the Endangered Species Act (ESA), yet current assessment methods are inefficient when applied to hundreds of pesticides potentially impacting multiple species across a continent. Thus, many pesticides remain on the market without complete review. We assessed the value of using high resolution pesticide usage data in the risk assessment process to rapidly improve process efficiency. By using data available only in California, we found that high resolution data increased the number of species deemed not likely to be adversely affected by pesticides from <5 % to nearly 50 %. Across the contiguous US, we predicted that 48 % of species would be deemed not likely to be adversely affected using high resolution data, compared to 20 % without. However, if such data were available in just 11 states, 68 % of the available gains in efficiency could be obtained. Overall, using existing high-resolution data in California and a focused collection of such information from 11 other states could reduce risk assessment burden across the contiguous U.S. by one-quarter.